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90c7b22c90821be20ca3d7dadb1b9b842af1c415..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~JqiLr422W55Nx)zoW=uqgF*BJUO-k61i?b==jgutu;6MfA}^4ET z-rJ!i>uPEXjdsx-J~ZE~HpRd++C>XCFs&{OBtQZq0@K)ccK+|+pXUEj3%4Xd0)Iw8 zXWRXDgOAFy_3iU4f5@z@8yxE82rriaZ0smr!rkz^*aBKpTPQLxUIZKi0||VUz!L$j B5n})V diff --git a/.gitignore b/.gitignore index 92d7fcdc..dce309ec 100644 --- a/.gitignore +++ b/.gitignore @@ -49,6 +49,9 @@ BenchmarkDotNet.Artifacts/ # Temporary files and agent workspace temp/ +temp_pandas_ta/ +temp_decompile/ +Invoke-WebRequest/ .temp/ *.tmp *.temp @@ -78,4 +81,15 @@ plans/ # SonarLint IDE plugin .sonarlint/ -.obsidian/ \ No newline at end of file +.obsidian/ + +# macOS +.DS_Store +**/.DS_Store + +# Python cache +__pycache__/ +.pytest_cache/ + +# PowerShell accidents +$null diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/.github/workflows/test.yml b/temp_pandas_ta/pandas_ta-0.4.71b0/.github/workflows/test.yml deleted file mode 100644 index 3af85728..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/.github/workflows/test.yml +++ /dev/null @@ -1,68 +0,0 @@ -on: - push: - branches: - - main - - development - pull_request: - branches: - - main - - development - workflow_dispatch: - -jobs: - test: - if: github.actor != 'dependabot[bot]' && github.ref != 'refs/heads/master' - runs-on: ${{ matrix.os }} - strategy: - # If failure occurs on one OS, it is still relevant to see which others may work - fail-fast: false - matrix: - python-version: ["3.12"] - os: [ubuntu-latest] - - steps: - - name: Github Actor Info - run: | - echo "Workflow triggered by github actor ${{ github.actor }}" - - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Checkout - uses: actions/checkout@v3 - - - name: Activate Cache for pip - uses: actions/cache@v3 - id: cache-python-env - with: - path: ${{ env.pythonLocation }} - key: "v3-${{ env.pythonLocation }}-${{ hashFiles('setup.py') }}" - - - name: Update pip and friends - run: | - python -m pip install --upgrade pip setuptools wheel - if: steps.cache-python-env.outputs.cache-hit != 'true' - - - name: Install TA-lib - # Inspiration for this step is taken from here: https://github.com/mrjbq7/ta-lib/blob/master/.github/workflows/tests.yml - shell: bash - run: | - wget https://raw.githubusercontent.com/mrjbq7/ta-lib/master/tools/build_talib_from_source.bash - chmod +x build_talib_from_source.bash - ./build_talib_from_source.bash $DEPS_PATH - python -m pip install TA-lib - env: - DEPS_PATH: ${{ github.workspace }}/dependencies - TA_INCLUDE_PATH: ${{ github.workspace }}/dependencies/include - TA_LIBRARY_PATH: ${{ github.workspace }}/dependencies/lib - - - name: Install pandas-ta - run: | - python -m pip install ".[test]" - - - name: Run tests - shell: bash - run: | - make tests diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/.gitignore b/temp_pandas_ta/pandas_ta-0.4.71b0/.gitignore deleted file mode 100644 index 61b4b0e1..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/.gitignore +++ /dev/null @@ -1,147 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - - -.DS_Store - -# Distribution / packaging -.pypirc -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# pycharm -.idea/* - -# pytest -.pytest_cache/ - -# pytest debug logs generated via --debug -pytestdebug.log - -# dotenv -.env - -# virtualenv -.venv -venv/ -ENV/ -env/** -.env*/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mypy -.mypy_cache/ - -# vscode settings -.vscode/** - -# zed settings -.zed -.zed/** - -# misc -dev.sh -Dockerfile -flake.nix -flake.lock -Makefile -scripts/ -setup.py* -ta-lib/ -venv.sh -website.tar.gz - -# Data & NB Exclusions -csv/ -# data/ -jnb/ -notes/ - -# mkdocs documentation -all_tests.sh -mkdocs.yml -docs/ -site/ -examples/ - -*.bak diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/CODE_OF_CONDUCT.md b/temp_pandas_ta/pandas_ta-0.4.71b0/CODE_OF_CONDUCT.md deleted file mode 100644 index d01d7560..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,73 +0,0 @@ -# Contributor Covenant Code of Conduct - -## Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to making participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, sex characteristics, gender identity and expression, -level of experience, education, socio-economic status, nationality, personal -appearance, race, religion, or sexual identity and orientation. - -## Our Standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or - advances -* Trolling, insulting/derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or electronic - address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Our Responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -## Scope - -This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. Examples of -representing a project or community include using an official project e-mail -address, posting via an official social media account, or acting as an appointed -representative at an online or offline event. Representation of a project may be -further defined and clarified by project maintainers. - -## Guidelines - -Not everyone is familiar with open-source communities or intuitively understands what is acceptable behavior. To assist, the following guidelines and examples provide a quick overview to help you avoid common pitfalls: - -1. Do **not** feel entitled to free software, support, or advice, especially if you are **not** a contributor, member, or business customer. Do **not** expect others to give you status reports as if they work for you or owe you something, even if you've made a donation. Please refrain from using GitHub Issues or other development tools for general discussions, technical support, or personal opinions. - -2. Slow down and **read** the documentation and use the troubleshooting checklists provided to identify the root cause of a problem **before** submitting _invalid_ bug reports, inciting public disputes, or insulting community members in public spaces. Such actions are disruptive and prevent maintainers and contributors from working on features and improvements that benefit users. - -3. Ignorant, reckless, or harsh communication is unacceptable, whether public or private. - -Many issues that new users become upset about in old issue comments have been resolved. If not, rest assured that we are working diligently to improve the software. - -## Enforcement - -We will enforce our community standards as necessary appropriate to the circumstances to protect everyone's well-being if there are instances of abusive, harassing, or otherwise unacceptable behavior. - -Initial warnings may be issued in the form of a snarky comment, especially if you seem to be exhibiting behaviour from Guideline #3 above. For serious cases, we will refer you to this Code of Conduct to prevent any misunderstandings. We also reserve the right to remove rants, personal attacks, spam, and unsolicited advertising from our community forums. - -In addition, we may use technical measures to temporarily or permanently restrict your access to our infrastructure. diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/LICENSE b/temp_pandas_ta/pandas_ta-0.4.71b0/LICENSE deleted file mode 100644 index 564dae29..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -The MIT License (MIT) - -Copyright (c) 2019+ Kevin Johnson - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/PKG-INFO b/temp_pandas_ta/pandas_ta-0.4.71b0/PKG-INFO deleted file mode 100644 index cbba7fc7..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/PKG-INFO +++ /dev/null @@ -1,52 +0,0 @@ -Metadata-Version: 2.4 -Name: pandas-ta -Version: 0.4.71b0 -Summary: A Comprehensive Python 3 Technical Analysis Library with Pandas Dataframe Extension for Quantitative Researchers, Traders, and Investors. -Project-URL: Homepage, https://www.pandas-ta.dev -Project-URL: Documentation, https://www.pandas-ta.dev/api -Project-URL: Repository, https://github.com/twopirllc/pandas-ta -Author-email: Pandas TA Support -License-File: LICENSE -Keywords: ai,dataframe extension,finance,indicators,library,machine learning,pandas,ta,technical analysis,trading -Classifier: Development Status :: 4 - Beta -Classifier: Intended Audience :: Developers -Classifier: Intended Audience :: Financial and Insurance Industry -Classifier: Intended Audience :: Science/Research -Classifier: Natural Language :: English -Classifier: Operating System :: OS Independent -Classifier: Programming Language :: Python :: 3.12 -Classifier: Topic :: Office/Business :: Financial -Classifier: Topic :: Office/Business :: Financial :: Investment -Classifier: Topic :: Scientific/Engineering -Classifier: Topic :: Scientific/Engineering :: Information Analysis -Requires-Python: >=3.12 -Requires-Dist: numba==0.61.2 -Requires-Dist: numpy>=2.2.6 -Requires-Dist: pandas>=2.3.2 -Requires-Dist: tqdm>=4.67.1 -Description-Content-Type: text/markdown - -

- Pandas TA -

- -# Pandas TA -A popular and comprehensive Technical Analysis Library in Python 3 that leverages [_numba_](http://numba.pydata.org/) and [_numpy_](https://numpy.org/) for accuracy and performance, and [_pandas_](https://pandas.pydata.org/) for simplicity and bulk processing. The library contains more than 150 indicators and utilities as well as 60 Candlestick Patterns when [TA Lib](https://ta-lib.org) is installed. - -
- -![Price Chart](https://www.pandas-ta.dev/assets/images/SPY_Chart.png) -![Volume Chart](https://www.pandas-ta.dev/assets/images/SPY_VOL.png) - -
- -## Details - -* [Getting Started](https://www.pandas-ta.dev/getting-started/installation/) -* [Documentation](https://www.pandas-ta.dev/api/) -* [Support](https://www.pandas-ta.dev/support/) -* [License](https://www.pandas-ta.dev/legal/license/) - -
- -Copyright © 2025 - Pandas TA diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/README.md b/temp_pandas_ta/pandas_ta-0.4.71b0/README.md deleted file mode 100644 index 2b171752..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/README.md +++ /dev/null @@ -1,24 +0,0 @@ -

- Pandas TA -

- -# Pandas TA -A popular and comprehensive Technical Analysis Library in Python 3 that leverages [_numba_](http://numba.pydata.org/) and [_numpy_](https://numpy.org/) for accuracy and performance, and [_pandas_](https://pandas.pydata.org/) for simplicity and bulk processing. The library contains more than 150 indicators and utilities as well as 60 Candlestick Patterns when [TA Lib](https://ta-lib.org) is installed. - -
- -![Price Chart](https://www.pandas-ta.dev/assets/images/SPY_Chart.png) -![Volume Chart](https://www.pandas-ta.dev/assets/images/SPY_VOL.png) - -
- -## Details - -* [Getting Started](https://www.pandas-ta.dev/getting-started/installation/) -* [Documentation](https://www.pandas-ta.dev/api/) -* [Support](https://www.pandas-ta.dev/support/) -* [License](https://www.pandas-ta.dev/legal/license/) - -
- -Copyright © 2025 - Pandas TA diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/data/ADX_D.csv b/temp_pandas_ta/pandas_ta-0.4.71b0/data/ADX_D.csv deleted file mode 100644 index 6878f664..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/data/ADX_D.csv +++ /dev/null @@ -1,31 +0,0 @@ -,date,high,low,close -0,2020-6-19,11382,11150,11286 -1,2020-6-20,11351,11155,11325 -2,2020-6-21,11355,11225,11238 -3,2020-6-22,11675,11232,11593 -4,2020-6-23,11649,11488,11509 -5,2020-6-24,11553,11110,11233 -6,2020-6-25,11232,10842,11147 -7,2020-6-26,11185,10901,11045 -8,2020-6-27,11051,10791,10913 -9,2020-6-28,11163,10756,10970 -10,2020-6-29,11033,10814,10972 -11,2020-6-30,11004,10882,10903 -12,2020-7-1,11111,10887,11051 -13,2020-7-2,11077,10772,10888 -14,2020-7-3,10920,10821,10837 -15,2020-7-4,10960,10814,10884 -16,2020-7-5,10900,10700,10831 -17,2020-7-6,11102,10796,11065 -18,2020-7-7,11097,10952,10988 -19,2020-7-8,11208,10968,11138 -20,2020-7-9,11149,10906,10961 -21,2020-7-10,11062,10880,11043 -22,2020-7-11,11044,10910,10957 -23,2020-7-12,11061,10915,11036 -24,2020-7-13,11063,10908,11000 -25,2020-7-14,11040,10925,11010 -26,2020-7-15,11017,10955,10975 -27,2020-7-16,11003,10835,10934 -28,2020-7-17,10980,10875,10937 -29,2020-7-18,10990,10894,10905 \ No newline at end of file diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/data/ALT_D.csv b/temp_pandas_ta/pandas_ta-0.4.71b0/data/ALT_D.csv deleted file mode 100644 index 40efdc38..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/data/ALT_D.csv +++ /dev/null @@ -1,248 +0,0 @@ -Date,Open,High,Low,Close,VWAP,Volume -2020-10-26,343.7,344.9,336.7,339.8,341.19,8695206 -2020-10-27,338.3,339.75,331.05,334.05,333.74,9910942 -2020-10-28,335.75,339.85,334.4,335.65,337.29,8984606 -2020-10-29,333.65,340.0,331.15,337.45,335.96,7239961 -2020-10-30,339.25,345.0,338.45,340.7,341.18,11496123 -2020-11-02,341.75,341.75,333.7,334.8,336.22,4831133 -2020-11-03,335.5,339.45,332.65,335.65,336.36,4757798 -2020-11-04,338.0,351.55,337.5,342.95,345.59,15381900 -2020-11-05,347.95,349.0,344.05,345.4,346.41,5602436 -2020-11-06,346.15,349.0,343.4,345.75,344.92,5457793 -2020-11-09,350.0,352.8,347.0,351.95,350.46,7107285 -2020-11-10,350.75,350.75,341.15,342.3,343.63,9049620 -2020-11-11,344.5,347.75,341.5,346.75,344.53,7637879 -2020-11-12,348.0,350.7,344.3,345.55,346.81,5114424 -2020-11-13,344.5,346.25,342.7,344.15,344.04,4727888 -2020-11-14,347.15,347.55,343.1,345.45,345.82,749242 -2020-11-17,347.0,350.5,343.7,348.7,347.99,6653834 -2020-11-18,349.75,352.35,341.8,345.3,345.38,7407808 -2020-11-19,347.7,349.5,341.6,342.6,345.72,11056851 -2020-11-20,343.8,348.5,342.05,346.3,345.96,6181393 -2020-11-23,348.0,357.4,346.0,355.85,352.34,10333347 -2020-11-24,359.0,361.4,355.0,355.5,357.97,10092441 -2020-11-25,357.9,358.8,349.55,350.0,352.95,6812338 -2020-11-26,351.0,356.4,347.2,354.85,352.19,7366370 -2020-11-27,354.85,357.5,347.85,350.5,351.8,12402084 -2020-12-01,352.7,353.9,346.25,352.85,350.99,10693567 -2020-12-02,354.45,360.3,354.2,358.9,357.61,9434077 -2020-12-03,364.0,364.0,358.0,360.3,360.33,10697045 -2020-12-04,361.1,362.4,359.0,360.8,360.69,7427393 -2020-12-07,362.0,362.75,357.5,358.45,359.01,8392383 -2020-12-08,360.3,365.0,358.4,363.7,361.67,11813301 -2020-12-09,367.75,367.75,357.5,359.5,362.22,18137688 -2020-12-10,356.2,358.0,352.75,355.9,355.84,6897397 -2020-12-11,357.4,360.0,352.65,353.5,355.85,5863616 -2020-12-14,353.5,354.55,347.1,349.35,349.28,11501839 -2020-12-15,349.3,353.9,347.5,352.7,351.95,7231781 -2020-12-16,353.9,358.95,353.2,358.4,356.44,6666272 -2020-12-17,358.4,359.25,355.5,356.9,357.27,4165371 -2020-12-18,358.95,365.8,357.5,363.55,362.94,17398081 -2020-12-21,362.5,367.45,348.35,353.95,360.92,9665063 -2020-12-22,354.0,365.7,349.45,364.2,357.14,8186039 -2020-12-23,374.0,387.6,371.7,385.55,379.89,44874433 -2020-12-24,386.45,386.7,378.65,382.2,382.71,12240172 -2020-12-28,383.45,386.4,382.0,382.9,384.06,4725879 -2020-12-29,384.0,390.5,383.1,385.0,387.04,11459126 -2020-12-30,385.0,386.6,382.8,384.4,384.43,7188435 -2020-12-31,381.2,387.6,381.2,386.25,385.2,6394605 -2021-01-01,385.05,390.75,385.05,388.1,388.0,5042336 -2021-01-04,390.0,397.95,387.8,396.4,393.91,9755721 -2021-01-05,394.1,409.8,393.5,406.3,403.28,25156747 -2021-01-06,405.0,417.4,403.3,406.4,410.04,22486300 -2021-01-07,412.25,413.0,403.85,406.75,406.88,12344755 -2021-01-08,407.5,432.65,407.25,430.2,423.56,40993230 -2021-01-11,436.0,451.0,435.0,446.8,444.84,47334018 -2021-01-12,447.95,460.75,442.4,457.7,451.3,22134465 -2021-01-13,461.0,467.45,452.8,459.0,460.7,29190193 -2021-01-14,452.0,466.0,430.0,454.35,450.54,71551807 -2021-01-15,454.75,459.4,436.6,438.55,447.14,25307779 -2021-01-18,441.0,445.0,429.1,431.55,438.58,21510587 -2021-01-19,433.0,439.55,427.7,430.25,432.24,15677790 -2021-01-20,434.0,447.85,433.0,444.95,443.71,24419679 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-1993-02-23,25.74472009197666,25.763069856831954,25.524522913713138,25.652971267700195,373700,0.0,0 -1993-02-24,25.671326483710335,25.983272552490234,25.671326483710335,25.983272552490234,26300,0.0,0 -1993-02-25,25.9649157641722,26.05666458312687,25.909866472799397,26.038314819335938,44500,0.0,0 -1993-02-26,26.09337723884341,26.09337723884341,25.94657905465864,26.075027465820312,66200,0.0,0 -1993-03-01,26.166767238813467,26.166767238813467,25.96491980569499,26.001619338989258,66500,0.0,0 -1993-03-02,26.019969460032414,26.386964797973633,25.983269926238293,26.386964797973633,182400,0.0,0 -1993-03-03,26.4236636175013,26.515412449506513,26.386964084699216,26.49706268310547,280100,0.0,0 -1993-03-04,26.533747808514864,26.533747808514864,26.350250244140625,26.350250244140625,89500,0.0,0 -1993-03-05,26.38694636382204,26.497044888288613,26.258498085277708,26.27684783935547,40000,0.0,0 -1993-03-08,26.331906846312226,26.864049911499023,26.331906846312226,26.864049911499023,50800,0.0,0 -1993-03-09,26.809005971311134,26.82735573583633,26.717257148685153,26.772306442260742,169300,0.0,0 -1993-03-10,26.772301333384377,26.827350616455078,26.662202767242974,26.827350616455078,194400,0.0,0 -1993-03-11,26.845704660821188,26.919103716626577,26.717256313161755,26.753955841064453,70900,0.0,0 -1993-03-12,26.533759468062755,26.552109232563488,26.313562294053934,26.478710174560547,643600,0.0,0 -1993-03-15,26.46037002826559,26.607168197631836,26.46037002826559,26.607168197631836,310800,0.0,0 -1993-03-16,26.607168197631836,26.680567282314957,26.607168197631836,26.607168197631836,30800,0.0,0 -1993-03-17,26.570455398930186,26.570455398930186,26.405307540787664,26.442007064819336,21800,0.0,0 -1993-03-18,26.552118884119494,26.71726682465652,26.552118884119494,26.607168197631836,59300,0.0,0 -1993-03-19,26.714374457524105,26.714374457524105,26.566883087158203,26.566883087158203,66900,0.213,0 -1993-03-22,26.308793997648124,26.474721920548497,26.290357561770303,26.41941261291504,183400,0.0,0 -1993-03-23,26.493148801054463,26.511585230282755,26.43783951336959,26.474712371826172,55200,0.0,0 -1993-03-24,26.43783951336959,26.585330947195917,26.30878450877155,26.474712371826172,37200,0.0,0 -1993-03-25,26.493158308774955,26.695959103066205,26.456285437085636,26.640649795532227,110100,0.0,0 -1993-03-26,26.622196141762625,26.64063256568351,26.474704750395517,26.493141174316406,101500,0.0,0 -1993-03-29,26.51159665325162,26.732833899314915,26.51159665325162,26.603778839111328,87100,0.0,0 -1993-03-30,26.640651692335737,26.67752456665039,26.603778818021087,26.67752456665039,56000,0.0,0 -1993-03-31,26.75125309507547,26.824998796233498,26.65907096862793,26.65907096862793,111600,0.0,0 -1993-04-01,26.695938036228366,26.695938036228366,26.51157382327099,26.566883087158203,129500,0.0,0 -1993-04-02,26.345665026691094,26.345665026691094,26.013809204101562,26.013809204101562,59400,0.0,0 -1993-04-05,26.21660231467867,26.21660231467867,26.06911088112211,26.14285659790039,172200,0.0,0 -1993-04-06,26.198167791461,26.198167791461,25.995367055566508,26.06911277770996,129700,0.0,0 -1993-04-07,26.10598374219576,26.161293029785156,26.050674454606362,26.161293029785156,28000,0.0,0 -1993-04-08,26.271919272025034,26.271919272025034,26.013809187949,26.124427795410156,180800,0.0,0 -1993-04-12,26.382522630791076,26.511577598237295,26.3456497829493,26.493141174316406,348500,0.0,0 -1993-04-13,26.474702872170344,26.64063067568673,26.45626644955741,26.54844856262207,146100,0.0,0 -1993-04-14,26.566898345947266,26.58533477783203,26.493152618408203,26.51158905029297,119600,0.0,0 -1993-04-15,26.493152618408203,26.566898345947266,26.400970458984375,26.51158905029297,148600,0.0,0 -1993-04-16,26.530025482177734,26.566898345947266,26.474716186523438,26.51158905029297,47900,0.0,0 -1993-04-19,26.51158905029297,26.58533477783203,26.38253402709961,26.400970458984375,157000,0.0,0 -1993-04-20,26.36410907946135,26.400981959292764,26.105998920641447,26.271926879882812,279500,0.0,0 -1993-04-21,26.32721899332625,26.32721899332625,26.17972757039445,26.25347328186035,67900,0.0,0 -1993-04-22,26.142858488970088,26.36409565530834,25.921621322631836,25.921621322631836,97700,0.0,0 -1993-04-23,25.866310111454556,25.940055828094483,25.774127965654646,25.81100082397461,106000,0.0,0 -1993-04-26,25.82944107055664,25.92162322998047,25.53445816040039,25.608203887939453,62600,0.0,0 -1993-04-27,25.571338561185744,25.88475799560547,25.571338561185744,25.88475799560547,156800,0.0,0 -1993-04-28,25.84787941115146,25.903188710890014,25.792570111412903,25.829442977905273,85900,0.0,0 -1993-04-29,25.884763689183476,25.940073013305664,25.73727215819098,25.940073013305664,85000,0.0,0 -1993-04-30,26.032232264240555,26.124414389822142,25.9769229888916,25.9769229888916,88500,0.0,0 -1993-05-03,26.0138015935384,26.14285659790039,25.903183018370978,26.14285659790039,80500,0.0,0 -1993-05-04,26.198165898916173,26.327220903344333,26.161293040508127,26.23503875732422,149100,0.0,0 -1993-05-05,26.235034964012783,26.40096280285756,26.235034964012783,26.308780670166016,109000,0.0,0 -1993-05-06,26.27191352441844,26.29034995496189,26.198167802244633,26.216604232788086,54700,0.0,0 -1993-05-07,26.23503874657102,26.23503874657102,26.124420171392227,26.161293029785156,68000,0.0,0 -1993-05-10,26.198167802244633,26.364095677135698,26.198167802244633,26.216604232788086,113900,0.0,0 -1993-05-11,26.216600442133032,26.327219009399414,26.14285473062211,26.327219009399414,42600,0.0,0 -1993-05-12,26.216623258122556,26.308805477736207,26.216623258122556,26.290369033813477,31000,0.0,0 -1993-05-13,26.179723665171096,26.179723665171096,25.958486563775285,25.9769229888916,129100,0.0,0 -1993-05-14,25.976930616931483,26.05067633905194,25.94005775587125,25.958494186401367,63500,0.0,0 -1993-05-17,26.03223419729396,26.050670623779297,25.921615638381944,26.050670623779297,34000,0.0,0 -1993-05-18,26.06912232525963,26.08755876254765,25.940067264243496,26.032249450683594,105200,0.0,0 -1993-05-19,26.032226869581805,26.566883087158203,25.866299077920164,26.566883087158203,50200,0.0,0 -1993-05-20,26.56690405215359,26.640649795532227,26.511594744619615,26.640649795532227,98200,0.0,0 -1993-05-21,26.659080505371094,26.695953369140625,26.38253402709961,26.400970458984375,221400,0.0,0 -1993-05-24,26.456279754638672,26.603771209716797,26.456279754638672,26.51158905029297,30500,0.0,0 -1993-05-25,26.622192351045417,26.622192351045417,26.566883087158203,26.566883087158203,191800,0.0,0 -1993-05-26,26.64063840634697,26.917184825790017,26.622201978384098,26.89874839782715,102400,0.0,0 -1993-05-27,26.935638482829727,26.935638482829727,26.769710525029957,26.806583404541016,53800,0.0,0 -1993-05-28,26.788143189594344,26.788143189594344,26.548469506549107,26.67752456665039,79100,0.0,0 -1993-06-01,26.76970099227874,27.027811056942586,26.73282812589819,26.93562889099121,28300,0.0,0 -1993-06-02,26.86187554190141,26.990930537641496,26.75125697412419,26.89874839782715,20300,0.0,0 -1993-06-03,26.843456284052078,26.843456284052078,26.751274085274428,26.806583404541016,21600,0.0,0 -1993-06-04,26.732837684899877,26.732837684899877,26.60378260642105,26.714401245117188,32000,0.0,0 -1993-06-07,26.769695260848366,26.769695260848366,26.622203826904297,26.622203826904297,121400,0.0,0 -1993-06-08,26.54845231733982,26.54845231733982,26.382524490356445,26.382524490356445,104500,0.0,0 -1993-06-09,26.474712371826172,26.585330947195917,26.43783951336959,26.474712371826172,43300,0.0,0 -1993-06-10,26.45626832647463,26.511577598237295,26.400959054711965,26.493141174316406,17900,0.0,0 -1993-06-11,26.585342401939386,26.64065171345521,26.49316021607968,26.603778839111328,647400,0.0,0 -1993-06-14,26.640628772341152,26.65906519363689,26.566883087158203,26.566883087158203,64200,0.0,0 -1993-06-15,26.603771209716797,26.622207641601562,26.51158905029297,26.51158905029297,142400,0.0,0 -1993-06-16,26.51157382327099,26.566883087158203,26.437828138088037,26.566883087158203,330900,0.0,0 -1993-06-17,26.622198118048917,26.65907096862793,26.566888842180393,26.65907096862793,37400,0.0,0 -1993-06-18,26.643773123119654,26.643773123119654,26.43953514099121,26.43953514099121,58500,0.318,0 -1993-06-21,26.51380444040813,26.51380444040813,26.45810317057534,26.495237350463867,29300,0.0,0 -1993-06-22,26.532370571328812,26.532370571328812,26.476669303509368,26.513803482055664,137500,0.0,0 -1993-06-23,26.513798722445753,26.513798722445753,26.27242660522461,26.27242660522461,227600,0.0,0 -1993-06-24,26.346707204395756,26.625213623046875,26.346707204395756,26.625213623046875,243700,0.0,0 -1993-06-25,26.60665512084961,26.680923523140883,26.58808802027679,26.60665512084961,44800,0.0,0 -1993-06-28,26.73662647697496,26.903730392456055,26.699492273534716,26.903730392456055,439900,0.0,0 -1993-06-29,26.866583863839033,26.866583863839033,26.736614211422395,26.77374839782715,207500,0.0,0 -1993-06-30,26.8108825842319,26.866583863839033,26.736614211422395,26.77374839782715,437600,0.0,0 -1993-07-01,26.81088261610941,26.81088261610941,26.662345870313793,26.699480056762695,605700,0.0,0 -1993-07-02,26.606640831740584,26.62520792234194,26.458104106929753,26.550939559936523,285400,0.0,0 -1993-07-06,26.513798722445753,26.588067066206108,26.235292433344434,26.27242660522461,246400,0.0,0 -1993-07-07,26.25386521848177,26.383834848276237,26.25386521848177,26.34670066833496,343700,0.0,0 -1993-07-08,26.365282783109553,26.699490593036295,26.32814858200658,26.643789291381836,248200,0.0,0 -1993-07-09,26.64378352427002,26.71805191040039,26.588082234672243,26.71805191040039,378200,0.0,0 -1993-07-12,26.680912963538244,26.71804714998715,26.643778777089338,26.699480056762695,373700,0.0,0 -1993-07-13,26.71803665293864,26.79230499665772,26.532365793640942,26.6809024810791,389600,0.0,0 -1993-07-14,26.699480025017643,26.848016770636654,26.680912931815264,26.77374839782715,617300,0.0,0 -1993-07-15,26.75517943841833,26.75517943841833,26.606642703159935,26.662343978881836,443800,0.0,0 -1993-07-16,26.68091683947174,26.699483935393435,26.550947168019896,26.58808135986328,216400,0.0,0 -1993-07-19,26.588068964096486,26.588068964096486,26.495233527769333,26.569501876831055,188200,0.0,0 -1993-07-20,26.55095378862441,26.643789291381836,26.420984084764008,26.643789291381836,68500,0.0,0 -1993-07-21,26.606646528470133,26.625213623046875,26.53237815016317,26.625213623046875,142700,0.0,0 -1993-07-22,26.588071855266442,26.625206033835248,26.43953514099121,26.43953514099121,632400,0.0,0 -1993-07-23,26.495233527769333,26.569501876831055,26.476666440503905,26.569501876831055,286200,0.0,0 -1993-07-26,26.64378352427002,26.77375319999817,26.64378352427002,26.71805191040039,121300,0.0,0 -1993-07-27,26.7366142432116,26.755181336436053,26.606644590640435,26.699480056762695,92800,0.0,0 -1993-07-28,26.643789291381836,26.699490593036295,26.606655090278867,26.643789291381836,30800,0.0,0 -1993-07-29,26.699490471177025,26.866594375377716,26.662356270243535,26.792325973510742,331000,0.0,0 -1993-07-30,26.792326095793722,26.792326095793722,26.606655090278867,26.643789291381836,75300,0.0,0 -1993-08-02,26.680917717335205,26.81088739306335,26.680917717335205,26.71805191040039,41300,0.0,0 -1993-08-03,26.773742683728535,26.84801104068756,26.64377305905024,26.736608505249023,81600,0.0,0 -1993-08-04,26.736608505249023,26.79230977296829,26.662340148289996,26.736608505249023,434000,0.0,0 -1993-08-05,26.73660373886841,26.73660373886841,26.64376830921956,26.6809024810791,36800,0.0,0 -1993-08-06,26.755186103465576,26.77375319999817,26.680917717335205,26.71805191040039,402300,0.0,0 -1993-08-09,26.792322108724154,26.940858891031706,26.736620815358823,26.86659049987793,828200,0.0,0 -1993-08-10,26.84801483154297,26.86658192340434,26.810880647820223,26.84801483154297,604900,0.0,0 -1993-08-11,26.829447739681598,26.922283198988453,26.829447739681598,26.84801483154297,542200,0.0,0 -1993-08-12,26.92228514344616,26.92228514344616,26.680912931815264,26.77374839782715,303700,0.0,0 -1993-08-13,26.792311663112482,26.829445844211737,26.792311663112482,26.81087875366211,103500,0.0,0 -1993-08-16,26.829459089221377,27.033697186094344,26.829459089221377,26.959428787231445,241800,0.0,0 -1993-08-17,26.940862565312223,27.052265167236328,26.940862565312223,27.052265167236328,369300,0.0,0 -1993-08-18,27.145089127993014,27.25649168255386,27.126522035566207,27.200790405273438,414300,0.0,0 -1993-08-19,27.219357497700248,27.219357497700248,27.163656220419824,27.200790405273438,28500,0.0,0 -1993-08-20,27.145086267635246,27.2193546295166,27.12651917716491,27.2193546295166,80700,0.0,0 -1993-08-23,27.1079606677438,27.18222905313488,27.1079606677438,27.16366195678711,15600,0.0,0 -1993-08-24,27.16366178139873,27.460735321044922,27.16366178139873,27.460735321044922,273400,0.0,0 -1993-08-25,27.46072292070131,27.590692535606593,27.42358874501409,27.479290008544922,242300,0.0,0 -1993-08-26,27.49786376953125,27.646400508326828,27.386461215434565,27.49786376953125,120000,0.0,0 -1993-08-27,27.42358874501409,27.479290008544922,27.42358874501409,27.479290008544922,25700,0.0,0 -1993-08-30,27.497855180044677,27.627824785892287,27.497855180044677,27.59069061279297,183500,0.0,0 -1993-08-31,27.572142530607696,27.66497802734375,27.535008331913275,27.66497802734375,66500,0.0,0 -1993-09-01,27.572146365719455,27.68354897729812,27.572146365719455,27.62784767150879,136500,0.0,0 -1993-09-02,27.646396663971874,27.683530843507096,27.5164270355986,27.53499412536621,472400,0.0,0 -1993-09-03,27.516429888270295,27.590698255040255,27.479295704885313,27.553564071655273,630500,0.0,0 -1993-09-07,27.553571817509354,27.59070601133349,27.330766654564535,27.367900848388672,196400,0.0,0 -1993-09-08,27.367907605258846,27.367907605258846,27.08940108281727,27.27507209777832,269900,0.0,0 -1993-09-09,27.16365901413171,27.349329957495563,27.16365901413171,27.33076286315918,239200,0.0,0 -1993-09-10,27.405028279622396,27.59069920292607,27.36789409496166,27.572132110595703,106500,0.0,0 -1993-09-13,27.664958958991605,27.664958958991605,27.59069061279297,27.59069061279297,66900,0.0,0 -1993-09-14,27.516424184232147,27.516424184232147,27.386454569326865,27.479290008544922,184500,0.0,0 -1993-09-15,27.367893154730375,27.572131163347766,27.275057696267925,27.553564071655273,101000,0.0,0 -1993-09-16,27.516433751954118,27.53500084625368,27.423598280456297,27.44216537475586,54300,0.0,0 -1993-09-17,27.42632927055411,27.445012056160753,27.35159812812753,27.38896369934082,200900,0.286,0 -1993-09-20,27.42632334629148,27.48237169100461,27.164764404296875,27.164764404296875,57800,0.0,0 -1993-09-21,27.202129566727052,27.239495129318712,26.791108378218816,27.07135009765625,318200,0.0,0 -1993-09-22,27.164752811377053,27.33289777375834,27.127387264181213,27.2955322265625,439700,0.0,0 -1993-09-23,27.3702723903669,27.463686289310132,27.332906830789607,27.445003509521484,88500,0.0,0 -1993-09-24,27.407644881160593,27.426327665673995,27.332913743106985,27.37027931213379,53500,0.0,0 -1993-09-27,27.57578888557635,27.66920280456543,27.57578888557635,27.66920280456543,274600,0.0,0 -1993-09-28,27.68789606520223,27.68789606520223,27.594482110866192,27.6131649017334,158300,0.0,0 -1993-09-29,27.613141582356935,27.687872682715142,27.482362156730076,27.51972770690918,221000,0.0,0 -1993-09-30,27.51973432813372,27.575782666887555,27.407637650626047,27.463685989379883,99300,0.0,0 -1993-10-01,27.42633161499602,27.631842274236455,27.38896604058867,27.5944766998291,22700,0.0,0 -1993-10-04,27.6318359375,27.6318359375,27.55710480582319,27.6318359375,1038500,0.0,0 -1993-10-05,27.68789063584748,27.68789063584748,27.501062763810722,27.5944766998291,436500,0.0,0 -1993-10-06,27.613142605396465,27.725239260086845,27.575777053833004,27.575777053833008,209200,0.0,0 -1993-10-07,27.61314379391463,27.61314379391463,27.482364357813548,27.501047134399414,59400,0.0,0 -1993-10-08,27.575791016039076,27.594473801280294,27.332914807903233,27.53842544555664,54400,0.0,0 -1993-10-11,27.5944766998291,27.65052506144013,27.5944766998291,27.5944766998291,467100,0.0,0 -1993-10-12,27.6318359375,27.6505187204192,27.613153154580797,27.6318359375,26200,0.0,0 -1993-10-13,27.6505187204192,27.6505187204192,27.594470371661593,27.6318359375,139100,0.0,0 -1993-10-14,27.743921254441958,27.986797332763672,27.669190153419894,27.986797332763672,108100,0.0,0 -1993-10-15,28.11759917701225,28.1923303376156,28.042868016408892,28.136281967163086,1502500,0.0,0 -1993-10-18,28.11758130907854,28.11758130907854,28.02416741768625,28.061532974243164,722400,0.0,0 -1993-10-19,28.024184152154135,28.080232520458445,27.85603904724121,27.85603904724121,880100,0.0,0 -1993-10-20,27.949427528662405,27.949427528662405,27.837330894189158,27.89337921142578,230400,0.0,0 -1993-10-21,27.912087415545518,27.912087415545518,27.81867346837167,27.85603904724121,56200,0.0,0 -1993-10-22,27.89338953604274,28.005486212008083,27.725244522094727,27.725244522094727,390700,0.0,0 -1993-10-25,27.743929912967065,27.837343818397933,27.669198788622374,27.799978256225586,114500,0.0,0 -1993-10-26,27.781286239624023,27.79996901449936,27.687872365247344,27.781286239624023,186200,0.0,0 -1993-10-27,27.743929912967065,27.81866103731176,27.743929912967065,27.799978256225586,118400,0.0,0 -1993-10-28,27.837346212477428,28.08022238748562,27.837346212477428,28.005491256713867,129600,0.0,0 -1993-10-29,27.98680847402093,28.024174039406805,27.96812569132799,28.005491256713867,80700,0.0,0 -1993-11-01,27.968114986153182,28.09889441494615,27.968114986153182,28.080211639404297,36400,0.0,0 -1993-11-02,28.042850195964707,28.09889853080008,27.89338796973705,28.061532974243164,262100,0.0,0 -1993-11-03,28.04285593476556,28.04285593476556,27.575786382221164,27.706565856933594,479100,0.0,0 -1993-11-04,27.706559176662786,27.706559176662786,27.388951957510212,27.407634735107422,130400,0.0,0 -1993-11-05,27.332914807903233,27.53842544555664,27.22081809645592,27.53842544555664,363200,0.0,0 -1993-11-08,27.557094278051277,27.650508156959926,27.48236317492436,27.575777053833008,367600,0.0,0 -1993-11-09,27.762621784662183,27.78130457186586,27.575793912625425,27.5944766998291,246900,0.0,0 -1993-11-10,27.59446766427768,27.799978256225586,27.51973653993299,27.799978256225586,46500,0.0,0 -1993-11-11,27.799975639404956,27.874706756715184,27.706561742767168,27.725244522094727,88900,0.0,0 -1993-11-12,27.781307889502134,27.949452994415058,27.762625100067364,27.85603904724121,108200,0.0,0 -1993-11-15,27.912078163607806,27.912078163607806,27.762615897671488,27.83734703063965,243300,0.0,0 -1993-11-16,27.89340467586785,27.986818623208332,27.78130793905927,27.968135833740234,492600,0.0,0 -1993-11-17,27.98679808852975,27.98679808852975,27.74392200364932,27.818653106689453,39600,0.0,0 -1993-11-18,27.781294470122365,27.837342811353277,27.66919778766054,27.743928909301758,240800,0.0,0 -1993-11-19,27.65051209813992,27.725243211918677,27.631829319695232,27.687877655029297,106000,0.0,0 -1993-11-22,27.613141582356935,27.631824357446487,27.42631383146142,27.51972770690918,165300,0.0,0 -1993-11-23,27.66920280456543,27.687885588363248,27.5197405341829,27.66920280456543,89700,0.0,0 -1993-11-24,27.743920689873352,27.79996901449936,27.70655514012268,27.781286239624023,77200,0.0,0 -1993-11-26,27.856026599484103,27.856026599484103,27.781295475139412,27.799978256225586,1019800,0.0,0 -1993-11-29,27.87470543947619,27.930753774810256,27.687877655029297,27.687877655029297,517500,0.0,0 -1993-11-30,27.669200292730043,27.837345331646027,27.650517510628266,27.706565856933594,230000,0.0,0 -1993-12-01,27.856025591763583,27.874708372173885,27.743928909301758,27.743928909301758,379200,0.0,0 -1993-12-02,27.79997033092942,27.837335882449484,27.74392200364932,27.818653106689453,352000,0.0,0 -1993-12-03,27.799983751175795,27.930763244628906,27.799983751175795,27.930763244628906,306000,0.0,0 -1993-12-06,27.968106353759765,28.06152020263672,27.968106353759765,28.024154663085938,99500,0.0,0 -1993-12-07,28.024174039406805,28.042856822099743,27.96812569132799,28.005491256713867,88800,0.0,0 -1993-12-08,28.005491256713867,28.005491256713867,27.96812569132799,28.005491256713867,146700,0.0,0 -1993-12-09,28.005487034758094,28.04285259451094,27.87470757562313,27.912073135375977,416500,0.0,0 -1993-12-10,27.930770204980288,27.930770204980288,27.799990678936904,27.85603904724121,412900,0.0,0 -1993-12-13,27.856009735107424,28.024154663085938,27.81864419555664,28.024154663085938,273200,0.0,0 -1993-12-14,28.04284641580985,28.04284641580985,27.818653106689453,27.818653106689453,41900,0.0,0 -1993-12-15,27.856017339125366,27.874700114000703,27.781286239624023,27.781286239624023,82600,0.0,0 -1993-12-16,27.89339286456479,27.89339286456479,27.83734451989386,27.874710083007812,78200,0.0,0 -1993-12-17,27.93383783302051,28.04670182426504,27.91502716781309,28.027891159057617,104700,0.317,0 -1993-12-20,28.009085419989784,28.084328094052886,27.990274751474008,28.06551742553711,68800,0.0,0 -1993-12-21,28.027905718784808,28.027905718784808,27.933852343889555,27.971473693847656,205700,0.0,0 -1993-12-22,28.046723780507794,28.178398540040696,27.99029174070798,28.159587860107422,410300,0.0,0 -1993-12-23,28.140777587890625,28.197209628508052,28.10315622747901,28.140777587890625,533800,0.0,0 -1993-12-27,28.140766752527114,28.29125213623047,28.140766752527114,28.29125213623047,447100,0.0,0 -1993-12-28,28.27243645964827,28.366489807817423,28.25362579001444,28.347679138183594,880600,0.0,0 -1993-12-29,28.36648487078391,28.385295537143847,28.291242205344165,28.3100528717041,266700,0.0,0 -1993-12-30,28.29126180148745,28.29126180148745,28.140776366373156,28.197208404541016,219900,0.0,0 -1993-12-31,28.253630274178434,28.29125161941702,28.027902202746915,28.04671287536621,312900,0.0,0 -1994-01-03,28.04671639376386,28.084337743721957,27.933852343889555,27.971473693847656,960900,0.0,0 -1994-01-04,28.009100242660736,28.08434295654297,27.97147888571962,28.08434295654297,164300,0.0,0 -1994-01-05,28.121966907684815,28.159588268096435,28.00910282644996,28.140777587890625,710900,0.0,0 -1994-01-06,28.178398948302245,28.197209628508052,28.10315622747901,28.140777587890625,201000,0.0,0 -1994-01-07,28.197188873544484,28.328863538064038,28.121946208104738,28.3100528717041,775500,0.0,0 -1994-01-10,28.347672846312594,28.648643493652344,28.272430184477656,28.648643493652344,593700,0.0,0 -1994-01-11,28.6298397390466,28.6298397390466,28.498165049050982,28.59221839904785,295200,0.0,0 -1994-01-12,28.611040640349433,28.611040640349433,28.404123186671693,28.49817657470703,158400,0.0,0 -1994-01-13,28.42293930053711,28.47937134415168,28.3853179381274,28.42293930053711,244300,0.0,0 -1994-01-14,28.516976911611437,28.59221959475553,28.516976911611437,28.53578758239746,137200,0.0,0 -1994-01-17,28.53578758239746,28.573408923969506,28.47935557003939,28.53578758239746,17700,0.0,0 -1994-01-18,28.573421478271484,28.611042836373223,28.516989441118874,28.573421478271484,166400,0.0,0 -1994-01-19,28.535797929921163,28.573419285135298,28.44174454188583,28.49817657470703,200800,0.0,0 -1994-01-20,28.535800120169743,28.592232157322353,28.516989441118874,28.573421478271484,281100,0.0,0 -1994-01-21,28.611034492704043,28.611034492704043,28.498170451312706,28.51698112487793,85600,0.0,0 -1994-01-24,28.498174781041428,28.629849516003336,28.40412139892578,28.40412139892578,373800,0.0,0 -1994-01-25,28.42293207534891,28.44174275177204,28.34768936965639,28.40412139892578,310400,0.0,0 -1994-01-26,28.404106470206298,28.498159802889102,28.36648513713318,28.47934913635254,145100,0.0,0 -1994-01-27,28.53578512331578,28.780323822460872,28.498163784985767,28.74270248413086,344500,0.0,0 -1994-01-28,28.855566819093866,28.91199882721465,28.81794548034668,28.81794548034668,356500,0.0,0 -1994-01-31,28.93082225253191,29.08130767387148,28.89320089719702,29.02487564086914,313800,0.0,0 -1994-02-01,28.987246052993623,28.987246052993623,28.83676067439275,28.87438201904297,303600,0.0,0 -1994-02-02,28.96843519488585,29.062488555908203,28.949624522681376,29.062488555908203,307600,0.0,0 -1994-02-03,29.006047872635104,29.006047872635104,28.836751873378482,28.930805206298828,466100,0.0,0 -1994-02-04,28.930805668224913,28.968427001993735,28.272432327270508,28.272432327270508,1403200,0.0,0 -1994-02-07,28.197203958271352,28.4793641046183,28.197203958271352,28.40412139892578,516400,0.0,0 -1994-02-08,28.460560662946822,28.460560662946822,28.310075213307975,28.42293930053711,188200,0.0,0 -1994-02-09,28.441734228467343,28.554598253183485,28.404112886895298,28.53578758239746,144600,0.0,0 -1994-02-10,28.516980211785498,28.535790884748415,28.29125213623047,28.29125213623047,883900,0.0,0 -1994-02-11,28.310076029115834,28.460561483091197,28.178401256887387,28.385318756103516,519400,0.0,0 -1994-02-14,28.328885894512833,28.51699270656139,28.29126453210312,28.42293930053711,2742100,0.0,0 -1994-02-15,28.479368083017132,28.592232157322353,28.441746724915394,28.573421478271484,374700,0.0,0 -1994-02-16,28.611033688420836,28.611033688420836,28.498169650202215,28.554601669311523,287600,0.0,0 -1994-02-17,28.68628966405425,28.70510034580117,28.310076029115834,28.385318756103516,342400,0.0,0 -1994-02-18,28.40413314819336,28.42294383239746,28.140783569335937,28.216026306152344,313300,0.0,0 -1994-02-22,28.291259121029288,28.49817657470703,28.291259121029288,28.49817657470703,154500,0.0,0 -1994-02-23,28.535803387766247,28.5546140689711,28.328885894512833,28.42293930053711,391700,0.0,0 -1994-02-24,28.32887296465561,28.32887296465561,28.027902202746915,28.04671287536621,770800,0.0,0 -1994-02-25,28.084329526160204,28.178382873535156,28.027897517735234,28.178382873535156,531300,0.0,0 -1994-02-28,28.253625551435118,28.328868229335075,28.178382873535156,28.178382873535156,333000,0.0,0 -1994-03-01,28.178381436631764,28.23481344217909,27.915032077410906,28.06551742553711,423600,0.0,0 -1994-03-02,27.708127106049933,28.103151321411133,27.632884398362084,28.103151321411133,581500,0.0,0 -1994-03-03,28.06551248947246,28.06551248947246,27.952648498227934,28.027891159057617,223200,0.0,0 -1994-03-04,28.027908613723284,28.17839402909898,27.933855229113476,28.103151321411133,595800,0.0,0 -1994-03-07,28.197207185463327,28.291260578343458,28.197207185463327,28.253639221191406,539800,0.0,0 -1994-03-08,28.27245234933129,28.27245234933129,28.10315622747901,28.140777587890625,880300,0.0,0 -1994-03-09,28.17837899284845,28.29124299415492,28.046704324657572,28.272432327270508,2500100,0.0,0 -1994-03-10,28.253630274178434,28.253630274178434,27.952659512269744,28.04671287536621,207400,0.0,0 -1994-03-11,28.084344328205294,28.23482976331959,27.933858893091,28.197208404541016,576600,0.0,0 -1994-03-14,28.310071664361615,28.310071664361615,28.197207591281106,28.23482894897461,345900,0.0,0 -1994-03-15,28.29126904296875,28.347701095581055,28.197215621948242,28.216026306152344,748600,0.0,0 -1994-03-16,28.253625710805256,28.44173240661621,28.15957236289978,28.44173240661621,455500,0.0,0 -1994-03-17,28.347679058710735,28.4793537457784,28.347679058710735,28.44173240661621,133000,0.0,0 -1994-03-18,28.303104931088345,28.47337762118179,28.284185743300185,28.43553924560547,365500,0.271,0 -1994-03-21,28.340951717241833,28.416628490852624,28.284194137033737,28.35987091064453,324800,0.0,0 -1994-03-22,28.359862494452827,28.49229680896995,28.303104931088345,28.43553924560547,435700,0.0,0 -1994-03-23,28.416610717773438,28.49228744405246,28.39769153620368,28.416610717773438,698500,0.0,0 -1994-03-24,28.30309139364813,28.35984892986534,27.943626997605808,28.076061248779297,1200500,0.0,0 -1994-03-25,28.09499078867387,28.17066753153898,27.811203002929688,27.811203002929688,100900,0.0,0 -1994-03-28,27.84903335571289,27.886871716250543,27.603084012218144,27.84903335571289,1117200,0.0,0 -1994-03-29,27.849046734910992,27.867965924268947,27.300390243530273,27.300390243530273,338400,0.0,0 -1994-03-30,27.319292166328566,27.394968875930584,26.92198944091797,26.92198944091797,1123900,0.0,0 -1994-03-31,26.92199250176476,27.054426758625162,26.354417115220176,26.997669219970703,788800,0.0,0 -1994-04-04,26.240908013883434,26.65713005880444,26.240908013883434,26.581453323364258,2627300,0.0,0 -1994-04-05,26.84632391510648,27.130111694335938,26.84632391510648,27.130111694335938,1179000,0.0,0 -1994-04-06,27.167950064899863,27.186869250181825,26.940919841516298,27.130111694335938,516500,0.0,0 -1994-04-07,27.111191157249014,27.319302184973882,26.959837682540016,27.262544631958008,666100,0.0,0 -1994-04-08,27.20578538054353,27.20578538054353,26.92199763318042,27.054431915283203,242400,0.0,0 -1994-04-11,27.13012273464362,27.281476278491006,27.06390555921039,27.16796112060547,203300,0.0,0 -1994-04-12,27.19632884282281,27.243626806027716,27.073354138490046,27.130111694335938,1409200,0.0,0 -1994-04-13,27.120644940103183,27.18686207110448,26.78955928509669,26.988210678100586,364100,0.0,0 -1994-04-14,26.940911681316244,27.13956306660685,26.884154142661785,26.997669219970703,419900,0.0,0 -1994-04-15,26.98820963019496,27.11118429727962,26.95037127109199,26.997669219970703,387300,0.0,0 -1994-04-18,27.026041143911854,27.10171784294976,26.732793935139973,26.817930221557617,369100,0.0,0 -1994-04-19,26.789554898274453,26.959827492966877,26.628741892176055,26.855772018432617,472200,0.0,0 -1994-04-20,26.8841443741103,26.95036147848003,26.68549306100111,26.8273868560791,508000,0.0,0 -1994-04-21,26.940921154300952,27.2247089473589,26.84632522328164,27.186870574951172,200300,0.0,0 -1994-04-22,27.25308897735266,27.272008164424758,27.082816293703807,27.158493041992188,301800,0.0,0 -1994-04-25,27.14903054012179,27.461197093370576,27.14903054012179,27.442277908325195,394600,0.0,0 -1994-04-26,27.404435062342632,27.45173301723104,27.357137107454225,27.413894653320312,399000,0.0,0 -1994-04-28,27.357138163354772,27.39497652872594,27.130107971127764,27.21524429321289,287000,0.0,0 -1994-04-29,27.16795591802458,27.338228622246184,27.13011753930867,27.300390243530273,481900,0.0,0 -1994-05-02,27.300380888064044,27.67876454555627,27.20578497369099,27.470653533935547,275000,0.0,0 -1994-05-03,27.480116278415956,27.489575870938648,27.281464835439454,27.442277908325195,183400,0.0,0 -1994-05-04,27.499015846963744,27.499015846963744,27.29090495992102,27.394960403442383,401900,0.0,0 -1994-05-05,27.423350994352816,27.470648943635933,27.357133865356445,27.357133865356445,659800,0.0,0 -1994-05-06,27.224711844375012,27.224711844375012,26.997681585770078,27.09227752685547,216300,0.0,0 -1994-05-09,27.016585024531018,27.092261733283205,26.808474075462502,26.855772018432617,499300,0.0,0 -1994-05-10,26.988215629473636,27.14902869140881,26.969296445716555,27.063892364501953,583400,0.0,0 -1994-05-11,27.03550073129159,27.073339080810545,26.74225352251971,26.817930221557617,210600,0.0,0 -1994-05-12,26.988222517185605,27.016601300063826,26.88416697996547,26.95038414001465,305200,0.0,0 -1994-05-13,27.026060894356565,27.026060894356565,26.817949819916297,26.95038414001465,321300,0.0,0 -1994-05-16,27.007140651502105,27.09227699681332,26.950383087961296,26.959842681884766,450400,0.0,0 -1994-05-17,26.97875027109149,27.357133865356445,26.940911911664994,27.357133865356445,470200,0.0,0 -1994-05-18,27.37605391979381,27.65984162456016,27.253079247728394,27.5557861328125,824800,0.0,0 -1994-05-19,27.499034853571303,27.76390343833222,27.499034853571303,27.6882266998291,531000,0.0,0 -1994-05-20,27.640907825265895,27.659826995577507,27.54631197370783,27.584150314331055,370400,0.0,0 -1994-05-23,27.55579777412585,27.565257368281056,27.451742238418543,27.53687858581543,262700,0.0,0 -1994-05-24,27.612556062085638,27.75444997812924,27.612556062085638,27.650394439697266,549600,0.0,0 -1994-05-25,27.546332430571105,27.744983866368493,27.451736508762824,27.726064682006836,738200,0.0,0 -1994-05-26,27.71660299135319,27.81119890599945,27.688224216959316,27.74498176574707,369600,0.0,0 -1994-05-27,27.697690178977698,27.773366928100586,27.65039221077589,27.773366928100586,162000,0.0,0 -1994-05-31,27.68823668933988,27.792292242323395,27.640938710711012,27.73553466796875,160000,0.0,0 -1994-06-01,27.669303890804688,27.858495712280273,27.58416757114067,27.858495712280273,200500,0.0,0 -1994-06-02,27.87741613679804,27.87741613679804,27.782820221844535,27.83011817932129,55100,0.0,0 -1994-06-03,27.849021506817127,28.08551115955844,27.763885231830255,27.990915298461914,550400,0.0,0 -1994-06-06,28.04769298045356,28.132829316650554,27.962556644256573,27.981475830078125,99300,0.0,0 -1994-06-07,27.934172534748924,27.981470490276774,27.867955397009933,27.943632125854492,120600,0.0,0 -1994-06-08,28.009851581326117,28.009851581326117,27.716604232788086,27.716604232788086,131900,0.0,0 -1994-06-09,27.839571671184842,27.867950439453125,27.782814134648277,27.867950439453125,80500,0.0,0 -1994-06-10,27.89633922195564,27.97201596450418,27.89633922195564,27.915258407592773,83100,0.0,0 -1994-06-13,27.877408235165877,27.962544534492814,27.86794864635177,27.95308494567871,109700,0.0,0 -1994-06-14,28.038227652606746,28.180121517245446,28.038227652606746,28.17066192626953,161000,0.0,0 -1994-06-15,28.18011883602541,28.18957842610128,28.038224984887314,28.057144165039062,142800,0.0,0 -1994-06-16,28.05714359899549,28.13282031807572,28.028764829340403,28.113901138305664,44000,0.0,0 -1994-06-17,28.128389643063663,28.156956050961153,27.918902651815387,27.95699119567871,403800,0.305,0 -1994-06-20,27.75702164288609,27.804632314657763,27.690366702405747,27.71893310546875,137400,0.0,0 -1994-06-21,27.699893680448263,27.699893680448263,27.366618919769095,27.480884552001953,139200,0.0,0 -1994-06-22,27.55705257411821,27.680840301645347,27.528486175458102,27.58561897277832,279900,0.0,0 -1994-06-23,27.63321768310335,27.6713061981731,27.385642335149978,27.423730850219727,922500,0.0,0 -1994-06-24,27.29043320324404,27.29043320324404,26.814326552803635,26.852415084838867,353800,0.0,0 -1994-06-27,26.957159181304327,27.319000244140625,26.823849316048847,27.319000244140625,371200,0.0,0 -1994-06-28,27.32851617014763,27.32851617014763,26.966675189497593,27.185684204101562,5382300,0.0,0 -1994-06-29,27.22378405445781,27.433271025146187,27.19521764936394,27.271394729614258,311800,0.0,0 -1994-06-30,27.31898836978958,27.32851049888327,27.00475810969789,27.099979400634766,271900,0.0,0 -1994-07-01,27.233292979578817,27.233292979578817,27.042850371330015,27.157115936279297,406900,0.0,0 -1994-07-05,27.21425807222407,27.338045810131316,27.12855886905751,27.299957275390625,112000,0.0,0 -1994-07-06,27.19520688231825,27.309472457454042,27.09998556970509,27.26186180114746,174800,0.0,0 -1994-07-07,27.261880662337592,27.37614631652832,27.23331424878991,27.37614631652832,66700,0.0,0 -1994-07-08,27.204733316319306,27.414220237201008,27.195211183551955,27.366609573364258,148400,0.0,0 -1994-07-11,27.385660349989738,27.433271025146187,27.1380848391762,27.271394729614258,124000,0.0,0 -1994-07-12,27.28090283262181,27.31899135315601,27.128548750485002,27.30946922302246,257300,0.0,0 -1994-07-13,27.319009080482534,27.44279685254467,27.319009080482534,27.357097625732422,532700,0.0,0 -1994-07-14,27.490396923270108,27.709405973923108,27.471352657995933,27.652273178100586,494200,0.0,0 -1994-07-15,27.633224454475968,27.718923634383025,27.623702323375184,27.66179084777832,49100,0.0,0 -1994-07-18,27.66179714006247,27.776062739264106,27.66179714006247,27.709407806396484,72300,0.0,0 -1994-07-19,27.757016637108542,27.76653876974563,27.652273178100586,27.652273178100586,609500,0.0,0 -1994-07-20,27.671312493793035,27.671312493793035,27.461825613248145,27.528480529785156,185300,0.0,0 -1994-07-21,27.528486175458102,27.604663238551726,27.471353378137884,27.58561897277832,86300,0.0,0 -1994-07-22,27.652276384181437,27.671320651663656,27.576099314252563,27.63323211669922,151600,0.0,0 -1994-07-25,27.64275737179614,27.699890180694098,27.61419096734716,27.671323776245117,120900,0.0,0 -1994-07-26,27.661795959446376,27.680840225167163,27.614185295144402,27.642751693725586,489600,0.0,0 -1994-07-27,27.642751693725586,27.642751693725586,27.52848609940085,27.642751693725586,83200,0.0,0 -1994-07-28,27.64274971425308,27.776059564752405,27.64274971425308,27.69988250732422,828200,0.0,0 -1994-07-29,27.89033114171661,28.06172955774628,27.88080900749274,27.976030349731445,459100,0.0,0 -1994-08-01,27.995063657683087,28.128373484624436,27.966497266195656,28.109329223632812,486300,0.0,0 -1994-08-02,28.204555583742216,28.26168837695709,28.052201468502556,28.147422790527344,505500,0.0,0 -1994-08-03,28.13790572911128,28.175994264785206,28.090295059518876,28.156949996948242,144100,0.0,0 -1994-08-04,28.13790340812839,28.166469807527506,27.985549277999777,27.995071411132812,231700,0.0,0 -1994-08-05,27.852230657896488,27.937929829151557,27.823664267478133,27.89984130859375,138400,0.0,0 -1994-08-08,27.928427946579713,27.995082905879425,27.899861535451265,27.966516494750977,328600,0.0,0 -1994-08-09,27.861754565884027,28.014108657836914,27.861754565884027,28.014108657836914,105900,0.0,0 -1994-08-10,28.033153437050245,28.14741900812518,27.9950649133586,28.118852615356445,840300,0.0,0 -1994-08-11,28.052197180825136,28.175984880536856,27.87127669663108,28.014108657836914,876200,0.0,0 -1994-08-12,28.06172349296171,28.24264400411416,28.06172349296171,28.233121871948242,184100,0.0,0 -1994-08-15,28.26169273695643,28.3283476726568,28.223604202270508,28.223604202270508,325900,0.0,0 -1994-08-16,28.24264629242608,28.452133217049603,28.128380697176883,28.423566818237305,1089500,0.0,0 -1994-08-17,28.461645182610518,28.480689441682188,28.385468146323838,28.404512405395508,133700,0.0,0 -1994-08-18,28.309295654296875,28.375950571747286,28.28072926110384,28.309295654296875,620000,0.0,0 -1994-08-19,28.318807624398318,28.347374007341557,28.22358634792085,28.290241241455078,103200,0.0,0 -1994-08-22,28.299771031279448,28.299771031279448,28.20454972902077,28.261682510375977,79700,0.0,0 -1994-08-23,28.385478286487572,28.556876679361366,28.34738975473784,28.423566818237305,268600,0.0,0 -1994-08-24,28.452127774661847,28.728269577026367,28.44260564354583,28.728269577026367,254700,0.0,0 -1994-08-25,28.699708366203875,28.766363296052393,28.575920639342346,28.652097702026367,147400,0.0,0 -1994-08-26,28.71874769518291,29.118677205526968,28.71874769518291,29.06154441833496,339500,0.0,0 -1994-08-29,29.13772659301758,29.242470054626466,29.03298313140869,29.042505264282227,350300,0.0,0 -1994-08-30,29.032993172570098,29.166303078905287,28.994904627902905,29.11869239807129,36000,0.0,0 -1994-08-31,29.07107166290283,29.185337257385253,29.004416732788087,29.042505264282227,356200,0.0,0 -1994-09-01,28.947280883789062,28.966325147528398,28.84253743322272,28.947280883789062,294600,0.0,0 -1994-09-02,29.080589600667963,29.080589600667963,28.775881392671444,28.8234920501709,99600,0.0,0 -1994-09-06,28.804447140687365,28.86157992840443,28.718747959111766,28.8330135345459,229800,0.0,0 -1994-09-07,28.899672314352237,28.899672314352237,28.775884591094716,28.80445098876953,27900,0.0,0 -1994-09-08,28.861581696962055,28.985369411267733,28.85205956509239,28.947280883789062,284800,0.0,0 -1994-09-09,28.661622392370344,28.718755194481382,28.528312520777924,28.642578125,488400,0.0,0 -1994-09-12,28.642578247111015,28.68066678201409,28.50926837495026,28.5568790435791,129400,0.0,0 -1994-09-13,28.61401172394448,28.718755194481382,28.556878921833444,28.642578125,389200,0.0,0 -1994-09-14,28.547364344196197,28.709240659690305,28.547364344196197,28.671152114868164,423500,0.0,0 -1994-09-15,28.74732923022877,29.03299331665039,28.74732923022877,29.03299331665039,779800,0.0,0 -1994-09-16,28.81678065575409,28.9030010765991,28.720980188148527,28.82636070251465,571300,0.288,0 -1994-09-19,28.8934284580023,29.017969097907482,28.826368113437972,28.8551082611084,167300,0.0,0 -1994-09-20,28.70181234395242,28.730552476473065,28.309030532836914,28.309030532836914,355600,0.0,0 -1994-09-21,28.395250930398856,28.395250930398856,28.040789295977547,28.309030532836914,397500,0.0,0 -1994-09-22,28.37611367679839,28.37611367679839,28.213252794791106,28.241992950439453,266400,0.0,0 -1994-09-23,28.251569292835836,28.30904959658525,28.098288482837408,28.146188735961914,176600,0.0,0 -1994-09-26,28.251551038081306,28.309031304689814,28.1940707714728,28.289871215820312,223000,0.0,0 -1994-09-27,28.28987580547895,28.36651617339085,28.203655391578067,28.270715713500977,479500,0.0,0 -1994-09-28,28.424001829689914,28.529382355516198,28.404841734085135,28.49106216430664,324000,0.0,0 -1994-09-29,28.4910421535831,28.4910421535831,28.261121167811076,28.347341537475586,195900,0.0,0 -1994-09-30,28.33777066535756,28.471891283787244,28.309030532836914,28.309030532836914,5200,0.0,0 -1994-10-03,28.328213417384486,28.35695357303283,28.194092691025546,28.241992950439453,72600,0.0,0 -1994-10-04,28.38567830511361,28.38567830511361,27.801295457792676,27.82045555114746,84200,0.0,0 -1994-10-05,27.772552075279354,27.830032348632812,27.59053120966007,27.830032348632812,461900,0.0,0 -1994-10-06,27.849177747800205,27.86833782881004,27.695897099721495,27.743797302246094,345200,0.0,0 -1994-10-07,27.743817992618546,27.944998993255627,27.71507784967039,27.868358612060547,188700,0.0,0 -1994-10-10,27.992899563847757,28.213240662399603,27.98331951608463,28.165340423583984,213600,0.0,0 -1994-10-11,28.289873532890955,28.66349529644759,28.289873532890955,28.586854934692383,461400,0.0,0 -1994-10-12,28.625185012817383,28.663505206944315,28.558124673095257,28.625185012817383,162400,0.0,0 -1994-10-13,28.950916189762495,29.01797655155215,28.69225479428811,28.72099494934082,1564500,0.0,0 -1994-10-14,28.81679702905752,28.855117237872758,28.62519598498134,28.845537185668945,72800,0.0,0 -1994-10-17,28.816787938071965,28.855108134798122,28.759307642982723,28.788047790527344,282300,0.0,0 -1994-10-18,28.759315156077765,28.759315156077765,28.625194432498454,28.72099494934082,132100,0.0,0 -1994-10-19,28.61560703052151,28.96048880256663,28.606026981298033,28.8551082611084,136700,0.0,0 -1994-10-20,28.85510460705681,28.85510460705681,28.577283214757788,28.663503646850586,290300,0.0,0 -1994-10-21,28.586841204342427,28.625161366814737,28.423980513835115,28.548521041870117,87800,0.0,0 -1994-10-24,28.558111681349185,28.673072211431773,28.299450488663364,28.309030532836914,149600,0.0,0 -1994-10-25,28.21323558557187,28.395256460317494,28.203655539532626,28.33777618408203,91800,0.0,0 -1994-10-26,28.44316291809082,28.471903062501152,28.30904224417594,28.44316291809082,201000,0.0,0 -1994-10-27,28.548542159354763,28.615602493286133,28.481481825423394,28.615602493286133,210300,0.0,0 -1994-10-28,28.67308024296995,29.247883054389327,28.67308024296995,29.21914291381836,192100,0.0,0 -1994-10-31,29.152083998489005,29.18082414045268,29.104183761882876,29.1137638092041,36300,0.0,0 -1994-11-01,28.98922882333968,28.98922882333968,28.778467741345803,28.788047790527344,435200,0.0,0 -1994-11-02,28.701825401071247,28.96048671142803,28.625185012817383,28.625185012817383,115600,0.0,0 -1994-11-03,28.720977366684913,28.826357870698768,28.682657183407148,28.778457641601562,87000,0.0,0 -1994-11-04,28.912579097393994,28.912579097393994,28.404836654663086,28.404836654663086,124400,0.0,0 -1994-11-07,28.471902068701862,28.567702546725755,28.395261686282744,28.49106216430664,115000,0.0,0 -1994-11-08,28.586864172318478,28.80720528356624,28.538963930742877,28.71140480041504,308300,0.0,0 -1994-11-09,29.0179691911935,29.0179691911935,28.51022658071702,28.759307861328125,318500,0.0,0 -1994-11-10,28.778459891262006,28.835940170671986,28.538958727053767,28.55811882019043,172100,0.0,0 -1994-11-11,28.51981533962468,28.548555496164443,28.376114556925867,28.452754974365234,302200,0.0,0 -1994-11-14,28.577294174077274,28.730575001025056,28.577294174077274,28.72099494934082,181400,0.0,0 -1994-11-15,28.692245352539512,28.874266274642434,28.404843896587533,28.625185012817383,316900,0.0,0 -1994-11-16,28.673094690919637,28.72099494934082,28.577294174077274,28.72099494934082,106900,0.0,0 -1994-11-17,28.740139917049575,28.740139917049575,28.414418331323013,28.52937889099121,104700,0.0,0 -1994-11-18,28.53896240331859,28.586862642330537,28.366521542875574,28.49106216430664,269300,0.0,0 -1994-11-21,28.47189267821934,28.558113080003988,28.20365142822266,28.203651428222656,283100,0.0,0 -1994-11-22,28.0982572555542,28.2036376953125,27.59051513671875,27.59051513671875,483600,0.0,0 -1994-11-23,27.609676735177224,27.753377342751012,27.351015641544404,27.743797302246094,601600,0.0,0 -1994-11-25,27.762976750639297,27.916257505646275,27.762976750639297,27.87793731689453,77300,0.0,0 -1994-11-28,27.868360783031775,28.01206150896697,27.80130044426202,28.00248146057129,79900,0.0,0 -1994-11-29,27.992883682250977,28.050363936218638,27.89708325897154,27.992883682250977,105500,0.0,0 -1994-11-30,28.136599300821157,28.174919490539676,27.954578399658203,27.954578399658203,218800,0.0,0 -1994-12-01,27.98331933897975,27.98331933897975,27.619277526285046,27.6767578125,439000,0.0,0 -1994-12-02,27.619254879500147,27.935396194458008,27.619254879500147,27.935396194458008,318500,0.0,0 -1994-12-05,27.992889608165587,28.088690051725358,27.897089164605813,27.964149475097656,139200,0.0,0 -1994-12-06,27.858770117780924,27.992890744207656,27.81086989405709,27.98331069946289,249900,0.0,0 -1994-12-07,27.81086560282214,27.897085992221175,27.753385343222785,27.78212547302246,531900,0.0,0 -1994-12-08,27.897108518977685,27.935428722987268,27.437266070862666,27.513906478881836,261500,0.0,0 -1994-12-09,27.51387462688741,27.638415145741707,27.398914147944986,27.61925506591797,235900,0.0,0 -1994-12-12,27.56179316959079,27.801294326782227,27.56179316959079,27.801294326782227,151100,0.0,0 -1994-12-13,27.78213093708849,27.91625156919857,27.78213093708849,27.90667152404785,70900,0.0,0 -1994-12-14,27.86835789875906,28.174919415692816,27.86835789875906,28.050378799438477,203300,0.0,0 -1994-12-15,28.0887021593062,28.222822838102346,28.0887021593062,28.1366024017334,130100,0.0,0 -1994-12-16,28.225746625108144,28.331967361637805,28.19677733332733,28.274028778076172,266100,0.363,0 -1994-12-19,28.274040201351067,28.331978808321047,28.187132290896095,28.312665939331055,1120200,0.0,0 -1994-12-20,28.341632895872603,28.360945763263317,28.22575569152832,28.22575569152832,675600,0.0,0 -1994-12-21,28.245062285689944,28.59269382151382,28.245062285689944,28.52509880065918,544500,0.0,0 -1994-12-22,28.52509260323335,28.554061891591406,28.409215449801124,28.43818473815918,222000,0.0,0 -1994-12-23,28.41889806033151,28.53477532051635,28.41889806033151,28.467180252075195,125800,0.0,0 -1994-12-27,28.56371784210205,28.67959499359131,28.544404983520508,28.62165641784668,95200,0.0,0 -1994-12-28,28.650622347485378,28.650622347485378,28.40921164351263,28.476806640625,358200,0.0,0 -1994-12-29,28.58303404872355,28.58303404872355,28.467156883661158,28.496126174926758,220100,0.0,0 -1994-12-30,28.554047873644837,28.583017147781103,28.15813446044922,28.15813446044922,2209500,0.0,0 -1995-01-03,28.245056627553478,28.331964494099797,28.23540019793722,28.293338775634766,324300,0.0,0 -1995-01-04,28.418869496039722,28.428525924682617,28.2740230663963,28.428525924682617,351800,0.0,0 -1995-01-05,28.447838781968407,28.496120925182883,28.399556638753932,28.428525924682617,89800,0.0,0 -1995-01-06,28.48645256542132,28.583016811405802,28.37057547023995,28.457483291625977,448400,0.0,0 -1995-01-09,28.44785116680598,28.48647689819336,28.428538301112287,28.48647689819336,36800,0.0,0 -1995-01-10,28.554056160497616,28.66993329067211,28.515430450439453,28.515430450439453,229800,0.0,0 -1995-01-11,28.61199568540312,28.61199568540312,28.312646422410374,28.534744262695312,222400,0.0,0 -1995-01-12,28.50578117112507,28.56371975074118,28.447842591508962,28.54440689086914,40300,0.0,0 -1995-01-13,28.69890737836475,28.88237953186035,28.66028166183936,28.88237953186035,170600,0.0,0 -1995-01-16,28.872725948537852,29.06585455020031,28.872725948537852,29.056198120117188,105100,0.0,0 -1995-01-17,28.99825240290442,29.085160252263773,28.95962669207804,29.065847396850586,89500,0.0,0 -1995-01-18,29.046531197713147,29.09481333068807,28.969279784953272,29.036874771118164,84500,0.0,0 -1995-01-19,28.940327187605128,28.969296484089217,28.85341929815286,28.872732162475586,139100,0.0,0 -1995-01-20,28.834091648794573,28.843748076004484,28.70855809506573,28.766496658325195,78700,0.0,0 -1995-01-23,28.573379937903468,28.853416442871094,28.554067075491908,28.853416442871094,53700,0.0,0 -1995-01-24,28.84376000401808,28.892042160034176,28.82444714161164,28.89204216003418,32400,0.0,0 -1995-01-25,28.74720514043172,29.075523909250894,28.74720514043172,28.969303131103516,15700,0.0,0 -1995-01-26,28.949972633516794,29.036880489988324,28.89203406253577,28.998254776000977,9800,0.0,0 -1995-01-27,29.191391005128573,29.191391005128573,28.998262384519055,29.114139556884766,91200,0.0,0 -1995-01-30,29.0562138979948,29.07552676864817,28.949993109401266,28.988618850708008,26600,0.0,0 -1995-01-31,29.04655167972638,29.15277244717219,28.99826951270556,29.104490280151367,127500,0.0,0 -1995-02-01,29.14311362974595,29.249334388502483,29.046549303603655,29.094831466674805,380200,0.0,0 -1995-02-02,29.085155072994098,29.268627166748047,29.085155072994098,29.268627166748047,131700,0.0,0 -1995-02-03,29.452100820795778,29.732137189255802,29.40381868830267,29.683855056762695,405100,0.0,0 -1995-02-06,29.674197242373513,29.89629504145408,29.645227964232568,29.80938720703125,405400,0.0,0 -1995-02-07,29.857682706005402,29.857682706005402,29.75146197192841,29.848026275634766,702900,0.0,0 -1995-02-08,29.809400554152223,29.954247009711757,29.790087693410953,29.848026275634766,521500,0.0,0 -1995-02-09,29.799744123781586,29.848026275634766,29.741805541557774,29.848026275634766,390700,0.0,0 -1995-02-10,29.809404354680915,29.886655807495117,29.722496470264936,29.886655807495117,148300,0.0,0 -1995-02-13,29.867339607357057,29.954247482063234,29.867339607357057,29.89630889892578,79700,0.0,0 -1995-02-14,29.992863644015404,29.992863644015404,29.8190479500063,29.934925079345703,170200,0.0,0 -1995-02-15,29.934950245088366,30.13773539190993,29.90598093839957,30.099109649658203,431500,0.0,0 -1995-02-16,30.060474395751957,30.08944369327436,29.97356650318473,30.060474395751953,99300,0.0,0 -1995-02-17,30.03149557252039,30.05080843140947,29.934931278074988,29.94458770751953,49100,0.0,0 -1995-02-21,29.944581506790655,29.992863644015404,29.925268651900755,29.934925079345703,168000,0.0,0 -1995-02-22,29.963901510614257,30.15703010559082,29.9445886511166,30.15703010559082,386400,0.0,0 -1995-02-23,30.272915384653583,30.37913614038921,30.195663925936763,30.205320358276367,402800,0.0,0 -1995-02-24,30.24393272886471,30.29221486923646,30.166681304269908,30.28255844116211,307600,0.0,0 -1995-02-27,30.195651995794677,30.282559852514265,29.992866996782304,30.041149139404297,280900,0.0,0 -1995-02-28,30.012177038998374,30.292213439941406,30.012177038998374,30.292213439941406,493500,0.0,0 -1995-03-01,30.263269054228044,30.301894796479772,30.07014034296941,30.099109649658203,242600,0.0,0 -1995-03-02,30.089449380460895,30.137731552124023,30.002541471467268,30.137731552124023,488600,0.0,0 -1995-03-03,30.041148672647616,30.14736938476562,29.9638972456527,30.147369384765625,290000,0.0,0 -1995-03-06,29.944577764152108,30.166675567626953,29.90595205919996,30.166675567626953,85700,0.0,0 -1995-03-07,30.060458636130058,30.128053628244707,29.7997350951164,29.934925079345703,180900,0.0,0 -1995-03-08,29.983218664368145,30.050813682291036,29.876997921917887,30.012187957763672,155900,0.0,0 -1995-03-09,30.002525329589844,30.031494617001744,29.925273896491447,30.002525329589844,63500,0.0,0 -1995-03-10,30.03149368329688,30.5239715539876,29.992867967948587,30.446720123291016,192100,0.0,0 -1995-03-13,30.4274134651063,30.523977773151703,30.388787741888137,30.417757034301758,261300,0.0,0 -1995-03-14,30.504658705347385,30.678474424466174,30.504658705347385,30.639848709106445,223300,0.0,0 -1995-03-15,30.59156751617634,30.639849661877374,30.466033937353647,30.581911087036133,278500,0.0,0 -1995-03-16,30.552935646378014,30.78468989906862,30.552935646378014,30.765377044677734,20400,0.0,0 -1995-03-17,30.718299478480375,30.834803774858937,30.698882095750616,30.795969009399414,89900,0.268,0 -1995-03-20,30.834803774858937,30.834803774858937,30.737716861210135,30.795969009399414,91700,0.0,0 -1995-03-21,30.79600190453222,30.990175939239233,30.66978878197266,30.718332290649414,104400,0.0,0 -1995-03-22,30.77657024454611,30.77657024454611,30.65035718045176,30.74744415283203,74900,0.0,0 -1995-03-23,30.708604972119513,30.854235409862728,30.66977018872132,30.766857147216797,220500,0.0,0 -1995-03-24,30.863953778516752,31.203758239746097,30.863953778516752,31.203758239746094,134000,0.0,0 -1995-03-27,31.252282696166272,31.329952239990234,31.17461315234231,31.329952239990234,132100,0.0,0 -1995-03-28,31.252282696166272,31.329952239990234,31.213447924254293,31.329952239990234,121900,0.0,0 -1995-03-29,31.300834073978997,31.621221023247394,31.14549494706099,31.32025146484375,246100,0.0,0 -1995-03-30,31.38821025516676,31.38821025516676,31.135784190634023,31.26199722290039,298400,0.0,0 -1995-03-31,31.019289312906864,31.17462847691516,30.78628056689442,31.135793685913086,541300,0.0,0 -1995-04-03,31.12606743297903,31.21344566345215,31.10665004842945,31.21344566345215,193300,0.0,0 -1995-04-04,31.22316730626581,31.417341232299805,31.22316730626581,31.417341232299805,66900,0.0,0 -1995-04-05,31.349380358187908,31.446467321204903,31.329962965584507,31.417341232299805,107200,0.0,0 -1995-04-06,31.495011115896293,31.562971990683984,31.43675893750684,31.533845901489258,352500,0.0,0 -1995-04-07,31.601822960358373,31.601822960358373,31.35910542610063,31.504735946655273,361400,0.0,0 -1995-04-10,31.456177358589184,31.562973022460938,31.436759965157954,31.562973022460938,285400,0.0,0 -1995-04-11,31.640655421622007,31.65036412227301,31.436772707950922,31.45619010925293,250300,0.0,0 -1995-04-12,31.504720842167252,31.562973022460938,31.44646866187357,31.562973022460938,150900,0.0,0 -1995-04-13,31.621218062570314,31.74743109137394,31.601800673523602,31.737722396850586,243400,0.0,0 -1995-04-17,31.85423036780724,31.883356454702522,31.456173846905045,31.553260803222656,178900,0.0,0 -1995-04-18,31.582393937141816,31.582393937141816,31.36880258558414,31.44647216796875,329500,0.0,0 -1995-04-19,31.446467321204903,31.5144281953168,31.252293395170913,31.417341232299805,223000,0.0,0 -1995-04-20,31.50472376751922,31.553267255604766,31.339675908028365,31.46588897705078,207900,0.0,0 -1995-04-21,31.475596804175233,31.630935961752904,31.475596804175233,31.621227264404297,145000,0.0,0 -1995-04-24,31.601823372616572,31.990171432495117,31.601823372616572,31.990171432495117,169000,0.0,0 -1995-04-25,31.941605775151686,31.990149248974106,31.883353606564782,31.90277099609375,293200,0.0,0 -1995-04-26,31.84453507838332,31.95133077529253,31.766865480631164,31.931913375854492,204400,0.0,0 -1995-04-27,31.88335422635042,32.02898465064861,31.863936836443994,32.00956726074219,502200,0.0,0 -1995-04-28,31.999853308600848,32.10664893554096,31.795970748078815,32.05810546875,130800,0.0,0 -1995-05-01,32.029006833477006,32.09696774521824,31.970754623413086,31.970754623413086,518700,0.0,0 -1995-05-02,31.9998691258286,32.08724740924257,31.93190823872884,32.03870391845703,228400,0.0,0 -1995-05-03,32.14550510709755,32.48530960083008,32.14550510709755,32.48530960083008,724700,0.0,0 -1995-05-04,32.52414881327506,32.737740238317464,32.38822699733899,32.46589660644531,311400,0.0,0 -1995-05-05,32.60181577990869,32.60181577990869,32.35909827112438,32.427059173583984,314900,0.0,0 -1995-05-08,32.39792603006261,32.76685656321885,32.368799935339744,32.66006088256836,183100,0.0,0 -1995-05-09,32.795991211482296,32.83482601457464,32.58239979447442,32.630943298339844,180600,0.0,0 -1995-05-10,32.71829367605042,32.7280023685359,32.51441113385545,32.66975021362305,330300,0.0,0 -1995-05-11,32.71832156902103,32.75715637207031,32.6115258606355,32.75715637207031,351700,0.0,0 -1995-05-12,32.630923683609446,32.854223667162856,32.621214988672335,32.776554107666016,94600,0.0,0 -1995-05-15,32.863958179950714,32.93191909790039,32.79599726200104,32.93191909790039,147200,0.0,0 -1995-05-16,32.92219074318953,33.02898640764694,32.863938562576394,32.951316833496094,221600,0.0,0 -1995-05-17,32.951316817700246,32.961025514464446,32.79597766947302,32.825103759765625,189200,0.0,0 -1995-05-18,32.76685062719851,32.78626802016277,32.3493766784668,32.3493766784668,577800,0.0,0 -1995-05-19,32.300832613399805,32.42704566539085,32.252289131864785,32.36879348754883,363900,0.0,0 -1995-05-22,32.475597361222604,32.77656702286622,32.45617996369721,32.728023529052734,216000,0.0,0 -1995-05-23,32.78628181444064,33.02899932861328,32.68919480877158,33.02899932861328,136800,0.0,0 -1995-05-24,33.08722153607537,33.19401714549345,32.80566947488224,33.009552001953125,370800,0.0,0 -1995-05-25,32.91249842703991,33.06783765265425,32.75715920142556,33.03871154785156,379900,0.0,0 -1995-05-26,32.912487036833156,32.922195735074105,32.53384780543596,32.66006088256836,518600,0.0,0 -1995-05-30,32.73773076298192,32.8251090479365,32.54355679641619,32.650352478027344,61500,0.0,0 -1995-05-31,32.59210628995109,33.3299674987793,32.59210628995109,33.3299674987793,564500,0.0,0 -1995-06-01,33.184320612488506,33.49499886281023,33.08723365926297,33.24257278442383,810100,0.0,0 -1995-06-02,33.106657773438926,33.504714362503734,32.99015340590776,33.271705627441406,112900,0.0,0 -1995-06-05,33.271703308530306,33.58238160029365,33.242577218677496,33.4755859375,257200,0.0,0 -1995-06-06,33.44646923767834,33.56297362979798,33.41734313964844,33.41734313964844,165800,0.0,0 -1995-06-07,33.34936843297192,33.368785823617024,33.203738003133616,33.24257278442383,41900,0.0,0 -1995-06-08,33.23286642012049,33.3299533801559,33.1649055480957,33.1649055480957,141100,0.0,0 -1995-06-09,33.096942869035885,33.13577765092973,32.776555918411596,32.97072982788086,304900,0.0,0 -1995-06-12,33.077535186365445,33.291126549470825,33.0581177897195,33.15520477294922,378500,0.0,0 -1995-06-13,33.3105407252925,33.53384076512979,33.28141463313981,33.51442337036133,120000,0.0,0 -1995-06-14,33.46588838643079,33.52414058553685,33.35909268806969,33.48530578613281,389400,0.0,0 -1995-06-15,33.53384486852562,33.69889274425374,33.475592677092166,33.63093185424805,274500,0.0,0 -1995-06-16,33.564741615070105,33.73075866699219,33.564741615070105,33.73075866699219,325100,0.316,0 -1995-06-19,33.828423859088154,34.13116090863542,33.808892436536716,34.111629486083984,134600,0.0,0 -1995-06-20,34.013976963187744,34.131165514309835,33.994445538000726,34.02374267578125,287800,0.0,0 -1995-06-21,34.13117717696907,34.16047432476046,34.00422286987305,34.00422286987305,158200,0.0,0 -1995-06-22,34.150689338069924,34.472957781923604,34.150689338069924,34.453426361083984,297000,0.0,0 -1995-06-23,34.37530890272206,34.39484032823497,34.27765177515751,34.385074615478516,315400,0.0,0 -1995-06-26,34.287428907157015,34.346023203210265,33.97492599487305,33.97492599487305,132900,0.0,0 -1995-06-27,33.96514945315875,34.18976084977251,33.88702374998875,33.90655517578125,127700,0.0,0 -1995-06-28,33.90655360180874,34.219056399982094,33.79913076493665,34.08233642578125,212600,0.0,0 -1995-06-29,34.0823369513423,34.17022836468387,33.78936557353706,34.02374267578125,89200,0.0,0 -1995-06-30,34.092114313247215,34.20930290441277,33.935862858359805,34.00422286987305,714100,0.0,0 -1995-07-03,34.04327313992087,34.13116455078125,34.04327313992087,34.13116455078125,9500,0.0,0 -1995-07-05,34.22882053979854,34.41436906768903,34.15069484384465,34.25811767578125,409300,0.0,0 -1995-07-06,34.26787952151461,34.69757080078125,34.19951954526765,34.69757080078125,202500,0.0,0 -1995-07-07,34.629228409681375,34.86360559011915,34.60969697797823,34.85383987426758,481700,0.0,0 -1995-07-10,34.88313684006827,34.97102828227449,34.81477682946343,34.87337112426758,400400,0.0,0 -1995-07-11,34.84407552655789,34.87337267526295,34.65852691809247,34.70735549926758,420500,0.0,0 -1995-07-12,34.77570776438435,35.19563346892479,34.70734776597079,35.13703918457031,203400,0.0,0 -1995-07-13,35.088203691572865,35.16632938863175,35.01984370664634,35.05890655517578,215600,0.0,0 -1995-07-14,34.873358284892035,35.058906816791485,34.84406114827633,35.02960968017578,543700,0.0,0 -1995-07-17,35.07844410201734,35.26399266491777,35.049146960506754,35.22492980957031,171500,0.0,0 -1995-07-18,35.166319092914826,35.166319092914826,34.89287923326428,34.922176361083984,221200,0.0,0 -1995-07-19,34.78545589948529,34.824518735981066,33.877244950958584,34.54131317138672,486600,0.0,0 -1995-07-20,34.58039013714858,34.736641546974724,34.39484158798003,34.66828155517578,318900,0.0,0 -1995-07-21,34.638984768389314,34.76593904016519,34.49249907018637,34.62921905517578,94000,0.0,0 -1995-07-24,34.678049133446024,34.922191974430575,34.678049133446024,34.88312911987305,108400,0.0,0 -1995-07-25,34.92218201582416,35.1956219197512,34.814759196424255,35.146793365478516,107400,0.0,0 -1995-07-26,35.127269832920845,35.29328695473337,35.107738406825256,35.11750411987305,167800,0.0,0 -1995-07-27,35.283507206659586,35.43975855327086,35.283507206659586,35.41046142578125,187700,0.0,0 -1995-07-28,35.43976818744322,35.43976818744322,35.15656254472185,35.18585968017578,415600,0.0,0 -1995-07-31,35.21516525446871,35.24446239694331,35.01985097130471,35.09797668457031,342500,0.0,0 -1995-08-01,35.146809618342814,35.146809618342814,34.84407244185806,35.03938674926758,141000,0.0,0 -1995-08-02,35.24445596407246,35.49836448584189,34.90265603092129,34.96125030517578,240400,0.0,0 -1995-08-03,34.717115160805385,34.97102373863406,34.65852087361415,34.95149230957031,1193600,0.0,0 -1995-08-04,35.019848134974815,35.049145275076576,34.95148814140403,34.99055099487305,240500,0.0,0 -1995-08-07,35.06869125366211,35.17611414527734,35.04915981882298,35.06869125366211,193100,0.0,0 -1995-08-08,35.08822268850125,35.19564558011647,34.96126836204689,35.06869125366211,951900,0.0,0 -1995-08-09,35.18586081289647,35.18586081289647,35.0296094187787,35.05890655517578,107100,0.0,0 -1995-08-10,35.0979662756225,35.176091965718484,34.83429207154854,34.980777740478516,280800,0.0,0 -1995-08-11,34.95149767177953,34.99056053589998,34.63899475881586,34.78548049926758,257200,0.0,0 -1995-08-14,34.87336525893171,35.09797668457031,34.795239545666114,35.09797668457031,254800,0.0,0 -1995-08-15,35.08820395340719,35.08820395340719,34.785466875044925,35.02960968017578,44400,0.0,0 -1995-08-16,35.000319276480404,35.12727355957031,34.96125642014504,35.12727355957031,374900,0.0,0 -1995-08-17,35.14681699301864,35.14681699301864,34.94173692720775,35.06869125366211,353800,0.0,0 -1995-08-18,35.244439330120905,35.25420503788764,35.07842229808654,35.10771942138672,85400,0.0,0 -1995-08-21,35.254221236522895,35.40070692033116,35.0003127179219,35.01007843017578,266700,0.0,0 -1995-08-22,35.05890638100809,35.13703207767886,34.883123563498856,35.07843780517578,220200,0.0,0 -1995-08-23,35.10774374208027,35.127275171144014,34.95149230957031,34.95149230957031,176500,0.0,0 -1995-08-24,34.961253854771286,35.05891098844383,34.84406529436423,34.99055099487305,167400,0.0,0 -1995-08-25,35.049139714724085,35.244453951083656,35.049139714724085,35.18585968017578,195000,0.0,0 -1995-08-28,35.283517934220086,35.283517934220086,35.000312282381614,35.05890655517578,293000,0.0,0 -1995-08-29,35.09796481120583,35.15655907633305,34.853822039842406,35.146793365478516,1133100,0.0,0 -1995-08-30,35.17610124038599,35.26399266491777,35.13703838503853,35.22492980957031,437400,0.0,0 -1995-08-31,35.215152398106795,35.312809504036096,35.185855266328005,35.254215240478516,491900,0.0,0 -1995-09-01,35.24444437000677,35.469055680760476,35.21514724251715,35.41046142578125,629900,0.0,0 -1995-09-05,35.517915499536066,35.76205844358017,35.45932119296548,35.74252700805664,272200,0.0,0 -1995-09-06,35.771789217892476,35.84991488367002,35.762023509670286,35.81085205078125,214500,0.0,0 -1995-09-07,35.84993761779502,35.869469046625206,35.78157761688939,35.830406188964844,258800,0.0,0 -1995-09-08,35.967124938964844,35.967124938964844,35.73274780116002,35.967124938964844,107300,0.0,0 -1995-09-11,36.025703091367035,36.13312589809109,36.025703091367035,36.06476593017578,260700,0.0,0 -1995-09-12,36.07454160088799,36.23079299926758,35.98665018929947,36.23079299926758,139500,0.0,0 -1995-09-13,36.19172108585981,36.4456295446381,36.181955375906796,36.39680099487305,239800,0.0,0 -1995-09-14,36.52375993878243,36.75813700790831,36.426102826646655,36.72883987426758,457600,0.0,0 -1995-09-15,36.69907236558231,36.80706856569505,36.571440492721806,36.71870803833008,431200,0.312,0 -1995-09-18,36.5910899653207,36.610725645473806,36.40455100386615,36.58127212524414,307500,0.0,0 -1995-09-19,36.64999175071716,36.777623653411865,36.52235984802246,36.75798797607422,549600,0.0,0 -1995-09-20,36.816878372022025,36.9346923828125,36.78742486932441,36.9346923828125,290800,0.0,0 -1995-09-21,36.885635003810975,36.91508853189493,36.4831034533302,36.63037109375,508500,0.0,0 -1995-09-22,36.335818076057066,36.67944240263421,36.30636456235045,36.64017105102539,449900,0.0,0 -1995-09-25,36.679450526009695,36.679450526009695,36.45364020424893,36.58127212524414,130000,0.0,0 -1995-09-26,36.68925951759287,36.77762005697161,36.48308492570916,36.57144546508789,466600,0.0,0 -1995-09-27,36.41436789921059,36.561635496538216,36.18855758330823,36.54199981689453,654400,0.0,0 -1995-09-28,36.57145614624023,36.81690216064453,36.54200262451172,36.81690216064453,456200,0.0,0 -1995-09-29,36.78743647993996,37.01324673855716,36.69907594395932,36.748165130615234,606600,0.0,0 -1995-10-02,36.74818068134541,36.8365412547176,36.47328111974304,36.561641693115234,293100,0.0,0 -1995-10-03,36.56163882390624,36.65981723105959,36.414371213176224,36.60091018676758,839700,0.0,0 -1995-10-04,36.620548742030024,36.620548742030024,36.512552485686236,36.561641693115234,248800,0.0,0 -1995-10-05,36.60089184864458,36.66961669921875,36.50271349068147,36.66961669921875,268800,0.0,0 -1995-10-06,36.689244513718315,36.807058518043874,36.689244513718315,36.69906234741211,75300,0.0,0 -1995-10-09,36.561625214899074,36.58126088902093,36.32599712543678,36.39472198486328,358200,0.0,0 -1995-10-10,36.02163803471245,36.37508010864258,35.98236669316466,36.37508010864258,360800,0.0,0 -1995-10-11,36.50273013275286,36.50273013275286,36.36528036893937,36.49291229248047,228100,0.0,0 -1995-10-12,36.52235131085238,36.738343711077846,36.52235131085238,36.71870803833008,199800,0.0,0 -1995-10-13,36.885625608440144,36.98380401037903,36.8365364074707,36.8365364074707,488100,0.0,0 -1995-10-16,36.72853398233668,36.79725885212453,36.65980911254883,36.65980911254883,443600,0.0,0 -1995-10-17,36.69907975862812,36.94452571742044,36.630354890166274,36.90525436401367,146000,0.0,0 -1995-10-18,37.08197712986792,37.13106632384444,36.89543819275712,37.01325225830078,228100,0.0,0 -1995-10-19,36.9936151554869,37.189971923828125,36.944525963401595,37.189971923828125,500600,0.0,0 -1995-10-20,37.160510013777895,37.18996352237534,36.96415328979492,36.96415328979492,748400,0.0,0 -1995-10-23,36.787434120777284,36.95433734470395,36.787434120777284,36.88561248779297,533000,0.0,0 -1995-10-24,36.934698817388266,36.973970161160075,36.836520457958734,36.92488098144531,172200,0.0,0 -1995-10-25,36.95433560696108,37.00342478816055,36.62052917480469,36.62052917480469,302700,0.0,0 -1995-10-26,36.67944526672363,36.7088987827301,35.992196559906006,36.2867317199707,606800,0.0,0 -1995-10-27,36.247470765569666,36.561641693115234,36.08056746031109,36.561641693115234,905800,0.0,0 -1995-10-30,36.64997750285341,36.816880695338945,36.64997750285341,36.797245025634766,488700,0.0,0 -1995-10-31,36.89543483648269,36.97397753970033,36.64017105102539,36.64017105102539,508200,0.0,0 -1995-11-01,36.620521687371244,36.9346923828125,36.591068184673624,36.9346923828125,415700,0.0,0 -1995-11-02,36.87579872868896,37.17033386230469,36.87579872868896,37.17033386230469,326000,0.0,0 -1995-11-03,37.21943664550781,37.22925448688093,37.111440390403466,37.21943664550781,615000,0.0,0 -1995-11-06,37.209614773412554,37.209614773412554,37.08198284938766,37.091800689697266,311200,0.0,0 -1995-11-07,37.072171250767965,37.072171250767965,36.83654304366563,36.9543571472168,358200,0.0,0 -1995-11-08,37.003433469598576,37.30778646444006,37.003433469598576,37.288150787353516,357200,0.0,0 -1995-11-09,37.43542361384549,37.43542361384549,37.24888465537104,37.42560577392578,503700,0.0,0 -1995-11-10,37.21942497013122,37.455053089171884,37.20960713183786,37.40596389770508,795600,0.0,0 -1995-11-13,37.337238075228285,37.48450564586421,37.26851320893152,37.36669158935547,818500,0.0,0 -1995-11-14,37.297952094553516,37.37649476808771,37.12123107910156,37.12123107910156,341800,0.0,0 -1995-11-15,37.16051723483221,37.504141568575704,37.1408815586183,37.49432373046875,584700,0.0,0 -1995-11-16,37.49432208240032,37.73976802428563,37.48450424472491,37.700496673583984,577700,0.0,0 -1995-11-17,37.75940656810535,37.82813143705406,37.710317375999125,37.81831359863281,267000,0.0,0 -1995-11-20,37.87721582345027,37.88703366062587,37.61213421970917,37.621952056884766,448800,0.0,0 -1995-11-21,37.661241207967116,37.92632293701172,37.651423366150645,37.92632293701172,119200,0.0,0 -1995-11-22,37.89683981642603,37.93611115302336,37.80847930908203,37.80847930908203,351600,0.0,0 -1995-11-24,37.847774019515505,37.906681060791016,37.81832049887775,37.906681060791016,62400,0.0,0 -1995-11-27,37.98522497519874,38.10303906144903,37.916500091552734,37.916500091552734,273000,0.0,0 -1995-11-28,37.88703824433491,38.31902313232422,37.79867769906437,38.31902313232422,479000,0.0,0 -1995-11-29,38.387745138870834,38.387745138870834,38.23065973676804,38.35829162597656,549200,0.0,0 -1995-11-30,38.358299980535456,38.45647837823327,38.2306680635283,38.26993942260742,286200,0.0,0 -1995-12-01,38.31902313232422,38.377930162504576,38.23066258705368,38.31902313232422,465200,0.0,0 -1995-12-04,38.407364806897185,38.790260314941406,38.35827563919921,38.790260314941406,631700,0.0,0 -1995-12-05,38.75101230190662,39.08481884314422,38.72155878356212,39.04554748535156,567700,0.0,0 -1995-12-06,39.143717526073615,39.29098508636735,38.957178616368225,39.1338996887207,272800,0.0,0 -1995-12-07,39.08480831978451,39.13389750380835,38.8982694204939,38.9277229309082,289700,0.0,0 -1995-12-08,39.09463023803772,39.10444807586747,38.80991294097517,39.05535888671875,296900,0.0,0 -1995-12-11,39.13389518078934,39.300798396562904,39.055352491013544,39.22225570678711,186500,0.0,0 -1995-12-12,39.04552795980972,39.18297764334941,39.04552795980972,39.104434967041016,299400,0.0,0 -1995-12-13,39.153527326450615,39.39897320989125,39.153527326450615,39.349884033203125,390700,0.0,0 -1995-12-14,39.38917426842485,39.45789914875361,38.967007146405344,39.065185546875,395000,0.0,0 -1995-12-15,39.118993352228514,39.118993352228514,38.94117974608202,39.0794792175293,416700,0.382,0 -1995-12-18,38.733728508113586,38.733728508113586,38.31883011552476,38.32870864868164,862300,0.0,0 -1995-12-19,38.34847128181487,38.7831268038138,38.29907860886044,38.733734130859375,1022600,0.0,0 -1995-12-20,38.85226397203656,38.89177809761198,38.35833740234375,38.35833740234375,1349800,0.0,0 -1995-12-21,38.58555143269825,38.674458233234425,38.40773783162591,38.55591583251953,857600,0.0,0 -1995-12-22,38.76337506292059,38.80288920671562,38.65471116748427,38.6942253112793,332800,0.0,0 -1995-12-26,38.85227281582065,38.90166548403298,38.773244546680914,38.88190841674805,432200,0.0,0 -1995-12-27,38.98069067927381,38.98069067927381,38.792998554867786,38.86214828491211,151800,0.0,0 -1995-12-28,38.783148193359374,38.980718994140624,38.723876953125,38.822662353515625,256200,0.0,0 -1995-12-29,38.86216507248836,38.901679221011484,38.72386555265744,38.87204360961914,339200,0.0,0 -1996-01-02,38.82262013382368,39.28691101074219,38.78310601663913,39.28691101074219,514400,0.0,0 -1996-01-03,39.41535643612968,39.514141790606196,39.198028656281345,39.395599365234375,610300,0.0,0 -1996-01-04,39.44499880730351,39.59317686443087,38.704108521666704,39.02022171020508,1129700,0.0,0 -1996-01-05,38.76337516471131,39.03997417200283,38.674468340939036,38.94118881225586,302400,0.0,0 -1996-01-08,39.079492190584915,39.13876341230977,39.030099505814206,39.08937072753906,179900,0.0,0 -1996-01-09,39.20790828509636,39.23754389226574,38.32871860573793,38.417625427246094,415500,0.0,0 -1996-01-10,38.36822326087653,38.44725152711933,37.70636153109314,37.91381072998047,787700,0.0,0 -1996-01-11,37.97309423771216,38.141029357910156,37.795280581031925,38.141029357910156,513200,0.0,0 -1996-01-12,38.23981316909097,38.249691705171855,37.72612929288515,38.081756591796875,390400,0.0,0 -1996-01-15,38.0916228605488,38.22004378824256,37.8446595380608,38.002716064453125,154200,0.0,0 -1996-01-16,38.19041912703864,38.496653734627415,37.864427447992526,38.467018127441406,454600,0.0,0 -1996-01-17,38.378104382357236,38.64482479891416,38.22004783921239,38.34846878051758,407600,0.0,0 -1996-01-18,38.49663999897831,38.50651853118231,38.17064843624639,38.47688293457031,425600,0.0,0 -1996-01-19,38.47689223149637,38.83251947676822,38.43737809313283,38.733734130859375,169800,0.0,0 -1996-01-22,38.70409624604532,38.832517188158285,38.634946507984495,38.74361038208008,288200,0.0,0 -1996-01-23,38.72384804255239,38.83251190185547,38.64481978124106,38.83251190185547,362200,0.0,0 -1996-01-24,38.891786364031475,39.227656502811534,38.87202929704441,39.14862823486328,1506500,0.0,0 -1996-01-25,39.178261444640476,39.19801851042194,38.96093372104439,39.01032638549805,298400,0.0,0 -1996-01-26,38.98068951590708,39.36595228608457,38.93129685306381,39.346195220947266,726300,0.0,0 -1996-01-29,39.316580243693735,39.5042724609375,39.28694463044472,39.5042724609375,254800,0.0,0 -1996-01-30,39.58327279349813,39.91914283966208,39.53388013965049,39.84011459350586,272100,0.0,0 -1996-01-31,39.830262114167944,40.26491775231858,39.81050503970655,40.25503921508789,498000,0.0,0 -1996-02-01,40.215490935309305,40.40318298339844,40.18585534876891,40.40318298339844,373900,0.0,0 -1996-02-02,40.3834303292308,40.47233709854662,40.146345611055274,40.235252380371094,496200,0.0,0 -1996-02-05,40.16613397723689,40.62054670791886,40.10686275149577,40.561275482177734,295300,0.0,0 -1996-02-06,40.58101223530326,40.986032075042175,40.52174103924391,40.94651794433594,301600,0.0,0 -1996-02-07,40.93664565421489,41.18360900878906,40.897131517483025,41.18360900878906,585100,0.0,0 -1996-02-08,41.12434936961576,41.66766890248361,41.04532107392589,41.62815475463867,1526600,0.0,0 -1996-02-09,41.608392338330574,41.96401962327357,41.42070016016621,41.61827087402344,804600,0.0,0 -1996-02-12,41.69729853923245,42.122075567706034,41.65778439704886,41.934383392333984,626500,0.0,0 -1996-02-13,41.75657123479222,42.1615912065515,41.72693562710251,41.84547805786133,1045900,0.0,0 -1996-02-14,41.81583152801885,41.954130994447446,41.460204328631015,41.47996139526367,431400,0.0,0 -1996-02-15,41.39106437853135,41.66766337676497,41.17373659420493,41.22312927246094,889500,0.0,0 -1996-02-16,41.14411314596226,41.24289853166685,40.92678529741216,41.055206298828125,606100,0.0,0 -1996-02-20,40.73909147501281,40.857633933233025,40.38346410035218,40.650184631347656,492300,0.0,0 -1996-02-21,40.72921454053743,41.18362731494071,40.72921454053743,41.15399169921875,434000,0.0,0 -1996-02-22,41.381177036883926,41.93437491562957,41.351541436236836,41.80595397949219,721400,0.0,0 -1996-02-23,42.04303562833235,42.11218535798421,41.30214566777668,41.687408447265625,1430400,0.0,0 -1996-02-26,41.56886947338398,41.65777627596488,41.074942792378934,41.09469985961914,1408400,0.0,0 -1996-02-27,41.19349585230275,41.203374388478366,40.77859733292704,40.96628952026367,601000,0.0,0 -1996-02-28,41.292278822078266,41.5589992833692,40.778594970703125,40.778594970703125,865900,0.0,0 -1996-02-29,40.541513767727665,41.06507620186255,40.38345718383789,40.38345718383789,914300,0.0,0 -1996-03-01,40.86750190159857,41.02555847167969,40.21551855001397,41.02555847167969,1452600,0.0,0 -1996-03-04,41.114456275390154,41.50959761393307,40.976156806900136,41.26263427734375,652600,0.0,0 -1996-03-05,41.16386276947253,41.647911071777344,41.14410569590907,41.647911071777344,402000,0.0,0 -1996-03-06,41.58861744030151,41.70715981780356,41.28238296508789,41.28238296508789,624700,0.0,0 -1996-03-07,41.361431931995206,41.50960997808547,41.242889495122995,41.48985290527344,534200,0.0,0 -1996-03-08,40.581021658078896,41.01567719677302,39.198026762233944,40.146366119384766,2289900,0.0,0 -1996-03-11,39.9883165556681,40.66993558786699,39.869774115285686,40.61066436767578,1511400,0.0,0 -1996-03-12,40.343929256168934,40.49210725294722,39.770974335292884,40.29453659057617,1385300,0.0,0 -1996-03-13,40.561273196331626,40.73908686353425,40.33406684379494,40.57115173339844,1019300,0.0,0 -1996-03-14,40.630419966264206,40.976168738162876,40.610662893584276,40.74896240234375,725600,0.0,0 -1996-03-15,40.68188323379725,40.850564213059336,40.48343502290068,40.72157287597656,869200,0.285,0 -1996-03-18,41.09862473807266,41.50544357299805,41.00932304260123,41.50544357299805,917600,0.0,0 -1996-03-19,41.872560823711986,41.872560823711986,41.27721635702414,41.41613006591797,858300,0.0,0 -1996-03-20,41.614583971403746,41.614583971403746,41.04900664990399,41.36652374267578,744300,0.0,0 -1996-03-21,41.41615110316165,41.43599592879805,41.12840113143389,41.26731491088867,650800,0.0,0 -1996-03-22,41.2871364758169,41.48558462037742,41.23752443967677,41.37643814086914,344500,0.0,0 -1996-03-25,41.75350039994012,41.793190039103564,41.18792304186108,41.29706954956055,802400,0.0,0 -1996-03-26,41.22760129624836,41.60465276418035,41.18791166804499,41.495506286621094,969400,0.0,0 -1996-03-27,41.55504759122046,41.58481481728867,41.078771974129104,41.11846160888672,809600,0.0,0 -1996-03-28,40.969623212630935,41.297062681271484,40.95970080449031,41.277217864990234,1001300,0.0,0 -1996-03-29,41.39628674419606,41.39628674419606,40.880321521114034,41.07876968383789,457700,0.0,0 -1996-04-01,41.27721864833303,41.564968489871895,41.14822734005699,41.55504608154297,773400,0.0,0 -1996-04-02,41.604681763226246,41.63444900512695,41.465767967689594,41.63444900512695,638800,0.0,0 -1996-04-03,41.4856127956234,41.66421624702767,41.38638865595437,41.63444900512695,288000,0.0,0 -1996-04-04,41.63443489538693,41.733659001429324,41.59474525296998,41.60466766357422,934900,0.0,0 -1996-04-08,40.56281541742419,40.890254974365234,40.38421202272907,40.890254974365234,2217200,0.0,0 -1996-04-09,41.049004349145186,41.049004349145186,40.65210800542611,40.73148727416992,1180900,0.0,0 -1996-04-10,40.662027665546965,40.84063100814819,39.84839021591913,40.00714874267578,939500,0.0,0 -1996-04-11,40.10636324393474,40.30481135498835,39.451484477457825,39.95752716064453,1276000,0.0,0 -1996-04-12,40.23537707363875,40.523126995990296,40.1163081402519,40.463592529296875,411300,0.0,0 -1996-04-15,40.67195016400823,40.85055350700705,40.54295886073131,40.800941467285156,484200,0.0,0 -1996-04-16,40.8803009270003,41.05890418347748,40.790999298761704,40.91999053955078,429700,0.0,0 -1996-04-17,40.88031232849625,40.99938119935595,40.54295052772711,40.820777893066406,525300,0.0,0 -1996-04-18,40.92001877201414,41.00932046186577,40.70172575237682,40.88032913208008,818600,0.0,0 -1996-04-19,41.078765850667516,41.18791232998088,40.91008492809231,40.97954177856445,885100,0.0,0 -1996-04-22,41.35660400830571,41.45582810621431,41.019242075416464,41.29706954956055,828900,0.0,0 -1996-04-23,41.227596346917984,41.455811681690335,41.13829469418098,41.42604446411133,225400,0.0,0 -1996-04-24,41.63442455805265,41.63442455805265,41.18791619172463,41.277217864990234,699000,0.0,0 -1996-04-25,41.36651613062494,41.634421128352656,41.11845594754371,41.46574020385742,475500,0.0,0 -1996-04-26,41.57489089820082,41.793183881437194,41.42605477326693,41.55504608154297,563300,0.0,0 -1996-04-29,41.3764427316223,41.634425348174375,41.3764427316223,41.55504608154297,219900,0.0,0 -1996-04-30,41.55503958972125,41.63441884395185,41.356591454144734,41.525272369384766,184400,0.0,0 -1996-05-01,41.515346558024675,41.78325151908267,41.45581212223401,41.61457061767578,561900,0.0,0 -1996-05-02,41.2871548709564,41.51537033888045,40.70173258367295,40.90018081665039,1299600,0.0,0 -1996-05-03,41.13830564738015,41.26729695790016,40.483426686278584,40.840633392333984,1329300,0.0,0 -1996-05-06,40.99938962617273,40.99938962617273,40.443734781287525,40.800941467285156,647500,0.0,0 -1996-05-07,40.800963282323146,40.800963282323146,40.49336847158579,40.63228225708008,582100,0.0,0 -1996-05-08,40.43383340702158,41.13832473754883,40.056781708992915,41.13832473754883,1694700,0.0,0 -1996-05-09,40.99941210754822,41.31692933587386,40.95972245400751,41.108558654785156,593400,0.0,0 -1996-05-10,41.515357971191406,41.65427169289234,41.31690979733294,41.515357971191406,925100,0.0,0 -1996-05-13,41.61458095068198,42.319071949131775,41.57489131696651,42.14046859741211,867500,0.0,0 -1996-05-14,42.30915869300605,42.507606904511704,42.26946905070491,42.398460388183594,632300,0.0,0 -1996-05-15,42.40838089632787,42.7457428407535,42.31907920515638,42.348846435546875,466100,0.0,0 -1996-05-16,42.11069382031387,42.507590086292325,42.09084900701495,42.438133239746094,514400,0.0,0 -1996-05-17,42.5472900390625,42.73581581115723,42.5472900390625,42.666358947753906,427700,0.0,0 -1996-05-20,42.96402015430488,43.02355459331778,42.537356674712484,42.954097747802734,788500,0.0,0 -1996-05-21,43.02357983065236,43.1029591292329,42.86482123349128,42.894588470458984,352700,0.0,0 -1996-05-22,42.9441811048471,43.301387786865234,42.79534498733954,43.301387786865234,1218100,0.0,0 -1996-05-23,43.36093678761889,43.460160899261034,42.88466105173657,43.122798919677734,967900,0.0,0 -1996-05-24,43.162493472959426,43.390708955690016,43.142648648374156,43.29148483276367,659100,0.0,0 -1996-05-28,43.42046242263699,43.42046242263699,42.74573860071301,42.85488510131836,456400,0.0,0 -1996-05-29,42.94417100741869,42.98386062942185,42.42820592137761,42.56711959838867,649600,0.0,0 -1996-05-30,42.507588275067214,43.00370858640087,42.38851940034714,42.78541564941406,895800,0.0,0 -1996-05-31,42.775509289714776,42.954112668795005,42.46791458129883,42.46791458129883,923500,0.0,0 -1996-06-03,42.47784002451617,42.616753773720376,42.358771096626846,42.58698654174805,503100,0.0,0 -1996-06-04,42.7854270952711,42.9243408196064,42.70604782422236,42.88465118408203,627100,0.0,0 -1996-06-05,42.904492981936954,43.3113117174271,42.815191308292775,43.26169967651367,428000,0.0,0 -1996-06-06,43.49982276112667,43.49982276112667,42.864788852205116,42.94416809082031,523300,0.0,0 -1996-06-07,42.03130684674557,42.954090495647215,42.03130684674557,42.94416809082031,1422800,0.0,0 -1996-06-10,42.88467717071236,43.013668564330885,42.73584094730637,42.8052978515625,548800,0.0,0 -1996-06-11,42.96403888428018,43.202176744146854,42.76559066772461,42.76559066772461,688700,0.0,0 -1996-06-12,42.90451133511666,43.01365787181562,42.616761374728505,42.68621826171875,1120600,0.0,0 -1996-06-13,42.78544238599053,42.844976860553594,42.43815795103931,42.68621826171875,1502000,0.0,0 -1996-06-14,42.70604705098185,42.70604705098185,42.36868515513302,42.507598876953125,1468200,0.0,0 -1996-06-17,42.527463934795236,42.68622254957749,42.368705320012985,42.47785186767578,1101000,0.0,0 -1996-06-18,42.44807517275853,42.567144107324474,42.12063560270219,42.130558013916016,718000,0.0,0 -1996-06-19,42.32901039436582,42.54730346250366,42.18017421154456,42.319087982177734,552100,0.0,0 -1996-06-20,42.358752039887435,42.49766572659589,42.0313126355032,42.2892951965332,1234400,0.0,0 -1996-06-21,42.49345725891855,42.65305709838867,42.37375737931597,42.65305709838867,573300,0.351,0 -1996-06-24,42.752787647906246,42.912387415140614,42.67298776428907,42.73283767700195,416500,0.0,0 -1996-06-25,42.85252376448526,42.88244871124817,42.6330741548906,42.68294906616211,454400,0.0,0 -1996-06-26,42.63308785292681,42.722862722060256,42.383713216445024,42.3936882019043,838200,0.0,0 -1996-06-27,42.39369018055569,42.802664603473985,42.234090405758295,42.69293975830078,1090500,0.0,0 -1996-06-28,42.87248858628367,43.091938271927745,42.75278875775054,42.84256362915039,1060800,0.0,0 -1996-07-01,42.95229355978504,43.2216182058868,42.89244363842909,43.21164321899414,471300,0.0,0 -1996-07-02,43.171762885375635,43.18173787680017,42.982238048309526,43.05206298828125,389400,0.0,0 -1996-07-03,43.03208991715657,43.091939833592114,42.812640223559576,42.96226501464844,308300,0.0,0 -1996-07-05,42.21414529956293,42.413645041054245,41.735345919983764,41.865020751953125,842900,0.0,0 -1996-07-08,41.89493042430239,42.12435504329262,41.57573095440294,41.695430755615234,942000,0.0,0 -1996-07-09,41.88497072610225,42.014645558071614,41.735345919983764,41.865020751953125,716300,0.0,0 -1996-07-10,41.85504572234224,42.03459548950195,41.44607125270067,42.03459548950195,1132300,0.0,0 -1996-07-11,41.855045938526466,41.93484583545407,40.77774733000386,41.18672180175781,1587400,0.0,0 -1996-07-12,41.25652514122578,41.436074815438076,40.90740077470187,41.21662521362305,1312000,0.0,0 -1996-07-15,41.316381187153574,41.316381187153574,39.99968338012695,39.99968338012695,1951300,0.0,0 -1996-07-16,40.1193876826348,40.53833703188161,38.54334013070633,40.09943771362305,4141000,0.0,0 -1996-07-17,40.69793161037749,40.85753134218289,40.21913241496128,40.57823181152344,1340500,0.0,0 -1996-07-18,40.6979188077318,41.20664279282845,40.428594345033574,41.136817932128906,1432500,0.0,0 -1996-07-19,41.037088967208234,41.09693887819589,40.61813959029458,40.837589263916016,1216000,0.0,0 -1996-07-22,40.558287676863785,40.69793746227354,40.22911318268363,40.53833770751953,519800,0.0,0 -1996-07-23,40.59818329625686,40.81763293569609,39.94980936155006,40.01963424682617,1479800,0.0,0 -1996-07-24,39.54083858129397,40.33883734176408,39.34133889117644,40.09943771362305,2463400,0.0,0 -1996-07-25,40.49844563124918,40.578245524118636,40.29894589907554,40.45854568481445,1989800,0.0,0 -1996-07-26,40.57824771763239,40.73784751199869,40.46852285900555,40.67799758911133,642300,0.0,0 -1996-07-29,40.618123541391434,40.6779734287314,40.179224367565,40.22909927368164,1297900,0.0,0 -1996-07-30,40.49844134767304,40.65804111653086,40.17924180995739,40.618141174316406,1868200,0.0,0 -1996-07-31,40.64807947643323,41.02712905191899,40.5483295881475,40.91740417480469,767100,0.0,0 -1996-08-01,40.957281307522344,41.7453049858697,40.897431407901024,41.595680236816406,1168600,0.0,0 -1996-08-02,42.134346803477115,42.513396324909905,41.97474700497909,42.493446350097656,1098500,0.0,0 -1996-08-05,42.503429311056486,42.513404300333896,42.11440472923738,42.24407958984375,710200,0.0,0 -1996-08-06,42.234087541727476,42.46351220244068,42.00466288101426,42.37373733520508,640000,0.0,0 -1996-08-07,42.53334585325515,42.61314575166651,42.214146259609706,42.48347091674805,917800,0.0,0 -1996-08-08,42.4934198445906,42.4934198445906,42.25402029617037,42.41361999511719,925100,0.0,0 -1996-08-09,42.56326138405037,42.62311129459743,42.17423696549449,42.27398681640625,648800,0.0,0 -1996-08-12,42.29394301063536,42.68296748644073,42.13434322568956,42.58321762084961,764200,0.0,0 -1996-08-13,42.503429311056486,42.53335427888872,42.134354707792205,42.24407958984375,805200,0.0,0 -1996-08-14,42.254038405474496,42.43358814374138,42.134338579963234,42.3936882019043,533800,0.0,0 -1996-08-15,42.28396740526256,42.53334206559084,42.2640174324363,42.31389236450195,632600,0.0,0 -1996-08-16,42.43358544112924,42.692935046552215,42.43358544112924,42.6729850769043,475100,0.0,0 -1996-08-19,42.682964772375946,42.72286471607529,42.533339983503396,42.69293975830078,436400,0.0,0 -1996-08-20,42.70290576416615,42.71288074799333,42.5632559905856,42.66300582885742,645700,0.0,0 -1996-08-21,42.55327807587114,42.65302790727262,42.37372837934848,42.51337814331055,266400,0.0,0 -1996-08-22,42.613153433699985,42.992203019486645,42.59320345550069,42.91240310668945,584800,0.0,0 -1996-08-23,42.83257379997666,42.90239867575678,42.543299314601875,42.68294906616211,757100,0.0,0 -1996-08-26,42.58321269345765,42.63308762048208,42.403662956169704,42.473487854003906,784700,0.0,0 -1996-08-27,42.59317959476198,42.67297946285521,42.45352982559883,42.633079528808594,389400,0.0,0 -1996-08-28,42.732841522095214,42.772741467493994,42.553291767800694,42.56326675415039,227600,0.0,0 -1996-08-29,42.51339597261869,42.51339597261869,41.894946758563705,42.14432144165039,680200,0.0,0 -1996-08-30,42.13434589698936,42.14432088418088,41.57574661426412,41.705421447753906,1500900,0.0,0 -1996-09-03,41.156794002300884,42.034592807972835,41.09694408373234,41.9747428894043,1782000,0.0,0 -1996-09-04,41.97472562001981,42.61312448876536,41.80515092050928,42.014625549316406,930200,0.0,0 -1996-09-05,41.8151450034547,41.98471978042482,41.50592041015625,41.50592041015625,616600,0.0,0 -1996-09-06,41.65554108099915,42.24406524857074,41.585716179761846,42.09444046020508,804900,0.0,0 -1996-09-09,42.25403593898657,42.60316040968169,42.184211044847544,42.59318542480469,719200,0.0,0 -1996-09-10,42.533338430560185,42.71288817065167,42.373738661589975,42.573238372802734,669900,0.0,0 -1996-09-11,42.49344536887077,42.85254490719926,42.393695497112866,42.812644958496094,713500,0.0,0 -1996-09-12,42.93233534121392,43.2415598755024,42.872485431351635,43.11188507080078,713300,0.0,0 -1996-09-13,43.610640999612315,43.81014072971118,43.49094116155299,43.770240783691406,2008800,0.0,0 -1996-09-16,43.830059775647854,44.14925911857473,43.77020989884906,43.9298095703125,1220200,0.0,0 -1996-09-17,43.96973554969756,44.039560447349075,43.560761149167256,43.83008575439453,604700,0.0,0 -1996-09-18,43.7702423659683,43.889942208354725,43.55079265492653,43.720367431640625,225500,0.0,0 -1996-09-19,43.65054627852006,43.91987094705755,43.5906963521784,43.88994598388672,410000,0.0,0 -1996-09-20,44.01553719509931,44.13585301431143,43.87516873935185,44.045616149902344,948400,0.352,0 -1996-09-23,43.935335551889,44.11580931727786,43.73480914590137,44.03559875488281,466800,0.0,0 -1996-09-24,43.99549095659423,44.43664902452726,43.83506984098222,44.025569915771484,1269800,0.0,0 -1996-09-25,44.236130219130935,44.236130219130935,43.955393218646876,44.05565643310547,1353300,0.0,0 -1996-09-26,44.11580931727786,44.396546285660534,43.88520395039209,44.03559875488281,773200,0.0,0 -1996-09-27,44.09576675651071,44.09576675651071,43.875187659502245,44.075714111328125,407600,0.0,0 -1996-09-30,44.13586195787662,44.31633572326549,44.00551979398467,44.03559875488281,578200,0.0,0 -1996-10-01,44.08574117007463,44.30632027076638,43.91529368317645,44.276241302490234,561400,0.0,0 -1996-10-02,44.436654060422114,44.6572331193863,44.37649613525006,44.577022552490234,609000,0.0,0 -1996-10-03,44.597072747310825,44.617125387934614,44.406572661384814,44.53691482543945,342200,0.0,0 -1996-10-04,44.71737202831185,45.13847732543945,44.597056229132534,45.13847732543945,754100,0.0,0 -1996-10-07,45.1184410939916,45.31896749885378,45.07833581301916,45.18862533569336,235100,0.0,0 -1996-10-08,45.208682246557665,45.359077064410485,44.9279452532324,45.008155822753906,386200,0.0,0 -1996-10-09,45.07833582754353,45.15854638951425,44.60709877596557,44.657230377197266,528800,0.0,0 -1996-10-10,44.65723059603816,44.82767804106121,44.51686211190154,44.566993713378906,1562200,0.0,0 -1996-10-11,44.73744613647461,45.118446350097656,44.73744613647461,45.118446350097656,331300,0.0,0 -1996-10-14,45.11846524580182,45.3590970604461,45.108438920191645,45.17862319946289,201600,0.0,0 -1996-10-15,45.63981506347656,45.63981506347656,44.8878409576416,45.118446350097656,776900,0.0,0 -1996-10-16,45.23876799254858,45.33903121948242,44.87782037558676,45.33903121948242,590100,0.0,0 -1996-10-17,45.4793994164856,45.579662642712414,45.268846641409276,45.459346771240234,792500,0.0,0 -1996-10-18,45.47939118757327,45.76012817021262,45.37912797948779,45.69997024536133,695800,0.0,0 -1996-10-21,45.74008332405023,45.96066241943144,45.519504228669014,45.63982009887695,879200,0.0,0 -1996-10-22,45.59969903051326,45.6097253495613,45.29890945907188,45.31896209716797,848900,0.0,0 -1996-10-23,45.2186985625716,45.48940917480873,45.038224821080185,45.469356536865234,735900,0.0,0 -1996-10-24,45.46936898469077,45.5495795584234,45.05828979431098,45.07834243774414,379300,0.0,0 -1996-10-25,45.06831474304199,45.27886749267578,44.99813049316406,45.118446350097656,568300,0.0,0 -1996-10-28,45.158537029633315,45.38914234750223,44.79758957557763,44.81764221191406,975400,0.0,0 -1996-10-29,44.95804403603897,45.28891278117485,44.72743854700487,45.17862319946289,815900,0.0,0 -1996-10-30,45.31896916934999,45.31896916934999,44.9780742685186,45.02820587158203,495400,0.0,0 -1996-10-31,45.088372834200996,45.4793994164856,45.03824122108759,45.459346771240234,726300,0.0,0 -1996-11-01,45.549566203062916,45.65985570960786,45.0883555393295,45.29890823364258,821300,0.0,0 -1996-11-04,45.35907684970343,45.599708557128906,45.25881363827614,45.599708557128906,1608400,0.0,0 -1996-11-05,45.7501077956386,46.07095010321266,45.740081473526914,45.86039733886719,764000,0.0,0 -1996-11-06,45.94061190434558,46.762770388884285,45.90050661241686,46.74271774291992,1665000,0.0,0 -1996-11-07,46.60234260191735,47.03347442891444,46.54218467256892,46.85300064086914,1446300,0.0,0 -1996-11-08,46.863016182525435,47.12370048307008,46.68254243599453,47.11367416381836,1355700,0.0,0 -1996-11-11,47.0033758786774,47.17382327414189,46.98332324391687,47.063533782958984,655900,0.0,0 -1996-11-12,47.19387493133544,47.22395388285319,46.85298014746772,46.92316436767578,719900,0.0,0 -1996-11-13,46.9732955100054,47.203900802797314,46.85297970507049,47.13371658325195,481900,0.0,0 -1996-11-14,46.96327861486001,47.444541931152344,46.94322597668116,47.444541931152344,558800,0.0,0 -1996-11-15,47.56488435808844,47.805516150793785,47.304199915991,47.50472640991211,2250900,0.0,0 -1996-11-18,47.59491235491947,47.64504392470557,47.314175564117335,47.51470184326172,1518200,0.0,0 -1996-11-19,47.57491474003755,47.86567818101986,47.50473046117975,47.85565185546875,1029300,0.0,0 -1996-11-20,47.84561133289955,48.136374687604935,47.68519017168279,47.935848236083984,730000,0.0,0 -1996-11-21,47.99598085765415,48.02605980951815,47.725270290878164,47.865638732910156,653000,0.0,0 -1996-11-22,47.93584744317248,48.28676872752952,47.93584744317248,48.236637115478516,965100,0.0,0 -1996-11-25,48.286765876857714,48.86829254231821,48.17647633685658,48.84823989868164,2058800,0.0,0 -1996-11-26,48.94849330550167,49.20917762052483,48.42712467545536,48.687808990478516,2861800,0.0,0 -1996-11-27,48.80812887479429,48.8883394399131,48.53741821751832,48.59757614135742,872000,0.0,0 -1996-11-29,48.76802696235868,48.90839546101679,48.67779007036419,48.778053283691406,1073400,0.0,0 -1996-12-02,48.7178927661213,48.848234936518416,48.37699785892885,48.79810333251953,1350600,0.0,0 -1996-12-03,48.80813433733273,49.13900295547817,47.96592330932617,47.96592330932617,1777800,0.0,0 -1996-12-04,48.04612465692058,48.166440494959616,47.544808665091274,48.096256256103516,2365100,0.0,0 -1996-12-05,48.02608123989807,48.216581353375744,47.82555480465841,47.96592330932617,1697700,0.0,0 -1996-12-06,47.00337582420561,47.965902291595484,46.622375764197116,47.68516540527344,3401800,0.0,0 -1996-12-09,47.92581317141201,48.39705024652841,47.85562892618191,48.38702392578125,1864600,0.0,0 -1996-12-10,48.48727490379243,48.55745913132067,48.1062748114963,48.15640640258789,1331600,0.0,0 -1996-12-11,47.24403003965964,47.885714658194914,47.043503596367366,47.715267181396484,1847900,0.0,0 -1996-12-12,47.98595398796929,48.04611189100477,46.80284856160482,46.92316436767578,2540200,0.0,0 -1996-12-13,46.883074052295214,47.21394262452056,46.46196859673567,47.043495178222656,1678300,0.0,0 -1996-12-16,47.16380863486176,47.284124473216,46.241387207479264,46.44191360473633,1831100,0.0,0 -1996-12-17,46.30155431404694,47.023449487414496,46.12108052070505,46.812896728515625,2023200,0.0,0 -1996-12-18,47.08359758123357,47.33425557517114,47.013413342931045,47.183860778808594,1666800,0.0,0 -1996-12-19,47.60495890058033,48.20653799367949,47.41445885443226,48.15640640258789,2269300,0.0,0 -1996-12-20,48.44345710133212,48.82632968241586,48.201642839595024,48.2620964050293,1543200,0.367,0 -1996-12-23,48.42328703039208,48.49381616256286,47.92958310519666,48.141170501708984,1263700,0.0,0 -1996-12-24,48.26208918698483,48.49382781982422,48.23186240878839,48.49382781982422,633000,0.0,0 -1996-12-26,48.644952298193864,48.89684206775784,48.60464993506363,48.866615295410156,1384300,0.0,0 -1996-12-27,48.86661326080021,49.02782270660903,48.685252634265275,48.927066802978516,410400,0.0,0 -1996-12-30,49.08827522002071,49.08827522002071,48.46358863060337,48.50389099121094,694100,0.0,0 -1996-12-31,48.54421079153738,48.60466435416694,47.61725616455078,47.61725616455078,1378100,0.0,0 -1997-01-02,47.95983372080824,47.95983372080824,46.9119718075805,47.7381706237793,2031900,0.0,0 -1997-01-03,47.959820623949476,48.44344906721619,47.7683843651564,48.42329788208008,2123200,0.0,0 -1997-01-06,48.42330079835368,48.64496384820049,47.91952113961092,48.000125885009766,1374100,0.0,0 -1997-01-07,48.00010754883116,48.66509644434393,47.79859576231214,48.58449172973633,939000,0.0,0 -1997-01-08,48.8464675839476,48.86661876694428,48.16132736206055,48.16132736206055,1802200,0.0,0 -1997-01-09,48.40314446920181,48.92707525862698,48.322539732367176,48.564353942871094,1415700,0.0,0 -1997-01-10,47.87921623643396,49.1688920946544,47.87921623643396,49.088287353515625,2369500,0.0,0 -1997-01-13,49.33007007061761,49.33007007061761,48.77591282921974,49.01772689819336,1364600,0.0,0 -1997-01-14,49.45098271991391,49.90438415071996,49.33007567169896,49.63234329223633,2111200,0.0,0 -1997-01-15,49.47115698237219,49.783500336028716,49.24949395719659,49.51145935058594,1583900,0.0,0 -1997-01-16,49.672695047032946,49.84398020236754,49.33012473636375,49.71299743652344,1308400,0.0,0 -1997-01-17,49.78351628290464,50.136162117735985,49.72306271121927,50.015254974365234,1604000,0.0,0 -1997-01-20,50.136148593219474,50.357811629604285,49.95478792708644,50.0756950378418,1889900,0.0,0 -1997-01-21,49.89433344614482,50.65000287434774,49.72304837575216,50.47871780395508,2785800,0.0,0 -1997-01-22,50.49887194729872,50.8414421081543,50.216755344241186,50.8414421081543,1201600,0.0,0 -1997-01-23,50.982507923408335,51.385531701458994,49.59207588913356,50.13615798950195,2601100,0.0,0 -1997-01-24,50.21675277610555,50.23690396100768,49.491310119628906,49.491310119628906,2176000,0.0,0 -1997-01-27,49.57191710279347,49.80365573965612,49.24949812976717,49.35025405883789,2108500,0.0,0 -1997-01-28,50.055543296888516,50.19660159120342,49.00768168197781,49.491310119628906,4376000,0.0,0 -1997-01-29,49.57191753387451,49.97494125366211,49.39055685997009,49.97494125366211,1122700,0.0,0 -1997-01-30,50.216776677757316,50.63995176212044,49.91450876035508,50.61980056762695,2126300,0.0,0 -1997-01-31,50.90189633724841,51.20416413022494,50.53917498567657,50.559326171875,3208100,0.0,0 -1997-02-03,50.76084244741541,50.871673981143395,50.529103785984184,50.71046447753906,755000,0.0,0 -1997-02-04,50.78098242881739,51.07317458961812,50.579470593782396,51.022796630859375,654100,0.0,0 -1997-02-05,51.09330752582515,51.24444134812596,49.73310312511792,50.06559753417969,2255400,0.0,0 -1997-02-06,50.15631042199819,50.42835147892747,49.914496149172166,50.39812469482422,2483100,0.0,0 -1997-02-07,50.821305534167166,51.1034222183378,50.15631620719353,51.08327102661133,2321000,0.0,0 -1997-02-10,51.1034273658792,51.28478810968555,50.56942073133827,50.599647521972656,1670000,0.0,0 -1997-02-11,50.80113325344296,51.18400573730469,50.37795840285895,51.18400573730469,1664300,0.0,0 -1997-02-12,51.18401166074764,52.00013468132256,51.11348251082142,51.909454345703125,2675400,0.0,0 -1997-02-13,52.11096488495576,52.594593329363704,52.030360144221106,52.47368621826172,1104800,0.0,0 -1997-02-14,52.35276412963867,52.534124744406476,52.17140351487087,52.35276412963867,1112900,0.0,0 -1997-02-18,52.45353428789677,52.83640680094712,52.070661774846435,52.796104431152344,833100,0.0,0 -1997-02-19,52.65503833781745,52.87670133885687,52.33261942721467,52.443450927734375,648100,0.0,0 -1997-02-20,52.25202281778307,52.46361026073977,51.6877896365652,51.80869674682617,1193900,0.0,0 -1997-02-21,51.849016242969086,52.08075494751403,51.677731113522825,51.82886505126953,1844400,0.0,0 -1997-02-24,51.56687451453305,52.47367770060338,51.56687451453305,52.443450927734375,787300,0.0,0 -1997-02-25,52.49384024157408,52.574444986666705,52.15127007493041,52.4837646484375,1653900,0.0,0 -1997-02-26,52.4333792572199,52.51398399089741,51.34521535257352,51.96990203857422,1597800,0.0,0 -1997-02-27,52.11095529521363,52.11095529521363,51.18400094734827,51.19407653808594,1813100,0.0,0 -1997-02-28,51.103391771583844,51.506415365996965,50.982484693259906,51.042938232421875,2961200,0.0,0 -1997-03-03,50.78101178337546,51.425850028243715,50.74070939307119,51.38554763793945,1210400,0.0,0 -1997-03-04,51.52658757501597,51.667645890630006,51.00265668844951,51.10341262817383,1478700,0.0,0 -1997-03-05,51.40566890283166,51.96990203857422,51.23438384376695,51.96990203857422,1254800,0.0,0 -1997-03-06,51.990059660675,52.171420333909914,51.506431198715234,51.667640686035156,1528500,0.0,0 -1997-03-07,51.86914196685331,52.332619148375315,51.748234876021485,52.131107330322266,1859300,0.0,0 -1997-03-10,52.1915729408877,52.675201416015625,51.93968311009191,52.675201416015625,1074900,0.0,0 -1997-03-11,52.71551598182091,52.74574276850773,52.393096923828125,52.393096923828125,624600,0.0,0 -1997-03-12,52.43337934832264,52.453530531777034,51.77846588605497,51.92959976196289,1055600,0.0,0 -1997-03-13,51.748240938889595,51.88929922805323,50.98249594057269,51.12355422973633,2514100,0.0,0 -1997-03-14,51.35532084521125,51.60721078461292,51.10343090580959,51.38554763793945,1642700,0.0,0 -1997-03-17,51.10341726936397,51.60719701373862,50.57948633521434,51.52659225463867,3013500,0.0,0 -1997-03-18,51.58703470918166,51.65756385819812,50.76083610641742,50.9724235534668,1352500,0.0,0 -1997-03-19,50.8414383857603,51.264613259363536,50.33765877432788,50.8011360168457,1510000,0.0,0 -1997-03-20,50.780987351705015,50.94219683536122,50.216754158908294,50.5391731262207,1808200,0.0,0 -1997-03-21,50.93499348249093,50.97545018899013,50.71248159674534,50.77316665649414,2810600,0.299,0 -1997-03-24,50.813634846205794,51.511513191028286,50.550666194533555,51.481170654296875,2145600,0.0,0 -1997-03-25,51.359787308223666,51.76435436067127,50.833850140041775,50.97544860839844,2125800,0.0,0 -1997-03-26,51.177742987082254,51.56208176840817,50.93500270413957,51.19797134399414,1335300,0.0,0 -1997-03-27,51.380017670718104,51.380017670718104,49.29649726911024,49.8426628112793,2898500,0.0,0 -1997-03-31,49.782003271441354,49.92360181792656,48.70989999091052,48.79081344604492,4270700,0.0,0 -1997-04-01,48.70986769388436,49.3167182050208,48.57838341647147,49.10432052612305,3210900,0.0,0 -1997-04-02,48.952612620872145,49.154896144098885,48.183935232610516,48.22439193725586,2672700,0.0,0 -1997-04-03,48.18395556854515,48.62897950746539,48.022128681665066,48.48738098144531,2284200,0.0,0 -1997-04-04,48.22440740823451,49.10434101656429,48.00189546130054,49.0942268371582,3706700,0.0,0 -1997-04-07,49.31672329651167,49.59992023094611,49.286380767822266,49.286380767822266,2596200,0.0,0 -1997-04-08,49.27628184647315,49.7010773796324,49.094226617976325,49.6403923034668,1822700,0.0,0 -1997-04-09,49.84266761648136,49.873010149527104,49.094218468019584,49.235816955566406,2453200,0.0,0 -1997-04-10,49.25605939884775,49.54937063551441,48.993090703905246,49.053775787353516,1996500,0.0,0 -1997-04-11,48.588504943977625,48.750331771434674,47.49617385864258,47.49617385864258,4221600,0.0,0 -1997-04-14,47.7288250523371,48.194077426231466,47.45574213722519,48.133392333984375,3988500,0.0,0 -1997-04-15,48.70987359669583,48.962728006977684,48.22439312895468,48.952613830566406,2760400,0.0,0 -1997-04-16,48.79079543842989,49.58981551298129,48.70988201315886,49.51901626586914,2273300,0.0,0 -1997-04-17,49.51903320512779,49.842687016926014,49.195379393329574,49.316749572753906,1386000,0.0,0 -1997-04-18,49.721301802031846,49.83255776619425,49.357191373864,49.55947494506836,1702100,0.0,0 -1997-04-21,49.6808407735707,49.78198255038986,48.8514782036536,49.235816955566406,2809400,0.0,0 -1997-04-22,49.26614941774301,50.318023681640625,49.19535018844221,50.318023681640625,3215000,0.0,0 -1997-04-23,50.26744575194087,50.51018593263435,49.96402052607402,50.368587493896484,2047700,0.0,0 -1997-04-24,50.52030492141677,50.67201754880841,49.761741784458565,50.11573791503906,2689900,0.0,0 -1997-04-25,49.88313535205098,49.97416297130655,49.46845397544228,49.53925323486328,1606200,0.0,0 -1997-04-28,49.519014166474484,50.36860509580126,49.43810074463384,50.024723052978516,1666500,0.0,0 -1997-04-29,50.87431820548314,51.64299577677871,50.68214881265925,51.6025390625,3197400,0.0,0 -1997-04-30,51.29910649817947,52.22961079585938,51.27887814388208,51.845272064208984,3372200,0.0,0 -1997-05-01,51.92619399279356,52.128477568924424,51.33957162201404,51.78459548950195,2149500,0.0,0 -1997-05-02,51.96663130894132,52.86679288669448,51.82503285850824,52.71508026123047,1448300,0.0,0 -1997-05-05,52.8567014376799,54.09063132198854,52.62407531194958,53.969261169433594,3636200,0.0,0 -1997-05-06,53.70630194885071,54.02995574590592,53.40287651411143,53.92881393432617,1721100,0.0,0 -1997-05-07,53.72653085907544,53.72653085907544,52.69488437044108,52.75556945800781,2327200,0.0,0 -1997-05-08,52.63420159019592,53.86813175814441,52.55328813655996,53.11968231201172,2923700,0.0,0 -1997-05-09,53.70627269802584,53.8074144545193,52.8566819434808,53.483760833740234,2558200,0.0,0 -1997-05-12,53.686087281931016,54.53567871593294,53.645630546978545,54.37385177612305,2357900,0.0,0 -1997-05-13,54.33336900177401,54.434510790682744,53.73663244721246,54.15131378173828,1195100,0.0,0 -1997-05-14,54.52553950060134,54.697480545214624,54.00971636676148,54.25245666503906,2772000,0.0,0 -1997-05-15,54.252436319986984,54.758145085087534,54.060266989248774,54.616546630859375,884100,0.0,0 -1997-05-16,54.454705972130746,54.576076044876956,53.72648553565352,53.86808395385742,1934200,0.0,0 -1997-05-19,53.92881500404098,54.313153895667675,53.76698810230342,54.02995681762695,1375800,0.0,0 -1997-05-20,53.908561046062644,54.818836936146255,53.56467904314217,54.67723846435547,1756400,0.0,0 -1997-05-21,54.81886165337426,55.01103109459458,54.15132569966159,54.55589294433594,1208000,0.0,0 -1997-05-22,54.61659218583788,54.67727728826659,54.09065463145574,54.37385177612305,907600,0.0,0 -1997-05-23,54.616548036940195,55.1627135173096,54.474949579066646,54.8795166015625,644400,0.0,0 -1997-05-27,54.61654747110106,55.3649964549643,54.54574824289778,55.102027893066406,1531000,0.0,0 -1997-05-28,55.10202549093466,55.31442316628518,54.7075726652837,55.09191131591797,716700,0.0,0 -1997-05-29,55.081815746022066,55.182957530351885,54.69747696556873,54.76827621459961,1282700,0.0,0 -1997-05-30,53.86811436504399,55.385241130863854,53.8074292944112,55.12227249145508,2143300,0.0,0 -1997-06-02,55.24362692180877,55.34476867743273,54.839059899312915,54.8795166015625,1479100,0.0,0 -1997-06-03,54.63678274352169,55.3043184082889,54.596326036566104,54.69746780395508,1562100,0.0,0 -1997-06-04,54.71769701503286,54.85929549159672,54.42438588500773,54.636783599853516,1063100,0.0,0 -1997-06-05,54.75813309357466,55.223385055581396,54.64687718961653,54.839046478271484,1180800,0.0,0 -1997-06-06,54.758151035060415,55.95162385037943,54.758151035060415,55.91116714477539,1511100,0.0,0 -1997-06-09,55.951618614773516,56.234815527501944,55.890933562045994,56.194358825683594,1823400,0.0,0 -1997-06-10,56.21461648220608,56.578726988388055,55.99210450620598,56.366329193115234,1217500,0.0,0 -1997-06-11,56.31571329446663,56.57868178326982,56.20445739535759,56.49776840209961,1673800,0.0,0 -1997-06-12,56.88211101375214,57.61033149614726,56.679827546420164,57.59010314941406,4273300,0.0,0 -1997-06-13,57.650808057316674,58.25765866844632,57.54966628879506,58.07560348510742,2132900,0.0,0 -1997-06-16,58.09584426879883,58.25767113305732,57.91378904650803,58.09584426879883,800400,0.0,0 -1997-06-17,57.8935605771372,58.409383705969134,57.569906849242656,58.014930725097656,2048100,0.0,0 -1997-06-18,57.69124055492702,58.01489407837683,57.59009882884896,57.8126106262207,1971900,0.0,0 -1997-06-19,58.00478820140228,58.58129612249729,57.8530755905878,58.40935516357422,2029900,0.0,0 -1997-06-20,58.44389719874078,58.70788978511452,58.159597490338285,58.210365295410156,1396900,0.35,0 -1997-06-23,58.09868244038849,58.42359642817116,56.75841224078498,56.79902648925781,3991500,0.0,0 -1997-06-24,57.347327313496315,58.403297929750494,57.184870295611056,58.240840911865234,4895100,0.0,0 -1997-06-25,58.149431343828695,58.646955725852024,57.144229020965234,57.83467102050781,4708900,0.0,0 -1997-06-26,57.73314862194075,58.240826678764,56.98178509784233,57.550384521484375,2569900,0.0,0 -1997-06-27,57.81436835827015,58.30173921903533,57.631604285483206,57.77375411987305,2617900,0.0,0 -1997-06-30,57.77375949500557,58.13928767458733,57.103624499105685,57.38792419433594,2384900,0.0,0 -1997-07-01,57.50976741826797,58.2002095412274,57.43869249384568,58.05805969238281,1749000,0.0,0 -1997-07-02,58.20021958689154,59.01250457763671,57.97684121443662,59.01250457763672,1961800,0.0,0 -1997-07-03,59.70294058929879,59.88570469314358,59.43894799485631,59.82478332519531,2539100,0.0,0 -1997-07-07,60.027837965223526,60.10906643337674,59.073403464423286,59.215553283691406,1158600,0.0,0 -1997-07-08,59.29679172787099,59.9060053415135,59.25617748696149,59.834930419921875,3396500,0.0,0 -1997-07-09,59.98724220710498,60.027856453690696,58.50482220672629,59.174957275390625,5390300,0.0,0 -1997-07-10,59.215594032008696,59.68265804529271,58.79929784625555,59.43897247314453,2072200,0.0,0 -1997-07-11,59.52018605311143,59.9060214454721,59.378036171715394,59.64202880859375,2258900,0.0,0 -1997-07-14,59.7130941506235,59.98724030639069,59.27649101366094,59.82478332519531,3186400,0.0,0 -1997-07-15,59.98725227373994,60.28170561089946,59.43895985282221,60.12940216064453,2983100,0.0,0 -1997-07-16,60.5152214050293,61.14474216461182,60.36291799545288,60.921363830566406,2621300,0.0,0 -1997-07-17,60.840119456126665,60.9619621519667,60.29182732484649,60.61674118041992,1794800,0.0,0 -1997-07-18,60.4441197219214,60.57611595178617,59.17492520399092,59.32722854614258,3961900,0.0,0 -1997-07-21,59.31710285131936,59.52017406957105,58.931267536641144,59.33740997314453,3810300,0.0,0 -1997-07-22,59.581107613801514,60.95183861718652,59.540493361849364,60.911224365234375,4243400,0.0,0 -1997-07-23,61.04319190406227,61.398566441095234,60.77919939083778,60.86042785644531,3204300,0.0,0 -1997-07-24,61.0432016454397,61.25642640168625,60.23091685973857,61.144737243652344,4152700,0.0,0 -1997-07-25,61.44934319102832,61.61180014592859,60.840129610152296,61.104122161865234,2364800,0.0,0 -1997-07-28,61.246278804047066,61.44935002024086,60.75890788518197,61.063514709472656,2794700,0.0,0 -1997-07-29,60.84012917078288,61.3884213896117,60.70813289587965,61.266578674316406,2737100,0.0,0 -1997-07-30,61.40871709579333,62.13977325169563,61.34779574946813,61.997623443603516,4839800,0.0,0 -1997-07-31,61.99765844126217,62.403800979364114,61.753972918401004,61.936737060546875,2138700,0.0,0 -1997-08-01,62.058559475485,62.2007093172155,60.961974982135466,61.693031311035156,7077700,0.0,0 -1997-08-04,61.59150285902418,62.089027362831004,61.34781738777186,61.85549545288086,2339500,0.0,0 -1997-08-05,61.79457111183427,62.180406422752725,61.67272838207055,61.8961067199707,1466700,0.0,0 -1997-08-06,62.01797313547429,62.72872266387691,61.774287582879104,62.40380859375,2478200,0.0,0 -1997-08-07,62.70840788294058,62.789636390560965,61.8148942991163,61.936737060546875,3641900,0.0,0 -1997-08-08,61.286867662605985,61.571167247521984,60.04813375690056,60.67765426635742,7113100,0.0,0 -1997-08-11,60.92134900267338,61.26656998035519,60.190292814641296,61.124420166015625,6355900,0.0,0 -1997-08-12,61.22595830192282,61.43918303232753,60.08875973976439,60.149681091308594,6132500,0.0,0 -1997-08-13,60.880762484531424,60.98229811242424,59.43895656845346,59.966941833496094,6982300,0.0,0 -1997-08-14,60.49490676910771,60.63705660038792,59.6013935439178,60.19029998779297,3462700,0.0,0 -1997-08-15,59.86540342729929,59.86540342729929,58.24083345640921,58.342369079589844,4907100,0.0,0 -1997-08-18,58.728208142476376,59.64202880859375,58.05807298732364,59.64202880859375,5638900,0.0,0 -1997-08-19,59.92629711750741,60.373053653117424,59.47954058189739,60.33243942260742,3802800,0.0,0 -1997-08-20,60.3933928208895,61.28690636632297,60.18016799754742,61.24629211425781,3564600,0.0,0 -1997-08-21,61.165070342245116,61.24629885531582,59.845107004846064,60.170021057128906,5392600,0.0,0 -1997-08-22,59.13432523223856,60.2613702358063,58.85002559169895,60.149681091308594,8087800,0.0,0 -1997-08-25,60.29185500705658,60.697997512998356,59.682641248143916,59.926326751708984,3888000,0.0,0 -1997-08-26,59.784174985221405,60.14970322901692,58.9414293120262,59.042964935302734,4290000,0.0,0 -1997-08-27,59.134334309895834,59.763855039156404,58.74849902422,59.3983268737793,5484300,0.0,0 -1997-08-28,59.25618802034033,59.743558997889394,58.48451730588765,58.49467086791992,4287900,0.0,0 -1997-08-29,58.56574582723826,59.205420235545475,58.30175321428607,58.72820281982422,2652300,0.0,0 -1997-09-02,58.9312787729865,60.66753802008862,58.870357395895205,60.63707733154297,7294000,0.0,0 -1997-09-03,60.626913256275934,61.08382350523463,60.27153861819694,60.31215286254883,2812500,0.0,0 -1997-09-04,60.47460826466657,60.90105781925287,60.271537048196905,60.6979866027832,2473700,0.0,0 -1997-09-05,61.06349484053779,61.36810156571507,60.169981780017785,60.52535629272461,3663300,0.0,0 -1997-09-08,60.901064486461394,61.08382860129239,60.63707187614995,60.78937530517578,803500,0.0,0 -1997-09-09,60.576161575954224,61.2666039137291,60.37309030013809,60.819847106933594,1885200,0.0,0 -1997-09-10,60.51521415723455,60.75889958337107,59.5607795715332,59.5607795715332,3623700,0.0,0 -1997-09-11,59.642026631941604,59.723255132632026,58.64697749848395,59.25619125366211,7055900,0.0,0 -1997-09-12,59.78415613801644,60.41367680387191,59.09371411740076,60.21060562133789,6708000,0.0,0 -1997-09-15,60.21059013057399,60.63703950418008,59.905983435141074,60.10905456542969,2060500,0.0,0 -1997-09-16,60.63704438483,61.91639260862246,60.45428035285965,61.611785888671875,6979000,0.0,0 -1997-09-17,61.977313952612576,62.05854241126607,61.40871474203815,61.611785888671875,2220000,0.0,0 -1997-09-18,61.73365503754974,62.6271684657248,61.71334791418212,61.87580490112305,5045400,0.0,0 -1997-09-19,61.73588565900427,62.19447180206389,61.57283280813862,61.980464935302734,1566400,0.348,0 -1997-09-22,62.286186861945104,62.7447729890373,62.10275241110822,62.32695007324219,3259000,0.0,0 -1997-09-23,62.32697620467037,62.418693468542514,61.797054235631315,62.04163360595703,1872800,0.0,0 -1997-09-24,62.20469788345685,62.6836659045123,61.49134125635299,61.5321044921875,2793100,0.0,0 -1997-09-25,61.470937747968044,61.79704351851429,61.06330553478523,61.104068756103516,6327800,0.0,0 -1997-09-26,61.59325058382156,61.83783000374408,61.43019763720655,61.61363220214844,3995800,0.0,0 -1997-09-29,61.634003805458,62.326978715969105,61.409806040292644,62.204689025878906,2065000,0.0,0 -1997-09-30,61.960130299953434,62.40852597975573,61.55249786376953,61.55249786376953,4137600,0.0,0 -1997-10-01,62.12319165398093,62.490060896032,61.81746728560504,62.36777114868164,3567500,0.0,0 -1997-10-02,62.306601072155814,62.73461501475159,62.16392975795723,62.7142333984375,2577500,0.0,0 -1997-10-03,63.57023729279626,63.7536717703965,62.18428790648329,63.03012466430664,6499900,0.0,0 -1997-10-06,63.46835677615324,63.66198214204067,63.20339574914939,63.447975158691406,2272100,0.0,0 -1997-10-07,63.61101050470985,64.24284047127378,63.3970035805511,64.03902435302734,1832800,0.0,0 -1997-10-08,64.15111236388866,64.16130316807673,63.09126872832958,63.59061813354492,4439400,0.0,0 -1997-10-09,63.12185284408123,63.661965566189124,62.754983825290964,63.366432189941406,3607600,0.0,0 -1997-10-10,62.81612584021783,63.25433048186763,62.77536261773878,63.1829948425293,2858100,0.0,0 -1997-10-13,63.48871613409379,63.621196601147275,63.081083927775374,63.244136810302734,1760300,0.0,0 -1997-10-14,63.570239060932835,63.59062067012197,62.73459308417802,63.2645149230957,2235700,0.0,0 -1997-10-15,62.81613248850324,63.30529121002306,62.795750875106584,63.121856689453125,2601700,0.0,0 -1997-10-16,63.46834008524737,63.590629757858636,61.96010078970842,62.12315368652344,9488100,0.0,0 -1997-10-17,61.97027020933299,62.204658667615284,60.65565668244531,61.491302490234375,8151300,0.0,0 -1997-10-20,61.85823053472183,62.44929764691521,61.40983479443719,62.36777114868164,3636400,0.0,0 -1997-10-21,62.754981445566855,63.590627512234036,62.6326917772741,63.58043670654297,5225400,0.0,0 -1997-10-22,63.48872252763107,63.5192949461837,62.95880060605212,63.16261672973633,4485200,0.0,0 -1997-10-23,61.91932678222656,62.449248604589265,61.491313002625915,61.91932678222656,8054100,0.0,0 -1997-10-24,62.7142465545776,62.7142465545776,61.09390771769463,61.30791473388672,6778700,0.0,0 -1997-10-27,60.665863005662054,61.26712050899383,56.64049497488152,56.86469268798828,10840700,0.0,0 -1997-10-28,55.03037005753809,60.57417030037156,55.03037005753809,60.146156311035156,19548000,0.0,0 -1997-10-29,60.3295768818157,61.14484143427267,59.534693943170154,59.983089447021484,10134400,0.0,0 -1997-10-30,59.106655664291544,60.370315199183295,58.535970713050105,58.658260345458984,9772900,0.0,0 -1997-10-31,59.88119372946019,60.329589325800605,58.98440253677938,60.044246673583984,7072700,0.0,0 -1997-11-03,60.77799259855392,61.55249418096339,60.57417639265669,61.30791473388672,5548500,0.0,0 -1997-11-04,61.22638825152782,61.59325742214284,60.83913746032309,61.30791473388672,3455700,0.0,0 -1997-11-05,61.430184664514535,62.14354115531016,61.205986910264485,61.51171112060547,4774900,0.0,0 -1997-11-06,61.32826725972307,61.532083368927864,60.98177987407492,61.277313232421875,3679800,0.0,0 -1997-11-07,60.248029758886915,60.81871474983713,59.6365815542974,60.614898681640625,10606800,0.0,0 -1997-11-10,61.063315245714826,61.20598654301789,60.003471322892004,60.248050689697266,4347900,0.0,0 -1997-11-11,60.451859490630355,60.69643882774686,60.02384565067646,60.26842498779297,3212400,0.0,0 -1997-11-12,59.758892217393736,60.472248707028385,58.943627657811284,59.02515411376953,6775300,0.0,0 -1997-11-13,59.65698586393166,60.105381384603504,58.76019482258796,59.88118362426758,8589600,0.0,0 -1997-11-14,60.20725646572997,60.91042178328843,59.74867038906141,60.696414947509766,5827900,0.0,0 -1997-11-17,61.55244484538887,62.16389297299207,61.32824719860103,61.817405700683594,5149900,0.0,0 -1997-11-18,61.837819522789324,62.02125405664071,61.236561884054225,61.4301872253418,3433600,0.0,0 -1997-11-19,61.10405959906693,62.00085033367632,60.492611370924166,61.73588943481445,4387900,0.0,0 -1997-11-20,62.16393292434816,62.95881600108573,62.14355130699592,62.6734733581543,4822700,0.0,0 -1997-11-21,63.0199330697072,63.142222719907146,62.388103210340795,63.10145950317383,5436500,0.0,0 -1997-11-24,62.61231753700658,62.83651528860394,61.71552653061717,61.960105895996094,4337200,0.0,0 -1997-11-25,62.36773303174597,62.49002270435724,61.73590305658776,62.12315368652344,4525000,0.0,0 -1997-11-26,62.53078112042258,62.602116757293985,62.163912130798195,62.306583404541016,2681100,0.0,0 -1997-11-28,62.44929764691521,62.77540363984949,62.34738952412325,62.36777114868164,1564700,0.0,0 -1997-12-01,62.75501181343324,63.977909088134766,62.63272208596309,63.977909088134766,4850900,0.0,0 -1997-12-02,63.71290778380173,63.90653306337503,63.42756526653583,63.59061813354492,1974900,0.0,0 -1997-12-03,63.67214743448698,64.26321410401327,63.18298881143074,63.77405548095703,3302500,0.0,0 -1997-12-04,64.24284307146714,64.38551436001417,63.50910501608236,63.712921142578125,2872500,0.0,0 -1997-12-05,63.50907986629484,64.56892330437164,63.34602702966764,64.52816009521484,3458800,0.0,0 -1997-12-08,64.54854261116171,64.56892421602788,64.07976569923979,64.3447265625,2289200,0.0,0 -1997-12-09,64.05940852095323,64.30398787260815,63.67215788083292,63.95750045776367,1703000,0.0,0 -1997-12-10,63.63136315537357,63.814797578307065,62.91800706618777,63.40716552734375,3558400,0.0,0 -1997-12-11,62.8161086088072,62.99954305964408,62.16389722805385,62.32695007324219,5072800,0.0,0 -1997-12-12,62.8976600288411,62.93842325633873,61.8989609551492,62.44926452636719,4478400,0.0,0 -1997-12-15,62.775357247939596,63.33585150908191,62.530777933986585,63.08108139038086,4674700,0.0,0 -1997-12-16,63.40718936736413,63.79443996137889,63.1829916550398,63.46833419799805,2885800,0.0,0 -1997-12-17,63.81485884969969,63.83524047181713,63.070929642413184,63.14226531982422,2236300,0.0,0 -1997-12-18,63.22375554677871,63.22375554677871,62.10276697325426,62.53078079223633,4658300,0.0,0 -1997-12-19,61.89971380488455,62.77961056310274,60.48778644867396,62.06341552734375,8556000,0.38,0 -1997-12-22,62.49311929838663,62.80005996291702,61.920163391263245,62.462425231933594,5136800,0.0,0 -1997-12-23,62.4931023605988,62.615878593134354,61.244877329820625,61.347190856933594,4436500,0.0,0 -1997-12-24,61.83830573400074,61.83830573400074,61.06072280286506,61.16303634643555,2019200,0.0,0 -1997-12-26,61.633701175739276,61.633701175739276,61.16305870328177,61.40861129760742,941800,0.0,0 -1997-12-29,61.94066637824563,62.67732445211971,61.83835275687423,62.615936279296875,2080000,0.0,0 -1997-12-30,62.820564807612,63.6799992447194,62.7591766335329,63.598148345947266,3616000,0.0,0 -1997-12-31,63.43441365690901,63.92551879489798,63.31163737241177,63.55718994140625,4359500,0.0,0 -1998-01-02,63.72085420020798,63.9459439228163,63.209286648825454,63.88455581665039,2360900,0.0,0 -1998-01-05,64.06876509347332,64.45755670534685,63.373032735383866,64.02783966064453,4191800,0.0,0 -1998-01-06,63.67998087115782,63.700443589946744,62.98424843233411,63.00471115112305,3154900,0.0,0 -1998-01-07,62.92285863581621,63.332113000927215,62.34990252466081,63.16841125488281,4424200,0.0,0 -1998-01-08,63.066116213331036,63.066116213331036,62.452234485102636,62.615936279296875,3831000,0.0,0 -1998-01-09,62.370352577824164,62.534054290626855,60.18084216908821,60.44685745239258,10258800,0.0,0 -1998-01-12,59.669272108280914,61.67461801589804,59.526033114879695,61.551841735839844,12097900,0.0,0 -1998-01-13,61.961109424028244,62.45221465063877,61.69509409294754,62.41128921508789,5224900,0.0,0 -1998-01-14,62.656847540454514,62.84101201722267,62.02250323158643,62.69777297973633,3770400,0.0,0 -1998-01-15,62.53405422243963,62.69775593506381,62.083874512723106,62.17595672607422,2875400,0.0,0 -1998-01-16,63.0251794477162,63.31165753611491,62.63638775631795,63.066104888916016,4374800,0.0,0 -1998-01-20,63.3116350332287,64.1813003496192,63.18885875326768,64.08921813964844,5091700,0.0,0 -1998-01-21,63.65953683760918,63.96647770953594,62.963804194575204,63.475372314453125,4699400,0.0,0 -1998-01-22,62.96378115239735,63.43442365044908,62.77961669663799,62.91262435913086,4543400,0.0,0 -1998-01-23,63.18888563721499,63.37305013550998,62.2066749796417,62.820556640625,6350300,0.0,0 -1998-01-26,63.107016955080994,63.34233819210545,62.472672750928105,62.77961349487305,4362900,0.0,0 -1998-01-27,62.73868607320721,63.843672716375245,62.636372495136094,63.41395568847656,7044200,0.0,0 -1998-01-28,63.78228461738588,64.24269571905133,63.63904560797886,63.98691177368164,4268600,0.0,0 -1998-01-29,64.06874733359456,65.19419642441146,63.88458293691543,64.33476257324219,8007700,0.0,0 -1998-01-30,64.68262730020668,64.80540356208623,64.17105954237525,64.37568664550781,3649100,0.0,0 -1998-02-02,65.41932609515163,65.80811783609873,65.31701247911292,65.43978881835938,5756300,0.0,0 -1998-02-03,65.4807206194716,66.01275147450481,65.29655609272935,65.93090057373047,2759600,0.0,0 -1998-02-04,65.66482703029331,66.23778282862557,65.43973725237707,65.84899139404297,3374000,0.0,0 -1998-02-05,66.3401207900166,66.52428522157432,65.50114949069807,65.80809020996094,5076200,0.0,0 -1998-02-06,66.13544662118984,66.5447006225586,65.93081962050547,66.5447006225586,5701200,0.0,0 -1998-02-09,66.6061573564105,66.62662007756455,65.95135027948112,66.3196792602539,2322200,0.0,0 -1998-02-10,66.4219490927997,67.09721844586883,66.2582474314496,66.9539794921875,3660400,0.0,0 -1998-02-11,66.8516832166726,67.0153849202947,66.59589930476308,66.89260864257812,4073200,0.0,0 -1998-02-12,66.60612883089956,67.40417461412693,66.05363559635754,67.17908477783203,5024700,0.0,0 -1998-02-13,66.9130747458514,67.12793324274176,66.70844760595584,66.79029846191406,2101300,0.0,0 -1998-02-17,67.32232579952333,67.50649021660409,66.89260882633488,67.1176986694336,3055500,0.0,0 -1998-02-18,66.99494003802988,67.762291977989,66.97447731963098,67.73159790039062,3007400,0.0,0 -1998-02-19,67.60879529114465,67.71110884334068,67.28139192411732,67.37347412109375,3387800,0.0,0 -1998-02-20,67.54745145975411,67.91578048922263,67.03588336327006,67.8748550415039,3707000,0.0,0 -1998-02-23,68.26358433628941,68.26358433628941,67.67016588732287,68.14080810546875,3227800,0.0,0 -1998-02-24,68.0385120844437,68.1612883468517,67.40416806200226,67.60879516601562,3386800,0.0,0 -1998-02-25,67.93620138353653,68.67285898890017,67.85435053849612,68.44776916503906,3481800,0.0,0 -1998-02-26,68.38640618522726,68.89797415489532,68.22270443493348,68.83658599853516,3187600,0.0,0 -1998-02-27,68.73427240460154,69.10260134276255,68.44779434158743,68.83658599853516,3442900,0.0,0 -1998-03-02,68.91842635912947,69.24582980976668,68.50917204583298,68.6933364868164,4252300,0.0,0 -1998-03-03,68.44778622496185,69.16398129756982,68.44778622496185,69.0821304321289,3349200,0.0,0 -1998-03-04,68.81608422540381,69.02071129714156,68.38636737475456,68.63191986083984,4404100,0.0,0 -1998-03-05,67.7724916846717,68.38637294993141,67.5474018874098,67.9975814819336,7268000,0.0,0 -1998-03-06,68.46823201137306,69.36859130859375,68.38638116617118,69.36859130859375,6896300,0.0,0 -1998-03-09,69.10256890702911,69.5527485090293,68.91840452439267,69.12303161621094,3362800,0.0,0 -1998-03-10,69.55276506485478,69.96201934590718,69.3686006383812,69.7778549194336,5481900,0.0,0 -1998-03-11,70.04386698761253,70.26895683186136,69.92109070893136,70.10525512695312,3439600,0.0,0 -1998-03-12,70.12573880040368,70.45314230807992,69.73694713503815,70.39175415039062,3191300,0.0,0 -1998-03-13,70.61685300436874,70.71916661347387,69.98250862791686,70.12574768066406,2879400,0.0,0 -1998-03-16,70.6168429306418,70.964709151975,70.41221574162225,70.88285827636719,3223600,0.0,0 -1998-03-17,70.92378451402804,71.08748626708984,70.4940674122408,71.08748626708984,4581900,0.0,0 -1998-03-18,70.88282751811415,71.35346984863281,70.7395885479563,71.35346984863281,1944100,0.0,0 -1998-03-19,71.35347140899239,71.61948664510277,71.1488443042921,71.53763580322266,2554800,0.0,0 -1998-03-20,71.94841039872414,72.35884057783836,71.49693720169849,72.15362548828125,3123300,0.313,0 -1998-03-23,72.05102070598537,72.44092939109323,71.84580560856017,71.98945617675781,4453100,0.0,0 -1998-03-24,72.27675837834316,72.76927461942216,72.19467233816333,72.6051025390625,3333600,0.0,0 -1998-03-25,73.15915506782261,73.24124107631386,71.702128417103,72.33829498291016,4597600,0.0,0 -1998-03-26,72.1536059459434,72.72820804107606,71.98943391876264,72.29725646972656,3333500,0.0,0 -1998-03-27,72.7282305274885,72.74875203723103,71.57902598190742,71.98945617675781,2611300,0.0,0 -1998-03-30,71.98946831937661,72.29729101743551,71.55851654209414,71.94842529296875,3108700,0.0,0 -1998-03-31,72.33830691061621,73.01551659233262,72.07152733903096,72.19465637207031,5926500,0.0,0 -1998-04-01,72.44092847973877,72.94370546210523,71.84580470469275,72.77953338623047,2929000,0.0,0 -1998-04-02,72.85137288241206,73.7132764489082,72.72824380148404,73.56962585449219,3920900,0.0,0 -1998-04-03,73.77481568035006,74.08263827706918,73.44647157718299,73.9389877319336,3787600,0.0,0 -1998-04-06,74.36996297724232,74.45204902909359,73.34388732910156,73.34388732910156,4550500,0.0,0 -1998-04-07,73.38491573709837,73.50804479034854,72.33831878447197,72.85135650634766,5583700,0.0,0 -1998-04-08,73.03603396537284,73.07707697406369,72.0715232611378,72.44091033935547,4854800,0.0,0 -1998-04-09,72.6050804828646,73.07707507051465,72.58455897905372,73.01551055908203,4481200,0.0,0 -1998-04-13,73.13866215547822,73.13866215547822,72.23571570911429,72.81031799316406,4350200,0.0,0 -1998-04-14,72.9744532133628,73.4259262084961,72.8308027149113,73.4259262084961,3279100,0.0,0 -1998-04-15,73.52857225688139,73.63117980957031,72.995012982899,73.63117980957031,3867200,0.0,0 -1998-04-16,73.09761599881193,73.22074505439667,72.56405675794468,72.76927185058594,7177900,0.0,0 -1998-04-17,72.70774210020257,73.83642564959888,72.52304842848316,73.73381805419922,5688800,0.0,0 -1998-04-20,73.54909238411749,73.91847956685021,73.4670063435102,73.71326446533203,3688300,0.0,0 -1998-04-21,73.8364199846859,74.30841488731174,73.4875541870929,74.0621566772461,4573200,0.0,0 -1998-04-22,74.12369104971968,74.49307821441928,74.08264803141974,74.26734161376953,2386100,0.0,0 -1998-04-23,73.95951770033155,74.20577580588204,73.38491545404706,73.54908752441406,5062000,0.0,0 -1998-04-24,73.38491712850967,73.85691184158453,72.46144921162406,72.76927185058594,11039700,0.0,0 -1998-04-27,71.82529994419643,72.03051508689413,70.67609514508929,71.39434814453125,14510200,0.0,0 -1998-04-28,72.09206404216549,72.11258555199815,71.00442402103404,71.2917251586914,6511400,0.0,0 -1998-04-29,71.59955474565614,72.21520010030494,71.20964602104523,71.78424835205078,7703100,0.0,0 -1998-04-30,72.60508826526403,73.4977737767222,72.50248073521136,73.11812591552734,8684500,0.0,0 -1998-05-01,73.38490705783917,73.9389877319336,73.09760596756799,73.9389877319336,4008800,0.0,0 -1998-05-04,74.02107493719527,74.41098356591905,73.6516878152464,73.7542953491211,4537200,0.0,0 -1998-05-05,73.54908697289157,73.65169451610151,72.97448473091585,73.24126434326172,5146400,0.0,0 -1998-05-06,73.63116833194984,73.63116833194984,72.35883487693845,72.37935638427734,5526100,0.0,0 -1998-05-07,72.56405966715937,72.6051026873331,71.80476379394531,71.80476379394531,6940400,0.0,0 -1998-05-08,72.23573562398938,73.13868231928925,72.23573562398938,72.9745101928711,7951000,0.0,0 -1998-05-11,73.2617541844904,73.69270567969329,72.4819371931709,72.72819519042969,6336300,0.0,0 -1998-05-12,72.76927464930856,73.52857052128385,72.46145199850775,73.50804901123047,6045200,0.0,0 -1998-05-13,73.59014908300074,73.91849330646086,73.28232637350688,73.69275665283203,4441500,0.0,0 -1998-05-14,73.24129344941254,74.00058957091106,73.11816434862898,73.32337951660156,4187500,0.0,0 -1998-05-15,73.54910023219814,73.69275081858915,72.76928276321836,72.91293334960938,6716100,0.0,0 -1998-05-18,72.70772692833152,73.28232934266775,72.12286375659642,72.62564086914062,4847200,0.0,0 -1998-05-19,72.89238934941149,73.3438624816432,72.74873880733776,73.11812591552734,5918700,0.0,0 -1998-05-20,73.61066469855862,73.87744435874296,72.81032571800557,73.81587982177734,5716600,0.0,0 -1998-05-21,73.7953606142835,74.0621402828001,73.09762917354779,73.34388732910156,6301500,0.0,0 -1998-05-22,73.38491610409169,73.59013119386823,72.85135687067267,73.05657196044922,4862200,0.0,0 -1998-05-26,73.61064745145902,73.61064745145902,71.70214725268966,71.8868408203125,6899300,0.0,0 -1998-05-27,71.27120333576963,72.17414976444049,70.64529728862279,71.98945617675781,10202600,0.0,0 -1998-05-28,72.15362273442489,72.4819668651845,71.62006352194051,72.31779479980469,4907600,0.0,0 -1998-05-29,72.64616857185922,72.76929767113356,71.59957122802734,71.59957122802734,4772600,0.0,0 -1998-06-01,71.55847704094249,72.3793371159748,71.29169751655698,71.92786407470703,6092200,0.0,0 -1998-06-02,72.23571429366804,72.46145090083576,71.68163353062002,71.98945617675781,6701100,0.0,0 -1998-06-03,72.15363243695344,72.35884754627355,70.84025573730469,70.84025573730469,6445100,0.0,0 -1998-06-04,71.08650702258426,72.27675454201552,70.96337796885,72.15362548828125,6566300,0.0,0 -1998-06-05,72.48196906702859,73.54908752441406,72.1536249262946,73.54908752441406,8463200,0.0,0 -1998-06-08,73.50804588701472,73.85691154309491,73.32335230438403,73.46700286865234,4159900,0.0,0 -1998-06-09,73.36441242937191,73.82614649361271,73.15919728970933,73.69275665283203,2725300,0.0,0 -1998-06-10,73.3028196668411,74.24680894586538,73.05656159405217,73.22073364257812,6186900,0.0,0 -1998-06-11,73.1797081501407,73.46700930384287,71.82528842554471,71.84580993652344,8056600,0.0,0 -1998-06-12,72.15362310490504,72.52301025390625,71.08650467445706,72.52301025390625,9779000,0.0,0 -1998-06-15,71.45589913742656,72.17415200066893,70.5939957015357,70.61451721191406,10234200,0.0,0 -1998-06-16,71.18911074118131,71.72266994535275,70.75815907627361,71.7021484375,7471500,0.0,0 -1998-06-17,72.33833749320348,73.40545623069187,72.1946868939262,73.20024108886719,12057700,0.0,0 -1998-06-18,73.01555307614437,73.17972520222902,72.72825185549625,72.87190246582031,3844600,0.0,0 -1998-06-19,73.16552999952448,73.27875746575897,72.21853664556328,72.50675201416016,3549100,0.352,0 -1998-06-22,72.6302726683087,73.16552977980984,72.50675179642383,72.85672760009766,5438300,0.0,0 -1998-06-23,73.18610224634601,73.82429329265732,73.12434182250942,73.72135925292969,5004600,0.0,0 -1998-06-24,73.88605374388538,74.89480733359015,73.52578460470512,74.70952606201172,8291900,0.0,0 -1998-06-25,75.03890059384528,75.38887626190986,74.31836245371234,74.58599090576172,5854800,0.0,0 -1998-06-26,74.6477794259246,74.99775522577035,74.5242585553908,74.89482116699219,4833100,0.0,0 -1998-06-29,75.26537148092453,75.55358679841166,74.96686275924144,75.16243743896484,5953400,0.0,0 -1998-06-30,75.03896915248528,75.22425055780005,74.48312493654095,74.64781951904297,4284800,0.0,0 -1998-07-01,75.14182010679754,75.71825050761682,74.85360490638791,75.51238250732422,3489500,0.0,0 -1998-07-02,75.57419266266076,75.67712673057504,75.26539045891793,75.65653991699219,3503900,0.0,0 -1998-07-06,75.61536531818325,76.41825103759766,75.47125762495502,76.41825103759766,3144400,0.0,0 -1998-07-07,76.4079231128358,76.50056373091684,75.94472002243063,76.27410888671875,4920700,0.0,0 -1998-07-08,76.33588031633873,77.03583175397506,76.25353308838152,76.82996368408203,6875000,0.0,0 -1998-07-09,76.60353062652597,76.8917460064699,76.17120755661007,76.31531524658203,6942500,0.0,0 -1998-07-10,76.43882020254829,77.03583765093877,75.80062913702741,76.72703552246094,7849700,0.0,0 -1998-07-13,76.78884667899987,76.97412807849344,76.45945752434464,76.74767303466797,6555100,0.0,0 -1998-07-14,77.03582834097055,77.83871377798226,77.03582834097055,77.61225891113281,6983600,0.0,0 -1998-07-15,77.77697233250254,77.92108001019643,77.42699654381738,77.46817016601562,4977500,0.0,0 -1998-07-16,77.52992895291959,78.12694646211625,77.1181927396805,78.00342559814453,6315600,0.0,0 -1998-07-17,78.14751211115103,78.39455377219788,77.94164406027866,78.10633850097656,3539500,0.0,0 -1998-07-20,78.22985933149998,78.54895481035216,77.69460239923183,78.10633850097656,2338400,0.0,0 -1998-07-21,78.3946188079441,78.3946188079441,76.6035652795063,76.74767303466797,5518600,0.0,0 -1998-07-22,76.6447389238382,77.06676877824022,76.10948154752346,76.74767303466797,10417300,0.0,0 -1998-07-23,76.62410034948056,76.80938162383448,75.03891611334139,75.22419738769531,15110900,0.0,0 -1998-07-24,75.7388680739391,75.82121530750685,74.35955191167926,75.16243743896484,11540500,0.0,0 -1998-07-27,74.85363374470951,75.75945329497553,74.33896354569474,75.69769287109375,9600000,0.0,0 -1998-07-28,75.3889058515409,75.53301353612876,73.7007872606544,74.42132568359375,13693000,0.0,0 -1998-07-29,74.91540890274959,75.18303745887373,73.94782873830079,74.13311004638672,7462400,0.0,0 -1998-07-30,74.85363974095043,75.49183083885183,74.70953207368238,75.24478912353516,6821600,0.0,0 -1998-07-31,75.32714913069353,75.43008319619051,73.33022826005201,73.63903045654297,7680500,0.0,0 -1998-08-03,73.63902525818796,74.06105489693351,73.14494177868097,73.3302230834961,10486500,0.0,0 -1998-08-04,73.92726407764114,73.92726407764114,70.48926544189453,70.48926544189453,15091600,0.0,0 -1998-08-05,71.10686463246391,71.72446913129828,69.56285338537798,71.45684051513672,21718400,0.0,0 -1998-08-06,71.23038304515495,72.13620294515114,70.85982035879287,71.76564025878906,13084400,0.0,0 -1998-08-07,72.25973050618909,72.85674822262199,71.45684461167588,71.88916778564453,12083800,0.0,0 -1998-08-10,71.6421115013861,72.23912909723097,71.27154885568926,71.4774169921875,6273000,0.0,0 -1998-08-11,70.34517617476614,70.77749946995786,69.50111640796327,70.40693664550781,14287100,0.0,0 -1998-08-12,70.98330223430087,71.7244271996242,70.81860779756235,71.60090637207031,11158400,0.0,0 -1998-08-13,71.68329884592396,72.23914292083492,70.7363052368164,70.7363052368164,9073900,0.0,0 -1998-08-14,71.37448267268348,71.62152443561172,69.68636395934053,69.9128189086914,8628500,0.0,0 -1998-08-17,69.8304750295109,71.76563559931455,69.50108599635281,71.39507293701172,11290000,0.0,0 -1998-08-18,71.80679628884131,72.85672364283526,71.66268861280292,72.71261596679688,7603900,0.0,0 -1998-08-19,73.18610638270079,73.18610638270079,72.21852635457508,72.52732849121094,6258600,0.0,0 -1998-08-20,72.25973391791604,72.7332307498568,71.90975799865548,72.09503936767578,7402800,0.0,0 -1998-08-21,71.2715510630237,71.62152690590972,69.50108503430619,71.51859283447266,16685400,0.0,0 -1998-08-24,71.97148132324219,72.42439110273628,71.35387707847752,71.97148132324219,7201700,0.0,0 -1998-08-25,72.71259232089945,73.28902283759966,71.57002468958294,72.13616180419922,9688300,0.0,0 -1998-08-26,71.33330733849999,72.21854030114227,70.98333151606002,71.72445678710938,9339700,0.0,0 -1998-08-27,70.48923369258283,70.85979625288262,68.16292428625636,68.34820556640625,24887800,0.0,0 -1998-08-28,69.15108530812569,69.64516868037785,67.29827266218008,68.10115814208984,23735900,0.0,0 -1998-08-31,68.34823315342267,68.52322109974921,62.583924333254494,63.24270248413086,22563100,0.0,0 -1998-09-01,63.283849093111364,66.24834950606127,61.67807803609682,65.91896057128906,24748500,0.0,0 -1998-09-02,65.7542595540851,67.03064154918631,65.07489494378929,65.4454574584961,13843200,0.0,0 -1998-09-03,64.31318195272868,65.46604308888514,63.73675138465045,64.9307861328125,17311300,0.0,0 -1998-09-04,65.50726481776712,65.75430667125966,63.078019925090665,64.39557647705078,17783800,0.0,0 -1998-09-08,66.45423783607853,67.85414123535156,65.75428613644202,67.85414123535156,14746500,0.0,0 -1998-09-09,67.68942642211914,67.93646812438965,66.166002591451,66.2071762084961,12109700,0.0,0 -1998-09-10,64.84842143082982,65.44543864400254,63.77790780720977,64.88959503173828,19889300,0.0,0 -1998-09-11,64.68376373497343,66.98948669433594,63.901464873761135,66.98948669433594,20379800,0.0,0 -1998-09-14,67.77177903097561,68.81141294381409,67.25710877709518,68.14234161376953,10686800,0.0,0 -1998-09-15,67.77177082783467,68.71876398034998,67.38062148223052,68.55406951904297,8337900,0.0,0 -1998-09-16,69.00698977879115,69.33637875148227,67.95706242833819,69.17168426513672,11024900,0.0,0 -1998-09-17,67.36001454147639,67.87468457923217,67.05121251882292,67.19532012939453,12945200,0.0,0 -1998-09-18,67.68059512472021,67.70125464704155,66.83355470954514,67.49465942382812,7019200,0.359,0 -1998-09-21,65.86255544571928,68.36235758151979,65.40804596648282,67.45333862304688,8834600,0.0,0 -1998-09-22,68.42439071636733,68.52768840766619,67.5360305711971,68.01119995117188,7270100,0.0,0 -1998-09-23,68.67228233480007,70.73823547363281,68.61030374063509,70.73823547363281,13688500,0.0,0 -1998-09-24,70.28368251594954,70.61423481467241,68.25904968627198,69.00279235839844,11129500,0.0,0 -1998-09-25,68.17643076395817,69.69490581279179,67.84587837237534,68.92017364501953,10247400,0.0,0 -1998-09-28,69.6226008704781,70.28370568586543,68.92017700412906,69.53996276855469,8847800,0.0,0 -1998-09-29,69.66392055418322,70.03579201621623,68.3417109113992,69.37468719482422,10618100,0.0,0 -1998-09-30,68.4243436246305,68.96149124970307,67.01949598982527,67.26741027832031,7908400,0.0,0 -1998-10-01,66.13113029812952,67.13311712082846,64.8502399268443,65.32540893554688,13335600,0.0,0 -1998-10-02,65.3667506147829,66.688960488154,64.27179556339748,66.58566284179688,14351800,0.0,0 -1998-10-05,65.84190114258662,66.0898154236381,63.69331070680721,65.2427749633789,12807200,0.0,0 -1998-10-06,66.60631641321439,66.9575283794131,64.47838508859867,65.1808090209961,12621000,0.0,0 -1998-10-07,65.20148111586404,66.13116002911306,63.30080422655496,64.20982360839844,14348500,0.0,0 -1998-10-08,62.51571636761481,63.96188297228534,60.96625214832495,63.858585357666016,20625000,0.0,0 -1998-10-09,64.12714887111481,65.30474148891557,62.18515402772409,65.13946533203125,12708500,0.0,0 -1998-10-12,66.52365553039951,67.06080305952696,65.90386991986784,66.08980560302734,9508500,0.0,0 -1998-10-13,65.82124503792603,66.3790521992644,65.2840974010817,65.86256408691406,6256800,0.0,0 -1998-10-14,65.40807289422261,67.30874968078878,65.34609430335632,66.48236846923828,8176900,0.0,0 -1998-10-15,66.193097055222,70.90346725765353,66.0691399446317,70.05642700195312,20541100,0.0,0 -1998-10-16,70.15974075389359,70.57293121769743,69.41599791904666,70.07710266113281,16573900,0.0,0 -1998-10-19,69.87049514734647,70.61423785145779,69.74653802999458,70.32500457763672,7915900,0.0,0 -1998-10-20,71.1101070386227,71.92615852846163,70.13910906337131,70.73823547363281,14611600,0.0,0 -1998-10-21,70.65560219016751,71.15143097746693,69.99449714043494,70.49032592773438,9178400,0.0,0 -1998-10-22,70.59359949846127,71.70921388913055,70.18040898339858,71.54393768310547,8277500,0.0,0 -1998-10-23,71.37868426563656,71.39934379798552,70.59362203637632,70.63494110107422,6237200,0.0,0 -1998-10-26,71.21336863846729,71.70919714073686,70.59358301063033,71.1513900756836,5838600,0.0,0 -1998-10-27,71.68857391749586,72.03978595109743,70.26306625170128,70.73823547363281,10089700,0.0,0 -1998-10-28,70.44900444392366,71.08944993886843,70.0977923983088,70.6142807006836,5650600,0.0,0 -1998-10-29,70.800204885309,72.3496695384745,70.49031195467589,72.32901000976562,9625600,0.0,0 -1998-10-30,72.8041881127791,73.3206763939424,72.39099748784845,72.72154998779297,9872800,0.0,0 -1998-11-02,73.25866632727937,73.96109008789062,72.84547587986098,73.96109008789062,6501400,0.0,0 -1998-11-03,73.77516211239757,73.91977878413847,73.21735494996835,73.4239501953125,6866000,0.0,0 -1998-11-04,74.35363921396981,74.76682976253313,73.44462000713051,74.20902252197266,8233600,0.0,0 -1998-11-05,73.73385044926636,75.32463399776552,73.46527660341584,75.2213363647461,6841500,0.0,0 -1998-11-06,75.01474306956831,75.73782651970185,74.91144543383496,75.44859313964844,5224800,0.0,0 -1998-11-09,75.32465917605164,75.53125451114778,74.37432063460942,74.91146850585938,6188700,0.0,0 -1998-11-10,74.70486450195312,75.3246504353211,74.20903575525875,74.70486450195312,14015200,0.0,0 -1998-11-11,75.24199889338095,75.36595605794996,73.97143795654874,74.20902252197266,10922400,0.0,0 -1998-11-12,74.25034572129266,74.78749346440718,73.8371551496661,74.12638854980469,5457400,0.0,0 -1998-11-13,74.18833688330064,74.84944152832031,74.12635832283004,74.84944152832031,5524600,0.0,0 -1998-11-16,75.51055469251659,75.60352254371121,74.63252498678965,75.40725708007812,6023700,0.0,0 -1998-11-17,75.13868328773812,76.44023320446276,74.70483331549657,75.40725708007812,9058900,0.0,0 -1998-11-18,75.57256010051387,76.02706975666786,75.03541232505914,75.8617935180664,4567700,0.0,0 -1998-11-19,76.21297248238879,76.62616289975061,75.77912254415887,76.52286529541016,5249300,0.0,0 -1998-11-20,76.92573529884363,77.18397934107152,76.58485316310282,77.1013412475586,5562000,0.0,0 -1998-11-23,77.6591492478136,79.08465637686365,77.45255401171939,78.91938018798828,7151300,0.0,0 -1998-11-24,78.67146010243101,79.10531006623118,78.30991846593088,78.42354583740234,5323700,0.0,0 -1998-11-25,78.63014105825957,78.79541723494535,78.15497205028795,78.42354583740234,4393500,0.0,0 -1998-11-27,78.98132412802724,79.14660024443431,78.68176116703943,79.00198364257812,4557900,0.0,0 -1998-11-30,78.68181168173307,78.91939625160045,76.77080535888672,76.77080535888672,8705400,0.0,0 -1998-12-01,76.77082411211686,78.03105561664837,76.17169765914285,77.76248168945312,8950600,0.0,0 -1998-12-02,77.49386009787548,77.94836954126478,76.68813881186718,77.5351791381836,7495500,0.0,0 -1998-12-03,77.51453394077231,78.21695775580064,76.08902678674426,76.25430297851562,12145300,0.0,0 -1998-12-04,77.10138211758864,78.34095417735695,77.01874398027076,78.25831604003906,10339500,0.0,0 -1998-12-07,78.0516604384935,78.98133876557984,78.01034140173411,78.630126953125,4290000,0.0,0 -1998-12-08,78.36156875396914,79.16729013055885,77.67980451223939,78.2789306640625,10102600,0.0,0 -1998-12-09,78.46486488157379,78.65080058034529,77.927717307345,78.42354583740234,5327900,0.0,0 -1998-12-10,78.56817498657732,78.56817498657732,77.16332726133744,77.2769546508789,5966600,0.0,0 -1998-12-11,76.97739047250981,77.57651670001995,76.39892377008623,77.43190002441406,8198200,0.0,0 -1998-12-14,76.79145170358511,76.95672789772789,75.20066833496094,75.20066833496094,9521400,0.0,0 -1998-12-15,75.8204375248129,77.18396582907384,75.71713992600525,77.14264678955078,9631500,0.0,0 -1998-12-16,77.4319063137031,77.4319063137031,76.5228871360609,77.03937530517578,7260000,0.0,0 -1998-12-17,77.49388348308925,78.4029026441812,77.36992632475852,78.26861572265625,6914700,0.0,0 -1998-12-18,78.47681207294706,79.01574422136139,78.1866178391855,78.60118103027344,4802400,0.392,0 -1998-12-21,79.09865905206132,80.48744578070075,78.93283377102975,79.69977569580078,8580700,0.0,0 -1998-12-22,79.86557065310842,80.40450261183689,79.0571727150157,80.0521240234375,5461100,0.0,0 -1998-12-23,80.38381715368686,81.73114776611328,80.13507919446968,81.73114776611328,7791000,0.0,0 -1998-12-24,81.68967605256573,81.751860530657,81.25438470592684,81.37875366210938,1507100,0.0,0 -1998-12-28,81.75185909407618,81.7933154120752,80.92273273409569,81.17147064208984,4203600,0.0,0 -1998-12-29,81.39949064988356,82.57063178095522,81.00565557925769,82.45662689208984,3935800,0.0,0 -1998-12-30,82.20788290388259,82.74681506613699,81.60676626136807,81.79331970214844,6810700,0.0,0 -1998-12-31,81.79331970214844,82.20788290388259,81.23365937980734,81.79331970214844,6790500,0.0,0 -1999-01-04,81.83476785587604,83.05772917895018,80.73617548091114,81.60675811767578,9450400,0.0,0 -1999-01-05,81.54458381350109,82.82972977090756,81.54458381350109,82.53953552246094,8031000,0.0,0 -1999-01-06,83.45156991312233,84.73671579852063,83.41011359423852,84.52943420410156,7737700,0.0,0 -1999-01-07,83.82468154478568,84.38434188150902,83.43084649301738,84.11487579345703,5504900,0.0,0 -1999-01-08,85.02690380566742,85.23418538491087,83.55520459303884,84.73670959472656,6224400,0.0,0 -1999-01-11,84.69528687397738,84.69528687397738,83.0577617484159,83.9283447265625,7578300,0.0,0 -1999-01-12,83.72103516484651,83.72103516484651,82.08351058499434,82.4151611328125,7768800,0.0,0 -1999-01-13,79.86560258024733,82.99555482255651,79.84487441970222,81.83477783203125,10810600,0.0,0 -1999-01-14,82.00057837851854,82.1871317671451,80.19722895512848,80.40451049804688,11400700,0.0,0 -1999-01-15,81.17143760949523,82.76750520294037,80.96415610385301,82.49803924560547,7817700,0.0,0 -1999-01-19,83.10954348182773,83.32718912506485,81.91767448314832,83.03699493408203,6535100,0.0,0 -1999-01-20,83.63814052111327,84.86110217929064,82.93338295538395,83.70032501220703,6534400,0.0,0 -1999-01-21,83.29608183506336,83.4722711334578,81.31654324722,81.48236846923828,6937500,0.0,0 -1999-01-22,81.00566599066315,82.14571502584391,80.77765618362699,81.29586029052734,7522300,0.0,0 -1999-01-25,81.77256037766497,82.24930787796568,80.86052168143753,82.12493896484375,5700300,0.0,0 -1999-01-26,82.33222509789834,83.61737060546875,82.00057464433178,83.61737060546875,6047000,0.0,0 -1999-01-27,83.82467058926619,83.9904958525486,82.35297137763466,82.6431655883789,7399400,0.0,0 -1999-01-28,83.07843401494377,84.21848238590731,83.03697771054509,84.03192901611328,5961700,0.0,0 -1999-01-29,84.46724843803788,85.0994572913778,83.18210257223215,84.67453002929688,6456800,0.0,0 -1999-02-01,85.35855270870447,85.35855270870447,84.17704772949219,84.17704772949219,9426800,0.0,0 -1999-02-02,84.29106048738991,84.38433720500784,82.75717668656196,83.65885162353516,9194500,0.0,0 -1999-02-03,83.36864885559906,84.86107618468984,83.34792069825058,84.50869750976562,10290700,0.0,0 -1999-02-04,84.48795187900741,84.57086449125374,82.76751517489612,83.2442626953125,7761100,0.0,0 -1999-02-05,83.34788415750269,83.34788415750269,81.73108859314426,82.2907485961914,7516100,0.0,0 -1999-02-08,82.97483093238704,82.97483093238704,81.81405388212133,82.45662689208984,8528400,0.0,0 -1999-02-09,82.49807120902155,82.58098384340246,80.60144469755785,80.61180877685547,8985700,0.0,0 -1999-02-10,81.0056435609875,81.58603200001197,80.4770755183045,81.13001251220703,6936700,0.0,0 -1999-02-11,81.62749351249947,83.63812501852091,81.25438663509343,82.99555206298828,9181800,0.0,0 -1999-02-12,82.78826511641485,83.24428460378618,81.33729402023332,82.00059509277344,10676600,0.0,0 -1999-02-16,82.74679445905093,83.32718279694006,81.50310516357422,81.50310516357422,6915500,0.0,0 -1999-02-17,81.71043665588752,83.1510443036765,81.08859162806493,81.42024230957031,7858100,0.0,0 -1999-02-18,81.71040358113412,82.49807363087615,81.06783064581825,82.06278228759766,9048900,0.0,0 -1999-02-19,82.24933585890342,83.41011277626698,81.83477267413073,82.4151611328125,5312200,0.0,0 -1999-02-22,82.53953443094541,84.71599126551328,82.43589362929931,84.61235046386719,10695800,0.0,0 -1999-02-23,84.63309971865486,85.23421651802812,83.96979842279472,84.57091522216797,7770700,0.0,0 -1999-02-24,84.79892902793047,85.4622303549639,82.91266587917914,83.0784912109375,7271000,0.0,0 -1999-02-25,82.60167082010649,83.09914637837062,81.37871007270718,82.2907485961914,11633000,0.0,0 -1999-02-26,82.74682237454357,82.80900686029598,81.46167633566039,81.95915222167969,9621300,0.0,0 -1999-03-01,82.02133900665244,82.45663041912141,81.17148434421303,82.18716430664062,7607200,0.0,0 -1999-03-02,82.58097510883826,83.11990717531161,81.13000416064081,81.46165466308594,9651500,0.0,0 -1999-03-03,81.64823469829632,81.95915714573843,80.77765184545844,81.89697265625,7881400,0.0,0 -1999-03-04,82.29081464451393,83.23394614736667,81.76224643961844,82.93338775634766,8256400,0.0,0 -1999-03-05,84.57084411822974,84.98540707959361,83.94899967618393,84.60193634033203,10703100,0.0,0 -1999-03-08,85.08910640653409,85.43112108514744,84.40507704930738,85.15129089355469,4802200,0.0,0 -1999-03-09,84.98547183065196,86.18770533459777,84.73673386431834,84.94401550292969,7893400,0.0,0 -1999-03-10,85.21346836985167,85.73167238572282,84.75744883588506,85.69021606445312,3950000,0.0,0 -1999-03-11,86.02185491169467,87.01680648657694,85.48292280863346,86.6436996459961,6583700,0.0,0 -1999-03-12,86.89243169959617,86.91315985601305,85.71092678383353,85.81456756591797,5286500,0.0,0 -1999-03-15,86.18767457668145,87.05825714816308,85.89748038618757,87.03752899169922,5394400,0.0,0 -1999-03-16,86.97533759459793,87.32771622641589,86.54004634352869,86.70587158203125,4547500,0.0,0 -1999-03-17,86.68511165696748,86.85093683469482,85.98035465162629,86.33273315429688,4524100,0.0,0 -1999-03-18,86.08402577960874,87.80446260592888,86.06329762507477,87.72154998779297,3506300,0.0,0 -1999-03-19,87.97587211884648,88.18365641766285,86.23048400878906,86.23048400878906,5526700,0.32,0 -1999-03-22,86.4798575250388,86.83309096519393,86.05389955308702,86.39674377441406,4603800,0.0,0 -1999-03-23,85.9811325337758,86.12658152549558,83.58122417039951,83.90328979492188,9713800,0.0,0 -1999-03-24,84.33964583850852,84.55781934954679,83.52928708322351,84.3812026977539,6280900,0.0,0 -1999-03-25,85.15002694737497,86.1058349609375,84.94224259660051,86.1058349609375,6639600,0.0,0 -1999-03-26,85.52404741390156,85.85650240870778,84.92147273581527,85.48249053955078,6159700,0.0,0 -1999-03-29,85.877242383941,87.39406761840209,85.877242383941,87.20706176757812,5863900,0.0,0 -1999-03-30,86.39673607375094,87.24865194172203,86.14739484410086,86.74996948242188,5401400,0.0,0 -1999-03-31,87.20713845710792,87.50842592616392,85.31629985820469,85.35785675048828,7413600,0.0,0 -1999-04-01,86.23050828865975,86.23050828865975,85.19158650204939,86.00194549560547,7683600,0.0,0 -1999-04-05,87.06163688625456,88.162893868348,86.60451134651765,88.03822326660156,5791100,0.0,0 -1999-04-06,87.89279491026576,88.42264509234892,87.20710643933461,87.83045959472656,5381300,0.0,0 -1999-04-07,88.22528010280836,88.68240590644984,87.3525853867655,88.53695678710938,6248600,0.0,0 -1999-04-08,88.55768616854247,89.7212784785937,87.9551115794088,89.65894317626953,5909100,0.0,0 -1999-04-09,89.38882755636617,90.09529434783907,88.8278098101965,89.67972564697266,4365800,0.0,0 -1999-04-12,88.74466120087187,90.69783332751247,88.57843378583864,90.635498046875,8213200,0.0,0 -1999-04-13,90.59399614039246,90.73944521676465,89.43040352941495,90.05375671386719,10071400,0.0,0 -1999-04-14,90.46935222977568,90.46935222977568,88.22528010280836,88.53695678710938,11551700,0.0,0 -1999-04-15,88.72392104006207,88.80703478342512,87.10320304448247,88.20446014404297,11150000,0.0,0 -1999-04-16,88.37066420366827,88.37066420366827,87.22785053538665,87.45641326904297,6476200,0.0,0 -1999-04-19,88.22523533883702,89.45116300840635,85.35781129814943,86.1058349609375,12487200,0.0,0 -1999-04-20,86.313638238025,87.22788958190394,85.77339880755109,87.18633270263672,9049600,0.0,0 -1999-04-21,87.14475657168751,90.17840808801235,86.70840943577778,89.67972564697266,5772200,0.0,0 -1999-04-22,89.84598104719014,90.67711870720115,89.35768767193366,90.53166961669922,6897400,0.0,0 -1999-04-23,90.34459640285247,90.92639233184968,89.76280047385526,90.30303955078125,4593100,0.0,0 -1999-04-26,90.76021260174481,90.96799697125431,90.0745241823635,90.82254791259766,3606500,0.0,0 -1999-04-27,91.17578212550428,91.42512337106173,90.32386620318303,91.25889587402344,5147200,0.0,0 -1999-04-28,90.71865096228716,91.25889029463242,89.76284291275321,90.0121841430664,5544300,0.0,0 -1999-04-29,90.13683587957411,90.46929078369425,88.9732437151536,89.32647705078125,9824300,0.0,0 -1999-04-30,89.82517851316013,90.17841192392203,87.43565838153546,88.59925079345703,10991900,0.0,0 -1999-05-03,88.7239157818584,90.32385524677714,88.4953530011557,90.21996307373047,10709700,0.0,0 -1999-05-04,89.8459527987186,90.30307836799977,88.51613296080973,88.93170166015625,10509300,0.0,0 -1999-05-05,89.05638731636822,89.80441110157336,87.66423193834753,89.63818359375,10032300,0.0,0 -1999-05-06,89.38881932383724,89.84594485268997,88.01744273727907,89.08753204345703,13507200,0.0,0 -1999-05-07,89.43038530702944,89.84595401198776,88.7239185086003,89.7628402709961,8680700,0.0,0 -1999-05-10,89.65894524491193,90.24074141336095,88.7862509922384,89.31610107421875,5580100,0.0,0 -1999-05-11,89.97062185841845,91.00954358888518,89.5758316008411,90.21996307373047,7000600,0.0,0 -1999-05-12,90.26148954736685,91.23807564513993,87.4356233921086,90.92639923095703,13865800,0.0,0 -1999-05-13,91.25890034229681,91.7575828578285,90.96800220823667,91.32123565673828,4485700,0.0,0 -1999-05-14,89.47194502861024,90.3446393368317,88.76547820766906,88.95248413085938,8370100,0.0,0 -1999-05-17,88.84857580702861,89.41998269072677,88.0174385216495,89.22258758544922,6268100,0.0,0 -1999-05-18,89.45117152966576,89.75245886698984,88.18368686919895,89.05638122558594,8218600,0.0,0 -1999-05-19,89.40960240932921,89.76283579091383,88.59924347510568,89.70050048828125,5052400,0.0,0 -1999-05-20,89.8459283406016,90.15760478026604,89.13946174402889,89.16024017333984,4670400,0.0,0 -1999-05-21,89.18103615238037,89.55504795357861,88.16289291578516,88.66157531738281,6418200,0.0,0 -1999-05-24,88.99405695076098,88.99405695076098,86.69803937124684,87.18633270263672,7952000,0.0,0 -1999-05-25,87.35254523550817,87.99667675365774,85.39937224499015,85.77338409423828,15406400,0.0,0 -1999-05-26,85.98118604834592,87.10322182568055,85.17082687582648,86.72920989990234,12575900,0.0,0 -1999-05-27,86.33440646374173,86.62530458419718,85.10847867039378,85.48249053955078,14515400,0.0,0 -1999-05-28,85.77337169658662,86.9369639482845,85.50325206672818,86.5733413696289,8748000,0.0,0 -1999-06-01,86.52137482924269,86.54215325739573,85.35778285267266,86.27203369140625,6705300,0.0,0 -1999-06-02,86.27205746809489,86.56295554240927,85.11885438777715,86.35517120361328,6981900,0.0,0 -1999-06-03,86.89538124013272,87.02005180145285,86.29280686041874,86.83304595947266,6076600,0.0,0 -1999-06-04,87.41486843466171,88.671963642719,87.06163507041421,88.57846069335938,9787200,0.0,0 -1999-06-07,88.72394647450999,89.22262907764541,88.37071296395573,88.80706024169922,5299100,0.0,0 -1999-06-08,88.6823343518551,88.96284314139353,87.49796390713726,87.93431091308594,4895700,0.0,0 -1999-06-09,88.03825566626543,88.49538137423282,87.64346528211176,87.85124969482422,7409700,0.0,0 -1999-06-10,87.3940887669727,87.41486719987022,86.16816122601897,87.04085540771484,7040700,0.0,0 -1999-06-11,87.24864341137096,87.91355329209587,85.83570991483055,86.31361389160156,12824800,0.0,0 -1999-06-14,86.89542627816249,86.93698315347486,86.13701330371175,86.39674377441406,6823500,0.0,0 -1999-06-15,86.74997570294828,87.53955632012482,86.49024523677178,86.9785385131836,5700900,0.0,0 -1999-06-16,88.01748532752616,89.01485060035931,88.01748532752616,88.64083862304688,7865800,0.0,0 -1999-06-17,88.34991868971892,90.13686436406414,88.18369118512867,89.47195434570312,8162800,0.0,0 -1999-06-18,89.40858808102969,89.8254113354867,88.95008250112697,89.5753173828125,2875100,0.405,0 -1999-06-21,89.6794932568195,90.20052215094464,89.13762320692935,89.78369903564453,4646400,0.0,0 -1999-06-22,89.36691231390982,90.15887655549764,89.03345368587284,89.15850067138672,5862400,0.0,0 -1999-06-23,88.69998403365045,89.1168072668913,88.11643150711329,88.7208251953125,9629200,0.0,0 -1999-06-24,88.61662067481046,88.65830299874028,87.13689817530162,88.05390930175781,9176900,0.0,0 -1999-06-25,88.42904848276841,88.7104041637765,87.53287853585371,87.83507537841797,4087000,0.0,0 -1999-06-28,88.49157822864244,89.11681311956549,88.36653125045784,88.90840148925781,5028900,0.0,0 -1999-06-29,88.69997691266548,90.15885811188694,88.55408879274333,89.74203491210938,6956200,0.0,0 -1999-06-30,89.78371710672866,91.70110382302835,89.26268810773418,91.36764526367188,16856100,0.0,0 -1999-07-01,91.36763123125141,92.36800675568118,90.74239652848281,92.05538940429688,9939500,0.0,0 -1999-07-02,92.11793213301084,93.03494322211773,91.97204400519837,92.97241973876953,3772800,0.0,0 -1999-07-06,92.86821921310083,93.86859500354716,92.43055480478056,92.95158386230469,11221100,0.0,0 -1999-07-07,92.74313303584002,93.18079725915523,92.37841284974402,93.07659149169922,3230700,0.0,0 -1999-07-08,92.74313363709936,93.78519131841509,92.53472210083622,93.1495361328125,7101500,0.0,0 -1999-07-09,93.36839994926045,93.7643820026167,93.07662369941902,93.70185852050781,2976800,0.0,0 -1999-07-12,93.99363363502918,93.99363363502918,93.03494025427278,93.2850341796875,4436300,0.0,0 -1999-07-13,92.95158386230469,93.31630420257157,92.47223712938248,92.95158386230469,6477600,0.0,0 -1999-07-14,93.36840466485506,93.51429279714391,92.53475819463314,93.47261047363281,4382500,0.0,0 -1999-07-15,93.88941662459723,94.28539862589965,93.57679925514796,94.06656646728516,3348600,0.0,0 -1999-07-16,94.20203049627365,94.80642405476745,93.8685719812426,94.5771713256836,2281500,0.0,0 -1999-07-19,94.82730190206019,94.86898423256659,93.74356130889379,93.95197296142578,4287000,0.0,0 -1999-07-20,93.4517743198454,93.639344785697,91.72195780143613,92.02415466308594,7060100,0.0,0 -1999-07-21,92.0970640633331,92.63893409402932,91.57603518766363,91.91991424560547,4793700,0.0,0 -1999-07-22,91.65940850798526,92.03454933407525,90.34641561667034,90.71113586425781,7804100,0.0,0 -1999-07-23,91.13839020562216,91.36764295939801,90.11717339334785,90.53399658203125,4603000,0.0,0 -1999-07-26,89.95043769086226,90.78408400866451,89.7837084273018,89.86707305908203,4333400,0.0,0 -1999-07-27,90.70073525183993,91.5031199731625,90.28391201998404,90.61737060546875,5908500,0.0,0 -1999-07-28,90.8674515938879,91.57605098705125,90.42978726281643,90.90913391113281,3955500,0.0,0 -1999-07-29,89.99210939671211,90.20052095159102,88.9083693113418,89.63780975341797,7760600,0.0,0 -1999-07-30,89.9087887241342,90.2630885406178,88.40822479549774,88.53327178955078,5994300,0.0,0 -1999-08-02,88.53324946756932,89.86708373450068,88.3665201842029,88.74166107177734,6065500,0.0,0 -1999-08-03,89.17931659405048,89.26268122746394,87.61622971754808,88.3248291015625,5445400,0.0,0 -1999-08-04,88.5124084709934,89.28353140799052,86.99100375745856,87.11605072021484,6497900,0.0,0 -1999-08-05,87.28280789414646,87.99140757619062,85.92813203141496,87.90804290771484,10322000,0.0,0 -1999-08-06,87.51204459939227,88.03307368131291,86.36578061916684,86.94933319091797,7537700,0.0,0 -1999-08-09,87.09519251086463,87.89757703148399,86.5324812886121,86.76173400878906,5565900,0.0,0 -1999-08-10,86.61588314594734,86.80345363399198,84.69849593482435,85.8030776977539,9793300,0.0,0 -1999-08-11,86.49081593369992,87.05352726628061,85.78221647785756,86.8451156616211,8003400,0.0,0 -1999-08-12,87.15771951903271,87.90800117916785,86.6366905883833,86.79299926757812,7036800,0.0,0 -1999-08-13,87.78300481445771,88.8667456146362,87.44954610671049,88.8042221069336,5857700,0.0,0 -1999-08-16,88.7833415949456,89.34605291491357,88.19978911497883,89.20016479492188,3927000,0.0,0 -1999-08-17,89.6586521010545,89.92958712599957,88.78332355892425,89.78369903564453,4808000,0.0,0 -1999-08-18,89.50233922730492,89.61696557862642,88.86668400634024,89.13761901855469,4355500,0.0,0 -1999-08-19,88.28317139880491,88.85630341331557,87.8246657871964,88.40821838378906,6448100,0.0,0 -1999-08-20,88.74168609298653,89.4502857470874,88.4915920974215,89.30439758300781,3663700,0.0,0 -1999-08-23,89.90878496460492,91.06546961122883,89.80457914058475,91.01336669921875,5637800,0.0,0 -1999-08-24,90.74241571341534,92.01372654449442,90.28391016778025,91.34680938720703,9279500,0.0,0 -1999-08-25,91.49271135407734,92.55561073427278,90.80495293159795,92.28467559814453,6005300,0.0,0 -1999-08-26,92.2221577671596,92.31594301234654,91.034211328125,91.18009948730469,4322300,0.0,0 -1999-08-27,91.28429559357241,91.40934257383758,90.07550811767578,90.07550811767578,6247400,0.0,0 -1999-08-30,90.26308199438718,90.36728781520729,88.03307742883663,88.40821838378906,4652000,0.0,0 -1999-08-31,88.65832071805305,89.20019103743937,87.28280375345702,88.07476806640625,11453700,0.0,0 -1999-09-01,88.65831272933124,89.18976241776518,88.24148944428502,89.15850067138672,6863900,0.0,0 -1999-09-02,88.11645390867552,88.48117433048296,87.13691906153556,88.10603332519531,10896300,0.0,0 -1999-09-03,89.95043770665822,91.03417791999145,89.82539075896592,90.67987823486328,9160800,0.0,0 -1999-09-07,90.74240229165541,91.11754313714226,90.17969102342511,90.34642028808594,4560800,0.0,0 -1999-09-08,89.9296393856496,90.74244493282,89.38776902086933,89.90879821777344,6159600,0.0,0 -1999-09-09,89.86707305908203,90.20053158620293,89.15847368895012,89.86707305908203,6177800,0.0,0 -1999-09-10,90.86746454458228,90.94040861015706,89.99213575768492,90.61737060546875,2934500,0.0,0 -1999-09-13,90.11717459619135,90.34642735302717,89.70035140194439,90.00254821777344,2320500,0.0,0 -1999-09-14,89.40856170654297,89.7420202117422,88.95005626189403,89.40856170654297,3736000,0.0,0 -1999-09-15,90.32557307414565,90.32557307414565,87.94968121202,87.99136352539062,6984500,0.0,0 -1999-09-16,88.36655075291044,88.57496242921448,86.9076690187822,88.33528900146484,15377600,0.0,0 -1999-09-17,88.69902849286515,89.57682283704526,88.38553051280084,89.45142364501953,8542300,0.372,0 -1999-09-20,89.57677940257977,89.61857911298695,89.01248331208289,89.32598114013672,2803900,0.0,0 -1999-09-21,88.4482159587211,88.59451499267928,87.0479252051213,87.44502258300781,9335200,0.0,0 -1999-09-22,87.77941454967139,88.17651190120561,86.7762212405323,87.35096740722656,13009100,0.0,0 -1999-09-23,88.15559883900752,88.15559883900752,85.35501793705707,85.522216796875,12204100,0.0,0 -1999-09-24,85.43865203857422,85.85664935774533,84.47725820448068,85.43865203857422,13792900,0.0,0 -1999-09-27,86.10742135586744,86.77621686154409,85.79392346258152,86.00292205810547,6970300,0.0,0 -1999-09-28,85.56404091106124,86.14923708729712,83.97565128984955,85.83573913574219,11063400,0.0,0 -1999-09-29,85.89840749380564,86.35820432223964,84.76981527855857,84.81161499023438,7580300,0.0,0 -1999-09-30,85.22960511364982,86.56719577752271,84.91610730180463,86.1073989868164,7498900,0.0,0 -1999-10-01,85.56401395693338,85.98201109399704,84.68621996909967,85.9193115234375,11127100,0.0,0 -1999-10-04,86.40002948330647,87.52862203098391,86.1074314153901,87.44502258300781,6341600,0.0,0 -1999-10-05,87.4241268961053,88.26012141579075,86.06563580161647,87.3614273071289,11976000,0.0,0 -1999-10-06,87.44499849396759,88.82438900558371,87.40319878149437,88.69898986816406,10308200,0.0,0 -1999-10-07,88.85574081113553,88.94979016633961,87.94659704416284,88.19739532470703,6535600,0.0,0 -1999-10-08,88.11379811120412,89.53498840332031,87.76895046679357,89.53498840332031,9674300,0.0,0 -1999-10-11,89.34686454289533,89.7021620159314,89.15876588069976,89.38866424560547,4282000,0.0,0 -1999-10-12,89.03337781657461,89.15877694025994,87.73758687182635,88.00928497314453,8686100,0.0,0 -1999-10-13,87.40322060587347,87.82121783497855,85.73123168945312,85.73123168945312,10469300,0.0,0 -1999-10-14,85.92979097959326,86.40003792037908,84.76984852565494,85.71034240722656,10562200,0.0,0 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-2000-12-18,89.47075041035974,90.4234678188941,89.26962117966916,89.91535186767578,7235400,0.0,0 -2000-12-19,89.74593857099426,91.43965762871531,88.08397674560547,88.08397674560547,9616600,0.0,0 -2000-12-20,87.14187047977259,87.35358542262918,85.4269794426342,85.5328369140625,9994300,0.0,0 -2000-12-21,85.36345193238385,87.30064336388511,85.04587956656397,86.12562561035156,14331500,0.0,0 -2000-12-22,87.3959395743213,88.82501562113978,87.29008208937178,88.70857238769531,10182900,0.0,0 -2000-12-26,88.64506113712171,89.66129302978516,88.26397417737292,89.66129302978516,4665300,0.0,0 -2000-12-27,89.25902923998898,90.60341926767856,88.9202852960042,90.3176040649414,4854100,0.0,0 -2000-12-28,89.97882317928376,90.69865376471805,89.83062276463554,90.5927963256836,8358700,0.0,0 -2000-12-29,90.82570708894116,90.97390754339864,88.8779296875,88.8779296875,8774600,0.0,0 -2001-01-02,89.42840602824782,89.53426351644413,86.42205336347244,87.26891326904297,8737500,0.0,0 -2001-01-03,86.93016113705104,92.13834906684028,86.48555972841051,91.46086120605469,19431600,0.0,0 -2001-01-04,91.41852629297698,91.77844175082335,90.10589344671375,90.47639465332031,9219000,0.0,0 -2001-01-05,90.42345154775647,90.52930901854523,87.52295684814453,87.52295684814453,12911400,0.0,0 -2001-01-08,87.98872452404885,88.200439453125,86.50672002051577,88.200439453125,6625300,0.0,0 -2001-01-09,88.78265465416202,89.08964129836981,87.6817370335548,87.9675521850586,5702400,0.0,0 -2001-01-10,87.39592737867342,89.51307678222656,87.26889841446024,89.51307678222656,8746100,0.0,0 -2001-01-11,88.8144102168669,90.43402937815185,88.8144102168669,89.5977554321289,7245100,0.0,0 -2001-01-12,89.89414036273956,90.59279948472977,88.94142337820747,89.4283676147461,7244000,0.0,0 -2001-01-16,89.42837737627912,90.23289402881193,89.10021926800916,90.00000762939453,8542200,0.0,0 -2001-01-17,91.35502699903464,91.49264175581186,89.86243617552782,90.41289520263672,7851400,0.0,0 -2001-01-18,90.40227835620055,91.93721165381754,90.0635344560368,91.31265258789062,8107000,0.0,0 -2001-01-19,92.26537954288858,92.26537954288858,90.69868883931498,90.79396057128906,7782500,0.0,0 -2001-01-22,90.95273749674253,91.99014069444746,90.48696463246684,91.3973388671875,7050900,0.0,0 -2001-01-23,91.10093296360515,92.58293745057989,90.88921803689446,92.1171646118164,8463100,0.0,0 -2001-01-24,92.30769380312464,93.02752444287378,92.03246444086761,92.39237976074219,6199900,0.0,0 -2001-01-25,92.3077005587472,92.98518826927013,91.90544223062422,92.15950012207031,10818300,0.0,0 -2001-01-26,91.56670557213356,92.22302184328643,91.09034698823228,92.05364990234375,7136800,0.0,0 -2001-01-29,91.7995896659353,92.74806837613515,91.7115129508301,92.54483032226562,6705900,0.0,0 -2001-01-30,92.34163220667143,93.43915964569229,91.99610665168467,93.35786437988281,7069100,0.0,0 -2001-01-31,93.0867762883932,93.96751208627377,92.54479457059463,92.82933807373047,9706900,0.0,0 -2001-02-01,92.88355619283011,93.45941446325163,92.30768758476798,93.44586181640625,8239100,0.0,0 -2001-02-02,93.08681756905284,93.48654321900538,91.2914790729901,91.32535552978516,8276600,0.0,0 -2001-02-05,91.32537207829299,92.09770788001174,91.29149561535938,91.99607849121094,4352900,0.0,0 -2001-02-06,91.66411859445782,92.61259755723928,91.6099183197781,91.72509002685547,7106700,0.0,0 -2001-02-07,91.27117830386575,91.73186515210054,90.5665850402003,91.2508544921875,5748700,0.0,0 -2001-02-08,91.32536453255697,91.73185104123911,90.17363725149124,90.18717956542969,5943300,0.0,0 -2001-02-09,90.34299757704147,90.34299757704147,88.92704029190833,89.31998443603516,9913000,0.0,0 -2001-02-12,89.22512617609415,90.44460607838727,89.22512617609415,90.34298706054688,5804400,0.0,0 -2001-02-13,90.58012981959197,90.89854995839823,89.42840246148067,89.60454559326172,6587600,0.0,0 -2001-02-14,89.86873225959413,89.86873225959413,88.5205384578529,89.4690170288086,8400100,0.0,0 -2001-02-15,89.99748349207084,90.45818060553886,89.42162502229105,90.33622741699219,5929800,0.0,0 -2001-02-16,88.75089528374679,88.94736218275872,87.59916817583739,88.34439849853516,6434900,0.0,0 -2001-02-20,88.77800471129548,88.84575763241844,86.78619908459939,86.982666015625,5760000,0.0,0 -2001-02-21,86.65067386532805,87.28750875914349,85.0247024265932,85.10600280761719,10910800,0.0,0 -2001-02-22,85.60060057926934,85.72932494248516,83.34456448816695,85.23475646972656,21281600,0.0,0 -2001-02-23,84.7401672714314,85.05181100977713,82.51800832404723,84.65886688232422,20173000,0.0,0 -2001-02-26,85.22797841461295,86.46100616455078,84.34724209230491,86.46100616455078,11503700,0.0,0 -2001-02-27,85.90547138510256,86.6100542820455,85.03151133724391,85.66157531738281,11415000,0.0,0 -2001-02-28,85.87156944989694,85.93254086433016,83.51390988421016,83.97460174560547,14825800,0.0,0 -2001-03-01,84.04234765081603,84.41496276855469,82.48412394006675,84.41496276855469,14672000,0.0,0 -2001-03-02,82.99225086579833,85.12633835039743,82.85675538089295,83.7442626953125,12564300,0.0,0 -2001-03-05,84.11010374618715,84.53691916055307,83.8797552273713,84.50981903076172,5293200,0.0,0 -2001-03-06,85.60055242409808,86.54903604465278,85.0179127072782,85.41763305664062,6917000,0.0,0 -2001-03-07,85.97319689500111,86.0680447669215,85.20086127003708,86.02739715576172,6371700,0.0,0 -2001-03-08,85.76994166115372,86.20353337501572,85.45829793986672,86.12223815917969,6055000,0.0,0 -2001-03-09,85.43115835094196,85.43115835094196,83.40547251383153,83.57484436035156,10020300,0.0,0 -2001-03-12,82.88386694001902,82.9922674847073,79.77419996999416,79.99777221679688,13972900,0.0,0 -2001-03-13,80.89204886133524,81.59663683285581,79.62514474396336,81.31208801269531,12888000,0.0,0 -2001-03-14,79.29998248660607,80.81755435107165,78.41924590780098,79.70647430419922,19883400,0.0,0 -2001-03-15,80.24842417211673,80.52619663844656,79.50996498492636,79.72676086425781,10370300,0.0,0 -2001-03-16,79.5678161022104,79.75123325240038,77.99181240201762,78.12767791748047,58514600,0.316,0 -2001-03-19,78.63718125243128,79.94825399789259,77.99862612977672,79.71728515625,10067800,0.0,0 -2001-03-20,80.09084892953169,80.47126181575538,77.51625701401198,77.577392578125,15083900,0.0,0 -2001-03-21,77.56380626927312,78.29746411277014,76.01497649927052,76.25952911376953,19004600,0.0,0 -2001-03-22,76.09650679381654,76.4905088880847,73.3928486881565,75.4851303100586,28624800,0.0,0 -2001-03-23,76.9320755817863,77.88990788469133,75.74327971186906,77.76763153076172,12861700,0.0,0 -2001-03-26,78.59638371683188,78.9835912873984,77.96462345088248,78.7594223022461,9943800,0.0,0 -2001-03-27,78.54208411596235,80.60039940173874,78.3111152562158,80.36943054199219,12880700,0.0,0 -2001-03-28,79.41156130674126,79.44552379173899,78.0529375211092,78.14804077148438,10953300,0.0,0 -2001-03-29,77.91705478961067,79.1398179991735,76.17801838733223,78.4469223022461,12060300,0.0,0 -2001-03-30,78.49450561390886,79.2689208984375,77.78122592317699,79.2689208984375,9183600,0.0,0 -2001-04-02,79.00395126693762,79.73760394997807,77.30567204952914,77.577392578125,10561000,0.0,0 -2001-04-03,77.42799080044392,77.54347260666891,74.76508358907051,74.9892578125,12836000,0.0,0 -2001-04-04,75.1115283916328,76.15087493289629,74.24880442332093,75.3017349243164,14884300,0.0,0 -2001-04-05,76.96605021394389,78.45373994147701,76.42259855115115,78.15484619140625,21522800,0.0,0 -2001-04-06,77.43477861083504,77.7132990371887,76.12370615578736,76.96605682373047,14937800,0.0,0 -2001-04-09,77.44155508055351,78.11407239719156,76.61279374957611,77.82196807861328,9034300,0.0,0 -2001-04-10,78.42654484140188,79.98896404661772,78.23633836759912,79.2417221069336,17873600,0.0,0 -2001-04-11,80.688706665875,80.83136161368823,78.89532277516426,79.2961196899414,12722300,0.0,0 -2001-04-12,79.00396408159854,80.79734681145457,78.77299537554406,80.7362060546875,9233200,0.0,0 -2001-04-16,80.35581954304799,80.76340571888214,79.41837082035548,79.88709259033203,7350000,0.0,0 -2001-04-17,79.69004863500903,81.2864351713585,79.49304763301832,81.01470947265625,10924700,0.0,0 -2001-04-18,82.23751631982233,85.59331955448273,81.98617417459195,84.23469543457031,32481600,0.0,0 -2001-04-19,84.4045223941398,85.48462606824917,83.94938456351785,85.35556030273438,13809900,0.0,0 -2001-04-20,84.84605899273771,85.18571494932775,84.00371367156124,84.57433319091797,7626700,0.0,0 -2001-04-23,83.99690993961553,84.15994332982969,82.81490879077137,83.03907775878906,8451800,0.0,0 -2001-04-24,83.22931676068781,83.99694275570572,82.26469481618146,82.59076690673828,10044700,0.0,0 -2001-04-25,82.48204921309501,84.01050097992207,82.16277179256409,83.67084503173828,8249000,0.0,0 -2001-04-26,84.0512815711258,85.06345506656159,83.8950375204394,84.04448699951172,10590400,0.0,0 -2001-04-27,84.85963902333295,85.48460471067823,84.37053363924069,85.44384765625,7938700,0.0,0 -2001-04-30,85.89900403656365,86.45603969331039,84.68982945358216,86.04166412353516,10766900,0.0,0 -2001-05-01,84.96155475873857,86.37452501458436,84.6422772777529,86.30659484863281,10578000,0.0,0 -2001-05-02,86.55114161070497,86.74134813531992,85.5933092339437,86.15034484863281,9572900,0.0,0 -2001-05-03,85.68160631626917,85.6951954563228,84.38412330901988,85.056640625,9926200,0.0,0 -2001-05-04,83.99692070286746,86.51037275646331,83.85426582030821,86.50357818603516,12145300,0.0,0 -2001-05-07,86.17750763924606,86.53074754516311,85.7495430100649,85.75633239746094,7185200,0.0,0 -2001-05-08,86.17750080295147,86.34053419407711,85.29439333410635,85.71556854248047,6952600,0.0,0 -2001-05-09,85.0838344456017,85.966942185598,84.95476349734724,85.35556030273438,9507400,0.0,0 -2001-05-10,86.44924605330363,86.61227948116,85.43706744779469,85.60689544677734,6872400,0.0,0 -2001-05-11,85.59329847160338,85.92615984782618,84.50640047994023,85.01588439941406,7734400,0.0,0 -2001-05-14,84.84606636497165,85.21289545189111,84.54716743872488,85.18572235107422,7914000,0.0,0 -2001-05-15,85.28760760179605,85.93295180709394,84.8120861817581,85.5797119140625,9782200,0.0,0 -2001-05-16,84.80529483621692,87.76709512533408,84.65585057219668,87.5972671508789,14909000,0.0,0 -2001-05-17,87.63123114508622,88.36488920783249,87.3323322640996,87.73312377929688,11824600,0.0,0 -2001-05-18,87.69239022708969,88.13394927978516,87.01987793011368,88.13394927978516,6683100,0.0,0 -2001-05-21,88.20188137765321,89.56050558999715,87.73315436591773,89.43143463134766,11531500,0.0,0 -2001-05-22,89.55367360962975,89.7302909416641,89.03740038604657,89.31591033935547,8342700,0.0,0 -2001-05-23,89.0238347727809,89.0238347727809,87.80107116699219,87.80107116699219,12330800,0.0,0 -2001-05-24,87.95051933082826,88.31055383646176,87.32555357830448,88.05921173095703,7902800,0.0,0 -2001-05-25,88.07279991941193,88.10676759376712,86.88400905807266,87.0198745727539,7425000,0.0,0 -2001-05-29,87.1013728558726,87.18968672003678,86.20468135303429,86.32695770263672,9003900,0.0,0 -2001-05-30,85.99408266935691,86.33373858671638,84.91397933986804,85.1177749633789,10041800,0.0,0 -2001-05-31,85.20612427671428,86.10961069168184,85.09064245358802,85.55936431884766,9874200,0.0,0 -2001-06-01,85.7291859981265,86.34056793217134,84.99553286001276,86.08922576904297,8848300,0.0,0 -2001-06-04,86.1367543067335,86.56471895441122,85.64764889067969,86.50357818603516,5623500,0.0,0 -2001-06-05,86.60547445540723,87.78747573447369,86.45602500510508,87.49537658691406,9115400,0.0,0 -2001-06-06,87.51578941978384,87.51578941978384,86.51719968564267,86.76854705810547,12064900,0.0,0 -2001-06-07,86.30657094357107,87.18967840455781,86.2726032786339,87.08098602294922,7355300,0.0,0 -2001-06-08,86.74813746600724,86.86362444108039,85.68841243943284,86.27262115478516,8170600,0.0,0 -2001-06-11,86.07558968115332,86.2657961385136,85.19248753158956,85.66120910644531,7012200,0.0,0 -2001-06-12,84.81891420045906,86.09601915765332,84.2618784530643,85.51181030273438,9364400,0.0,0 -2001-06-13,85.70877587671404,85.98729622619769,84.67622413299847,84.77812194824219,7629400,0.0,0 -2001-06-14,84.35698408752462,84.43850341236761,82.70625533432256,82.87608337402344,12603000,0.0,0 -2001-06-15,82.36920910662299,83.38426114443642,82.02177323422498,83.00957489013672,16821100,0.346,0 -2001-06-18,82.87332019915998,83.4115034955753,82.36920117621183,82.60763549804688,11368300,0.0,0 -2001-06-19,83.37063567353424,83.71807155559549,82.35558360748915,82.96870422363281,7732300,0.0,0 -2001-06-20,82.5599601473972,83.69763635415372,82.4986454861011,83.40470123291016,8787200,0.0,0 -2001-06-21,83.26164181318957,84.68543926645658,83.21395494148875,84.35163116455078,12259100,0.0,0 -2001-06-22,84.12679916350379,84.19492250705039,83.22074881912594,83.69080352783203,12212000,0.0,0 -2001-06-25,83.98375948457064,84.0927610193557,82.77114599622266,82.9210205078125,8406800,0.0,0 -2001-06-26,82.36238909202109,83.37744105441331,82.1375773561454,82.8051986694336,8005800,0.0,0 -2001-06-27,82.83928380966863,83.27527962991844,82.36922890196563,82.75753784179688,10105100,0.0,0 -2001-06-28,83.11180919705855,84.4334248753535,83.06412230132017,83.21399688720703,10269300,0.0,0 -2001-06-29,83.65675972193675,84.48106428348329,83.28888736267585,83.5205078125,9824200,0.0,0 -2001-07-02,83.65678491081968,84.6922737469281,83.53416075414417,84.56283569335938,8522200,0.0,0 -2001-07-03,84.46061246509292,84.5423583984375,83.82705549428262,84.5423583984375,3303100,0.0,0 -2001-07-05,83.84072790018084,84.23585080437881,82.88017623029933,82.893798828125,5517900,0.0,0 -2001-07-06,82.64171195901484,82.76433609282283,81.10210418701172,81.10210418701172,11665900,0.0,0 -2001-07-09,81.40184599658355,82.11715429219987,81.20428460829447,81.54490661621094,8339300,0.0,0 -2001-07-10,81.94686687414325,82.18530130881497,80.52987766597865,80.5639419555664,8630700,0.0,0 -2001-07-11,80.4549357518259,80.99311927192615,79.76687768214323,80.64568328857422,15328600,0.0,0 -2001-07-12,81.4086629478484,82.75071454101217,81.27922491678807,82.55996704101562,12002800,0.0,0 -2001-07-13,82.32150324963169,83.3297464211773,82.17163397049691,83.2752456665039,10433800,0.0,0 -2001-07-16,82.95506515434103,83.30250098916217,81.94682712041858,82.23294830322266,6915300,0.0,0 -2001-07-17,81.88556731906907,83.07774491013902,81.63351024048835,83.0028076171875,7469800,0.0,0 -2001-07-18,82.13079205925408,82.86653697967726,81.79016998840783,82.4373550415039,7709300,0.0,0 -2001-07-19,83.23438396475753,83.77938119029909,82.26701882702606,83.1594467163086,10082900,0.0,0 -2001-07-20,82.5327451612658,83.07092876355371,82.37605669073382,82.66217803955078,6705800,0.0,0 -2001-07-23,82.97550767471465,83.03000323569557,81.0339584350586,81.0339584350586,8065200,0.0,0 -2001-07-24,81.06799383586389,81.20424048600917,79.53519563308494,80.25050354003906,12269000,0.0,0 -2001-07-25,80.33228609828357,81.39503020464572,80.01891450200978,81.13615417480469,12088500,0.0,0 -2001-07-26,81.10891126630078,82.32833868441956,80.7682892566859,81.98771667480469,12898200,0.0,0 -2001-07-27,82.3147371084175,82.66898180197563,81.68799369809426,82.30110931396484,8478800,0.0,0 -2001-07-30,82.55995664124224,82.66895297144774,81.95364993656474,82.32833099365234,8547700,0.0,0 -2001-07-31,82.43050338223921,83.5749932238752,82.29425669530933,82.66893768310547,11918100,0.0,0 -2001-08-01,83.09134041793875,83.58864568065522,82.8052191546652,83.18671417236328,11940800,0.0,0 -2001-08-02,83.94967864462818,83.96330123420593,83.03680921858198,83.52730560302734,11070100,0.0,0 -2001-08-03,83.35701355732691,83.35701355732691,82.36239797322257,83.07089233398438,10816300,0.0,0 -2001-08-06,82.66898526006491,82.77798683032283,81.81742998297837,81.95368194580078,8550100,0.0,0 -2001-08-07,81.93321983197325,82.56677700811508,81.68797675497994,82.27384185791016,8865100,0.0,0 -2001-08-08,81.83107553733015,82.53957030532442,80.67977088965421,80.74789428710938,15183800,0.0,0 -2001-08-09,80.86362775314899,81.0475664687461,80.29138552124047,80.98625183105469,14118500,0.0,0 -2001-08-10,80.93175599740985,81.64024489227609,79.93713559264606,81.26556396484375,11173300,0.0,0 -2001-08-13,81.47678111820717,81.6470921177007,80.93859773611011,81.2860336303711,7431600,0.0,0 -2001-08-14,81.84464484101397,81.9877054417541,80.9317804751068,81.25196075439453,13178100,0.0,0 -2001-08-15,81.22471291673953,81.4835837290268,80.4412813880689,80.55027770996094,8520600,0.0,0 -2001-08-16,80.25051084353495,80.89769028683887,79.70551379840124,80.8295669555664,10734700,0.0,0 -2001-08-17,80.14836083729831,80.29823535487476,79.0311210485204,79.53524017333984,11604600,0.0,0 -2001-08-20,79.56927766659949,80.31864480472697,79.39896671713971,80.27095794677734,10417400,0.0,0 -2001-08-21,80.25055316145095,80.75467231567292,78.88806502762313,78.90168762207031,14480800,0.0,0 -2001-08-22,79.53522841405146,79.99847449194252,78.87442097378995,79.71916198730469,11752300,0.0,0 -2001-08-23,79.67830798008066,80.05980300650846,79.35812242683737,79.43305969238281,8744100,0.0,0 -2001-08-24,79.84859993748806,81.15658702792678,79.52841446069263,81.08164978027344,11687700,0.0,0 -2001-08-27,81.04761556722035,81.2042988076711,80.56393285509978,80.59799194335938,7425100,0.0,0 -2001-08-28,80.57752319235735,80.72058377972718,79.4194107055664,79.4194107055664,12046600,0.0,0 -2001-08-29,79.79409756684572,79.82816184144798,78.45886012259848,78.71092224121094,16180000,0.0,0 -2001-08-30,78.24091140700715,78.84721845378925,76.32661647486242,77.1986083984375,17692600,0.0,0 -2001-08-31,77.25308212181265,78.18638321414478,77.069143303885,77.7640151977539,15985400,0.0,0 -2001-09-04,77.55962058436931,79.1401062980105,77.23262640774216,77.26668548583984,24473400,0.0,0 -2001-09-05,77.45744323730469,77.79125655033407,76.2652661985754,77.45744323730469,21477100,0.0,0 -2001-09-06,76.74215282223084,77.18496254933255,75.46141052246094,75.46141052246094,21653000,0.0,0 -2001-09-07,74.95047167169511,75.7884041800829,74.00354281279404,74.06485748291016,33133900,0.0,0 -2001-09-10,73.36997399084635,75.17527156316547,73.36997399084635,74.97090148925781,23408700,0.0,0 -2001-09-17,68.80564187491451,72.4843604007919,68.12439789595496,71.05374908447266,32388700,0.0,0 -2001-09-18,71.07417029306856,71.73497764219886,70.41336294393827,70.8834228515625,22029200,0.0,0 -2001-09-19,70.91751875240361,71.19001746641233,67.14342533670666,69.45284271240234,42771800,0.0,0 -2001-09-20,68.39688639769733,69.35743756308693,67.14339495936028,67.2455825805664,36210900,0.0,0 -2001-09-21,64.46869481800229,67.68939377325744,64.14047357836164,66.52009582519531,49782100,0.369,0 -2001-09-24,68.1885594246049,69.17323342024682,67.73724877094179,68.85868072509766,25549600,0.0,0 -2001-09-25,68.89286005643427,69.74761018120392,68.31163101498706,69.57666015625,25466200,0.0,0 -2001-09-26,69.98691446213157,70.02110654184229,68.67401998611062,69.3304672241211,18587500,0.0,0 -2001-09-27,69.2347845533365,69.94593753429263,68.3800341267521,69.93225860595703,20536800,0.0,0 -2001-09-28,70.41771419890388,74.95814500157658,70.08948795981638,71.41606140136719,21687200,0.0,0 -2001-10-01,71.04687051474706,71.33406546265142,70.31520421395244,71.29987335205078,20457400,0.0,0 -2001-10-02,71.11524981388989,72.1956558227539,70.87592068964372,72.1956558227539,19833100,0.0,0 -2001-10-03,71.52550928563835,73.7683734019655,71.35455921315022,73.40596008300781,32045800,0.0,0 -2001-10-04,74.04870486529383,74.5136890880343,72.9956522074116,73.46747589111328,32674100,0.0,0 -2001-10-05,73.33754150079571,73.59054934958702,72.1545653424215,73.32386779785156,29796100,0.0,0 -2001-10-08,72.66746343276841,73.37178220009176,72.39394745681768,72.84525299072266,12970300,0.0,0 -2001-10-09,72.89991536069702,72.99564693932464,72.20927592890084,72.4554443359375,15976300,0.0,0 -2001-10-10,72.34607001502832,74.22652074095126,72.15460155885128,74.06924438476562,19987400,0.0,0 -2001-10-11,74.50004043667789,75.42317501326218,74.50004043667789,75.21803283691406,23006300,0.0,0 -2001-10-12,74.63680010061226,75.14281083685538,73.37177065151349,74.87612915039062,31360500,0.0,0 -2001-10-15,74.28120693833952,74.84192284150316,73.89144106927434,74.7393569946289,16873000,0.0,0 -2001-10-16,75.08125553550559,75.63513196528352,74.50002125780969,75.21117401123047,15877100,0.0,0 -2001-10-17,75.94971063053919,76.0044159149113,73.61111450195312,73.61111450195312,28542300,0.0,0 -2001-10-18,73.7273021206087,73.95979679032851,72.99563646495528,73.45378112792969,16510000,0.0,0 -2001-10-19,73.16663102492087,73.78889179292912,72.48967019862287,73.40596008300781,21912500,0.0,0 -2001-10-22,73.37176842785867,74.92399268964871,73.31022369293402,74.8556137084961,17540600,0.0,0 -2001-10-23,75.19065078174708,75.40946658298503,74.11024548070584,74.47266387939453,22062500,0.0,0 -2001-10-24,74.52055204734809,74.84193377614397,74.00086240700587,74.27438354492188,16065200,0.0,0 -2001-10-25,73.47435182106517,75.62832415166285,72.98885415697211,75.6078109741211,27467900,0.0,0 -2001-10-26,75.18382069216483,76.44201561884918,74.99235749433872,75.43682861328125,18623300,0.0,0 -2001-10-29,75.32742234012271,75.5941039646672,73.47431945800781,73.47431945800781,18727500,0.0,0 -2001-10-30,73.40592729635412,73.64525624754513,72.18192469800026,72.59220886230469,26178600,0.0,0 -2001-10-31,73.09821774098161,73.75466508528719,72.34603881835938,72.34603881835938,28124200,0.0,0 -2001-11-01,72.89308189918073,74.54104246985663,72.27766083094117,74.19914245605469,29806800,0.0,0 -2001-11-02,74.15128562844505,74.79405418456345,73.76151977426338,74.70516204833984,17575900,0.0,0 -2001-11-05,75.30011020752004,75.96339228620117,75.18386016182791,75.68303680419922,15929500,0.0,0 -2001-11-06,75.45733859355465,76.9138361184368,75.11543854988926,76.859130859375,23245800,0.0,0 -2001-11-07,76.42835772590233,77.3514923104111,76.3394655644456,76.75658416748047,19716000,0.0,0 -2001-11-08,77.1805166989574,78.00791416748181,76.51722978843455,76.99588775634766,22563500,0.0,0 -2001-11-09,76.75657783146511,77.24207538146594,76.20270130006645,77.07796478271484,15895800,0.0,0 -2001-11-12,75.90183428271159,77.03010583571594,75.21803397385834,76.60614776611328,26068800,0.0,0 -2001-11-13,77.57031165528923,78.32933044433594,77.39252210747205,78.32933044433594,15296200,0.0,0 -2001-11-14,78.75326006845253,78.91053639452001,77.75491250027326,78.40452575683594,17571300,0.0,0 -2001-11-15,78.20621854467677,78.76009495078651,77.90534483420694,78.5481185913086,19470200,0.0,0 -2001-11-16,78.69173122328058,78.70540493232222,77.54294663545964,78.19939422607422,18134900,0.0,0 -2001-11-19,78.5823145977499,79.21824896706558,78.26092768185104,79.16354370117188,13625400,0.0,0 -2001-11-20,78.89003294397035,79.18406724607354,78.39085648989501,78.5002670288086,16209700,0.0,0 -2001-11-21,78.29510770766198,78.41135246703035,77.61814710177386,77.98056030273438,11470200,0.0,0 -2001-11-23,77.98059569110973,79.14989371082127,77.95324304996653,79.1020278930664,6717100,0.0,0 -2001-11-26,79.14987260431386,79.55331221538128,78.68488826284036,79.27295684814453,13726000,0.0,0 -2001-11-27,79.06096922029917,79.93623251114876,78.01475068794416,78.93104553222656,19261400,0.0,0 -2001-11-28,78.45925178345414,78.75328614670038,77.44039070861689,77.5019302368164,20195500,0.0,0 -2001-11-29,77.72072110449726,78.58230546578336,77.26941053879251,78.5481185913086,16354700,0.0,0 -2001-11-30,78.22675582081548,78.57549543660973,77.96690836642742,77.9874267578125,13680300,0.0,0 -2001-12-03,77.71387834440345,78.00791258070637,77.27624672064756,77.52241516113281,15220400,0.0,0 -2001-12-04,77.89852651765621,78.8421742413758,77.50876056737529,78.83533477783203,17239900,0.0,0 -2001-12-05,79.05415687375313,80.68843925140173,79.01996477032817,80.27816009521484,25204000,0.0,0 -2001-12-06,80.23710632324219,80.64739058287132,79.95675097727542,80.23710632324219,17972900,0.0,0 -2001-12-07,79.93623009070622,80.06614855786421,79.11566686764557,79.7037353515625,18857800,0.0,0 -2001-12-10,79.21822233418209,79.5874749293512,78.19252241992774,78.2130355834961,13862700,0.0,0 -2001-12-11,78.56864391147649,79.1293598659985,77.88484371245055,78.05579376220703,20833300,0.0,0 -2001-12-12,78.32929751340133,78.4865686027882,77.34462377575011,78.14466857910156,16171500,0.0,0 -2001-12-13,77.5771298000195,77.74807984824959,76.61297424083219,76.62664794921875,19026700,0.0,0 -2001-12-14,76.81127748600186,77.60448300277493,76.58562216860808,77.35831451416016,16721900,0.0,0 -2001-12-17,77.26259663503355,78.19940495080745,77.2010571104337,78.15837860107422,13925900,0.0,0 -2001-12-18,78.38401779879653,78.73959683678534,78.18571510779927,78.62335205078125,13663700,0.0,0 -2001-12-19,78.01476729061544,79.26612298851826,77.95322777284079,79.17723083496094,20143400,0.0,0 -2001-12-20,78.97888817131101,79.18403022737883,78.32928035539494,78.39765930175781,14867900,0.0,0 -2001-12-21,78.92807763015605,78.95552434318328,78.35857011342254,78.87318420410156,14037700,0.393,0 -2001-12-24,78.79085079971232,78.93494211965417,78.63989649265172,78.72223663330078,5728800,0.0,0 -2001-12-26,78.66732036439204,79.73771570740357,78.66732036439204,79.15448760986328,10304800,0.0,0 -2001-12-27,79.11333957221778,79.64853737498565,79.07902987309673,79.63481140136719,9407300,0.0,0 -2001-12-28,79.79265087004299,80.10828029698338,79.53877263677663,79.59366607666016,10593800,0.0,0 -2001-12-31,79.69657311605947,79.86124816596332,78.37916224697776,78.42719268798828,14619500,0.0,0 -2002-01-02,78.98295846379344,79.42209533236635,78.09095875485974,79.2711410522461,18651900,0.0,0 -2002-01-03,79.35351104525357,80.24550585499165,79.27803387678537,80.17002868652344,15743000,0.0,0 -2002-01-04,80.3964742553178,80.95226125079829,79.97106312400143,80.70524597167969,20140700,0.0,0 -2002-01-07,80.7601151043466,80.95909987856587,79.9779019835818,80.1357192993164,13106500,0.0,0 -2002-01-08,80.13570344951526,80.3209622109805,79.57305894349582,79.950439453125,12683700,0.0,0 -2002-01-09,80.0602285050172,80.81499477998264,79.14078195429825,79.29859924316406,16610300,0.0,0 -2002-01-10,79.3809149717877,79.83377250354538,79.11331614963008,79.64851379394531,12823400,0.0,0 -2002-01-11,79.73774397043135,79.78577441349024,78.70165271595997,78.8663330078125,13708400,0.0,0 -2002-01-14,78.6673524855355,78.7977178466173,78.1939057820766,78.37230682373047,12301100,0.0,0 -2002-01-15,78.59874668589606,79.17511209506252,78.1527466517021,79.01043701171875,20219900,0.0,0 -2002-01-16,78.42721276040801,78.49582694585173,77.32250709950569,77.41170501708984,17067000,0.0,0 -2002-01-17,78.05669051388315,78.38604070067056,77.80967525632919,77.99493408203125,17283400,0.0,0 -2002-01-18,77.53519106222005,78.11841932853487,77.30875960185097,77.63811492919922,17028000,0.0,0 -2002-01-22,78.049816356805,78.17332396790951,76.86277069258237,77.1029281616211,11689300,0.0,0 -2002-01-23,77.2813205435483,77.91258454725285,76.86276728198452,77.6930160522461,12438900,0.0,0 -2002-01-24,77.97432632644507,78.39287953751817,77.75475785792517,77.93315887451172,12142800,0.0,0 -2002-01-25,77.61754435016525,78.34486405947902,77.56265091382143,77.91259002685547,12810700,0.0,0 -2002-01-28,78.1527290011106,78.35171374788546,77.48029760735677,78.12528228759766,10589200,0.0,0 -2002-01-29,78.11841658004356,78.31053833301827,75.5110417062186,75.66885375976562,27720800,0.0,0 -2002-01-30,75.7443269181641,77.80278313712301,74.37888575870032,76.75983428955078,34711800,0.0,0 -2002-01-31,76.95195561251832,77.74103153913202,76.58829585340783,77.65869140625,19909200,0.0,0 -2002-02-01,77.59694734406516,77.74104390826668,76.96568859630652,77.2950439453125,15838500,0.0,0 -2002-02-04,77.00688639956329,77.00688639956329,75.09252056930139,75.37384033203125,24243400,0.0,0 -2002-02-05,75.06502091891267,75.81292399719501,74.46806686534035,74.9072036743164,33614000,0.0,0 -2002-02-06,75.23659509145303,75.29834628105053,74.14561029422798,74.58474731445312,29486000,0.0,0 -2002-02-07,74.59845074402863,75.3806636520682,74.1044204368616,74.1181411743164,23445400,0.0,0 -2002-02-08,74.53670874992574,75.99135324284968,74.31028251113162,75.53849029541016,19277800,0.0,0 -2002-02-11,75.51106196363011,76.6020415970967,75.35324463282016,76.46481323242188,18792400,0.0,0 -2002-02-12,76.13545723851219,76.6500714544461,75.49733540135706,76.22465515136719,13942500,0.0,0 -2002-02-13,76.4922309992826,77.21955047962943,76.40302788457431,77.03428649902344,16781100,0.0,0 -2002-02-14,77.19899996032551,77.51462940330566,76.56773583943837,76.89022827148438,20453800,0.0,0 -2002-02-15,76.95197084671925,77.01372203241203,75.96390999622761,76.08741760253906,18366800,0.0,0 -2002-02-19,75.57968213044357,75.67574304030747,74.52300688701379,74.62593078613281,15988100,0.0,0 -2002-02-20,74.824884086363,75.88155364990234,73.98091475089483,75.88155364990234,29242800,0.0,0 -2002-02-21,75.42869047627643,75.9089947880814,74.28281787888143,74.31026458740234,26288600,0.0,0 -2002-02-22,74.34458417044361,75.43556880765524,74.01523407104325,75.22972106933594,26572900,0.0,0 -2002-02-25,75.29835976076694,76.71869495996245,75.27091303772366,76.4716796875,17458700,0.0,0 -2002-02-26,76.57458636068623,76.87649501130497,75.86785041485233,76.31385040283203,22346500,0.0,0 -2002-02-27,76.82159220486585,77.4391301717308,75.92273458555013,76.60888671875,28597900,0.0,0 -2002-02-28,76.73241477807176,77.36367365260193,76.18349083113361,76.26583099365234,23755400,0.0,0 -2002-03-01,76.65692079045408,78.1184230188383,76.51282946384653,78.0429458618164,26273600,0.0,0 -2002-03-04,78.15270855597001,79.5867636261194,77.9811705675833,79.42208862304688,27184600,0.0,0 -2002-03-05,79.13394656331609,79.86812932000261,78.88693131481159,79.16825103759766,22718900,0.0,0 -2002-03-06,78.97611656945999,80.38273066268063,78.95553284120543,80.10826873779297,20143200,0.0,0 -2002-03-07,80.52678998529235,80.62285083557914,79.29857741671269,79.93669891357422,19330800,0.0,0 -2002-03-08,80.5405252094951,80.89732720468314,79.92299255653637,80.2729263305664,19930100,0.0,0 -2002-03-11,80.20431346442018,80.89732850725485,79.88868414662537,80.44446563720703,15621800,0.0,0 -2002-03-12,79.6622485168497,80.45132438724899,79.55246691575277,80.39643096923828,17153600,0.0,0 -2002-03-13,80.02591332552964,80.10825345776794,79.34662424806926,79.62108612060547,17175300,0.0,0 -2002-03-14,79.62110125594386,79.88870019293076,79.3397763435123,79.51131439208984,11168100,0.0,0 -2002-03-15,79.8010310495772,80.47538362919792,79.75286300817574,80.26895141601562,21220100,0.331,0 -2002-03-18,80.57859043833938,80.89512398498945,79.89047266303345,80.28269958496094,17548900,0.0,0 -2002-03-19,80.71621402785992,81.0189822673613,80.38591519849632,80.81942749023438,17912000,0.0,0 -2002-03-20,80.16568980975968,80.2207404704682,79.26425755281745,79.29866027832031,17114500,0.0,0 -2002-03-21,79.33995149277632,79.7528209513412,78.52797261596675,79.33306884765625,25846800,0.0,0 -2002-03-22,79.47755937368666,79.78033270841767,78.9270633559792,79.16102600097656,15235400,0.0,0 -2002-03-25,79.19548211212249,79.38127687457168,77.96375576071519,78.17707061767578,17499600,0.0,0 -2002-03-26,78.11509929411757,79.1472755838121,78.08069656774647,78.63118743896484,19947600,0.0,0 -2002-03-27,78.46603950757012,79.1403969831984,78.28025008821947,78.8376235961914,19020300,0.0,0 -2002-03-28,79.11290169416395,79.6633927660368,78.78948549885362,78.8032455444336,17532900,0.0,0 -2002-04-01,78.60366616071875,79.20232503967527,78.10133806408625,78.8376235961914,17711000,0.0,0 -2002-04-02,78.4316925254858,79.09916258044767,78.28718312449254,78.40416717529297,15669500,0.0,0 -2002-04-03,78.45229430804389,78.58991573730637,77.1792777126424,77.85363006591797,25658500,0.0,0 -2002-04-04,77.48204437806028,78.03254058408935,77.22744421395657,77.53021240234375,23549000,0.0,0 -2002-04-05,77.88802697484594,78.19079512616302,77.19302678106033,77.5439682006836,19404900,0.0,0 -2002-04-08,76.60124441427966,77.7779247787872,76.53931635558358,77.70911407470703,16470100,0.0,0 -2002-04-09,77.88118586444917,77.88118586444917,77.02103847031535,77.16554260253906,15122700,0.0,0 -2002-04-10,77.13799852027779,78.12888975762276,77.13111587281796,78.03943634033203,17199300,0.0,0 -2002-04-11,77.68160309472961,77.7917044415352,76.03700228874945,76.09893035888672,25453700,0.0,0 -2002-04-12,76.394803521188,76.82832091170447,75.7204513024823,76.67005157470703,14950600,0.0,0 -2002-04-15,76.80772335752363,76.97287018670494,75.83059193088688,76.08519744873047,17394900,0.0,0 -2002-04-16,76.8627362866034,77.97748872434791,76.84209359792825,77.89491271972656,15040900,0.0,0 -2002-04-17,78.02566529500234,78.21833741030373,77.48205168401832,77.72977447509766,12920100,0.0,0 -2002-04-18,77.6884765264363,78.07382069470631,76.48427075067198,77.39258575439453,25204800,0.0,0 -2002-04-19,77.89494881283763,77.92247416046546,77.45455375063894,77.67475128173828,10499200,0.0,0 -2002-04-22,77.33065818874732,77.36506617058022,76.27095639732926,76.38105773925781,13922900,0.0,0 -2002-04-23,76.44297753322999,76.71134821265484,75.80991063542048,76.05075073242188,16967000,0.0,0 -2002-04-24,76.07831103635127,76.93845838526053,75.28009702652332,75.28697967529297,18902700,0.0,0 -2002-04-25,75.14931216068555,75.51401359868626,74.81213605290074,75.3282241821289,25451500,0.0,0 -2002-04-26,75.54840968347222,75.69979635087311,73.82811619013306,73.89692687988281,19769800,0.0,0 -2002-04-29,74.26853509566372,74.49561517073107,73.37398008777946,73.5322494506836,17724400,0.0,0 -2002-04-30,73.64236860677667,74.75712139270394,73.38088571868519,74.22039031982422,19473500,0.0,0 -2002-05-01,74.29605572886177,75.1768453886222,73.49095942266706,75.12867736816406,24575600,0.0,0 -2002-05-02,75.07365634040205,75.63103536851816,74.16534098893872,74.8396987915039,15666800,0.0,0 -2002-05-03,74.72955696000788,74.83965827630082,73.7661914421235,74.02767944335938,18185500,0.0,0 -2002-05-06,74.06895037370369,74.30979039374776,72.46563570468089,72.57573699951172,23630400,0.0,0 -2002-05-07,73.00930007496287,73.16068683622744,72.18356078614825,72.32118225097656,21910000,0.0,0 -2002-05-08,73.66299358123614,75.25254360907772,73.48408152942618,75.01170349121094,27917400,0.0,0 -2002-05-09,74.76399852488494,75.0736494287238,74.02771270360577,74.14469146728516,18085600,0.0,0 -2002-05-10,74.29610648949237,74.3511571933193,72.66526482560727,72.74784088134766,18958900,0.0,0 -2002-05-13,73.09184651616044,74.28228692980876,72.7959557616263,74.22724151611328,14677700,0.0,0 -2002-05-14,75.43145269733607,75.94754089388344,75.00481789821882,75.84432220458984,34201200,0.0,0 -2002-05-15,75.34887666391367,76.31912499297253,75.20437259893225,75.54843139648438,29535300,0.0,0 -2002-05-16,75.48649843449031,76.0232345711044,75.23189828306533,75.94065856933594,28092000,0.0,0 -2002-05-17,76.147101464402,76.55308835684927,75.7617520114909,76.31224822998047,27823700,0.0,0 -2002-05-20,76.13333586773062,76.16774384996933,75.34199952552947,75.48650360107422,13833800,0.0,0 -2002-05-21,75.76862628738984,76.02323167904984,74.53689520328406,74.79837799072266,16877200,0.0,0 -2002-05-22,74.4681011502056,75.0874081536107,74.31671441049306,74.96354675292969,15844200,0.0,0 -2002-05-23,75.18373298402739,75.9406613195956,74.64700207789575,75.76174926757812,13879800,0.0,0 -2002-05-24,75.67916852364574,75.83054997084149,74.7364456142824,74.79149627685547,11877000,0.0,0 -2002-05-28,75.03923588784016,75.09428131287501,73.93824338840201,74.3855209350586,24236900,0.0,0 -2002-05-29,74.05524235525561,74.33048528583353,73.7180608404296,73.83504486083984,14773300,0.0,0 -2002-05-30,73.31895777632408,73.97955027115086,72.8716801314111,73.62860870361328,18217900,0.0,0 -2002-05-31,73.90382963694512,74.70204330513238,73.52536286544218,73.77996826171875,19826300,0.0,0 -2002-06-03,73.69051559902711,74.04145706564971,71.65368801319721,71.81884002685547,26056300,0.0,0 -2002-06-04,71.6674633840862,72.3899838582408,71.25459379172335,71.99775695800781,25856200,0.0,0 -2002-06-05,72.21793614087453,72.71338162624917,71.80506665303577,72.6720962524414,19695900,0.0,0 -2002-06-06,72.62393578116881,72.66522115926756,70.97933505457053,71.1926498413086,22998500,0.0,0 -2002-06-07,70.0366225422545,71.50919408176163,69.99533715794438,71.1100845336914,24011600,0.0,0 -2002-06-10,71.041263750477,71.88076811205438,70.88987702954718,71.38532257080078,18759900,0.0,0 -2002-06-11,71.65368296993799,71.93581370263936,70.00220494896038,70.16046905517578,19990700,0.0,0 -2002-06-12,69.98846328835859,70.74539184519924,69.34851350168226,70.58712768554688,31266000,0.0,0 -2002-06-13,70.27742285850718,70.87608684121439,69.73380952361829,69.8783187866211,21043900,0.0,0 -2002-06-14,69.02508779950399,69.88523496179438,67.77959638848245,69.77513885498047,39267500,0.0,0 -2002-06-17,70.13296537783208,71.79820920481609,70.08479734151179,71.6468276977539,17647200,0.0,0 -2002-06-18,71.38532530097628,72.27299772135173,71.30963193761643,72.2317123413086,21628500,0.0,0 -2002-06-19,71.22014896966334,71.86009834431505,70.35311959918577,70.54579162597656,21540700,0.0,0 -2002-06-20,70.36691962800504,70.9105332576518,69.4723644906275,69.64439392089844,25691000,0.0,0 -2002-06-21,69.3771552623971,69.69479662316424,68.14111296193396,68.55542755126953,31190700,0.353,0 -2002-06-24,68.09277728247399,69.52907279563189,67.15366139066275,68.91450500488281,37169700,0.0,0 -2002-06-25,69.25978246583466,69.66719040974925,67.35392860995873,67.36773681640625,33355000,0.0,0 -2002-06-26,65.73806688451836,67.77512155312041,65.73116541834273,67.47819519042969,37913600,0.0,0 -2002-06-27,68.01682990102658,68.7004493558732,66.68411413994973,68.65901947021484,31616400,0.0,0 -2002-06-28,68.5278114998591,69.39787589667166,68.27922167219836,68.33446502685547,28184200,0.0,0 -2002-07-01,68.4863871093401,68.91451523981617,66.90508151648899,67.00175476074219,20270200,0.0,0 -2002-07-02,66.88435563199383,67.1191319672587,65.44115340466244,65.57926177978516,34213900,0.0,0 -2002-07-03,65.33757307957062,66.18001038790828,64.71609861580072,65.95214080810547,30565800,0.0,0 -2002-07-05,66.82913357301001,68.7280808724916,66.74627378983395,68.57616424560547,19013500,0.0,0 -2002-07-08,68.34830648965053,68.84548108027855,67.3677549819685,67.71992492675781,19118600,0.0,0 -2002-07-09,67.48510592658593,67.9063219249525,65.60687442966078,66.0142822265625,28620400,0.0,0 -2002-07-10,66.29049457142756,66.33883117965408,63.556012302551586,63.61125564575195,50522500,0.0,0 -2002-07-11,63.362681199032096,64.46061525221378,62.36832185404003,64.12916564941406,59476500,0.0,0 -2002-07-12,64.44682800478441,64.83352108989888,63.19697168045682,63.42484664916992,39018600,0.0,0 -2002-07-15,63.279851457231146,63.80465310070731,60.69037694233474,63.76321792602539,77317200,0.0,0 -2002-07-16,62.92074439777459,63.79080354963516,62.05758671420913,62.5340461730957,53282400,0.0,0 -2002-07-17,63.846034596538836,64.4260791875789,61.97470973154771,62.658329010009766,48880600,0.0,0 -2002-07-18,62.63071041032797,62.90692188302854,60.593660677215624,60.62818908691406,32656700,0.0,0 -2002-07-19,59.91004938332599,60.455565666137964,58.211355711070425,58.494468688964844,77572600,0.0,0 -2002-07-22,58.07324252414458,59.323098316762085,56.24334735061406,56.7612419128418,78134000,0.0,0 -2002-07-23,57.00291787450485,57.479377185817995,55.06944315485352,55.20754623413086,74484100,0.0,0 -2002-07-24,53.950782346985605,58.777561794356316,53.6400478242046,58.50135040283203,107022800,0.0,0 -2002-07-25,58.190620600215404,59.28165288645098,56.34691754288118,58.004180908203125,87176600,0.0,0 -2002-07-26,58.453041550147525,59.33691420793483,57.86609535723844,59.109039306640625,41206800,0.0,0 -2002-07-29,60.42104090543757,62.38213273290023,60.282937776400956,61.98853302001953,53492900,0.0,0 -2002-07-30,61.67783099760166,63.11412748045898,61.26351612285806,62.796485900878906,47532200,0.0,0 -2002-07-31,62.48569638889801,63.2176575327916,61.62944562232122,62.9483528137207,44669900,0.0,0 -2002-08-01,62.75503209108542,63.07958031489123,60.994193079214625,61.304927825927734,66571900,0.0,0 -2002-08-02,61.11155451087617,61.39467271592313,59.122841413210345,59.930755615234375,51772900,0.0,0 -2002-08-05,59.72359527452007,60.027428415685584,57.693452314656156,57.84536361694336,47191300,0.0,0 -2002-08-06,58.85353397673449,60.69723715360475,58.770668959192605,59.79264450073242,64730000,0.0,0 -2002-08-07,60.67655100317299,61.11158069799522,59.22644148383652,60.83536911010742,43289400,0.0,0 -2002-08-08,61.05632571678936,62.906936154953534,60.62820284185456,62.8033561706543,48339800,0.0,0 -2002-08-09,62.21640015955575,63.48697091496663,61.69850558785862,63.03812789916992,41879200,0.0,0 -2002-08-12,62.1404352054088,63.02430767008327,61.83660733836161,62.575469970703125,25841700,0.0,0 -2002-08-13,62.25092863992701,63.29362452054368,61.21513949539676,61.43610763549805,49690500,0.0,0 -2002-08-14,61.4706339562372,63.96343371436882,60.78010782419622,63.680320739746094,57423600,0.0,0 -2002-08-15,64.10843637143635,64.90254238777592,63.66650011801699,64.5641860961914,45551500,0.0,0 -2002-08-16,64.09463049449528,64.96469482987007,63.52839931998378,64.37084197998047,36517300,0.0,0 -2002-08-19,64.53653798513618,66.1178426343835,64.28794828588285,65.87615966796875,33669900,0.0,0 -2002-08-20,65.47569080517307,65.87619724557379,64.64706151385126,65.17876434326172,30508300,0.0,0 -2002-08-21,65.64141702455987,66.13859671738739,64.612534490862,66.11788177490234,39628900,0.0,0 -2002-08-22,65.93832789581288,67.08460361294689,65.64830823303032,66.76005554199219,38399800,0.0,0 -2002-08-23,66.29739746836772,66.39407067922424,65.01301878893688,65.3237533569336,33716400,0.0,0 -2002-08-26,65.53783504475471,66.0419161503161,64.56419070955049,65.7795181274414,33830100,0.0,0 -2002-08-27,66.08334884643679,66.46314032705276,64.56419346056553,65.01994323730469,35335700,0.0,0 -2002-08-28,64.41229055090814,64.55730043750218,63.39031460682712,63.597469329833984,38970600,0.0,0 -2002-08-29,63.02432800881756,64.25346914922854,62.7066865685868,63.62508773803711,42965700,0.0,0 -2002-08-30,63.3074437932788,64.48824299230213,63.11409729542192,63.376495361328125,30366700,0.0,0 -2002-09-03,62.651409008859,62.83784865935946,60.86985115610422,60.959617614746094,76586400,0.0,0 -2002-09-04,61.187507285490234,62.31996946708501,60.80771589056114,61.82969665527344,51099500,0.0,0 -2002-09-05,61.10467455446675,61.75377100207837,60.42105495071917,61.304927825927734,67250900,0.0,0 -2002-09-06,61.974711174435086,62.54094230813821,61.69159297343576,62.147342681884766,38622200,0.0,0 -2002-09-09,61.52587925523271,63.07956310141685,61.3187245700527,62.60310363769531,33998400,0.0,0 -2002-09-10,62.93452759522162,63.37646373213725,62.534021324799845,63.32122039794922,41416600,0.0,0 -2002-09-11,63.85296990775372,64.44682296678377,62.906946948997046,62.92766189575195,27711200,0.0,0 -2002-09-12,62.66524124069568,62.727386058598654,61.44991387484419,61.767555236816406,43601700,0.0,0 -2002-09-13,61.24275913899988,62.07829505566339,60.93892598083284,61.919471740722656,41131000,0.0,0 -2002-09-16,61.6708742779624,62.071380615234375,61.08392824205426,62.071380615234375,28167600,0.0,0 -2002-09-17,62.76195034944109,62.96911039747218,60.59369765812426,60.64894104003906,47609700,0.0,0 -2002-09-18,60.082679656808715,61.11156210871383,59.57859326712717,60.04124450683594,54719400,0.0,0 -2002-09-19,59.378324108264934,59.937653702131676,58.48754501342774,58.487545013427734,48510500,0.0,0 -2002-09-20,58.90231811295457,59.09653122558997,58.2988709832532,58.506954193115234,46325600,0.378,0 -2002-09-23,58.021451278910966,58.30583339968537,57.355575139144285,58.02838897705078,46893800,0.0,0 -2002-09-24,57.182165618155906,58.021447107688964,56.772926127938184,57.09199142456055,69507000,0.0,0 -2002-09-25,57.82720829889133,58.79827386206834,56.904690721962744,58.506954193115234,59294400,0.0,0 -2002-09-26,58.9717153665287,59.630659165179864,58.57635120410347,59.46419143676758,53638000,0.0,0 -2002-09-27,58.957858359345494,59.394838227978354,57.39720916748047,57.39720916748047,64648300,0.0,0 -2002-09-30,56.876979349330846,57.43187882560195,56.11399653839327,56.731319427490234,73096400,0.0,0 -2002-10-01,57.175243828956525,59.49193804586444,56.50936755178692,59.45726013183594,67198100,0.0,0 -2002-10-02,58.74279142298954,59.325430793048874,57.29312010897117,57.67461395263672,56749100,0.0,0 -2002-10-03,57.66769861477411,58.68038550194407,56.84228722861039,57.09199142456055,55547000,0.0,0 -2002-10-04,57.43186745266893,57.51509865328824,55.19840517623333,56.04462432861328,68483700,0.0,0 -2002-10-07,55.53134455586728,56.32207277008829,54.483979732064924,54.88627624511719,53188000,0.0,0 -2002-10-08,55.35794808842792,56.398380674228704,54.241216689648645,55.74637985229492,79531000,0.0,0 -2002-10-09,54.858524503644034,55.2816340621177,53.949881988845995,54.17184066772461,79956400,0.0,0 -2002-10-10,54.06088444835932,56.231918763734406,53.457431581032495,55.92672348022461,76749000,0.0,0 -2002-10-11,56.94635282820233,58.77058168057855,56.75213955690328,58.37521743774414,82308300,0.0,0 -2002-10-14,57.70930065139041,58.85377706515079,57.5983239747841,58.70117950439453,40631900,0.0,0 -2002-10-15,60.33812374630806,61.53809108963021,60.241017174237044,61.52421569824219,82320300,0.0,0 -2002-10-16,60.62945479412546,60.89996674211519,59.595955001126946,60.03293991088867,62977800,0.0,0 -2002-10-17,61.64214838091222,61.94040585583395,60.93465144416187,61.22597122192383,68534100,0.0,0 -2002-10-18,60.795936314224676,61.808623248773316,60.29652789895948,61.48262023925781,47448700,0.0,0 -2002-10-21,61.121936488423124,62.772753969493586,60.74044249670529,62.54385757446289,45859100,0.0,0 -2002-10-22,61.76701979638815,62.43289605043693,61.39939581866827,62.093017578125,40966800,0.0,0 -2002-10-23,61.57278763973309,62.61322004879316,60.81674262926764,62.564666748046875,54905800,0.0,0 -2002-10-24,62.946142020452925,63.05018628878635,61.10804425293347,61.288387298583984,54987400,0.0,0 -2002-10-25,61.18436123376279,62.696456546606605,60.99708572857365,62.564666748046875,43672100,0.0,0 -2002-10-28,63.223596061243896,63.32070264063809,61.62826461363357,62.155418395996094,39416100,0.0,0 -2002-10-29,61.787815280976645,62.07219739384821,60.345081038951776,61.43406677246094,59508200,0.0,0 -2002-10-30,61.510343510542,62.39817802042788,61.19821600975469,62.03055953979492,41688600,0.0,0 -2002-10-31,62.190151835620206,62.63406949780251,61.17052663619911,61.399417877197266,41620600,0.0,0 -2002-11-01,61.28150533337956,62.994752595339726,61.073421899389125,62.613258361816406,51878900,0.0,0 -2002-11-04,63.6744740597562,64.46520237295006,63.05021347252063,63.20974349975586,49080600,0.0,0 -2002-11-05,63.008581503208575,63.86173837120642,63.008581503208575,63.70914077758789,37270800,0.0,0 -2002-11-06,64.14611196293781,64.5553460651684,62.973890056698494,64.53453826904297,65013100,0.0,0 -2002-11-07,63.82709638504232,63.965823965133254,62.578579915720404,62.9531364440918,51572000,0.0,0 -2002-11-08,62.7935746117489,63.51494184875353,62.09301518001811,62.183189392089844,37905400,0.0,0 -2002-11-11,62.08605912316675,62.12073702144241,60.89996704713765,61.219032287597656,33505100,0.0,0 -2002-11-12,61.4964836842525,62.37738064582985,61.29533281764401,61.704566955566406,37724500,0.0,0 -2002-11-13,61.26064090418736,62.24558200714188,60.65718839028137,61.76698684692383,63891500,0.0,0 -2002-11-14,62.47450769533751,63.119576102345725,62.252548958082734,62.93230056762695,31896800,0.0,0 -2002-11-15,62.425954598759375,63.50107037805906,62.39127139610568,63.39702606201172,39152100,0.0,0 -2002-11-18,63.917219571637055,63.917219571637055,62.668698807841835,62.758872985839844,28934800,0.0,0 -2002-11-19,62.43982884291977,63.188941617812205,62.259490994527,62.675662994384766,32806600,0.0,0 -2002-11-20,62.41203941636572,64.10447158686607,62.391226334657304,64.06979370117188,36688400,0.0,0 -2002-11-21,64.2293532916132,65.33221428764621,64.11143890033847,65.26284790039062,55128200,0.0,0 -2002-11-22,64.83973947937933,65.38769580184251,64.69407430899979,64.7981185913086,32513800,0.0,0 -2002-11-25,64.80505374842876,65.38076083498662,64.3472610409745,64.83973693847656,33846400,0.0,0 -2002-11-26,64.55538932508685,64.81896373915473,63.54963969770001,63.605125427246094,42284700,0.0,0 -2002-11-27,64.173877641144,65.6512952555435,64.11145422798474,65.3946533203125,37764000,0.0,0 -2002-11-29,65.75536957584934,65.8594086452581,65.04093445044097,65.18659973144531,19385700,0.0,0 -2002-12-02,66.22009808521909,66.62240014503847,64.65944846977284,65.29064178466797,49911900,0.0,0 -2002-12-03,64.68021554373873,64.78425984428283,64.05595503238708,64.41664123535156,34407700,0.0,0 -2002-12-04,63.65366506675127,64.60392876034868,63.417836214101456,64.12532806396484,64040800,0.0,0 -2002-12-05,64.31951852235875,64.36807181152962,63.18891218723286,63.417808532714844,36724900,0.0,0 -2002-12-06,62.5091663558066,63.931087138981546,62.41205977712779,63.833980560302734,49824100,0.0,0 -2002-12-09,63.1681371688275,63.43864924770776,62.06528104599481,62.07915115356445,36789600,0.0,0 -2002-12-10,62.43981324208486,63.18892582980854,62.2594754387502,62.911476135253906,33319900,0.0,0 -2002-12-11,62.717271934041094,63.54962113711357,62.536934104835225,62.966976165771484,39201000,0.0,0 -2002-12-12,63.258287098404075,63.459437995192374,62.564665506132485,62.96002960205078,34465600,0.0,0 -2002-12-13,62.36352048405518,62.75888458366822,61.91959779229306,61.9681510925293,36862200,0.0,0 -2002-12-16,62.30109801821239,63.66753355110069,62.1901213031093,63.57042694091797,37098400,0.0,0 -2002-12-17,63.376208202660564,63.6328448106935,62.93922320297136,63.01552200317383,32353900,0.0,0 -2002-12-18,62.6479124047418,62.70340341139667,61.96122839610723,62.2872314453125,35612600,0.0,0 -2002-12-19,61.97510347569874,62.91149179751749,61.45488715336453,61.843318939208984,39264200,0.0,0 -2002-12-20,62.175084096692856,62.746648577962304,62.105382148814684,62.725738525390625,31176900,0.436,0 -2002-12-23,62.44691993073127,63.060309781182724,62.251752366648454,62.74664306640625,22599300,0.0,0 -2002-12-24,62.44692593764259,62.63512704819542,62.20993824929344,62.279640197753906,10937000,0.0,0 -2002-12-26,62.52360711075862,63.157907148381895,61.92416071046877,62.30752944946289,17485600,0.0,0 -2002-12-27,62.00779531456861,62.237816517402045,60.90648651123047,60.90648651123047,22205700,0.0,0 -2002-12-30,61.1922871384948,61.66626792781748,60.79498010317423,61.41533660888672,29968000,0.0,0 -2002-12-31,61.33166756052057,61.638362459001264,60.71828308147599,61.498958587646484,34036600,0.0,0 -2003-01-02,61.93113065702954,63.638857795056836,61.715053002169235,63.478538513183594,44516300,0.0,0 -2003-01-03,63.367020187151155,63.694619925228125,63.08123524284234,63.673709869384766,32222600,0.0,0 -2003-01-06,63.597051095944614,65.16536946854616,63.54825918151381,64.79594421386719,40984500,0.0,0 -2003-01-07,64.75409870156489,65.08170370625143,64.26617443065722,64.63560485839844,38640400,0.0,0 -2003-01-08,64.26621890571738,64.40562821584743,63.46463797363567,63.70162582397461,38702200,0.0,0 -2003-01-09,64.00131727466815,64.94927883910017,63.71553763778783,64.69137573242188,34804900,0.0,0 -2003-01-10,64.09192712753595,65.2699099107321,63.98737684801324,64.86563110351562,37768900,0.0,0 -2003-01-13,65.20020582564649,65.42325527788591,64.4334736659434,64.84471893310547,31649900,0.0,0 -2003-01-14,64.60770377523139,65.40231744370023,64.4125362521571,65.05380249023438,30733000,0.0,0 -2003-01-15,65.20021562688828,65.22112568525428,64.06405813967528,64.40560150146484,33511800,0.0,0 -2003-01-16,64.47531639561255,64.77503970076303,63.7434322985483,64.14073944091797,44812100,0.0,0 -2003-01-17,63.4227802204316,64.33589345349047,62.83727736984611,63.19276428222656,35618300,0.0,0 -2003-01-21,63.33913263918914,63.37398095648535,62.00083068856403,62.20994186401367,36826200,0.0,0 -2003-01-22,61.875366548747586,62.59331209899469,61.33865565167941,61.457149505615234,42286500,0.0,0 -2003-01-23,61.86145042824288,62.30057768302874,61.303823959186445,61.83356857299805,55919800,0.0,0 -2003-01-24,61.749885437144464,61.812620902755604,60.06307410552961,60.209449768066406,68633900,0.0,0 -2003-01-27,59.75637796006253,60.50219976896715,58.89902881027826,59.38694763183594,57884400,0.0,0 -2003-01-28,59.68671300904594,60.2234294110108,59.33819779870203,59.82612228393555,46929100,0.0,0 -2003-01-29,59.5403116312003,60.76708613259378,59.087240989182234,60.27916717529297,53712200,0.0,0 -2003-01-30,60.49527696084919,60.55800715244643,58.82937451255027,58.850284576416016,49845900,0.0,0 -2003-01-31,58.655105898613115,60.090986739483235,58.655105898613115,59.98643112182617,55317000,0.0,0 -2003-02-03,60.04217251505262,60.509181406736204,59.88882504186276,60.10490798950195,39696000,0.0,0 -2003-02-04,59.46364020076457,59.770335167497734,58.75964358046235,59.51243209838867,43633400,0.0,0 -2003-02-05,59.77035668161884,60.3210113346786,58.88513041525688,59.143028259277344,55270600,0.0,0 -2003-02-06,58.80843459328501,59.170887924765275,58.306572043606224,58.864192962646484,53638000,0.0,0 -2003-02-07,59.18484204081884,59.24060042010567,57.8326016321346,58.146263122558594,43165000,0.0,0 -2003-02-10,58.1741416271233,58.64115059905833,57.609548849647176,58.55751037597656,45455900,0.0,0 -2003-02-11,58.80843883779369,59.1639204045415,57.73501171082106,58.153228759765625,46861000,0.0,0 -2003-02-12,57.96503669298051,58.28566996181169,57.21920923893054,57.22618103027344,35873600,0.0,0 -2003-02-13,57.26102868189969,57.6165155523573,56.459442934675614,57.40043258666992,57934100,0.0,0 -2003-02-14,57.41437747050557,58.6899386500554,57.03100874681249,58.65509033203125,59580900,0.0,0 -2003-02-18,58.919955398389746,59.80518663191355,58.82237160668256,59.68668746948242,39492700,0.0,0 -2003-02-19,59.47061923804746,59.575174836530564,58.74570715792823,59.3730354309082,31432000,0.0,0 -2003-02-20,59.393905671928394,59.54028128451343,58.58535390937555,58.780521392822266,29280100,0.0,0 -2003-02-21,58.81540911761075,59.76337065946524,58.17414257986173,59.3730354309082,60955800,0.0,0 -2003-02-24,59.19874409395154,59.247535969680534,58.264721349819276,58.411102294921875,30628600,0.0,0 -2003-02-25,57.81865440213824,58.89208157500975,57.30982532150603,58.878143310546875,56770600,0.0,0 -2003-02-26,58.56445738229262,58.919944165980944,57.90925280829697,58.020774841308594,37787200,0.0,0 -2003-02-27,58.34143762666526,59.07332161454423,57.96504592092582,58.78753662109375,51126000,0.0,0 -2003-02-28,58.87814088089745,59.40788531324147,58.66206322416902,59.17786407470703,43666800,0.0,0 -2003-03-03,59.428791485500376,59.79124482000272,58.35536442705249,58.61326217651367,42923100,0.0,0 -2003-03-04,58.51568291916405,58.5575083471744,57.609546853688386,57.67924880981445,31440500,0.0,0 -2003-03-05,57.58165729959585,58.22989550883805,57.40739977364813,58.16716003417969,43974700,0.0,0 -2003-03-06,57.76289434791676,58.21595972169541,57.48408120549411,57.67924880981445,41217700,0.0,0 -2003-03-07,56.88463239642417,58.54356228066201,56.759166753554545,58.07655334472656,63538000,0.0,0 -2003-03-10,57.574685164430484,57.755914479346735,56.52914001501134,56.68248748779297,41014100,0.0,0 -2003-03-11,56.79401626286615,57.156469583757385,56.09698614599106,56.12486267089844,48102700,0.0,0 -2003-03-12,56.02728951205217,56.52915214230561,55.330259262904335,56.50127029418945,62459800,0.0,0 -2003-03-13,57.28193116824331,58.4877955250783,56.82886056071858,58.45294189453125,72117800,0.0,0 -2003-03-14,58.68992042445558,59.08722728839479,58.097446022850306,58.64112854003906,63951800,0.0,0 -2003-03-17,58.174126472241774,60.60675982165377,58.00684075173913,60.48826599121094,88217500,0.0,0 -2003-03-18,60.74619669300459,60.88560063727547,60.139778367795124,60.843780517578125,50792300,0.0,0 -2003-03-19,60.83678743768284,61.45017734085142,60.41857043328827,61.310768127441406,49630800,0.0,0 -2003-03-20,60.87166119147982,61.74991559563773,60.18856911477083,61.4432258605957,67321600,0.0,0 -2003-03-21,62.14584563437148,62.90166940279262,61.536982407654556,62.754703521728516,71165300,0.354,0 -2003-03-24,61.599961867599134,61.683944654413324,60.431229449406125,60.6691780090332,65398900,0.0,0 -2003-03-25,60.704182315336865,61.76794319553053,60.494233303791944,61.250057220458984,61040100,0.0,0 -2003-03-26,61.27804392583687,61.48099834949735,60.74616889075452,60.94212341308594,45740800,0.0,0 -2003-03-27,60.466223721001484,61.34802429638031,60.1792865367225,60.99110412597656,53120200,0.0,0 -2003-03-28,60.515251935818235,61.08212143545648,60.36128606199968,60.68321228027344,32583000,0.0,0 -2003-03-31,59.73138847368106,60.599189240306856,59.06654210251831,59.30448532104492,61119500,0.0,0 -2003-04-01,59.66141586052148,60.45923378036378,59.423472600627676,60.214290618896484,53574000,0.0,0 -2003-04-02,61.26402842584804,62.124829116132915,61.23603416950468,61.66993713378906,50431200,0.0,0 -2003-04-03,62.19485154697264,62.278829022714596,61.34104494785363,61.376033782958984,48755500,0.0,0 -2003-04-04,61.88691800481719,61.99888974883561,61.32004870345116,61.739952087402344,36250100,0.0,0 -2003-04-07,63.22362164719743,63.5805419859995,61.565002202114265,61.62099075317383,69776200,0.0,0 -2003-04-08,61.795938901483346,62.040882090326974,61.38303009226604,61.718955993652344,39712700,0.0,0 -2003-04-09,61.83792441828568,62.355805039079975,60.72517493351219,60.907135009765625,55647300,0.0,0 -2003-04-10,60.9491277105626,61.32704235229255,59.98334940793792,61.243064880371094,41812400,0.0,0 -2003-04-11,61.697944566363695,62.07585375930932,60.78814972866305,60.99110412597656,47730100,0.0,0 -2003-04-14,61.21505904655219,62.285814326176485,60.88613310536353,62.25082015991211,36711700,0.0,0 -2003-04-15,62.1738360576501,62.831687927246094,61.87990428843657,62.831687927246094,49709800,0.0,0 -2003-04-16,62.92267865364152,63.02765048507379,61.60697474650954,61.76094055175781,51788000,0.0,0 -2003-04-17,61.7959539393693,62.789726741287524,61.718971012804694,62.67774963378906,37403100,0.0,0 -2003-04-21,62.88767709826489,63.097631415194485,62.32780247935935,62.7407112121582,32052700,0.0,0 -2003-04-22,62.35581243243125,64.07742011057148,62.20884650934919,63.92345428466797,59763600,0.0,0 -2003-04-23,64.11939015199509,64.6302707398601,63.85344733808673,64.51129913330078,44227100,0.0,0 -2003-04-24,64.05640294426576,64.44131737203632,63.65749405519421,63.93743133544922,49692400,0.0,0 -2003-04-25,63.895454816377494,64.01442644791791,62.999654280589446,63.14662551879883,43917200,0.0,0 -2003-04-28,63.29362659523386,64.51834843901554,63.19564927488041,64.23841094970703,46432900,0.0,0 -2003-04-29,64.48331278421914,64.94521014545691,63.96543223261078,64.46231842041016,52017100,0.0,0 -2003-04-30,64.32231903076172,64.78421081276393,63.9723989107102,64.32231903076172,48709100,0.0,0 -2003-05-01,64.32934785649427,64.89622238370968,63.33557546762003,64.31535339355469,50240400,0.0,0 -2003-05-02,64.07739407232772,65.41409198189515,64.02840545120719,65.23213195800781,50201500,0.0,0 -2003-05-05,65.41408689825414,65.63103572644788,64.73524081594091,65.10615539550781,35437800,0.0,0 -2003-05-06,65.11315986149313,66.05094355564962,65.08516560115662,65.7220230102539,44401000,0.0,0 -2003-05-07,65.37913341008586,65.88301969553241,65.06420715136284,65.35813903808594,41413100,0.0,0 -2003-05-08,64.7492831481623,65.31615795546337,64.58132281309963,64.7002944946289,40570700,0.0,0 -2003-05-09,64.96620409944657,65.64505033490823,64.81223831333855,65.59606170654297,33608100,0.0,0 -2003-05-12,65.43509321362602,66.56883685778566,65.28112743411992,66.40087127685547,35662300,0.0,0 -2003-05-13,66.15594221451687,66.61083974906344,65.96698756998633,66.28191375732422,39253600,0.0,0 -2003-05-14,66.54784074996438,66.65281790887572,65.72203395957412,66.14193725585938,32195100,0.0,0 -2003-05-15,66.40787046482217,66.7158020205294,65.95997293253517,66.56183624267578,43879200,0.0,0 -2003-05-16,66.40788484580402,66.79979393658522,65.96698711867518,66.39389038085938,38905000,0.0,0 -2003-05-19,65.89000150513155,66.07895614060371,64.61629167307865,64.84024047851562,41606000,0.0,0 -2003-05-20,64.95918019338778,65.10614602417378,64.09837422523202,64.70723724365234,55404600,0.0,0 -2003-05-21,64.46232586821061,65.00120084588808,64.32235986707276,64.84024047851562,49333800,0.0,0 -2003-05-22,65.05017440764718,65.82000325361274,64.8612198159571,65.48407745361328,38421800,0.0,0 -2003-05-23,65.45610733028633,65.77103888505322,65.18316985310521,65.61707305908203,26155900,0.0,0 -2003-05-27,65.29512922394083,67.0727196250256,65.13416353882089,66.7647933959961,43719200,0.0,0 -2003-05-28,67.07971449135626,67.51361754703349,66.78578274687791,66.95374298095703,37727100,0.0,0 -2003-05-29,67.10073472459011,67.75858679246781,66.54086528200968,66.77880859375,50844200,0.0,0 -2003-05-30,67.11472641359514,67.94753324332403,66.87677779127188,67.84955596923828,52529500,0.0,0 -2003-06-02,68.25547290132907,68.8993251608971,67.65360938149236,68.12950134277344,50305500,0.0,0 -2003-06-03,67.98950533908399,68.47239165116207,67.77955105427839,68.40940856933594,38254500,0.0,0 -2003-06-04,68.34642607429117,69.52915289339643,68.283437652565,69.39618682861328,49360700,0.0,0 -2003-06-05,68.99028366857016,69.7391128540039,68.77332946426111,69.7391128540039,46262400,0.0,0 -2003-06-06,70.26398902747928,70.96382954500525,69.37518858018004,69.46617126464844,60356800,0.0,0 -2003-06-09,69.12326080833903,69.35420951149284,68.42342013649882,68.75934600830078,37808500,0.0,0 -2003-06-10,68.90629286181547,69.46616738027323,68.71733828196749,69.45916748046875,29965900,0.0,0 -2003-06-11,69.39619008990543,70.25699095886192,69.08125864519293,70.19400787353516,37610200,0.0,0 -2003-06-12,70.5089422251382,70.61391938565312,69.71812424939812,70.41096496582031,36442000,0.0,0 -2003-06-13,70.41097471755998,70.50895199044747,69.24923656852141,69.67613983154297,48628300,0.0,0 -2003-06-16,69.95611052058636,71.17383244914082,69.8441387293702,71.14584350585938,36326300,0.0,0 -2003-06-17,71.43277555908362,71.50975850151626,70.84491164163094,71.14584350585938,36801800,0.0,0 -2003-06-18,70.88683971803579,71.48170303179809,70.68388534710816,71.08279418945312,35521000,0.0,0 -2003-06-19,71.1318137051722,71.19480214940774,69.87209821407374,69.9980697631836,43551700,0.0,0 -2003-06-20,70.51079211607639,70.5880531004005,69.82949363175355,69.84354400634766,41545000,0.36,0 -2003-06-23,69.85053669319932,69.99803877588347,68.77591387617252,69.1270980834961,34237500,0.0,0 -2003-06-24,68.98664219134122,69.59769942114889,68.84616526136185,69.19734954833984,36213600,0.0,0 -2003-06-25,69.2044059389963,69.84356420164536,68.50203704833984,68.50203704833984,47743400,0.0,0 -2003-06-26,68.67761620632804,69.52046192996923,68.10167403989833,69.39403533935547,33477300,0.0,0 -2003-06-27,69.35889427140769,69.66793814799156,68.53712435446978,68.59331512451172,54208800,0.0,0 -2003-06-30,68.98668903973062,69.30275296918889,68.4599122515331,68.5722885131836,33349000,0.0,0 -2003-07-01,68.30534528411577,69.42913395484996,67.72940325544906,69.20437622070312,51322800,0.0,0 -2003-07-02,69.37292607874805,70.08934486378814,69.23245453903019,70.0752944946289,34662100,0.0,0 -2003-07-03,69.58364292361478,70.1314894635021,68.76187311378376,69.35186004638672,30792800,0.0,0 -2003-07-07,69.99105446234039,70.86901549333149,69.99105446234039,70.7285385131836,31391100,0.0,0 -2003-07-08,70.58803035133758,71.14290207872891,70.35624747306409,71.04457092285156,30952500,0.0,0 -2003-07-09,70.88305176119921,71.22019111328397,70.25794401299774,70.64424896240234,36528500,0.0,0 -2003-07-10,70.12447542284772,70.26495235196818,69.27461009659501,69.74520111083984,49777700,0.0,0 -2003-07-11,69.80843648917573,70.55294572252099,69.80843648917573,70.40544891357422,39976300,0.0,0 -2003-07-14,71.07968140747523,71.57134251283712,70.55290509657405,70.74957275390625,42114900,0.0,0 -2003-07-15,71.20618870463669,71.26237952761093,70.20180051824224,70.59513092041016,42573600,0.0,0 -2003-07-16,70.80577164181699,70.84791739859625,69.69603340803596,70.1806640625,39894200,0.0,0 -2003-07-17,69.63986751240108,70.15259373497376,68.9445239016148,69.1833267211914,52253500,0.0,0 -2003-07-18,69.54854331831568,70.09639538196485,69.15521857803722,69.89270782470703,35698100,0.0,0 -2003-07-21,69.85056340831814,69.8786587986869,68.72677458680643,69.02879333496094,34786000,0.0,0 -2003-07-22,69.31676618365232,69.82247248654814,68.77593930358128,69.65390014648438,49968300,0.0,0 -2003-07-23,69.681963254181,69.85053015750323,69.02876047533148,69.70303344726562,37275400,0.0,0 -2003-07-24,70.2298609706175,70.47568900044645,69.09202684436653,69.17630767822266,40896200,0.0,0 -2003-07-25,69.29567347739449,70.44053211564001,68.86020311161812,70.39839172363281,43241100,0.0,0 -2003-07-28,70.49675172989798,70.92519708614267,70.00509040112384,70.13854217529297,34382800,0.0,0 -2003-07-29,70.33521161973373,70.41949780534628,69.30975380870026,69.81546020507812,53472100,0.0,0 -2003-07-30,69.95590435332704,70.06126070431097,69.48531872539833,69.64686584472656,28363300,0.0,0 -2003-07-31,70.22278785674966,70.87599061674479,69.6538658666603,69.80838775634766,54937200,0.0,0 -2003-08-01,69.66794903748179,69.90675180951607,69.00069559589359,69.19033813476562,49321000,0.0,0 -2003-08-04,69.04986118770346,69.53449727743462,68.12975995869857,69.19033813476562,55214100,0.0,0 -2003-08-05,69.12009901491578,69.36592696213732,67.66619081567556,67.72238159179688,61415600,0.0,0 -2003-08-06,67.91200465261053,68.87424596425879,67.72236217722944,68.11569213867188,50096900,0.0,0 -2003-08-07,68.2491555786359,68.88128839039044,67.96118723912097,68.83212280273438,43427400,0.0,0 -2003-08-08,69.0568887253297,69.21843588028815,68.66356397746254,69.02879333496094,27357300,0.0,0 -2003-08-11,69.01474243704484,69.5625891059239,68.71974354263915,69.28866577148438,34631400,0.0,0 -2003-08-12,69.33080681971316,69.94888928495955,69.12711927398884,69.9207992553711,43285600,0.0,0 -2003-08-13,70.1104151724419,70.13148536756913,69.20435927145053,69.56256866455078,36152000,0.0,0 -2003-08-14,69.60474623866088,70.06128702534271,69.14820545197905,69.75224304199219,35525100,0.0,0 -2003-08-15,69.78736856259124,70.08938737896594,69.6188015409504,69.96998596191406,12567000,0.0,0 -2003-08-18,70.18770780915432,70.65829357490026,70.05425603491514,70.57401275634766,22873800,0.0,0 -2003-08-19,70.7214799988292,70.89707210897932,70.23684406004817,70.84088134765625,37437700,0.0,0 -2003-08-20,70.44054130091398,70.86196135187609,70.3492353201103,70.55291748046875,21295300,0.0,0 -2003-08-21,70.97436244819593,71.30447125067444,70.5178217010771,70.77769470214844,46494200,0.0,0 -2003-08-22,71.46598631587713,71.51515189064297,70.04720447360194,70.0752944946289,52040300,0.0,0 -2003-08-25,70.03317640617392,70.81983121157943,69.7311576768229,70.18769836425781,23473000,0.0,0 -2003-08-26,69.88573284178827,70.51084097821364,69.41514678109078,70.31417846679688,45065800,0.0,0 -2003-08-27,70.27197159616723,70.48970411958467,69.93483235343226,70.3351821899414,18919500,0.0,0 -2003-08-28,70.51780728011501,70.93922736102535,69.99805602561382,70.77066040039062,27402400,0.0,0 -2003-08-29,70.66530850610246,71.27637114515835,70.57400251304017,71.24827575683594,28706600,0.0,0 -2003-09-02,71.38874864008422,72.25968425935282,70.9743537117374,72.20349884033203,49419100,0.0,0 -2003-09-03,72.36503409861828,72.83561976217439,72.18944195323344,72.59681701660156,44912400,0.0,0 -2003-09-04,72.4142200424437,72.73028920817964,72.17541722973738,72.6319580078125,28381000,0.0,0 -2003-09-05,72.30182457244157,72.73026984223672,71.92254488973865,72.22456359863281,31637300,0.0,0 -2003-09-08,72.37207107218015,72.96205820461456,72.29481008693547,72.82158660888672,32632800,0.0,0 -2003-09-09,72.60385723994227,72.6670678532219,72.11922110353926,72.34397888183594,35053200,0.0,0 -2003-09-10,72.01382937781175,72.20347184186889,71.32551136401857,71.61347961425781,45904900,0.0,0 -2003-09-11,71.71185243017051,72.17541840399916,71.52923504423964,71.82423400878906,38396300,0.0,0 -2003-09-12,71.57841291011924,72.09113917575318,71.1850827056524,71.95768737792969,42524800,0.0,0 -2003-09-15,72.00685280700098,72.08411380990407,71.60650277316181,71.704833984375,21312800,0.0,0 -2003-09-16,71.80313868045414,72.79347554195682,71.76099292389355,72.75133514404297,37892600,0.0,0 -2003-09-17,72.68109624090881,72.89882876564486,72.37907756531698,72.61085510253906,31885800,0.0,0 -2003-09-18,72.62491795983216,73.5379939705096,72.46337081591255,73.46775817871094,30243600,0.0,0 -2003-09-19,73.51710826135384,73.74978094881101,72.90370529524397,73.09407043457031,34331600,0.4,0 -2003-09-22,72.51593675117668,72.5934943342704,71.93778364579946,72.3044204711914,36677400,0.0,0 -2003-09-23,72.33260682722555,72.82615549375137,72.17044490963242,72.57938385009766,32489200,0.0,0 -2003-09-24,72.70632905350482,72.7768345012419,71.26094316840084,71.28914642333984,41694700,0.0,0 -2003-09-25,71.50062221622728,71.83199814564912,70.64748909520323,70.70389556884766,52673100,0.0,0 -2003-09-26,70.81674586969247,70.97186106442422,70.4007546311646,70.47126007080078,42914700,0.0,0 -2003-09-29,70.71798922971622,71.20448019880646,70.35840473839305,71.16217803955078,36771600,0.0,0 -2003-09-30,70.844949121391,71.0423665040604,69.97771661412727,70.47126007080078,70764500,0.0,0 -2003-10-01,70.67571530038495,72.04354314508642,70.64751205439718,71.97303771972656,65797600,0.0,0 -2003-10-02,71.86727816105719,72.31146718996463,71.65575650202359,72.2339096069336,44591500,0.0,0 -2003-10-03,73.09406951307531,73.5241594889948,72.67808384609421,72.89665222167969,48806900,0.0,0 -2003-10-06,72.96011226951019,73.31969146615205,72.76268959506514,73.22803497314453,20226500,0.0,0 -2003-10-07,72.80500587372774,73.54532081960284,72.5582342251027,73.51007080078125,42602900,0.0,0 -2003-10-08,73.55943973089542,73.60174191356502,72.91078116027012,73.32676696777344,30920800,0.0,0 -2003-10-09,73.94724616981308,74.18697116720031,73.20693083792892,73.52420806884766,40066500,0.0,0 -2003-10-10,73.51710085483263,73.74977351884907,73.26328248770784,73.72862243652344,22682800,0.0,0 -2003-10-13,73.82733205274744,74.23627091385785,73.68632124922809,73.96129608154297,23825600,0.0,0 -2003-10-14,73.89083760846525,74.33502676295502,73.58060718863196,74.22221374511719,38484400,0.0,0 -2003-10-15,74.63815035844344,74.65930143899152,73.77797055470914,74.02474212646484,39021700,0.0,0 -2003-10-16,73.80619259952634,74.54651288269599,73.78504151358747,74.32089233398438,32772200,0.0,0 -2003-10-17,74.3631987170098,74.47600630814473,73.31265348188123,73.51007080078125,32790300,0.0,0 -2003-10-20,73.64402175684783,74.06001281738281,73.2844425547928,74.06001281738281,27657000,0.0,0 -2003-10-21,73.90492510683953,74.22925441409141,73.55239254949356,73.9331283569336,26729100,0.0,0 -2003-10-22,73.34792946602036,73.46074247630985,72.7556772996618,73.00244903564453,33914500,0.0,0 -2003-10-23,72.54412478150097,73.29149182994949,72.50886938595532,72.86845397949219,45830500,0.0,0 -2003-10-24,72.50181813501804,73.0306167602539,72.04352491730339,73.0306167602539,51723600,0.0,0 -2003-10-27,73.14341448518667,73.45364469155831,72.81203319732914,73.06585693359375,32460200,0.0,0 -2003-10-28,73.31264579442382,74.1375703815636,73.19983283589978,74.06001281738281,34956200,0.0,0 -2003-10-29,73.86964275024094,74.33498813819554,73.2350886818926,74.15872192382812,30955400,0.0,0 -2003-10-30,74.58881000105467,74.71572188730758,73.8907973162725,74.31383514404297,39123100,0.0,0 -2003-10-31,74.3138296826958,74.55354912843546,74.18691780576967,74.24332427978516,25761600,0.0,0 -2003-11-03,74.5606290338382,75.16698540714597,74.53242578633325,74.72984313964844,37589300,0.0,0 -2003-11-04,74.72982701423506,74.92724432495098,74.44075320332051,74.56766510009766,31421600,0.0,0 -2003-11-05,74.37729756551812,74.71573109698147,73.96131184104662,74.62406921386719,33558800,0.0,0 -2003-11-06,74.45483642988668,75.04709361632973,74.10230405533304,75.01889038085938,28392300,0.0,0 -2003-11-07,75.18810703048945,75.2445135033927,74.43368793786478,74.4618911743164,31723200,0.0,0 -2003-11-10,74.55355652266138,74.62406193256476,74.0388621890706,74.15872192382812,25530800,0.0,0 -2003-11-11,74.09528094158101,74.26450042072473,73.89081681791602,74.13758850097656,26558600,0.0,0 -2003-11-12,74.19400343424009,75.06828265906258,74.1446490920135,74.96957397460938,28000000,0.0,0 -2003-11-13,74.74391293160785,75.11759637482406,74.58174567815823,74.99068450927734,29714800,0.0,0 -2003-11-14,75.01888743743604,75.40666980669982,74.23626516629635,74.35612487792969,49158500,0.0,0 -2003-11-17,73.96834583497616,74.130507715839,73.35493756321759,73.9824447631836,44382100,0.0,0 -2003-11-18,74.20100458058346,74.34906751620841,73.11520434011722,73.21391296386719,41155000,0.0,0 -2003-11-19,73.34789986539981,74.03886582652243,73.27034229693038,73.83439636230469,29827000,0.0,0 -2003-11-20,73.32674599041422,74.20102491719581,73.15047977409111,73.171630859375,53578700,0.0,0 -2003-11-21,73.49597408736997,73.55943273455831,73.05883723010852,73.47482299804688,30016000,0.0,0 -2003-11-24,73.81323045174709,74.58174844777362,73.80617829806124,74.44778442382812,28906400,0.0,0 -2003-11-25,74.54653009969543,75.03302132630233,74.34910736716081,74.72984313964844,37580000,0.0,0 -2003-11-26,75.03300697623285,75.05415806301156,74.3067909743325,74.99775695800781,33053600,0.0,0 -2003-11-28,74.93430132241414,75.20222944360084,74.87789483593077,75.05416107177734,10507500,0.0,0 -2003-12-01,75.33616877447648,75.92137381818647,75.3009187638925,75.8649673461914,38699000,0.0,0 -2003-12-02,75.7098716016309,75.98484649891381,75.49130319076977,75.67462158203125,35352000,0.0,0 -2003-12-03,75.90022879005252,76.20340687718695,75.49128990429581,75.55474853515625,39078600,0.0,0 -2003-12-04,75.56178928323949,75.94957705418399,75.39962740027524,75.8649673461914,36089500,0.0,0 -2003-12-05,75.52657511733693,76.00601959840337,75.17404256888277,75.3362045288086,28824400,0.0,0 -2003-12-08,75.258674166532,75.89323405917833,75.21637196676282,75.84387969970703,32482900,0.0,0 -2003-12-09,76.07652216764646,76.09767325908211,75.11763324161981,75.2586441040039,43596100,0.0,0 -2003-12-10,75.27978477926202,75.42785315726312,74.70868387264206,75.2515869140625,36915400,0.0,0 -2003-12-11,75.21631756800845,76.21750835274638,75.20926541340667,76.09764862060547,45304000,0.0,0 -2003-12-12,76.12586016445142,76.2880220834577,75.7169212204225,76.24571990966797,34142200,0.0,0 -2003-12-15,76.97191880781263,77.01422635141755,75.78036301741605,75.8649673461914,38693400,0.0,0 -2003-12-16,75.92138552581966,76.49953850488137,75.80857256443294,76.25981903076172,32894200,0.0,0 -2003-12-17,76.18930435814565,76.4995346069336,76.00599137405986,76.4995346069336,23198800,0.0,0 -2003-12-18,76.53480126469732,77.36677810771822,76.42198829245515,77.35972595214844,29353100,0.0,0 -2003-12-19,77.4277230235399,77.47731055173068,76.91767678807169,77.14436340332031,41465100,0.516,0 -2003-12-22,77.06643254379344,77.6827392578125,77.0593470676411,77.6827392578125,27611300,0.0,0 -2003-12-23,77.55524646685845,77.8881882616531,77.48440249747111,77.73234558105469,24741200,0.0,0 -2003-12-24,77.58357497829087,77.83859812658832,77.51982189353444,77.6544189453125,8055800,0.0,0 -2003-12-26,77.718160305662,77.9802688920476,77.6614873024486,77.71107482910156,8308400,0.0,0 -2003-12-29,77.99443465686892,78.82325706378212,77.76774804802166,78.7453384399414,22483700,0.0,0 -2003-12-30,78.69577823669735,78.82328984499344,78.52576456051501,78.75953674316406,19559500,0.0,0 -2003-12-31,78.7878888538756,79.00040464001489,78.51869462493877,78.83039093017578,31501800,0.0,0 -2004-01-02,79.1562077566734,79.47498936146897,78.44073132437855,78.79492950439453,38072300,0.0,0 -2004-01-05,79.12080984343011,79.70877485341526,79.04996586700892,79.65210723876953,27959800,0.0,0 -2004-01-06,79.45372924051644,79.85751490332879,79.34038324166085,79.7300033569336,20472800,0.0,0 -2004-01-07,79.61665664840386,80.09128085395298,79.26245851208935,79.99919128417969,30170400,0.0,0 -2004-01-08,80.22587308221289,80.3392190732864,79.88584051362666,80.31796264648438,36438400,0.0,0 -2004-01-09,79.92834951927122,80.40297370570808,79.53164394742204,79.61665344238281,54084300,0.0,0 -2004-01-12,79.7300189469264,80.22589427729379,79.59542189812565,80.20464324951172,31564100,0.0,0 -2004-01-13,80.11254026350667,80.21172072432074,79.17037722980756,79.73709106445312,54239700,0.0,0 -2004-01-14,79.87877284064848,80.51633062780658,79.81501435961509,80.40298461914062,30112800,0.0,0 -2004-01-15,80.45258626794437,80.79969902229813,79.75127528129278,80.60134887695312,38408700,0.0,0 -2004-01-16,80.7855155819117,80.97678020695902,80.49507050151826,80.92011260986328,31922700,0.0,0 -2004-01-20,81.13262154662556,81.217631042188,80.62966085830865,80.89884948730469,29863000,0.0,0 -2004-01-21,80.84926980707114,81.67809760644255,80.55883012717534,81.53641510009766,30725000,0.0,0 -2004-01-22,81.56475046591063,81.73476406652277,81.16805026293734,81.32389831542969,29888500,0.0,0 -2004-01-23,81.46559915149238,81.72770780747503,80.72178064955895,81.06181335449219,44245300,0.0,0 -2004-01-26,81.03343472893796,82.12436544260822,81.02634925417875,82.08186340332031,30460600,0.0,0 -2004-01-27,81.99689856264949,82.52819596154355,81.2176634585322,81.2389144897461,35322800,0.0,0 -2004-01-28,81.45139319368015,81.66390879978395,80.0062643727123,80.31087493896484,52621300,0.0,0 -2004-01-29,80.44548443161709,80.65092001313137,79.73708811107201,80.3888168334961,60117100,0.0,0 -2004-01-30,80.4171479302391,80.55883043712912,80.1125372962193,80.3888168334961,30984400,0.0,0 -2004-02-02,80.54466135888957,81.23889214722216,80.13379559093266,80.73593139648438,38832400,0.0,0 -2004-02-03,80.57301236981297,80.85637203657845,80.36049667321593,80.60134887695312,25093500,0.0,0 -2004-02-04,80.18339299506042,80.56592771273424,79.90003334441658,79.94253540039062,39332600,0.0,0 -2004-02-05,80.16922220066752,80.43133082384969,79.89294802701086,80.17630767822266,37226800,0.0,0 -2004-02-06,80.34632601425396,81.25307262754559,80.1904779267252,81.07597351074219,37216000,0.0,0 -2004-02-09,81.23181301390645,81.3734955351582,80.96262433048143,81.09722137451172,24851300,0.0,0 -2004-02-10,80.95554347313475,81.56476483769475,80.94137792251898,81.35932922363281,27908100,0.0,0 -2004-02-11,81.35934238360917,82.45027371510321,80.87763251521748,82.22358703613281,42965700,0.0,0 -2004-02-12,82.15271961940275,82.36523526621741,81.8764454952993,81.92603302001953,27814700,0.0,0 -2004-02-13,82.04647348393979,82.3156621789724,81.28848952762364,81.55767822265625,44739900,0.0,0 -2004-02-17,82.06771645927257,82.47858764135107,82.01104345388893,82.29440307617188,23984300,0.0,0 -2004-02-18,82.3156653131789,82.59902497405457,81.71352941171557,81.93313598632812,28618000,0.0,0 -2004-02-19,82.4077674915003,82.4502695527126,81.5081008951723,81.62853240966797,51146200,0.0,0 -2004-02-20,81.80564226197633,81.8623098828095,80.98389960656789,81.38059997558594,46728800,0.0,0 -2004-02-23,81.62144891175262,81.64978542146055,80.87763039467005,81.17515563964844,36357000,0.0,0 -2004-02-24,80.94845301204526,81.45849927698701,80.07004300277643,81.03346252441406,43953000,0.0,0 -2004-02-25,81.08308229757857,81.50811921916441,80.9839071960298,81.37352752685547,31213600,0.0,0 -2004-02-26,81.18932356025533,81.67103339800053,80.99805347803965,81.423095703125,29683000,0.0,0 -2004-02-27,81.60020600450532,81.98982089134594,81.20350023373602,81.47977447509766,39312000,0.0,0 -2004-03-01,81.77020644496586,82.4148444356309,81.64269486153464,82.28733825683594,33130800,0.0,0 -2004-03-02,82.13145700077136,82.8611042206377,81.62849634828714,81.80559539794922,38556400,0.0,0 -2004-03-03,81.64264969281943,82.08185721601062,81.40887768509027,81.954345703125,31346200,0.0,0 -2004-03-04,81.97563597616451,82.24482467465685,81.83395344698232,82.16690063476562,21060000,0.0,0 -2004-03-05,81.76310876487123,82.84695435997045,81.66393370418474,82.44316864013672,55905600,0.0,0 -2004-03-08,82.41481785916295,82.61317335080899,81.40181640115908,81.4372329711914,39281600,0.0,0 -2004-03-09,81.53641058760898,81.61433461062737,80.9271893556297,81.11137390136719,39746100,0.0,0 -2004-03-10,81.26724986288403,81.30266644619962,79.73711078124578,79.75128173828125,67671800,0.0,0 -2004-03-11,79.62374931475863,80.24005066863407,78.70283191532467,78.71700286865234,89134800,0.0,0 -2004-03-12,79.14914577555078,79.84337134046562,79.04288521633092,79.75128173828125,54012200,0.0,0 -2004-03-15,79.53168101772101,79.58835404077723,78.56118116990864,78.77369689941406,57677200,0.0,0 -2004-03-16,79.18458016930263,79.38293037532041,78.51868555941059,79.19166564941406,59832600,0.0,0 -2004-03-17,79.4678891018571,80.23295832548499,79.41121610648445,80.07711029052734,41607300,0.0,0 -2004-03-18,79.83625721893783,80.2400428753971,79.2907944707729,80.0983657836914,60014300,0.0,0 -2004-03-19,79.91004044390952,80.02377844790016,78.93613268373622,78.95034790039062,48636200,0.395,0 -2004-03-22,78.58065777930399,78.60198330662622,77.55698907917453,77.9479751586914,62752100,0.0,0 -2004-03-23,78.37450006590163,78.48113312349693,77.7418174607075,77.81290435791016,54080200,0.0,0 -2004-03-24,77.89821761821482,78.29630861069592,77.37927292565938,77.87689208984375,51584300,0.0,0 -2004-03-25,78.2536457273354,79.12091990723904,78.04748994493353,78.90765380859375,49873600,0.0,0 -2004-03-26,78.8792166006311,79.46924809622296,78.7654786606177,78.9289779663086,37409500,0.0,0 -2004-03-29,79.35551226221318,80.144589295386,79.3199715254039,80.03795623779297,44113600,0.0,0 -2004-03-30,79.83180963963481,80.37918473991957,79.77493794975038,80.30809783935547,39059900,0.0,0 -2004-03-31,80.32229260933343,80.61375587813139,79.88865557558816,80.4004898071289,48517600,0.0,0 -2004-04-01,80.37918707349642,80.94789314168574,80.36497186240406,80.8839111328125,45103800,0.0,0 -2004-04-02,81.61610277305057,81.63742829788485,80.96920501755486,81.49525451660156,50987700,0.0,0 -2004-04-05,81.36730431031523,82.02131244919579,81.35308910015812,81.943115234375,30251800,0.0,0 -2004-04-06,81.63030761354578,81.8791143944497,81.48102354500342,81.68006896972656,28420900,0.0,0 -2004-04-07,81.71567112847647,81.73700208880244,81.11853440539242,81.48818969726562,45890500,0.0,0 -2004-04-08,82.04263745538282,82.04263745538282,80.85546410893083,81.3033218383789,46929700,0.0,0 -2004-04-12,81.45261270945856,81.80805262658109,81.44550239251295,81.62322235107422,23085200,0.0,0 -2004-04-13,81.93599667207395,81.96443251122702,80.34362222099257,80.47869110107422,56210300,0.0,0 -2004-04-14,80.05217356724847,80.78437888300573,80.00952251540471,80.60665893554688,62322300,0.0,0 -2004-04-15,80.64930152673743,80.88389313443265,79.87444524042405,80.30097198486328,61602500,0.0,0 -2004-04-16,80.59954208948497,81.07583563201162,80.3151945354524,80.91944122314453,47059200,0.0,0 -2004-04-19,80.72039576588895,81.03317916004117,80.52134488505143,80.91944122314453,28277600,0.0,0 -2004-04-20,81.09717141521047,81.26778105392897,79.46214565477841,79.5616683959961,53299400,0.0,0 -2004-04-21,79.76072656607519,80.29388651751576,79.52614030936479,80.09484100341797,50177300,0.0,0 -2004-04-22,79.95978524546135,81.51660876636265,79.931349394468,81.2180404663086,62071500,0.0,0 -2004-04-23,81.33886229349294,81.44549534411807,80.89100998916602,81.29621124267578,29395700,0.0,0 -2004-04-26,81.39570774394255,81.70849647867901,80.75591506997274,81.18244171142578,35515200,0.0,0 -2004-04-27,81.20379393370132,81.83647645943238,81.01185336942044,81.25355529785156,43485500,0.0,0 -2004-04-28,80.96209528749189,81.04740281102866,79.97397285674842,80.20145416259766,50165800,0.0,0 -2004-04-29,80.13040163372843,80.55692858715535,79.02143047010148,79.49771881103516,69687600,0.0,0 -2004-04-30,79.73938293070015,79.88866703237804,78.83656800429131,78.87921905517578,48681400,0.0,0 -2004-05-03,79.17067339838378,79.82468141458558,79.1564527673115,79.72515869140625,33758000,0.0,0 -2004-05-04,79.79623982701985,80.51422978415525,79.37682344149304,79.66117095947266,51185100,0.0,0 -2004-05-05,79.9099973659748,80.30097798442988,79.7322774238819,80.17301940917969,34405000,0.0,0 -2004-05-06,79.63273591876727,80.03793710187696,78.90764106294301,79.48345184326172,54997000,0.0,0 -2004-05-07,79.0640526618986,79.78204278248036,78.16834259033203,78.16834259033203,60950000,0.0,0 -2004-05-10,77.7986883888943,78.0190593951474,77.0309368899975,77.36505126953125,75279400,0.0,0 -2004-05-11,77.81292526194441,78.23234728820609,77.72051281311309,78.01908111572266,48300600,0.0,0 -2004-05-12,77.89108283038276,78.58063695685712,76.81765304307997,78.51665496826172,90830500,0.0,0 -2004-05-13,78.02618266009094,78.77260333267704,77.9337647980683,78.18968200683594,57393700,0.0,0 -2004-05-14,78.1612300963329,78.72282577663032,77.67783162617705,78.22521209716797,54123100,0.0,0 -2004-05-17,77.40768461337413,77.84132163352962,77.06646541876145,77.55696868896484,55020400,0.0,0 -2004-05-18,77.83423178152914,78.15413096211095,77.72049382794961,77.9479751586914,30193100,0.0,0 -2004-05-19,78.55221809972842,79.03561658190083,77.59253145200479,77.6778335571289,54804100,0.0,0 -2004-05-20,77.80578968715952,78.10436331643491,77.51433179786359,77.92664337158203,38082900,0.0,0 -2004-05-21,78.17547283209278,78.58778452512605,77.82003283952972,78.06172943115234,47480400,0.0,0 -2004-05-24,78.57354028117757,78.73704501500929,77.96929356008015,78.38871002197266,40961500,0.0,0 -2004-05-25,78.1256734690293,79.60430442091054,77.91240740773789,79.51188659667969,48668000,0.0,0 -2004-05-26,79.37684785541848,79.82470021520538,79.27021479158837,79.78915405273438,35977000,0.0,0 -2004-05-27,80.00239267786591,80.35072217128781,79.66116807562942,80.23698425292969,45306900,0.0,0 -2004-05-28,80.13749174233696,80.24411938611391,79.87446423265992,80.22990417480469,23367200,0.0,0 -2004-06-01,79.94552532556318,80.22987827219086,79.52610893256289,80.12324523925781,41044700,0.0,0 -2004-06-02,80.3507499515285,80.6706491436477,79.94554864704385,80.42183685302734,39774200,0.0,0 -2004-06-03,80.1943293185207,80.46446704899165,79.66827991096432,79.6824951171875,38688300,0.0,0 -2004-06-04,80.31517028808594,80.7416969139472,80.12322978112117,80.31517028808594,32739500,0.0,0 -2004-06-07,80.63511814131165,81.61613058303485,80.62800782275006,81.53793334960938,31643800,0.0,0 -2004-06-08,81.3033275089686,81.69430815843066,81.16114828986932,81.65165710449219,32846500,0.0,0 -2004-06-09,81.40284075159236,81.53790421449771,80.84124510474416,80.89100646972656,36737600,0.0,0 -2004-06-10,81.06872965373171,81.28910064697266,80.99053244511417,81.28910064697266,21711000,0.0,0 -2004-06-14,80.91238979699999,80.93371533671542,80.23705572927702,80.4858627319336,34633000,0.0,0 -2004-06-15,80.96920229805914,81.36018289524746,80.69195963736094,81.05450439453125,37445000,0.0,0 -2004-06-16,81.040283203125,81.18245696913647,80.82701713052627,81.040283203125,26633400,0.0,0 -2004-06-17,80.93365643141338,81.09005084028047,80.56400135702188,80.91944122314453,28402400,0.0,0 -2004-06-18,80.8152845733518,81.49308886895561,80.75107445705676,81.07213592529297,31799800,0.414,0 -2004-06-21,81.1577713506792,81.43602613126814,80.71541494501857,80.7653579711914,25284000,0.0,0 -2004-06-22,80.7153737942784,81.25047970619762,80.38717615484008,81.1719970703125,37334000,0.0,0 -2004-06-23,81.05788537183439,81.93545646729325,80.92232339636449,81.87124633789062,35580000,0.0,0 -2004-06-24,81.73569221154739,81.99967991455505,81.52165298293606,81.6144027709961,35272100,0.0,0 -2004-06-25,81.62864013588005,82.00678084248688,81.10780122832172,81.22195434570312,32837900,0.0,0 -2004-06-28,81.70712041052873,81.77133596421334,80.91516849987184,80.9437026977539,40824500,0.0,0 -2004-06-29,81.00079037805237,81.4574137988257,80.92230771729002,81.27904510498047,28418100,0.0,0 -2004-06-30,81.38606617751606,81.89976887895207,81.08640808280519,81.71426391601562,52230600,0.0,0 -2004-07-01,81.51449953091452,81.62152184435777,80.32299786825263,80.57984924316406,57734700,0.0,0 -2004-07-02,80.73680294822388,80.82955270337904,80.33725339847663,80.53702545166016,34615100,0.0,0 -2004-07-06,80.17315874793704,80.23023259034922,79.6451835194271,79.8306884765625,38698200,0.0,0 -2004-07-07,79.77362715694787,80.31586960733664,79.73082040465296,80.06615447998047,29839800,0.0,0 -2004-07-08,79.7736104710096,80.13748411955083,79.33839043577292,79.50962829589844,45291100,0.0,0 -2004-07-09,79.70938594640329,79.86635123478582,79.4668017434359,79.71652221679688,27412900,0.0,0 -2004-07-12,79.5666847167375,79.93769455572343,79.19568032113075,79.75218963623047,35691300,0.0,0 -2004-07-13,79.85211003609587,79.92345643789713,79.62379828430286,79.8093032836914,26752000,0.0,0 -2004-07-14,79.38119666418362,80.18742111575001,79.28131064163132,79.56669616699219,54089400,0.0,0 -2004-07-15,79.72367932947508,79.84497419264055,78.98166482189518,79.05301666259766,38403500,0.0,0 -2004-07-16,79.60239654441334,79.67374294768122,78.79617184506122,78.98880767822266,40871200,0.0,0 -2004-07-19,79.01733130085816,79.16716035034486,78.47508894792016,78.65345764160156,39592800,0.0,0 -2004-07-20,78.86036345178499,79.83782582144566,78.66059139220596,79.6523208618164,46679800,0.0,0 -2004-07-21,79.78073789936815,79.95197029713296,78.08980096087882,78.18255615234375,56241100,0.0,0 -2004-07-22,78.02560195679453,78.76048010530283,77.60464900829972,78.3966064453125,72477100,0.0,0 -2004-07-23,78.2110997795047,78.27530989067233,77.54756808053894,77.74020385742188,49610500,0.0,0 -2004-07-26,77.90430843442164,78.07554084750576,77.2051007956427,77.59037780761719,49679100,0.0,0 -2004-07-27,77.8044028653524,78.56068415629353,77.74732359420015,78.31809997558594,51295100,0.0,0 -2004-07-28,78.16115055577288,78.74619955456261,77.47621009747206,78.55355834960938,65862300,0.0,0 -2004-07-29,78.86753552247848,79.10298360387551,78.48225832838772,78.8889389038086,52200500,0.0,0 -2004-07-30,78.71051569289821,79.12433225699745,78.55355041386892,79.0815200805664,41581700,0.0,0 -2004-08-02,78.61780290668536,79.45256725380693,78.51791685369892,79.24565887451172,38263100,0.0,0 -2004-08-03,79.14575223852792,79.23850199407109,78.59637908997499,78.63204956054688,40948800,0.0,0 -2004-08-04,78.40373303584745,79.0173217030526,78.22536436383153,78.62490844726562,40763200,0.0,0 -2004-08-05,78.6891268693603,78.75333698080527,77.24790489862087,77.34066009521484,50772000,0.0,0 -2004-08-06,76.79128113176677,77.02672909264379,76.07067559339288,76.23477172851562,74729000,0.0,0 -2004-08-09,76.35607923629637,76.68428247115928,76.24906235903819,76.34181213378906,37476300,0.0,0 -2004-08-10,76.56295268385405,77.34777904832612,76.5272822221646,77.32637023925781,55870600,0.0,0 -2004-08-11,76.82694052308587,77.29070007385747,76.41312397357405,77.1694107055664,52933200,0.0,0 -2004-08-12,76.82693700879535,77.01957273530596,76.07778658078814,76.32750701904297,50015900,0.0,0 -2004-08-13,76.41315564850818,76.59152436171357,76.0492819494225,76.47737121582031,41634700,0.0,0 -2004-08-16,76.44170694689483,77.5119193892464,76.41316729522627,77.26934051513672,45731900,0.0,0 -2004-08-17,77.59037786692475,77.9685186076572,77.43341255340255,77.70453643798828,40701600,0.0,0 -2004-08-18,77.42626806726409,78.60350235040077,77.40486469830556,78.50361633300781,43165400,0.0,0 -2004-08-19,78.34669126292034,78.4965203659389,77.89720395386465,78.27534484863281,39881600,0.0,0 -2004-08-20,78.20397435755437,78.93171626864806,78.13262796523237,78.82469940185547,44870900,0.0,0 -2004-08-23,78.87462894279182,79.03158880172673,78.51789159875997,78.62490844726562,33745100,0.0,0 -2004-08-24,78.93886486232994,79.00308043580961,78.37521902126024,78.7319564819336,30453100,0.0,0 -2004-08-25,78.71766332546487,79.38832582806256,78.41086899309002,79.26703643798828,38551400,0.0,0 -2004-08-26,79.16715041752948,79.41686546867648,79.08866776668616,79.26703643798828,26629500,0.0,0 -2004-08-27,79.33838547311888,79.64517981576148,79.23136862145986,79.51675415039062,24902900,0.0,0 -2004-08-30,79.35264313191965,79.43825659718887,78.8032646606733,78.86034393310547,26726500,0.0,0 -2004-08-31,78.95314649464788,79.30988399637309,78.55359679121568,79.27420806884766,44125300,0.0,0 -2004-09-01,79.16004641788797,79.65234589326025,78.82471775157487,79.42403411865234,52778300,0.0,0 -2004-09-02,79.36693893960502,80.40861154311521,79.36693893960502,80.322998046875,42736600,0.0,0 -2004-09-03,80.1446028770782,80.49420390585313,79.91629122351796,79.9947738647461,30480500,0.0,0 -2004-09-07,80.29446200348825,80.71540956124164,80.13749666216283,80.52277374267578,37338800,0.0,0 -2004-09-08,80.3515376934154,80.66546291859783,80.13035679357253,80.322998046875,32963100,0.0,0 -2004-09-09,80.31585043293722,80.53702585816676,79.93057342411852,80.25164031982422,34314800,0.0,0 -2004-09-10,80.2801892291784,80.81529537669174,79.96626399080316,80.66546630859375,27900600,0.0,0 -2004-09-13,80.84381680605556,81.15061114589483,80.62977766131326,80.92943572998047,44398100,0.0,0 -2004-09-14,80.8366546305847,81.11490922121122,80.75817200826667,81.093505859375,28048900,0.0,0 -2004-09-15,80.83665665547426,80.87946338021895,80.39430054072633,80.47991943359375,38295000,0.0,0 -2004-09-16,80.51562584539296,80.88663577012125,80.47995537330246,80.7225341796875,23911700,0.0,0 -2004-09-17,80.92241215180265,81.2161570497699,80.73614049878948,81.06570434570312,33683000,0.469,0 -2004-09-20,80.721839028063,80.95110115883801,80.4424259769947,80.57855224609375,37149400,0.0,0 -2004-09-21,80.77913559486078,81.29497662577558,80.62868287185046,80.9295883178711,40920800,0.0,0 -2004-09-22,80.60000022570149,80.61432670957407,79.86206332042326,79.91938018798828,49042100,0.0,0 -2004-09-23,79.95522259074397,80.02686596320281,79.48953247070312,79.48953247070312,44068700,0.0,0 -2004-09-24,79.64714983072884,80.04836254862333,79.61849138901931,79.85491943359375,34981100,0.0,0 -2004-09-27,79.5969763852332,79.66861973620077,79.22442768057955,79.34622192382812,39355100,0.0,0 -2004-09-28,79.46090156767822,79.89076738788089,79.10267913983591,79.72598266601562,41662900,0.0,0 -2004-09-29,79.67580858792209,80.13433272501236,79.5253558678805,80.12716674804688,33325700,0.0,0 -2004-09-30,79.91940201740287,80.22747343216567,79.71163241525691,80.06985473632812,43536700,0.0,0 -2004-10-01,80.42808485408482,81.42394256591797,80.39226043208934,81.42394256591797,62824300,0.0,0 -2004-10-04,81.74632809158636,81.98992209889424,81.53139799035552,81.56005096435547,33082400,0.0,0 -2004-10-05,81.56721818543471,81.7893197326122,81.34512210429584,81.60304260253906,36910600,0.0,0 -2004-10-06,81.50987533170132,82.1618423461914,81.44539796070526,82.1618423461914,42297800,0.0,0 -2004-10-07,81.94693336118581,81.96126531451384,81.21616225657732,81.2806396484375,39388800,0.0,0 -2004-10-08,81.06567879004838,81.50987078601423,80.49252112720889,80.6071548461914,51872600,0.0,0 -2004-10-11,80.80064132094871,80.97258653745989,80.70033948914721,80.936767578125,20229100,0.0,0 -2004-10-12,80.38507011282803,80.83643364271894,80.19879847976732,80.62149810791016,41754700,0.0,0 -2004-10-13,80.95821671898965,81.0083676080502,79.75458991549947,79.9122085571289,54212600,0.0,0 -2004-10-14,80.0125285340803,80.1916396737549,79.22444061271969,79.26742553710938,64082200,0.0,0 -2004-10-15,79.53968873955826,80.05552980169547,79.21729080874216,79.71163940429688,63482200,0.0,0 -2004-10-18,79.44652804065484,80.17013849677181,79.31040184016733,80.01251983642578,43535100,0.0,0 -2004-10-19,80.25612910878655,80.40658729881436,79.23161301740087,79.33908081054688,55851900,0.0,0 -2004-10-20,79.08119380800348,79.3964313438856,78.62983544475154,79.1814956665039,57118500,0.0,0 -2004-10-21,79.37490585603204,79.75462065757425,78.95936663447907,79.69730377197266,53218300,0.0,0 -2004-10-22,79.66149480620321,79.70447974277445,78.70862196124904,78.8017578125,48752400,0.0,0 -2004-10-25,78.62979356328002,78.89488002871593,78.34321463476196,78.70860290527344,43990900,0.0,0 -2004-10-26,78.90201933543315,79.95519347174168,78.72290823649733,79.9122085571289,54337400,0.0,0 -2004-10-27,79.79760172099242,81.02988737150294,79.61133005087434,80.87226867675781,73896000,0.0,0 -2004-10-28,80.80057700792628,81.3594026163216,80.59280753141394,81.11581420898438,54413300,0.0,0 -2004-10-29,81.0442358127669,81.41678469483271,80.886617085956,81.10154724121094,48820200,0.0,0 -2004-11-01,81.359442605657,81.56004620818969,81.10152212638975,81.32362365722656,36720900,0.0,0 -2004-11-02,81.43824950957897,82.08305068846693,81.11585165315401,81.35227966308594,56210000,0.0,0 -2004-11-03,82.41262711933902,82.64905516541411,81.84663519057088,82.3768081665039,76960200,0.0,0 -2004-11-04,82.23353001305786,83.58761019415427,82.16188662910288,83.50164031982422,55350300,0.0,0 -2004-11-05,83.85984847701275,84.28254819685489,83.45863575606755,84.02462768554688,63287200,0.0,0 -2004-11-08,83.80969619604203,83.9888073556227,83.62341905958726,83.90283203125,33863800,0.0,0 -2004-11-09,83.8813445840229,84.18225003826774,83.6520825046879,83.73805236816406,44658100,0.0,0 -2004-11-10,83.86699050315494,84.21805223986063,83.65206043512946,83.8025131225586,45265400,0.0,0 -2004-11-11,83.95298010244119,84.62643978885612,83.89566322146149,84.4401626586914,37863200,0.0,0 -2004-11-12,84.5189686006026,85.25690564887971,84.31119901494544,85.10645294189453,55583700,0.0,0 -2004-11-15,85.1502787623764,85.34428964357481,84.95626788117801,85.3155517578125,35297900,0.351,0 -2004-11-16,85.04972621853561,85.08565681873976,84.59703026005873,84.7048110961914,40028700,0.0,0 -2004-11-17,85.0568574889083,85.61015218533957,84.84128499395592,85.20775604248047,54494000,0.0,0 -2004-11-18,85.1718629157227,85.36587934293554,84.95629580822238,85.32276153564453,31854300,0.0,0 -2004-11-19,85.29401847209509,85.30839290199123,84.17305415869971,84.37425231933594,54276500,0.0,0 -2004-11-22,84.19459055143732,84.87723327300488,84.09399149474103,84.7766342163086,37560200,0.0,0 -2004-11-23,84.74070233276602,84.97783102658698,84.33830611215153,84.90597534179688,41968800,0.0,0 -2004-11-24,84.98502465206757,85.21496616492111,84.82694431629922,85.10718536376953,29724800,0.0,0 -2004-11-26,85.15749212302266,85.49521994791823,85.00659350637034,85.04251861572266,15487700,0.0,0 -2004-11-29,85.35866352760362,85.5167493094786,84.41734153110409,84.65446472167969,61460800,0.0,0 -2004-11-30,84.79099510111196,84.96344982730824,84.53231027069911,84.71195220947266,53685200,0.0,0 -2004-12-01,84.90597549314948,85.86885373197553,84.8628576964087,85.67484283447266,49898300,0.0,0 -2004-12-02,85.58143108147763,86.13473140406646,85.5167626055125,85.7467041015625,60163500,0.0,0 -2004-12-03,85.73232129747264,86.3287338790966,85.5742355064651,85.689208984375,49067900,0.0,0 -2004-12-06,85.6604995727539,85.9694840643291,85.3946274113671,85.6604995727539,33030500,0.0,0 -2004-12-07,85.86165506366042,85.95507237560452,84.81973406576006,84.86284637451172,52047200,0.0,0 -2004-12-08,84.94191762328192,85.3802445160654,84.79820622463488,85.35868835449219,43895100,0.0,0 -2004-12-09,84.89163195170487,85.84014159152461,84.59702189168537,85.6604995727539,60922800,0.0,0 -2004-12-10,85.45208864730965,85.91197163940966,85.40178911636865,85.7467041015625,47828600,0.0,0 -2004-12-13,86.05566134199866,86.51554418896137,85.7610459807253,86.49398803710938,38541000,0.0,0 -2004-12-14,86.35748007977877,86.91796209737184,86.35748007977877,86.79580688476562,41500700,0.0,0 -2004-12-15,86.73110835550756,87.02572370715343,86.45086742611727,86.8604507446289,46699200,0.0,0 -2004-12-16,86.74551981240505,87.11917271703877,86.25689340958365,86.81018829345703,51641800,0.0,0 -2004-12-17,86.2455845833256,86.61378676577195,86.0289992763414,86.23114776611328,70761900,0.568,0 -2004-12-20,86.45496375862018,86.84482396389399,86.03622440760752,86.25281524658203,47187400,0.0,0 -2004-12-21,86.34666436688047,86.9819945276402,86.24559011551116,86.9170150756836,33094200,0.0,0 -2004-12-22,86.90982815304037,87.41520511712748,86.85207534960004,87.12641906738281,31500700,0.0,0 -2004-12-23,87.2635610902062,87.55956268724897,87.11194971004277,87.19136047363281,25646100,0.0,0 -2004-12-27,87.50179867448155,87.61731524693703,86.91701117379243,87.0108642578125,29944100,0.0,0 -2004-12-28,87.09752832178245,87.59568405360051,87.06864916983727,87.48738861083984,23422900,0.0,0 -2004-12-29,87.4151763133301,87.64620397537377,87.32131771091196,87.61732482910156,22650600,0.0,0 -2004-12-30,87.64618862450203,87.76892084356238,87.38628186170777,87.45125579833984,21076900,0.0,0 -2004-12-31,87.57398603161329,87.83389277216669,87.21300505952607,87.26354217529297,28648800,0.0,0 -2005-01-03,87.76171143533617,87.90610716074609,86.56325720916936,86.85204315185547,55748000,0.0,0 -2005-01-04,86.96756017996128,87.02531847408078,85.50919905631552,85.79076385498047,69167600,0.0,0 -2005-01-05,85.7257903091464,86.09399257438368,85.19153982203166,85.19876098632812,65667300,0.0,0 -2005-01-06,85.509187633332,86.02178003186476,85.37923424299036,85.63191986083984,47814700,0.0,0 -2005-01-07,85.8918247909627,86.07953646366374,85.28537381305672,85.50918579101562,55847700,0.0,0 -2005-01-10,85.43699266331294,86.2455922019626,85.43699266331294,85.91349029541016,56563300,0.0,0 -2005-01-11,85.6535816969342,85.72577680412202,85.18430523801288,85.32147979736328,63099700,0.0,0 -2005-01-12,85.48033322769673,85.79799287857941,84.84500290966146,85.60306549072266,72720500,0.0,0 -2005-01-13,85.65362030761743,85.71859978907126,84.8305835967762,84.91722106933594,55537500,0.0,0 -2005-01-14,85.16986453877921,85.5741615271148,85.01825316815335,85.36479187011719,42032500,0.0,0 -2005-01-18,85.2276303594549,86.36111066606928,85.15542974104083,86.25281524658203,57391700,0.0,0 -2005-01-19,86.22394591700358,86.28891987121257,85.34315115016285,85.35037231445312,54378900,0.0,0 -2005-01-20,85.11212359512363,85.33593011525102,84.67894737533679,84.83055877685547,72049300,0.0,0 -2005-01-21,85.03994399779306,85.19155542698347,84.21690737753914,84.31076049804688,63160400,0.0,0 -2005-01-24,84.53456917851271,84.71505976656981,84.01476091174223,84.14471435546875,58441900,0.0,0 -2005-01-25,84.40459806176355,84.80889510537054,84.26742348991084,84.3829345703125,68245000,0.0,0 -2005-01-26,84.70057879232576,84.90272726427666,84.49843032037485,84.63560485839844,57195100,0.0,0 -2005-01-27,84.60673705353875,85.01103405160609,84.4551256792635,84.7800064086914,55878800,0.0,0 -2005-01-28,84.82332237044619,84.86664384033446,84.18799772946416,84.7800064086914,60738900,0.0,0 -2005-01-31,85.15542047784602,85.3720112932598,84.98215113039494,85.30703735351562,52532700,0.0,0 -2005-02-01,85.37205866350412,85.9712888115939,85.26376319570751,85.84855651855469,49841200,0.0,0 -2005-02-02,85.95681755443341,86.339456638175,85.84130646695222,86.10842895507812,52468900,0.0,0 -2005-02-03,85.95680814740096,86.0290087658264,85.60304825127845,85.88461303710938,48837100,0.0,0 -2005-02-04,85.91352043783573,86.94592682812842,85.899083614283,86.8015365600586,50024600,0.0,0 -2005-02-07,86.81595806025169,87.01088543855407,86.60658835281005,86.68600463867188,45412000,0.0,0 -2005-02-08,86.75818445358402,87.10472868603576,86.68598934502891,86.78706359863281,39263500,0.0,0 -2005-02-09,86.93870777756014,86.98924492342775,86.09401312171478,86.1373291015625,55279400,0.0,0 -2005-02-10,86.39000873964716,86.64991009197294,86.10122272712013,86.44776153564453,45858600,0.0,0 -2005-02-11,86.4188612462475,87.386293325367,86.24559188436554,87.19136047363281,53133000,0.0,0 -2005-02-14,87.13364023336376,87.25637251436113,86.98202879657836,87.12641906738281,32432100,0.0,0 -2005-02-15,87.21301252255593,87.66784660337939,87.12637509891351,87.45125579833984,43852700,0.0,0 -2005-02-16,87.30690751389699,87.68954667944547,87.11919575995665,87.5090560913086,55523000,0.0,0 -2005-02-17,87.52349891667906,87.59569405071397,86.79431539421441,86.8015365600586,58124000,0.0,0 -2005-02-18,86.80871966117832,86.9819945276402,86.56325519589103,86.9170150756836,47723300,0.0,0 -2005-02-22,86.56327079641703,86.97478903919449,85.61028089166216,85.62471771240234,80697600,0.0,0 -2005-02-23,85.86296252108768,86.32501787689273,85.63915600728238,86.2383804321289,68292600,0.0,0 -2005-02-24,86.08677646422818,86.86649700998989,85.89907024657558,86.8087387084961,68563600,0.0,0 -2005-02-25,86.8303907776835,87.84113895241065,86.76541682403158,87.6678695678711,60899900,0.0,0 -2005-02-28,87.46573261878115,87.57402806401176,86.66435403546389,87.0903091430664,69381300,0.0,0 -2005-03-01,87.22749132632707,87.73286277050417,87.19861217121186,87.52349853515625,47294400,0.0,0 -2005-03-02,87.18418472985522,88.02887945236647,87.10476842177609,87.48018646240234,64226500,0.0,0 -2005-03-03,87.83393102552905,88.00720042531596,87.14084241011159,87.51626586914062,61230800,0.0,0 -2005-03-04,88.11548032532973,88.67860990642257,87.9277686289208,88.60641479492188,56168500,0.0,0 -2005-03-07,88.55588902029392,88.98184408845573,88.36817729981745,88.64974212646484,43442400,0.0,0 -2005-03-08,88.56312968160363,88.80137860469084,88.15883248470617,88.31766510009766,44362000,0.0,0 -2005-03-09,88.05773955767073,88.28876726533122,87.32855611768748,87.33577728271484,73263600,0.0,0 -2005-03-10,87.50182915981303,87.71842005455987,86.92426262071353,87.53070831298828,65149000,0.0,0 -2005-03-11,87.58121887272006,87.87722597182558,86.75096688049128,86.9170150756836,57976500,0.0,0 -2005-03-14,87.07587222021685,87.47295368289187,86.83762334261185,87.45851135253906,36336400,0.0,0 -2005-03-15,87.66066023050934,87.68953938318461,86.69323340249609,86.7365493774414,62438500,0.0,0 -2005-03-16,86.41889420920745,86.75100174934717,85.84132761325377,86.00016021728516,74874200,0.0,0 -2005-03-17,86.13732344749585,86.44776746834378,85.8990800658359,86.17342376708984,62584200,0.0,0 -2005-03-18,86.33066840893288,86.6350821648709,85.63486316831663,85.91753387451172,60232000,0.467,0 -2005-03-21,86.04078299024602,86.09151863676446,85.35222699752853,85.5986557006836,61244300,0.0,0 -2005-03-22,85.79432798658289,86.20021303430512,84.72887420654297,84.72887420654297,92472400,0.0,0 -2005-03-23,84.76513422644521,85.32323185118456,84.62017682067349,84.80137634277344,70817300,0.0,0 -2005-03-24,85.13478245619991,85.51892455673783,84.84486211605622,84.90284729003906,51932500,0.0,0 -2005-03-28,85.10576733420785,85.48266533552768,85.02603912353516,85.02603912353516,46765500,0.0,0 -2005-03-29,84.90284350235518,85.45369157107673,84.25777367744284,84.4607162475586,71160300,0.0,0 -2005-03-30,84.64191312102461,85.6711249972384,84.63466359238363,85.65663146972656,62002100,0.0,0 -2005-03-31,85.66387546398046,85.85956848648293,85.43194033038466,85.4971694946289,64575400,0.0,0 -2005-04-01,85.9827771619284,86.24370486128501,84.73612935694271,85.11302185058594,95255300,0.0,0 -2005-04-04,85.06227972259161,85.42467866294697,84.61290304583413,85.25797271728516,71581200,0.0,0 -2005-04-05,85.36670195035829,85.80157961585176,85.28697373321137,85.66387176513672,46853900,0.0,0 -2005-04-06,85.85231588251385,86.21471486138435,85.65662286702239,85.9610366821289,53268200,0.0,0 -2005-04-07,85.82332578864988,86.43940292494081,85.75809109928507,86.4249038696289,46734600,0.0,0 -2005-04-08,86.3742043190464,86.4031969120127,85.52619171142578,85.52619171142578,63772900,0.0,0 -2005-04-11,85.73634779153709,85.83056953170723,85.40294140735504,85.59138488769531,44945000,0.0,0 -2005-04-12,85.44643135012254,86.29444363903713,84.85209724210938,86.03351593017578,86144800,0.0,0 -2005-04-13,85.93204794368518,86.10600344275754,84.8955865965675,85.01880645751953,65949000,0.0,0 -2005-04-14,85.09131388313112,85.16379260066671,83.9098892211914,83.9098892211914,96119800,0.0,0 -2005-04-15,83.88808240160478,84.2214886726877,82.69941464217261,82.73565673828125,128677300,0.0,0 -2005-04-18,82.71392818775199,83.3227556947452,82.59795788369512,82.98934936523438,100035200,0.0,0 -2005-04-19,83.42424989655674,83.73591326223998,83.2285568668347,83.64894104003906,64930100,0.0,0 -2005-04-20,83.62719419480014,83.80839370118234,82.30081801070763,82.48201751708984,107735900,0.0,0 -2005-04-21,83.19953944659008,84.2287509870464,82.90236969672642,84.08379364013672,86952200,0.0,0 -2005-04-22,83.8881374557076,84.43898560541791,82.82268327618057,83.76492309570312,88845800,0.0,0 -2005-04-25,83.97513218043954,84.43900265392482,83.8736608599754,84.31578826904297,52284100,0.0,0 -2005-04-26,84.04757501545367,84.63465962312645,83.46049040778091,83.4967269897461,72626000,0.0,0 -2005-04-27,83.250300142015,84.12730507339782,82.94588630396873,83.82289123535156,84131900,0.0,0 -2005-04-28,83.54747589694796,83.84464577701496,82.77194213867188,82.77194213867188,72481500,0.0,0 -2005-04-29,83.40251519512958,83.9823558734761,82.60523840973102,83.89537811279297,103993800,0.0,0 -2005-05-02,84.1272933284774,84.3737274937557,83.72865226542544,84.3664779663086,56026400,0.0,0 -2005-05-03,84.1272868544119,84.69262829685636,83.85186566007887,84.51142883300781,86000300,0.0,0 -2005-05-04,84.5476953341061,85.34497209914187,84.27951805951034,85.16377258300781,81055700,0.0,0 -2005-05-05,85.28700310142418,85.52618783522716,84.61294055237941,85.13479614257812,96906700,0.0,0 -2005-05-06,85.4754178987897,85.51890400394053,84.84484172525661,84.86658477783203,67415400,0.0,0 -2005-05-09,84.95357133322123,85.5841475721869,84.83760653086271,85.39569854736328,43750500,0.0,0 -2005-05-10,85.06227675144265,85.16374800881633,84.35922194694729,84.51142883300781,74613500,0.0,0 -2005-05-11,84.75066372733764,85.09131980621636,83.96788025201276,84.97534942626953,91647400,0.0,0 -2005-05-12,85.03331198627502,85.2290050381403,84.04033660888672,84.04033660888672,95086800,0.0,0 -2005-05-13,84.2940185500995,84.52595371350215,83.20682143481758,83.8736343383789,85267000,0.0,0 -2005-05-16,83.85914309343394,84.68541253565114,83.83015603385702,84.65642547607422,49207000,0.0,0 -2005-05-17,84.37373603725693,85.30872057114567,84.1925365411162,85.22174835205078,61071800,0.0,0 -2005-05-18,85.59141571944193,86.30896990995508,85.53343606678914,86.0987777709961,77944900,0.0,0 -2005-05-19,86.26546779732857,86.54814414492485,86.03353262552884,86.46116638183594,61768100,0.0,0 -2005-05-20,86.49741024062884,86.53365236073861,86.06253244816719,86.33795928955078,46345500,0.0,0 -2005-05-23,86.40315722616951,87.00474081192064,86.38866370122922,86.81629180908203,51047900,0.0,0 -2005-05-24,86.56987906077089,86.85254985200939,86.39592355095478,86.61336517333984,50654100,0.0,0 -2005-05-25,86.50462695771985,86.88152495672072,86.12773448848802,86.5481185913086,47608800,0.0,0 -2005-05-26,86.82357485020158,87.12798875937322,86.70036048645657,87.01202392578125,43256200,0.0,0 -2005-05-27,87.01923090943257,87.15694427490234,86.83078744378533,87.15694427490234,24596100,0.0,0 -2005-05-31,87.03373935936247,87.09896852072016,86.54087653106053,86.59886169433594,43377200,0.0,0 -2005-06-01,86.62787589369654,87.64259444993121,86.57714024237337,87.33818054199219,69611000,0.0,0 -2005-06-02,87.14246166249569,87.58458333323732,87.04823438810402,87.52660369873047,39704500,0.0,0 -2005-06-03,87.37441589945469,87.62084460687159,86.78008166005831,87.0844955444336,60999400,0.0,0 -2005-06-06,86.93951841691582,87.12071791801799,86.64960363896829,87.00475311279297,36046400,0.0,0 -2005-06-07,87.25844111609291,87.88176791232694,86.98301983417787,87.06999206542969,66501300,0.0,0 -2005-06-08,87.28741495152721,87.4033797448743,86.73656697626282,86.9105224609375,46881200,0.0,0 -2005-06-09,86.78729974276611,87.39613285947311,86.56986368876082,87.32365417480469,56653300,0.0,0 -2005-06-10,87.38164763960172,87.44688233697019,86.68584220303508,87.12071990966797,36465300,0.0,0 -2005-06-13,86.93227079116055,87.75854003932977,86.83804351546618,87.3961410522461,49383200,0.0,0 -2005-06-14,87.30188271124536,87.84548099843808,87.25114709229807,87.59905242919922,33857100,0.0,0 -2005-06-15,87.8165340185432,87.87451365967102,87.1424716137212,87.76579284667969,53195600,0.0,0 -2005-06-16,87.74404431955743,88.16442848770393,87.64257304169722,87.990478515625,46564500,0.0,0 -2005-06-17,88.4474958560733,88.70947670396208,88.21462460818199,88.3165054321289,51529400,0.488,0 -2005-06-20,88.11272511492596,88.66579074142906,88.01084431256554,88.34559631347656,41019400,0.0,0 -2005-06-21,88.41839148221165,88.52755110063214,88.07636060213554,88.39656066894531,39879800,0.0,0 -2005-06-22,88.54936457622424,88.73857417430055,88.10545295309579,88.46931457519531,46310100,0.0,0 -2005-06-23,88.28738989483985,88.49115152903079,87.20308374027339,87.22491455078125,62185600,0.0,0 -2005-06-24,87.23947263107534,87.33408021929586,86.48263968575382,86.58452606201172,58572500,0.0,0 -2005-06-27,86.57725388949083,86.89745396254081,86.41715385296584,86.70824432373047,48183800,0.0,0 -2005-06-28,86.89019669405594,87.50148183717869,86.86836587495732,87.43598937988281,41174200,0.0,0 -2005-06-29,87.59606577623623,87.61789658943333,87.15215406759903,87.20309448242188,42316500,0.0,0 -2005-06-30,87.48686651759145,87.55963771263856,86.56265512902571,86.73003387451172,62288800,0.0,0 -2005-07-01,86.92654490333042,87.18125249689255,86.75189286804654,86.9847640991211,49737500,0.0,0 -2005-07-05,86.78101232740228,87.7998261611606,86.73735070164179,87.68338775634766,51549000,0.0,0 -2005-07-06,87.61060481111149,87.7998144175806,86.89743910933157,86.94837951660156,52363600,0.0,0 -2005-07-07,86.08238751491683,87.2904052734375,86.06055670483634,87.2904052734375,103268800,0.0,0 -2005-07-08,87.30496630545579,88.28739166259766,87.12303547812996,88.28739166259766,64491200,0.0,0 -2005-07-11,88.29468233259396,88.85502691324747,88.28012475527939,88.73859405517578,49688300,0.0,0 -2005-07-12,88.77497763369269,89.24072017403657,88.52027554945603,88.97146606445312,51871100,0.0,0 -2005-07-13,88.9787459063976,89.1606767732278,88.77498422388209,89.09518432617188,41182300,0.0,0 -2005-07-14,89.49539077418446,89.83014273951953,89.13880245173226,89.44445037841797,63638800,0.0,0 -2005-07-15,89.35713371124356,89.53906451392496,89.04421250854807,89.39351654052734,56075900,0.0,0 -2005-07-18,89.1461011299174,89.24070315685462,88.81862787721411,89.03694152832031,56598400,0.0,0 -2005-07-19,89.29892081008633,89.59001122529189,89.08060716274805,89.5245132446289,59165700,0.0,0 -2005-07-20,89.21162313582371,90.04123305174815,89.00058820208334,89.83019256591797,69477000,0.0,0 -2005-07-21,89.9102174310577,89.95387905311559,89.12427492148618,89.30620574951172,101110900,0.0,0 -2005-07-22,89.42260843617811,89.91746034361111,89.24067766950549,89.90290832519531,52607100,0.0,0 -2005-07-25,89.80836208633595,90.2013279005011,89.40083314054012,89.64826202392578,57301600,0.0,0 -2005-07-26,89.67734080230808,89.89565445156184,89.47357362341482,89.75738525390625,42758800,0.0,0 -2005-07-27,89.87384418125085,90.15765587660346,89.54637085648683,90.08488464355469,43181600,0.0,0 -2005-07-28,90.20856947178353,90.70342148229768,89.97569826401731,90.65248107910156,47880700,0.0,0 -2005-07-29,90.53604831965976,90.69614276404884,89.8738175255223,90.04846954345703,62358100,0.0,0 -2005-08-01,90.11399214625389,90.26681339772013,89.83745369419101,89.98300170898438,40418200,0.0,0 -2005-08-02,90.14311635576537,90.67435142759619,90.04850874279744,90.52153015136719,45147400,0.0,0 -2005-08-03,90.4196192490503,90.77620210840554,90.32501721961388,90.76165008544922,36837200,0.0,0 -2005-08-04,90.40506835652091,90.46328200116224,89.92476831163468,90.03392791748047,50855600,0.0,0 -2005-08-05,89.83740167831535,90.22309953013185,89.26977862949771,89.42259979248047,53595500,0.0,0 -2005-08-08,89.619148915457,89.80835857746646,89.05879871891467,89.25528717041016,47616000,0.0,0 -2005-08-09,89.55361222654511,89.93930462133892,89.41534859857899,89.79376220703125,47170000,0.0,0 -2005-08-10,90.10669729745084,90.60154935537092,89.37897400945589,89.75011444091797,72863700,0.0,0 -2005-08-11,89.70647743626216,90.2595488757194,89.51727331525807,90.10672760009766,58570200,0.0,0 -2005-08-12,89.92475978862963,90.01208857544694,89.32802695893047,89.55361938476562,54776900,0.0,0 -2005-08-15,89.67009459087981,90.14311599756995,89.38628285728372,90.10672760009766,36208500,0.0,0 -2005-08-16,89.83016834918226,89.88838199516384,88.84773742603969,88.93506622314453,71942100,0.0,0 -2005-08-17,88.92047640118827,89.41532827025326,88.8040380545443,88.92774963378906,62275100,0.0,0 -2005-08-18,88.81862090271743,89.18975574608093,88.66579414122947,88.92050170898438,53388600,0.0,0 -2005-08-19,89.24075388691355,89.37902316434335,88.92783247889794,89.12432098388672,39842100,0.0,0 -2005-08-22,89.20437103375754,89.67739254151478,88.69496112075315,89.12432098388672,69912000,0.0,0 -2005-08-23,89.14612646882543,89.2261764959442,88.16370087938282,88.95691680908203,55168600,0.0,0 -2005-08-24,88.73858311737989,89.31348520448633,88.12001385580105,88.16368103027344,79104600,0.0,0 -2005-08-25,88.30924226934563,88.54211355844066,88.20736142737215,88.48389434814453,35631100,0.0,0 -2005-08-26,88.40381387001506,88.4110871042728,87.82163310833658,87.87985229492188,61956800,0.0,0 -2005-08-29,87.62517088785077,88.62214832002448,87.60333452368377,88.55665588378906,56179200,0.0,0 -2005-08-30,88.23647723730335,88.27286563431963,87.61063456283446,88.09093475341797,74160200,0.0,0 -2005-08-31,88.19281050791244,89.26256491415195,87.86533160995305,89.204345703125,102945200,0.0,0 -2005-09-01,89.16066290457476,89.6191321710107,88.15640667463303,89.13883209228516,74578700,0.0,0 -2005-09-02,89.40081777815905,89.42264859162913,88.8113636061116,88.9787368774414,47653400,0.0,0 -2005-09-06,89.26253611252761,90.09214016966646,89.25525732547544,90.01936340332031,57251300,0.0,0 -2005-09-07,89.96845108629135,90.33231282326881,89.8447394282205,90.1722183227539,41749700,0.0,0 -2005-09-08,89.99027110231467,90.23769437349759,89.73556349104126,89.87383270263672,39068700,0.0,0 -2005-09-09,90.11397194316676,90.77619721771967,90.09214113386437,90.67431640625,43093900,0.0,0 -2005-09-12,90.56518762163293,90.7252876804401,90.43419716916247,90.49241638183594,33017600,0.0,0 -2005-09-13,90.33229783422324,90.54333827290247,89.88838614460288,89.99027252197266,58427500,0.0,0 -2005-09-14,90.04848851943099,90.17947894850762,89.5245268031245,89.66279602050781,57694600,0.0,0 -2005-09-15,89.9393121856405,89.98297935527519,89.437186927587,89.6191177368164,73156900,0.0,0 -2005-09-16,90.11025431413329,90.43181184394642,89.79600098164732,90.25641632080078,75424100,0.522,0 -2005-09-19,90.23447214760061,90.2929391673195,89.62788936339146,89.95675659179688,53355300,0.0,0 -2005-09-20,90.03716380320965,90.33680315223307,89.06517590744288,89.19672393798828,84480300,0.0,0 -2005-09-21,89.0067070196252,89.06517404880813,88.26857610942828,88.36358642578125,94469100,0.0,0 -2005-09-22,88.39278334196909,88.91167107420328,88.02006870711355,88.67780303955078,84597200,0.0,0 -2005-09-23,88.6047403461364,89.07977513201462,88.35626387163755,88.75090789794922,59368100,0.0,0 -2005-09-26,89.17479083954196,89.3355723784731,88.48782277645799,88.85323333740234,70415400,0.0,0 -2005-09-27,88.80938476854168,89.1528716053107,88.44397418912322,88.83131408691406,66150800,0.0,0 -2005-09-28,89.1090061129692,89.24786388931935,88.57550432150958,88.91899108886719,58620500,0.0,0 -2005-09-29,88.8313052382831,89.7886790767625,88.48781843572935,89.64251708984375,66607700,0.0,0 -2005-09-30,89.61327592961878,89.92021942138673,88.97014986032755,89.92021942138672,47824200,0.0,0 -2005-10-03,89.8617593650452,90.13946936830052,89.4890390528881,89.59866333007812,50994800,0.0,0 -2005-10-04,89.73753028664525,89.91292580263328,88.54629381132044,88.59014129638672,60776300,0.0,0 -2005-10-05,88.61207109208124,88.65591858014277,87.38429124484863,87.42813873291016,106052100,0.0,0 -2005-10-06,87.53774626923116,87.8885428337315,86.3611239085819,87.1138687133789,140941800,0.0,0 -2005-10-07,87.47926150156918,87.73505328299946,87.06269380206537,87.41349029541016,75661400,0.0,0 -2005-10-10,87.46464957997573,87.48657331587428,86.45611869859411,86.67536163330078,52677000,0.0,0 -2005-10-11,86.96038251693518,87.25271200951667,86.47073382290095,86.5511245727539,75629800,0.0,0 -2005-10-12,86.52191320932471,87.06271928478915,85.80571162371125,85.8714828491211,100510400,0.0,0 -2005-10-13,85.84224386980208,86.29535494920678,85.41836630284821,85.82032012939453,99052900,0.0,0 -2005-10-14,86.3245989415899,86.82886175934888,85.91533529331508,86.72654724121094,88651000,0.0,0 -2005-10-17,86.82154296187802,87.16502417173817,86.5657511338251,87.048095703125,68109300,0.0,0 -2005-10-18,86.92383755785433,86.93845152218867,86.09070219777503,86.10531616210938,74996900,0.0,0 -2005-10-19,85.87149400081682,87.5523850498456,85.5937839423725,87.53776550292969,116563800,0.0,0 -2005-10-20,87.32577538893632,87.55963782072419,85.72528163652446,85.99568176269531,131966700,0.0,0 -2005-10-21,86.4488126929558,86.8069134049104,85.87877323188316,86.33187866210938,96579500,0.0,0 -2005-10-24,86.55845067412434,87.76430099798013,86.53652693379567,87.66929626464844,71308400,0.0,0 -2005-10-25,87.49390411376953,87.87392863735822,86.92386454052523,87.49390411376953,76594500,0.0,0 -2005-10-26,87.34045082240667,88.09319583221573,87.10658824095411,87.2381362915039,80855800,0.0,0 -2005-10-27,87.11384599954033,87.23808978573804,86.1857059423551,86.30994415283203,66623100,0.0,0 -2005-10-28,86.55114127091538,87.66198686617295,86.30996897482761,87.5523681640625,72322000,0.0,0 -2005-10-31,87.91045973344326,88.64859051282643,87.79352569580078,87.79352569580078,77698900,0.0,0 -2005-11-01,88.1223868888632,88.35624935762587,87.85929091454005,88.05661010742188,66365100,0.0,0 -2005-11-02,87.82278676342996,88.97748565673828,87.79355324484605,88.97748565673828,74012300,0.0,0 -2005-11-03,89.26981082267746,89.64253119641245,88.97748122711518,89.35750579833984,84897600,0.0,0 -2005-11-04,89.45252416358863,89.49637165536117,88.83132720200808,89.24058532714844,59156000,0.0,0 -2005-11-07,89.43056155713853,89.61326679443093,89.05053154116641,89.3282470703125,46765400,0.0,0 -2005-11-08,89.10899855527309,89.4670992591642,89.00668406844706,89.3282470703125,42152800,0.0,0 -2005-11-09,89.21860724163746,89.85441778798649,89.05782576876562,89.44515991210938,57666800,0.0,0 -2005-11-10,89.40862914937654,90.27099806256685,88.97744748064197,90.13945007324219,79048100,0.0,0 -2005-11-11,90.14677183781409,90.50487257695204,89.4744178365733,90.4464111328125,34867000,0.0,0 -2005-11-14,90.46836757344144,90.6364533466496,90.16872817013068,90.3952865600586,45092200,0.0,0 -2005-11-15,90.29296462134812,90.6876031763964,89.78869619163032,90.06640625,69592500,0.0,0 -2005-11-16,90.16138952039536,90.29293752450847,89.87636977410388,90.24908447265625,51133000,0.0,0 -2005-11-17,90.38794343888479,91.09684071618285,89.99329942134602,91.08953094482422,55717500,0.0,0 -2005-11-18,91.39647458778421,91.55725610361507,90.86297836786021,91.4476318359375,72437200,0.0,0 -2005-11-21,91.46222650381034,92.017651886789,91.33798830290154,91.90802764892578,50021200,0.0,0 -2005-11-22,91.76187707886737,92.46346452469459,91.65956259150016,92.30268859863281,66438800,0.0,0 -2005-11-23,92.26614220860013,93.11389716187284,92.23690870237819,92.836181640625,50854700,0.0,0 -2005-11-25,92.79969065334613,92.97508620324757,92.67544679543045,92.90930938720703,15270000,0.0,0 -2005-11-28,92.99696844447934,93.01158241056376,92.112675739752,92.25153350830078,54498500,0.0,0 -2005-11-29,92.55847924300257,92.79965150720966,92.1492156982422,92.14921569824219,51738900,0.0,0 -2005-11-30,92.20038322501075,92.4634736644455,91.56456692576182,91.65226745605469,56007200,0.0,0 -2005-12-01,92.09807601484367,92.83620691466564,92.0688480763858,92.58773040771484,65468200,0.0,0 -2005-12-02,92.64619148017955,92.87274982021863,92.4488722299238,92.70465850830078,46699400,0.0,0 -2005-12-05,92.59500389769815,92.61693320785331,92.21497949082153,92.50730895996094,59273400,0.0,0 -2005-12-06,92.85082293464419,93.35508570187784,92.5438738352625,92.68273162841797,57935200,0.0,0 -2005-12-07,92.64617707357039,92.7192636352182,91.84958484825,92.14191436767578,66816500,0.0,0 -2005-12-08,92.2442394797524,92.68273102149732,91.70343343079107,92.08345794677734,62608600,0.0,0 -2005-12-09,92.2003772504428,92.65348268828996,91.95189521857556,92.32461547851562,50744500,0.0,0 -2005-12-12,92.60235332951851,92.71197762436319,92.05423743101835,92.41233825683594,48389900,0.0,0 -2005-12-13,92.3903718819146,93.32582169160054,92.29536717727237,93.04080200195312,88630900,0.0,0 -2005-12-14,92.95315963730887,93.61089432342192,92.91661634331422,93.40626525878906,64375000,0.0,0 -2005-12-15,93.42815003797955,93.54508406941264,92.94581109762183,93.13582611083984,55900300,0.0,0 -2005-12-16,93.51201604190832,93.57079301064431,92.83609771728516,92.83609771728516,46238300,0.672,0 -2005-12-19,93.10790495876333,93.21076181602197,92.34382144392887,92.35851287841797,48733000,0.0,0 -2005-12-20,92.4687104847472,93.00503468034204,92.18952841068946,92.44667053222656,46603200,0.0,0 -2005-12-21,92.73319887778808,93.12993482937648,92.42462833942301,92.5936050415039,51806900,0.0,0 -2005-12-22,92.79934922300909,93.07853698730469,92.63037246541082,93.07853698730469,32247900,0.0,0 -2005-12-23,93.14462765137918,93.20340459440463,92.88013701304244,93.12993621826172,27977300,0.0,0 -2005-12-27,93.27689944973352,93.34302493072812,92.11607971920071,92.18220520019531,44499500,0.0,0 -2005-12-28,92.3805756726354,92.56424947819075,92.2042503887806,92.38792419433594,30764300,0.0,0 -2005-12-29,92.36587373271475,92.54219899652175,91.88097225064664,91.97648620605469,32788900,0.0,0 -2005-12-30,91.68997576196764,91.88099250433218,91.3667080359964,91.47691345214844,44645600,0.0,0 -2006-01-03,91.97648324751587,93.30627961207749,91.38872491335972,93.08586883544922,73256700,0.0,0 -2006-01-04,93.20343519336745,93.66629115977481,93.08588126872326,93.52670288085938,51899600,0.0,0 -2006-01-05,93.41647250384779,93.73973449963913,93.21810171036309,93.58544921875,47307500,0.0,0 -2006-01-06,94.05569476339781,94.46712233472677,93.57079317837702,94.36426544189453,62885900,0.0,0 -2006-01-09,94.34954929721486,94.81975374219844,94.32016642402262,94.60669708251953,43527400,0.0,0 -2006-01-10,94.32750374744894,94.76097116732531,94.23198979675979,94.70219421386719,44960800,0.0,0 -2006-01-11,94.79035371822569,95.09892427842549,94.57728588653968,95.00341033935547,49598900,0.0,0 -2006-01-12,94.83445215030434,94.98138893949888,94.36424769854676,94.62873840332031,40509200,0.0,0 -2006-01-13,94.45975978723918,94.702199318806,94.18791496152183,94.54056549072266,44856700,0.0,0 -2006-01-17,94.18793329119941,94.34956715073791,93.90140257848304,94.283447265625,52066600,0.0,0 -2006-01-18,93.73239667969688,94.70218832505795,93.42382610114758,93.90872192382812,75067600,0.0,0 -2006-01-19,94.13648280279274,94.60668719865257,93.90137499958429,94.26872253417969,81530400,0.0,0 -2006-01-20,94.2466969931859,94.26873695178789,92.54955291748047,92.54955291748047,114957800,0.0,0 -2006-01-23,92.72586454851582,93.17402899174755,92.6670875973723,92.8801498413086,67017400,0.0,0 -2006-01-24,93.03444884476055,93.4164934782174,92.88016352923046,92.97567749023438,53008800,0.0,0 -2006-01-25,93.33565486458377,93.43851171769548,92.45401748826878,93.05647277832031,87747700,0.0,0 -2006-01-26,93.48994572982144,93.79851630759214,93.12994672242229,93.57076263427734,71294000,0.0,0 -2006-01-27,93.79118190985223,94.52587703141981,93.63689100167757,94.43770599365234,65771200,0.0,0 -2006-01-30,94.36426544189453,94.63609911354533,94.29814555590819,94.36426544189453,33709600,0.0,0 -2006-01-31,94.27610818820769,94.43773084376829,93.66630412629833,93.67365264892578,72937000,0.0,0 -2006-02-01,93.90874621141339,94.35690515505448,93.83527780779745,94.32752227783203,63561000,0.0,0 -2006-02-02,94.11445617455519,94.14383904883844,93.15935020724883,93.23281860351562,83626900,0.0,0 -2006-02-03,92.9977407592934,94.3275375778597,92.67447300461308,92.76998138427734,86040400,0.0,0 -2006-02-06,92.8948570855875,93.15934778950718,92.69648625500815,93.01240539550781,45511900,0.0,0 -2006-02-07,92.85075810702291,93.05647742880521,92.13076009925919,92.18953704833984,71208100,0.0,0 -2006-02-08,92.46138121891477,94.11445087079635,92.27770743595892,93.027099609375,59422200,0.0,0 -2006-02-09,93.2475084894755,93.74710142317001,92.84342951409036,92.87281799316406,62023300,0.0,0 -2006-02-10,92.88753136381649,93.40181585584746,92.16752746969257,93.04181671142578,64508700,0.0,0 -2006-02-13,93.0124062621651,93.1520001364454,92.53485328645607,92.87281799316406,52308700,0.0,0 -2006-02-14,92.90953489282685,94.06300589143537,92.72586113123137,93.85729217529297,90964400,0.0,0 -2006-02-15,93.80588818875177,94.27609829781242,93.48262049042252,94.18792724609375,85471300,0.0,0 -2006-02-16,94.29075606797099,94.92994835781516,94.17320217437248,94.8932113647461,61017900,0.0,0 -2006-02-17,94.81242403504163,94.89324096077686,94.46711637149652,94.63609313964844,40342600,0.0,0 -2006-02-21,94.85650079857055,95.06955748083837,94.25404533395216,94.40099334716797,46456300,0.0,0 -2006-02-22,94.60668962638813,95.25321368068788,94.51851859264383,94.97403717041016,42326700,0.0,0 -2006-02-23,94.97403599736528,95.24586958920574,94.2466838333847,94.83444213867188,43423200,0.0,0 -2006-02-24,94.85648589285148,95.12831949557788,94.59933812069137,95.07689666748047,36777400,0.0,0 -2006-02-27,95.06954107945462,95.53974549317107,94.98138125518201,95.11363220214844,35858600,0.0,0 -2006-02-28,94.92261616760652,95.44425468439255,94.13649814494238,94.2099609375,74394800,0.0,0 -2006-03-01,94.48180862821704,95.13568691764968,94.40833462288603,95.04751586914062,48641600,0.0,0 -2006-03-02,94.7021888428408,95.08423341337217,94.48913220787648,95.04015350341797,60642300,0.0,0 -2006-03-03,94.53323009807862,95.56181010505978,94.51853305511284,94.59934997558594,73402500,0.0,0 -2006-03-06,94.87852581825541,94.9079086899361,93.93076845610659,94.1658706665039,57478400,0.0,0 -2006-03-07,93.9380963764468,94.08503312374296,93.60013737344354,94.01891326904297,61780800,0.0,0 -2006-03-08,93.82057399132577,94.36425247045311,93.43853495693311,94.21731567382812,66692400,0.0,0 -2006-03-09,94.24667585490208,94.5405493773891,93.58544921875001,93.58544921875,56313600,0.0,0 -2006-03-10,93.82788154052369,94.65808472895759,93.62951639117296,94.47441101074219,60490800,0.0,0 -2006-03-13,94.65809975003187,94.89320752772225,94.43034609699173,94.6507568359375,45479100,0.0,0 -2006-03-14,94.5626397223362,95.67936853893315,94.48916570599334,95.64263153076172,69877300,0.0,0 -2006-03-15,95.6205915094448,96.1422301198135,95.40019188167302,96.06875610351562,53398900,0.0,0 -2006-03-16,96.2524233169667,96.59038807391873,96.1275264662949,96.26712036132812,65526400,0.0,0 -2006-03-17,96.39175123677757,96.55402782176648,96.17047521646391,96.34749603271484,47286800,0.519,0 -2006-03-20,96.36225276775961,96.5540290728959,96.0450829644413,96.1926040649414,45538500,0.0,0 -2006-03-21,96.16310114401671,96.62043122554184,95.48449502078202,95.58776092529297,87102700,0.0,0 -2006-03-22,95.52874457242477,96.26636134815078,95.48448936670343,96.17047882080078,51605700,0.0,0 -2006-03-23,96.08195148433704,96.17784526116706,95.63938819810078,95.9713134765625,46704200,0.0,0 -2006-03-24,95.88281312317106,96.31063222331748,95.69840892078757,96.04508972167969,43209200,0.0,0 -2006-03-27,95.91230223017553,96.09670641215389,95.69839833134138,95.9049301147461,32523000,0.0,0 -2006-03-28,95.83854384060432,96.28111841535939,95.18944872080625,95.3148422241211,82079900,0.0,0 -2006-03-29,95.45497375729408,96.25897320775822,95.36645210544903,95.91229248046875,61505700,0.0,0 -2006-03-30,95.9713122763663,96.61303522370962,95.55824871232288,95.7426528930664,70571700,0.0,0 -2006-03-31,95.90491947138466,96.06719603392749,95.42546189836744,95.7647705078125,62925600,0.0,0 -2006-04-03,95.94183834618612,96.53192287905321,95.51401918125877,95.6910400390625,61624700,0.0,0 -2006-04-04,95.69105258650067,96.42866960444519,95.4181378915068,96.30327606201172,54809300,0.0,0 -2006-04-05,96.34011256171985,96.8343143740673,96.170463881096,96.6351547241211,50607200,0.0,0 -2006-04-06,96.51715426753606,96.78269674231609,96.03032452124965,96.53189849853516,57906200,0.0,0 -2006-04-07,96.67206117955595,96.92284820374076,95.41074268694783,95.5508804321289,80180900,0.0,0 -2006-04-10,95.69842529296875,95.94921234988178,95.34436106011849,95.69842529296875,41496500,0.0,0 -2006-04-11,95.77955289990092,95.93444613621043,94.59936143260387,94.88703155517578,72799400,0.0,0 -2006-04-12,94.98291809861095,95.24846059572948,94.8648967098607,95.06405639648438,43033700,0.0,0 -2006-04-13,94.85011513499981,95.33694478834313,94.6435833890607,94.93863677978516,51051800,0.0,0 -2006-04-17,95.02715739989715,95.3812102517472,94.4296896951698,94.90176391601562,64167700,0.0,0 -2006-04-18,95.10094714584922,96.5835644059311,95.10094714584922,96.40653228759766,92531800,0.0,0 -2006-04-19,96.44339838292954,96.67944116576001,96.06721785982526,96.5909194946289,87269000,0.0,0 -2006-04-20,96.62779244091278,97.26214328424186,96.33275024849199,96.72368621826172,86005500,0.0,0 -2006-04-21,97.1367378706463,97.21049278213238,96.3474826573012,96.73841857910156,72342600,0.0,0 -2006-04-24,96.5466611387459,96.67943801354373,96.17048062790694,96.56141662597656,52546400,0.0,0 -2006-04-25,96.6572954463379,96.71630613785722,95.83116827534465,96.16309356689453,84359800,0.0,0 -2006-04-26,96.25896971691903,96.73104390747086,96.11144864270966,96.1852035522461,67262400,0.0,0 -2006-04-27,95.8164449280355,97.0925304567885,95.58778545616137,96.64995574951172,124478600,0.0,0 -2006-04-28,96.4728944085573,97.18101147479032,96.41389497024856,96.97447967529297,55854400,0.0,0 -2006-05-01,96.97445874472773,97.21787358198459,96.12620412667155,96.1852035522461,64990300,0.0,0 -2006-05-02,96.6351704509444,96.96710700658788,96.43602202372496,96.90809631347656,49063500,0.0,0 -2006-05-03,96.73846062451643,96.86386541330737,96.22213099915437,96.54668426513672,60821300,0.0,0 -2006-05-04,96.68680926562558,97.08511737618187,96.60567096929056,96.89334106445312,42921400,0.0,0 -2006-05-05,97.40230448062802,97.95551710215169,97.2547833659163,97.74898529052734,62588200,0.0,0 -2006-05-08,97.74159657332685,97.9333841415886,97.6309585571289,97.6309585571289,30016700,0.0,0 -2006-05-09,97.67521753512115,97.93338792307034,97.59407923506335,97.82273864746094,29864000,0.0,0 -2006-05-10,97.66785987402149,97.91864692763052,97.28429592105755,97.77112579345703,64378200,0.0,0 -2006-05-11,97.74159992307904,97.77111089850253,96.27374966723224,96.5909194946289,80626900,0.0,0 -2006-05-12,96.15570928078654,96.42125173144468,95.29269912993183,95.32958221435547,91726500,0.0,0 -2006-05-15,94.99766467245023,95.69840965096562,94.86489904818797,95.52137756347656,84029300,0.0,0 -2006-05-16,95.7131202675769,95.89015229288945,95.15990785050253,95.38119506835938,62137600,0.0,0 -2006-05-17,94.90914312930741,95.22632417531385,93.50767021929174,93.56668090820312,144789500,0.0,0 -2006-05-18,93.93547939486652,94.23052718082899,93.02083632319447,93.09459686279297,87906300,0.0,0 -2006-05-19,93.5814270653817,94.0387457899337,92.79217746946908,93.7510757446289,124309400,0.0,0 -2006-05-22,93.14622711608376,93.80270546643801,92.57088704521763,93.03558349609375,110852800,0.0,0 -2006-05-23,93.81009532404109,94.14202059931104,92.32748413085938,92.32748413085938,92006500,0.0,0 -2006-05-24,92.70365580144221,93.59617122453768,92.02504987062768,93.0650863647461,168405000,0.0,0 -2006-05-25,93.61831640168128,94.21578979492188,93.25688577050948,94.21578979492188,78977900,0.0,0 -2006-05-26,94.42231037166346,94.69523620605467,94.05350764666801,94.69523620605469,62989700,0.0,0 -2006-05-30,94.39281115313155,94.414938753991,92.97658842243821,93.01346588134766,72419900,0.0,0 -2006-05-31,93.39704169763912,94.05352020263672,93.0872383631746,94.05352020263672,86926200,0.0,0 -2006-06-01,93.95761063670146,95.10829648381004,93.87647235172686,94.9533920288086,73721700,0.0,0 -2006-06-02,95.33695390982221,95.46971950699871,94.6509758141361,95.15254974365234,91702600,0.0,0 -2006-06-05,95.04192574644381,95.04929786232722,93.50767587971818,93.7658462524414,86105100,0.0,0 -2006-06-06,93.83221055742774,93.95760403355112,92.76266869802767,93.53716278076172,130498600,0.0,0 -2006-06-07,93.61095623491134,94.15679112294498,92.78482334492723,92.83645629882812,108599400,0.0,0 -2006-06-08,92.62992417572667,93.30853031833414,91.3686000538465,92.75531768798828,204957200,0.0,0 -2006-06-09,93.20524528621307,93.64781417416565,92.41599565396754,92.46025085449219,94972200,0.0,0 -2006-06-12,92.85120287756236,92.88808596943211,91.33171403569969,91.45710754394531,95815900,0.0,0 -2006-06-13,91.27271112687642,92.08408856910809,90.39495086669922,90.39495086669922,185688800,0.0,0 -2006-06-14,90.6088276526623,91.19154548833829,90.24001932331561,91.09565734863281,163566400,0.0,0 -2006-06-15,91.42760359517396,93.20526285219083,91.36122078187614,93.02823638916016,134057000,0.0,0 -2006-06-16,92.82451040936716,93.02454477780579,92.20958094138453,92.35034942626953,94253500,0.555,0 -2006-06-19,92.9059874199107,92.96525894318897,91.53536594565117,91.624267578125,95804400,0.0,0 -2006-06-20,91.87615894016517,92.46885715248928,91.66130393049816,91.9354248046875,65494700,0.0,0 -2006-06-21,91.86877026624639,93.12825921053172,91.83913450077041,92.61705780029297,75008200,0.0,0 -2006-06-22,92.57256177159536,92.65405867872134,91.89836676552989,92.20953369140625,74566100,0.0,0 -2006-06-23,92.1132281238418,92.83187963519981,91.89096278854511,92.1947250366211,54107000,0.0,0 -2006-06-26,92.2688338265023,92.6540874277286,92.05397879127169,92.60222625732422,37899600,0.0,0 -2006-06-27,92.61701878259319,92.82446340108845,91.69832739719716,91.80205535888672,69780200,0.0,0 -2006-06-28,92.00951104270356,92.4392153816461,91.60943680787355,92.42440032958984,62368100,0.0,0 -2006-06-29,92.75778219444467,94.3506686011244,92.73555679265597,94.29139709472656,110634800,0.0,0 -2006-06-30,94.49145009183438,94.5803573637345,94.06174008964788,94.29882049560547,54227800,0.0,0 -2006-07-03,94.40997833069116,94.83968279494233,94.32107103509907,94.6841049194336,23914000,0.0,0 -2006-07-05,94.30621280187947,94.42475015595393,93.73573447479353,94.14321899414062,69653400,0.0,0 -2006-07-06,94.23957432439656,94.72114561825407,94.15067268103128,94.41738891601562,50100300,0.0,0 -2006-07-07,94.23214684248039,94.50626771318227,93.56535641621124,93.80243682861328,81626500,0.0,0 -2006-07-10,94.04695227208825,94.40998051236758,93.65428826592671,93.98027038574219,60964100,0.0,0 -2006-07-11,93.80243015709634,94.39513397216797,93.30604396212829,94.39513397216797,73640800,0.0,0 -2006-07-12,94.20249855114183,94.38771777532715,93.14304436270412,93.38753509521484,82561300,0.0,0 -2006-07-13,92.94302095613168,93.1134251597466,91.8687515258789,91.8687515258789,102405700,0.0,0 -2006-07-14,91.9650886392287,92.0614062968129,91.00195163049942,91.51315307617188,103242500,0.0,0 -2006-07-17,91.5131035388733,91.94281345678579,91.23898272938283,91.37974548339844,81159000,0.0,0 -2006-07-18,91.65388987408315,91.90579098478501,90.67593249344627,91.84651947021484,122771000,0.0,0 -2006-07-19,92.00210846892722,93.54313402542566,91.66130571839366,93.12083435058594,133565300,0.0,0 -2006-07-20,93.43941276913128,93.57277086021604,92.35773298697403,92.48368072509766,112259800,0.0,0 -2006-07-21,92.72073588028542,92.7503716316107,91.73536834537558,91.83168029785156,101560000,0.0,0 -2006-07-24,92.19473089273382,93.5875775453973,92.19473089273382,93.5060806274414,92884000,0.0,0 -2006-07-25,93.3356809851454,94.31363833657947,93.14305139476582,93.8394775390625,95480700,0.0,0 -2006-07-26,93.7876242786778,94.42477900224536,93.48386762345702,93.96543884277344,84525800,0.0,0 -2006-07-27,94.3729121222988,94.60258784057716,93.49867701862634,93.87652587890625,87257100,0.0,0 -2006-07-28,94.12103278428452,94.93599650987719,93.98767467264615,94.81745910644531,82137000,0.0,0 -2006-07-31,94.5803828103891,94.80264257127382,94.42479362959224,94.72114562988281,49593100,0.0,0 -2006-08-01,94.34328090096048,94.37291666127477,93.79503346625448,94.25437927246094,65225600,0.0,0 -2006-08-02,94.52106397849809,95.17303918335708,94.49883857877634,94.8915023803711,64770900,0.0,0 -2006-08-03,94.34327645434999,95.23974261738556,94.20251366504574,95.14342498779297,63693800,0.0,0 -2006-08-04,95.63240599064021,95.89170616170547,94.4470039028573,94.98043060302734,96294200,0.0,0 -2006-08-07,94.7581558227539,94.89151388970076,94.38771737595069,94.7581558227539,45377300,0.0,0 -2006-08-08,94.8989248668036,95.17305702289416,94.05432560441416,94.39513397216797,90901300,0.0,0 -2006-08-09,94.9655977311119,95.27677598434194,93.80242685074093,94.07655334472656,78910600,0.0,0 -2006-08-10,93.78022308522382,94.46182864908566,93.55795769801324,94.36551666259766,69322300,0.0,0 -2006-08-11,94.21733041514692,94.23955582162455,93.63944723695583,94.09879302978516,47482200,0.0,0 -2006-08-14,94.55815097940615,94.95082062675861,94.03212886807208,94.17289733886719,57839000,0.0,0 -2006-08-15,95.01748196241093,95.47682211729706,94.75817612458113,95.29901885986328,68143000,0.0,0 -2006-08-16,95.81021789998785,96.23251190630636,95.60276194201835,96.09174346923828,71737600,0.0,0 -2006-08-17,96.00285786458979,96.58814581096186,95.9361816374501,96.33625030517578,70992800,0.0,0 -2006-08-18,96.45478729606138,96.82522583007814,96.01025653328654,96.82522583007812,58288400,0.0,0 -2006-08-21,96.44737416606519,96.52146639703254,96.1658485591412,96.41033935546875,42133600,0.0,0 -2006-08-22,96.3066172493572,96.69928120368756,96.07693585475451,96.40292358398438,60839900,0.0,0 -2006-08-23,96.44738991142036,96.64002522142891,95.71392870758494,96.13622283935547,66592100,0.0,0 -2006-08-24,96.30662484040336,96.38812177913455,95.86949872999708,96.05471801757812,57983000,0.0,0 -2006-08-25,96.04727769057924,96.4992130368435,95.98060148495433,96.17322540283203,41756000,0.0,0 -2006-08-28,96.05473078606275,96.92156711547157,96.04732608333495,96.63261413574219,52681500,0.0,0 -2006-08-29,96.67706766319884,96.92896319220328,96.17326530030132,96.74374389648438,61817800,0.0,0 -2006-08-30,96.95860218390476,97.08454995994006,96.72152734221763,96.80302429199219,50052200,0.0,0 -2006-08-31,96.94378957007102,97.04751192954328,96.74374955362833,96.78820037841797,37510300,0.0,0 -2006-09-01,97.15859845841855,97.4845861107312,96.93633312230679,97.36604309082031,48794500,0.0,0 -2006-09-05,97.43272650257622,97.68463326472937,97.20305645007414,97.55126953125,52348300,0.0,0 -2006-09-06,97.13638712086997,97.17343323306706,96.5585039462313,96.69185638427734,53795600,0.0,0 -2006-09-07,96.35849492627615,96.73634956517992,95.83247840077972,96.24736785888672,86269400,0.0,0 -2006-09-08,96.37333404340121,96.65487107441214,96.18811469245023,96.5215072631836,45096300,0.0,0 -2006-09-11,96.21028274388036,96.82521197896727,95.92874588073845,96.61776733398438,68496600,0.0,0 -2006-09-12,96.72892151328408,97.67724338656032,96.58815304219193,97.56611633300781,69875600,0.0,0 -2006-09-13,97.52904548689175,98.12915403370607,97.44014386658905,97.95875549316406,62898400,0.0,0 -2006-09-14,97.76611975491143,97.97356438051928,97.61053063326342,97.96614837646484,57805400,0.0,0 -2006-09-15,98.45656946612158,98.51610167065634,97.98776045203967,98.19612884521484,76703100,0.579,0 -2006-09-18,98.0695728578069,98.51605851894584,97.96539438415476,98.33002471923828,64154100,0.0,0 -2006-09-19,98.31515278387747,98.32260140386781,97.53381979794021,98.08447265625,92089100,0.0,0 -2006-09-20,98.41192131089021,98.79887495582595,98.27053374709021,98.60539245605469,75204100,0.0,0 -2006-09-21,98.67979176330434,98.78397026080764,97.79426885722401,98.1291275024414,88932500,0.0,0 -2006-09-22,97.9803067426533,97.98774400899448,97.48173738727101,97.83148193359375,65966800,0.0,0 -2006-09-25,98.0249520446634,98.8583914051391,97.51894542812903,98.58305358886719,92299100,0.0,0 -2006-09-26,98.59793613971833,99.4164895853616,98.52351806116727,99.40160369873047,73962700,0.0,0 -2006-09-27,99.33467987890826,99.69186183992615,99.17096911456468,99.52071380615234,82432200,0.0,0 -2006-09-28,99.52066851209237,99.70670235466832,99.17836124572655,99.48345947265625,58597500,0.0,0 -2006-09-29,99.56531438785913,99.66949288506785,99.3271856201794,99.40160369873047,47966600,0.0,0 -2006-10-02,99.37184459895845,99.58765024151676,98.93280740148609,99.02954864501953,51687400,0.0,0 -2006-10-03,98.88816282801604,99.61741241429598,98.70956622634073,99.23790740966797,73108100,0.0,0 -2006-10-04,99.14115208477246,100.45827486788514,99.02953632120555,100.39874267578125,80890500,0.0,0 -2006-10-05,100.39873776338683,100.7633681642055,100.27223611933236,100.59220886230469,60505900,0.0,0 -2006-10-06,100.4210671949423,100.53269431463276,100.01179046528681,100.46571350097656,64983600,0.0,0 -2006-10-09,100.34666044241547,100.68151909218608,100.1903870167333,100.5252456665039,41176800,0.0,0 -2006-10-10,100.5327085352188,100.79314914212692,100.33922604222869,100.65921020507812,56403700,0.0,0 -2006-10-11,100.33924895614452,100.77828626731794,99.93742069862846,100.54016876220703,104071800,0.0,0 -2006-10-12,100.79315563504153,101.4926448525288,100.75594658748446,101.4107894897461,59158600,0.0,0 -2006-10-13,101.32145153160657,101.73072816242399,101.23214758649965,101.67119598388672,53944000,0.0,0 -2006-10-16,101.58937432180096,101.98376520002444,101.51495623531865,101.8274917602539,42273000,0.0,0 -2006-10-17,101.55212309814353,101.72327100043834,100.95681271736898,101.50747680664062,90500600,0.0,0 -2006-10-18,101.97629997063818,102.22186599050968,101.2768223028089,101.64144134521484,86848600,0.0,0 -2006-10-19,101.49264023428482,101.85727071638084,101.37357582936286,101.80517578125,64063200,0.0,0 -2006-10-20,101.8274917602539,101.90934711354213,101.44798677024805,101.8274917602539,48094500,0.0,0 -2006-10-23,101.61912922148463,102.54186121253584,101.49262756502365,102.29629516601562,66219900,0.0,0 -2006-10-24,102.1548708815095,102.63855417958324,102.1102245899459,102.60135650634766,53234900,0.0,0 -2006-10-25,102.49721829060663,102.99578766651754,102.32605896626579,102.95114135742188,78105400,0.0,0 -2006-10-26,103.18179505120249,103.43479830756961,102.6757771838656,103.27108764648438,66843700,0.0,0 -2006-10-27,103.144595447363,103.24877393624968,102.41534602515637,102.62370300292969,80238000,0.0,0 -2006-10-30,102.43765489114175,102.83948290521168,102.24417247118775,102.54927062988281,49717800,0.0,0 -2006-10-31,102.74276927616953,102.8841454493686,102.13257286241009,102.5344009399414,71274100,0.0,0 -2006-11-01,102.85440906614569,102.92137853561543,101.73820582322378,101.8423843383789,83005600,0.0,0 -2006-11-02,101.5819400240975,101.9540077997614,101.47032423324131,101.7828598022461,60693100,0.0,0 -2006-11-03,102.14750577448058,102.23679842068384,100.91967511614023,101.60427856445312,71346400,0.0,0 -2006-11-06,101.91680077413386,102.89905370755325,101.90935215235386,102.75022888183594,63303300,0.0,0 -2006-11-07,102.8395093069232,103.41993400659184,102.69068449886035,103.14460754394531,63318900,0.0,0 -2006-11-08,102.69068531744453,103.47202975925916,101.84237141465854,103.36785125732422,87517800,0.0,0 -2006-11-09,103.44223382070734,103.53897503369302,102.61624326452853,102.82460021972656,95916300,0.0,0 -2006-11-10,102.79488274665565,102.94370758330746,102.4823471833047,102.86930084228516,48991500,0.0,0 -2006-11-13,102.82464578824644,103.46460282127771,102.74280177742178,103.12230682373047,59398200,0.0,0 -2006-11-14,103.41251852067512,103.98550607473297,102.77999875993268,103.89620208740234,96704000,0.0,0 -2006-11-15,103.85897376888042,104.51380514010782,103.82920199701313,104.19383239746094,76509600,0.0,0 -2006-11-16,104.50638489689482,104.68497013877099,103.79945848705447,104.46173858642578,76728800,0.0,0 -2006-11-17,104.12685518266775,104.49892286162465,103.97803038200588,104.49148559570312,56353800,0.0,0 -2006-11-20,104.40223105327688,104.72965254687979,104.13434177256617,104.55105590820312,69174200,0.0,0 -2006-11-21,104.54360424456453,104.67754318917302,104.3947680555235,104.65522003173828,51367900,0.0,0 -2006-11-22,104.73706949711504,105.04216777102212,104.5510356258946,104.86357116699219,45505300,0.0,0 -2006-11-24,104.35754437749078,104.80401876961916,104.32777260525303,104.43939971923828,30998000,0.0,0 -2006-11-27,104.38731961979597,104.43941455130825,102.97346681202673,103.00322723388672,84545100,0.0,0 -2006-11-28,102.8692823245664,103.55388553361358,102.77254108858409,103.44970703125,106652900,0.0,0 -2006-11-29,103.78456637342855,104.57334809001762,103.49435401818153,104.52870178222656,90034900,0.0,0 -2006-11-30,104.50637953675474,104.96030259425147,104.00036154997878,104.57334899902344,83994300,0.0,0 -2006-12-01,104.57334678308412,104.67008801849249,103.41249737660408,104.34266662597656,126080000,0.0,0 -2006-12-04,104.36496695962809,105.33234503837404,104.35008107560358,105.13886260986328,87813200,0.0,0 -2006-12-05,105.33983871116726,105.63749977803789,105.11659574966647,105.59284210205078,73374400,0.0,0 -2006-12-06,105.57051714938792,105.71935335760121,105.29519060248865,105.50354766845703,53253200,0.0,0 -2006-12-07,105.68953887203745,105.89045858826034,105.00493573837099,105.04214477539062,62857400,0.0,0 -2006-12-08,105.01986240713282,105.59283865224789,104.7594104014563,105.23565673828125,79625500,0.0,0 -2006-12-11,105.23561985993435,105.73418918200258,105.17608766936742,105.54071807861328,39779400,0.0,0 -2006-12-12,105.43653915264171,105.5704780649164,104.8412286040109,105.4588623046875,77451600,0.0,0 -2006-12-13,105.83838482913224,105.92024018510182,105.33981542626529,105.57049560546875,55520200,0.0,0 -2006-12-14,105.56306472447893,106.5899753697465,105.54817883487317,106.50067138671875,64755200,0.0,0 -2006-12-15,106.73485504359971,106.92192538764822,106.43554706026917,106.51036834716797,70857400,0.793,0 -2006-12-18,106.66004262075312,106.91446701461854,106.06890524501401,106.21855926513672,48954600,0.0,0 -2006-12-19,105.91926881938294,106.67502916815904,105.64988697126651,106.42061614990234,65023600,0.0,0 -2006-12-20,106.46549198657448,106.74984261317724,106.26345278252148,106.36073303222656,41469600,0.0,0 -2006-12-21,106.45801612538533,106.57773249221646,105.74715095776116,105.97162628173828,48698400,0.0,0 -2006-12-22,105.98661844280497,105.99409714929402,105.26078428782945,105.32064819335938,62069100,0.0,0 -2006-12-26,105.36549345113995,105.96412080553505,105.3430459244135,105.9416732788086,32696900,0.0,0 -2006-12-27,106.15868881544657,106.70494251106028,106.12876258041868,106.63758850097656,39727100,0.0,0 -2006-12-28,106.56276283041845,106.77975942460368,106.24848599511465,106.4131088256836,37288800,0.0,0 -2006-12-29,106.30087198073136,106.66004391696472,105.82945096463945,105.97162628173828,45461200,0.0,0 -2007-01-03,106.44303723416657,106.89948937954804,105.18592986726807,105.7845458984375,94807600,0.0,0 -2007-01-04,105.67978753344646,106.29338381200536,105.21585668137342,106.009033203125,69620600,0.0,0 -2007-01-05,105.75463147695052,105.80700524748706,105.04376629713062,105.16348266601562,76645300,0.0,0 -2007-01-08,105.37302653055258,105.8145099599799,104.94650051312932,105.64988708496094,71655000,0.0,0 -2007-01-09,105.73964645790917,105.9566544654077,105.05870762011074,105.56006622314453,75680100,0.0,0 -2007-01-10,105.1933918443427,105.93419447924104,104.98387398317557,105.91173553466797,72428000,0.0,0 -2007-01-11,105.94172005436823,106.71992796617049,105.8818561507108,106.37572479248047,54476800,0.0,0 -2007-01-12,106.36821758372533,107.18385314941406,106.33829134563938,107.18385314941406,55370600,0.0,0 -2007-01-16,107.05663850821189,107.33349900644883,106.80221412342908,106.97432708740234,44871300,0.0,0 -2007-01-17,106.89203482674964,107.34848707526258,106.8022332674892,107.01924133300781,50241400,0.0,0 -2007-01-18,107.13146281585743,107.1988054117197,106.48794025814409,106.66004180908203,68177300,0.0,0 -2007-01-19,106.66005377834857,107.079101063546,106.60020129833427,106.86958312988281,56973000,0.0,0 -2007-01-22,107.05669106551525,107.07913860785898,106.20363878495333,106.54037475585938,60253600,0.0,0 -2007-01-23,106.45052820117384,107.06412454703707,106.30087417614166,106.85460662841797,54064400,0.0,0 -2007-01-24,106.98180574360224,107.73756593951656,106.93691068097537,107.71511840820312,55834700,0.0,0 -2007-01-25,107.64777064299619,107.69266570346636,106.2634453831987,106.45051574707031,73583800,0.0,0 -2007-01-26,106.68250957467419,106.74236205291785,105.9417067053633,106.35326385498047,67255600,0.0,0 -2007-01-29,106.39816259283447,106.85461482581519,106.06143815621255,106.29340362548828,66114600,0.0,0 -2007-01-30,106.51789549282257,106.89951501689595,106.30088742429078,106.84712982177734,70407600,0.0,0 -2007-01-31,106.72740376092818,107.84982611359462,106.55529077931915,107.56547546386719,91868600,0.0,0 -2007-02-01,107.8647774783401,108.2464083642937,107.68519721941603,108.20899200439453,69312400,0.0,0 -2007-02-02,108.29876343740602,108.46338625824629,108.03687179906413,108.35862731933594,49607000,0.0,0 -2007-02-05,108.27633124604667,108.45592291727523,108.00694944813864,108.38858032226562,45705300,0.0,0 -2007-02-06,108.47839723038419,108.52329230480774,107.99949740674556,108.41853332519531,57081300,0.0,0 -2007-02-07,108.59063547210071,108.77022718858633,108.17908967586398,108.65798950195312,55669700,0.0,0 -2007-02-08,108.33619354868868,108.59060651579827,107.9545740980243,108.51578521728516,70641000,0.0,0 -2007-02-09,108.54569016674006,108.74772932705649,107.2960616897222,107.7076187133789,79084400,0.0,0 -2007-02-12,107.7076655471811,107.7824868635554,107.1464542939489,107.34100341796875,65657000,0.0,0 -2007-02-13,107.58041371706534,108.42596388494356,107.57292359607244,108.24638366699219,64081800,0.0,0 -2007-02-14,108.35116158371721,109.17426436856299,108.3361927569762,108.95726776123047,66039400,0.0,0 -2007-02-15,109.00213079231634,109.2116486336443,108.82253917780693,109.09941101074219,38715200,0.0,0 -2007-02-16,108.83008434033698,109.06952852958595,108.67294017093,109.04708099365234,39841800,0.0,0 -2007-02-20,108.91985790804281,109.39875763153094,108.50082207722447,109.2790298461914,56911800,0.0,0 -2007-02-21,108.95724752618149,109.3014619700826,108.76269848615658,109.2341079711914,63971600,0.0,0 -2007-02-22,109.28655976016206,109.56342037392837,108.62806811890793,109.15186309814453,79067400,0.0,0 -2007-02-23,109.05454933499398,109.09195427549753,108.52326444898348,108.72530364990234,71966200,0.0,0 -2007-02-26,109.12191275028775,109.21170289321982,108.31376721028403,108.62804412841797,69192800,0.0,0 -2007-02-27,107.6627143103242,107.9021583828344,104.01109800714745,104.38523864746094,274466500,0.0,0 -2007-02-28,105.05126281852972,106.24102792928137,104.60977937509944,105.45532989501953,177536300,0.0,0 -2007-03-01,104.26554893972545,105.6947694474094,103.30027075943156,105.14103698730469,212828600,0.0,0 -2007-03-02,104.79684587806841,105.25329813480214,103.75673485605178,103.76421356201172,162574000,0.0,0 -2007-03-05,103.21045958251867,104.4451308815409,102.76149754294912,102.7764663696289,143750400,0.0,0 -2007-03-06,103.8464566772397,104.84915067402893,103.05328048820152,104.53487396240234,143333300,0.0,0 -2007-03-07,104.45262331528554,105.10363605592849,104.31044798781328,104.43017578125,115144900,0.0,0 -2007-03-08,105.16345258139346,105.62739476788008,104.81177086244644,105.31311798095703,117891600,0.0,0 -2007-03-09,105.73963629986743,105.82194770551666,104.81925343151134,105.34304809570312,107765100,0.0,0 -2007-03-12,105.07365728992467,105.76207470604523,104.87910826510789,105.50018310546875,80366900,0.0,0 -2007-03-13,104.89410625040114,105.3356010595372,103.2927841282581,103.4499282836914,190605200,0.0,0 -2007-03-14,103.58462102254055,104.28052887883905,102.32751335910937,104.22066497802734,231853800,0.0,0 -2007-03-15,103.98863711773132,104.75188712288188,103.86143068651701,104.36277770996094,132435900,0.0,0 -2007-03-16,104.65654547739408,104.8969507725425,103.76255691368844,104.07057189941406,121531600,0.551,0 -2007-03-19,104.61900367320366,105.42284615918983,104.53636576775796,105.32518005371094,96161200,0.0,0 -2007-03-20,105.23502158316413,105.96373444768494,105.14487531972867,105.90363311767578,82147400,0.0,0 -2007-03-21,106.00133351308203,107.917011009924,105.79098452156087,107.64656066894531,152368700,0.0,0 -2007-03-22,107.78928096358938,107.93952857920036,107.27091694604692,107.56390380859375,118942200,0.0,0 -2007-03-23,107.6390112788053,108.03717253696084,107.54134519026135,107.72164916992188,74416800,0.0,0 -2007-03-26,107.80430322901344,107.91698606872845,106.74503868306182,107.57892608642578,113787500,0.0,0 -2007-03-27,107.51879939589539,107.54885578816996,106.97039060001144,107.32347869873047,99864600,0.0,0 -2007-03-28,106.782599681172,107.0305133829511,106.12149647642774,106.54220581054688,152907900,0.0,0 -2007-03-29,107.08309056004181,107.1356835233664,106.06891068989904,106.6548843383789,139432700,0.0,0 -2007-03-30,106.85771792171306,107.30846068018745,105.59561297904149,106.67741394042969,128194100,0.0,0 -2007-04-02,106.79764556884766,107.02302274080776,106.28678988152748,106.79764556884766,79416400,0.0,0 -2007-04-03,107.40616657956397,108.16492462194661,107.36109343332583,107.94706726074219,82417800,0.0,0 -2007-04-04,107.94706567460635,108.14238645982174,107.54890427113953,108.06726837158203,63995200,0.0,0 -2007-04-05,107.93203458254294,108.51050001598877,107.88696143817363,108.3602523803711,46822800,0.0,0 -2007-04-09,108.6682311278457,108.78092542611309,108.29260639326876,108.5104751586914,50967400,0.0,0 -2007-04-10,108.42784227909424,108.81849523082667,108.38276914296313,108.63819122314453,56620000,0.0,0 -2007-04-11,108.79591743954514,108.82596236452915,107.83430789275589,108.19491577148438,106365700,0.0,0 -2007-04-12,107.98457985519889,108.8710485379381,107.6840732535591,108.67572784423828,115534400,0.0,0 -2007-04-13,108.85601976957867,109.17155456542969,108.45035018844186,109.17155456542969,84287000,0.0,0 -2007-04-16,109.55468400576112,110.3284698480113,109.54717563968791,110.20826721191406,83064600,0.0,0 -2007-04-17,110.41112022379365,110.73415194881717,110.17071492439004,110.50126647949219,108424100,0.0,0 -2007-04-18,110.13317698657265,110.95954452642349,110.1106404195049,110.63651275634766,88345300,0.0,0 -2007-04-19,110.09557084913956,110.73412578136745,109.95283166924365,110.60641479492188,102947700,0.0,0 -2007-04-20,111.35015493616525,111.65065002441406,110.46367493847141,111.65065002441406,124114100,0.0,0 -2007-04-23,111.46284300445558,111.73329318194214,111.16234790198311,111.2299575805664,77270800,0.0,0 -2007-04-24,111.35769282228935,111.48540384180905,110.67406459581991,111.27505493164062,114471000,0.0,0 -2007-04-25,111.73330830950837,112.43197613892184,111.19992771093237,112.29674530029297,108418800,0.0,0 -2007-04-26,112.30429304368595,112.53717855699026,112.01130619418088,112.42448425292969,88741600,0.0,0 -2007-04-27,112.00374568270746,112.49206456329607,111.81593337397807,112.33429718017578,108191100,0.0,0 -2007-04-30,112.41696229424215,112.49209182119996,111.34268113031519,111.40277099609375,100874100,0.0,0 -2007-05-01,111.50045056251926,112.28926483827306,110.93701343171766,111.68826293945312,134342700,0.0,0 -2007-05-02,111.86105863422678,112.64987299120018,111.74837578172728,112.34185791015625,87129800,0.0,0 -2007-05-03,112.66491513089589,112.98794699013091,112.48461108143147,112.95039367675781,86569700,0.0,0 -2007-05-04,113.25086714095765,113.52882580005819,112.85270580537544,113.37857818603516,96409000,0.0,0 -2007-05-07,113.34850340725858,113.58889723377537,113.29591045145965,113.40108489990234,63461400,0.0,0 -2007-05-08,113.12313584058282,113.37855788500312,112.78507590377286,113.25084686279297,80584000,0.0,0 -2007-05-09,113.16819267921821,114.805922093592,112.9653521610698,113.55884552001953,102070100,0.0,0 -2007-05-10,113.23584250037517,113.4537112912421,112.13902456668426,112.37191009521484,153617800,0.0,0 -2007-05-11,112.49958599939859,113.38605469727982,112.477049436785,113.3334732055664,113408900,0.0,0 -2007-05-14,113.33348496669412,113.66403653686957,112.52964260620776,113.0855712890625,108027500,0.0,0 -2007-05-15,113.21325518961757,113.93445948478501,112.8301222140572,113.1156005859375,180673300,0.0,0 -2007-05-16,113.288410122604,113.91194849088085,112.97288675488969,113.88941192626953,114166500,0.0,0 -2007-05-17,113.72412567417653,114.15985165514198,113.52128514819239,113.66402435302734,101132800,0.0,0 -2007-05-18,114.19745320523985,114.65571594238283,114.04720557045653,114.65571594238281,99182000,0.0,0 -2007-05-21,114.625614556556,115.1139219949386,114.56551323960592,114.5955581665039,174664600,0.0,0 -2007-05-22,114.71578868718372,115.06136847786676,114.47539482409904,114.50543975830078,82148800,0.0,0 -2007-05-23,114.90364869340334,115.31683835473342,114.46041408541365,114.52051544189453,133786600,0.0,0 -2007-05-24,114.58808708876725,115.09894271750389,113.24335550677543,113.48374938964844,187593000,0.0,0 -2007-05-25,113.81427678028591,114.20492965284565,113.57387148543211,113.95701599121094,83309200,0.0,0 -2007-05-29,114.14480117812886,114.56549888089856,113.77668495662905,114.37017822265625,82020000,0.0,0 -2007-05-30,113.78422717907422,115.34681567885688,113.6940694656274,115.30174255371094,129013600,0.0,0 -2007-05-31,115.4445253322766,115.60980116229692,115.03133575722579,115.18159484863281,114866700,0.0,0 -2007-06-01,115.60228534042265,115.9929268560514,115.32431520976705,115.75253295898438,107771700,0.0,0 -2007-06-04,115.34683067460901,115.98539726560713,115.30175754360329,115.76753997802734,78008800,0.0,0 -2007-06-05,115.4970774714148,115.61726865362232,114.83597438507506,115.30926513671875,126917900,0.0,0 -2007-06-06,114.83597971927486,114.9035894117487,114.00209245596365,114.0697021484375,164096800,0.0,0 -2007-06-07,113.85932761019927,114.56550369659756,111.98120459877964,112.01126098632812,232414600,0.0,0 -2007-06-08,112.37193185393083,113.58144433324979,112.00381537303872,113.46875,175886000,0.0,0 -2007-06-11,113.386054309818,114.15233182552652,113.2132701759859,113.66402435302734,102015600,0.0,0 -2007-06-12,113.19076196054206,113.84434536409387,112.34936618004707,112.42448425292969,233898000,0.0,0 -2007-06-13,113.0630708923403,114.24253833021164,112.47709708981816,114.10730743408203,193208200,0.0,0 -2007-06-14,114.23497387671006,115.03129635046699,114.2124373133513,114.83597564697266,146396500,0.0,0 -2007-06-15,115.54221854034229,115.93455527489928,115.38377109535541,115.48941040039062,154030800,0.656,0 -2007-06-18,115.72329493367576,115.73083566405437,115.18006301971347,115.35359191894531,88537500,0.0,0 -2007-06-19,115.0970574469009,115.7232833827832,114.95370299605787,115.64028930664062,110851700,0.0,0 -2007-06-20,115.8741383619005,115.8741383619005,113.89738570467335,114.03318786621094,177119700,0.0,0 -2007-06-21,113.98789745668084,114.76501814646141,113.36167186454114,114.66693115234375,205262000,0.0,0 -2007-06-22,114.31991202957664,114.50853384320861,113.05991969602258,113.58805847167969,204964700,0.0,0 -2007-06-25,113.35422642137844,114.1162547881668,112.43375058571806,113.04488372802734,232014400,0.0,0 -2007-06-26,113.33153736484242,113.52015918146468,111.87537095398635,111.8829116821289,198445700,0.0,0 -2007-06-27,111.7622362275699,113.60318755588472,111.70941657693136,113.47491455078125,213638000,0.0,0 -2007-06-28,113.4598388671875,114.23696010497102,112.9241476533671,113.4598388671875,157705000,0.0,0 -2007-06-29,113.85209213710363,114.41795742906251,112.53173978919956,113.49748229980469,199701800,0.0,0 -2007-07-02,113.82947093119115,114.62168472933499,113.75402912485752,114.52359771728516,103357000,0.0,0 -2007-07-03,114.81786265261344,115.05930409543345,114.67451974757091,114.93858337402344,54048400,0.0,0 -2007-07-05,114.98384393890325,115.10456465314509,114.40289708766613,114.81785583496094,89279000,0.0,0 -2007-07-06,114.96879274277619,115.56483271830602,114.62926420554007,115.42147827148438,81109000,0.0,0 -2007-07-09,115.55723026806145,115.7081253637022,115.14980084601778,115.511962890625,72348100,0.0,0 -2007-07-10,114.90086959765314,115.14231106755503,113.75405715330798,113.86722564697266,180362600,0.0,0 -2007-07-11,113.73896919047905,114.7198050581501,113.56544032169957,114.6745376586914,175607600,0.0,0 -2007-07-12,114.96123053004152,116.75691405543115,114.93859683119736,116.48529815673828,133882500,0.0,0 -2007-07-13,116.62108856645482,117.29258169400944,116.48527488795666,116.83234405517578,111794300,0.0,0 -2007-07-16,116.93800437543008,117.34542249929927,116.6286617834279,116.8172836303711,98378700,0.0,0 -2007-07-17,116.89272969892559,117.30770007975593,116.70410784514492,116.75692749023438,126201300,0.0,0 -2007-07-18,116.36460367028927,116.79466707046632,115.66293582271484,116.54568481445312,237887400,0.0,0 -2007-07-19,117.09642757200467,117.34540976445672,116.75691058686557,116.99835205078125,145212700,0.0,0 -2007-07-20,116.8625030574686,116.9379563702965,115.30826151871095,115.81376647949219,245502500,0.0,0 -2007-07-23,116.32680894287532,116.73423840076057,115.66286816113751,116.16837310791016,121183900,0.0,0 -2007-07-24,115.52709156198483,116.40229959201504,113.74650107548592,114.1539306640625,256732400,0.0,0 -2007-07-25,114.69718288179408,114.97633954821126,113.36173690077601,114.3878402709961,265214500,0.0,0 -2007-07-26,113.31647377700355,113.77671158939606,110.44941911845503,111.67923736572266,467592500,0.0,0 -2007-07-27,111.82253422268376,112.32048694162089,109.43835230754341,109.4836196899414,422987600,0.0,0 -2007-07-30,110.10228889653158,111.52072822445093,109.61941764847201,111.19630432128906,283017500,0.0,0 -2007-07-31,111.91310580264006,112.7656801601307,109.43083606735887,109.94389343261719,316976700,0.0,0 -2007-08-01,109.53642154648095,110.91713419360782,108.60840572261347,110.47953033447266,467670000,0.0,0 -2007-08-02,110.72853433646782,111.48302151608753,109.59680356703825,111.36231231689453,294758400,0.0,0 -2007-08-03,111.12087439017944,111.34722285370032,108.04256521895435,108.4952621459961,359398200,0.0,0 -2007-08-06,108.80461643344478,110.78136943608972,107.53707180813488,110.31359100341797,324980000,0.0,0 -2007-08-07,110.10984212399289,112.41857065928147,109.57415125196978,111.49055480957031,232568700,0.0,0 -2007-08-08,111.97351266788787,113.61829018607492,111.16620537244489,113.04488372802734,274930600,0.0,0 -2007-08-09,111.23408037867416,112.38090453806126,109.6194776794666,109.69493103027344,357622100,0.0,0 -2007-08-10,108.94039757866263,110.53236590302429,107.98219568827317,109.18183898925781,411018400,0.0,0 -2007-08-13,110.53237494238424,110.82662449026084,109.4157371084629,109.57417297363281,181917200,0.0,0 -2007-08-14,109.92877266347023,110.20038849108144,107.68040423703076,107.89920043945312,264134500,0.0,0 -2007-08-15,107.68045543041362,108.99326780676432,106.09602709732019,106.41291046142578,323834000,0.0,0 -2007-08-16,105.46976370154785,107.84640535846793,103.36474941762111,107.21263885498047,546743700,0.0,0 -2007-08-17,109.7778787637545,110.01176793491851,106.67693616085326,109.18183898925781,388218100,0.0,0 -2007-08-20,109.52892093064212,109.75526942516157,108.11048111004536,109.12904357910156,187320400,0.0,0 -2007-08-21,109.09889912449606,110.13254333460829,108.75182980254277,109.34786987304688,157066400,0.0,0 -2007-08-22,110.16265181196299,110.75870303815921,109.64960613567355,110.64552307128906,173156700,0.0,0 -2007-08-23,111.16615896743902,111.40004819463245,109.8608991273372,110.5474853515625,203915300,0.0,0 -2007-08-24,110.5172875694141,111.91309356689453,110.36639242409336,111.91309356689453,128901900,0.0,0 -2007-08-27,111.55093256682066,111.91308318450534,110.70589910880898,110.87188720703125,113024300,0.0,0 -2007-08-28,110.27582565998829,110.3437267288074,108.23871300750658,108.43487548828125,219790700,0.0,0 -2007-08-29,108.92530998490524,110.71345242171242,108.61597899209312,110.56254577636719,207654200,0.0,0 -2007-08-30,109.74017935016833,111.05299144295958,109.6345515849481,110.26831817626953,191817300,0.0,0 -2007-08-31,111.40003423722214,112.04135298390543,110.78136069885979,111.35476684570312,185477500,0.0,0 -2007-09-04,111.24914025549653,113.15799203487657,111.21141359131653,112.47895812988281,120062000,0.0,0 -2007-09-05,111.81500353349215,111.9357242515255,110.9096211733647,111.50566101074219,166261800,0.0,0 -2007-09-06,111.62643905194533,112.12440363513005,111.00021295719495,111.76225280761719,127878400,0.0,0 -2007-09-07,110.51730837165698,110.82665096095589,109.59683284561287,110.20797729492188,235447600,0.0,0 -2007-09-10,110.54751115094057,110.69840634764589,108.89518169248825,109.9967269897461,192305900,0.0,0 -2007-09-11,110.33618808177266,111.43773270522942,110.25319404746632,111.279296875,162081900,0.0,0 -2007-09-12,111.12845119632291,111.99611866942044,110.89456192935663,111.56605529785156,149554600,0.0,0 -2007-09-13,112.07907689402121,112.75811077524622,111.81500176656641,112.35069274902344,154079000,0.0,0 -2007-09-14,111.63393893112483,112.48650169779856,111.46795081459116,112.34314727783203,121911000,0.0,0 -2007-09-17,111.89799885900838,112.15452175332183,111.38495307038146,111.73956298828125,109870800,0.0,0 -2007-09-18,112.2903515740039,115.05931871501505,111.76221274143121,115.02914428710938,263759500,0.0,0 -2007-09-19,115.74589469574609,116.48528899581959,115.21775590997932,115.70816802978516,193779900,0.0,0 -2007-09-20,115.693080506406,115.76098160079444,114.76506434540947,114.89332580566406,175186800,0.0,0 -2007-09-21,115.76438085829751,116.07517944111984,115.0290551703294,115.20340728759766,141457500,0.719,0 -2007-09-24,115.54454307034928,115.84777673801273,114.74099409360906,114.99115753173828,139450200,0.0,0 -2007-09-25,114.32404517593407,114.96840654289441,114.06630525602222,114.76372528076172,142289900,0.0,0 -2007-09-26,115.41564511119195,115.8098429999713,114.7637073427864,115.37016296386719,135547000,0.0,0 -2007-09-27,115.91599036486386,116.06002489916428,115.37018137550476,116.05243682861328,102713300,0.0,0 -2007-09-28,115.8704679348237,116.12820776599409,115.2109422210708,115.66578674316406,133372100,0.0,0 -2007-10-01,115.6809623111744,117.31080079715552,115.60515102789155,116.96967315673828,148162300,0.0,0 -2007-10-02,117.20466076622695,117.23497833634285,116.5982052593188,116.81046295166016,112978800,0.0,0 -2007-10-03,116.59822630297761,117.05307090704329,115.99177068661628,116.57548522949219,119055900,0.0,0 -2007-10-04,116.82566952173356,116.93937490951865,116.43147156544381,116.7574462890625,76864400,0.0,0 -2007-10-05,117.52304849723957,118.33418520398483,117.21982652429706,118.14466857910156,134579700,0.0,0 -2007-10-08,117.79596096403621,117.87177224605026,117.32596340364638,117.5154800415039,71280400,0.0,0 -2007-10-09,117.95517028975848,118.6374256185207,117.52306676561143,118.62226104736328,94054300,0.0,0 -2007-10-10,118.28869002710235,118.59192356345208,117.81111600401361,118.4251480102539,101711100,0.0,0 -2007-10-11,118.96338389813776,119.41065197534446,117.1516052186187,117.85661315917969,233529100,0.0,0 -2007-10-12,117.84903831307496,118.52371712202638,117.70500380883057,118.50855255126953,124546700,0.0,0 -2007-10-15,118.46303695166793,118.5312601500398,116.69674089059892,117.50786590576172,161151900,0.0,0 -2007-10-16,117.05307090704329,117.1364586988827,116.34048642535714,116.57548522949219,166525700,0.0,0 -2007-10-17,117.48517868750255,117.56856649056849,115.58243535759117,116.93179321289062,216687300,0.0,0 -2007-10-18,116.32534587913844,116.88631946438919,116.04486487010364,116.50728607177734,148367500,0.0,0 -2007-10-19,116.05240992320266,118.62225516598757,113.45224710678468,113.45982360839844,297169900,0.0,0 -2007-10-22,112.84580021879383,114.28612220442128,112.69418920459815,114.11934661865234,261989800,0.0,0 -2007-10-23,114.81680917146731,115.18825452167385,113.89954320154864,115.04421997070312,180085100,0.0,0 -2007-10-24,114.6272167074508,115.02899094349132,112.83059161054139,114.8318862915039,326694200,0.0,0 -2007-10-25,114.9608238578626,115.4459861398521,113.6190539704864,115.1048583984375,237374500,0.0,0 -2007-10-26,116.02964724278323,116.45416259765626,115.15028739787428,116.45416259765625,176484000,0.0,0 -2007-10-29,116.68920534325915,117.07582677572704,116.40114785364844,116.84082794189453,106841000,0.0,0 -2007-10-30,116.32530092584234,116.55272318695955,115.88562097226286,116.02965545654297,132981600,0.0,0 -2007-10-31,116.7270745633565,117.70498681032724,115.86287922987219,117.23497772216797,220954400,0.0,0 -2007-11-01,116.20401757756865,116.29499343020268,114.1572401845573,114.49079132080078,333040800,0.0,0 -2007-11-02,114.86983041807635,115.22612264373058,113.11112192949949,114.61966705322266,331228200,0.0,0 -2007-11-05,113.43711464288313,114.58937940909611,112.9292112787347,113.7479248046875,226841000,0.0,0 -2007-11-06,114.36192441457985,115.30950763703686,113.63417541017452,115.27919006347656,177800500,0.0,0 -2007-11-07,114.04351752682979,114.5665852232074,111.8527059694166,112.1256103515625,306639700,0.0,0 -2007-11-08,112.18627979311151,112.49707830665673,109.97272671913854,111.55708312988281,374509900,0.0,0 -2007-11-09,110.4427339609118,111.84515034687095,109.83627831719842,110.02579498291016,277745100,0.0,0 -2007-11-12,110.07888473922989,111.14017362870979,108.93419647216797,108.93419647216797,243087800,0.0,0 -2007-11-13,110.20012831721898,112.4288457773436,110.08642296401969,112.2544937133789,191117400,0.0,0 -2007-11-14,113.11871604790196,113.25516250424722,111.26903141402107,111.94371032714844,230558800,0.0,0 -2007-11-15,111.1098138979034,111.80723369888497,109.55577523931825,110.32899475097656,263111100,0.0,0 -2007-11-16,110.91273735268148,111.03403080116162,109.59370869379453,110.51853942871094,308770600,0.0,0 -2007-11-19,110.13192460842059,110.19257132992584,108.54756804832647,108.97966003417969,267746000,0.0,0 -2007-11-20,109.17676141495862,110.32143788433885,107.72885134737305,109.64675903320312,414767500,0.0,0 -2007-11-21,108.46416265218872,109.09335927263207,107.39528604756164,107.40286254882812,259012400,0.0,0 -2007-11-23,108.45659074430735,109.4193269689127,108.17609824508285,109.26013946533203,77688400,0.0,0 -2007-11-26,109.48758716289593,109.8287264915692,106.62968353020555,106.84951782226562,214232000,0.0,0 -2007-11-27,107.44835617544996,108.57786792369656,106.84947712770992,108.07755279541016,293897900,0.0,0 -2007-11-28,109.305622303084,111.7920797665277,109.26771666055055,111.53433990478516,258596900,0.0,0 -2007-11-29,111.14773486871717,111.98161285820834,110.75354851657568,111.57225036621094,199409900,0.0,0 -2007-11-30,112.98223314256556,113.61142976501135,111.68594582404347,112.69417572021484,222908000,0.0,0 -2007-12-03,112.33784649554077,112.53493956874028,111.6555798873756,111.95122528076172,146430400,0.0,0 -2007-12-04,111.17799897059872,111.84508940473795,110.91267115630237,110.95057678222656,136533900,0.0,0 -2007-12-05,112.14079710303547,113.10354501413806,112.06499737623349,112.80789947509766,171130000,0.0,0 -2007-12-06,112.67139979575524,114.6272122712358,112.62591766772587,114.42253112792969,154457400,0.0,0 -2007-12-07,114.78642051648565,114.84706722057084,114.1269064062203,114.39981079101562,148980100,0.0,0 -2007-12-10,114.68029530061409,115.41562070602,114.36190876948623,115.28675079345703,123914300,0.0,0 -2007-12-11,115.33222816639471,115.90077799860066,112.06496364806608,112.1256103515625,250346400,0.0,0 -2007-12-12,114.51358734099759,115.05181994496402,111.58744856055478,113.23245239257812,322435600,0.0,0 -2007-12-13,112.43642893952823,113.24755408037024,111.66319786998092,112.99739074707031,237551300,0.0,0 -2007-12-14,112.1407702276388,113.02771819937551,111.51158522053575,111.56464385986328,159152900,0.0,0 -2007-12-17,111.14013465419535,111.33722778821624,109.81351822397136,109.97271728515625,177269400,0.0,0 -2007-12-18,110.75355997795003,111.04161748552782,109.13129754911567,110.58678436279297,245569300,0.0,0 -2007-12-19,110.63226651834835,111.35242763806427,109.87419974660928,110.58678436279297,198917200,0.0,0 -2007-12-20,111.31449726207613,111.32966183295339,110.05610392436648,111.2841796875,214813800,0.0,0 -2007-12-21,112.30920136529878,113.10939820374118,112.09581709219023,112.88839721679688,146084400,0.775,0 -2007-12-24,113.41422890748734,113.91719942332251,113.15510949370436,113.72667694091797,45601400,0.0,0 -2007-12-26,113.28466262790316,114.06961431986929,113.17035379065351,113.97055053710938,67093100,0.0,0 -2007-12-27,113.56665266787964,113.57426938143406,112.27110202083705,112.53782653808594,122981700,0.0,0 -2007-12-28,113.20082814589023,113.25418002037547,111.95100118037165,112.25584411621094,116398100,0.0,0 -2007-12-31,112.10342277760724,112.49208441895247,111.31084281318726,111.42516326904297,108126800,0.0,0 -2008-01-02,111.66904035383217,112.01960685026161,109.64950662129158,110.44969177246094,204935600,0.0,0 -2008-01-03,110.43443860382182,110.876452094837,109.79428594589683,110.39633178710938,125133300,0.0,0 -2008-01-04,109.23794400087846,109.31415762871917,107.38607145573198,107.69090270996094,232330900,0.0,0 -2008-01-07,108.07197171337678,108.39204809032738,106.76880443783288,107.59947967529297,234991000,0.0,0 -2008-01-08,108.27773148172592,108.90263913610023,105.50372478531094,105.86190795898438,326365700,0.0,0 -2008-01-09,105.99909694327462,107.29464763753332,104.93979229208671,106.97457122802734,301824900,0.0,0 -2008-01-10,106.4487092618636,108.8264376039407,106.21246325873537,107.67567443847656,335701200,0.0,0 -2008-01-11,107.28702806958044,108.14056513889004,105.93050881504038,106.8069076538086,267076600,0.0,0 -2008-01-14,107.5766132357397,108.1100738408237,106.99741746858932,107.66806030273438,170365500,0.0,0 -2008-01-15,106.53254027079059,107.82810238256272,105.09219084837264,105.29795837402344,239940100,0.0,0 -2008-01-16,104.68070363044501,106.02197811022555,103.85764487174802,104.39110565185547,378802600,0.0,0 -2008-01-17,105.02358765854012,105.07693952329653,101.30458591945184,101.68563079833984,397892600,0.0,0 -2008-01-18,102.68399320078827,102.89737743395878,99.90998654100305,100.64158630371094,348561500,0.0,0 -2008-01-22,96.9454843886638,100.92358996886344,96.02335603241605,99.62042236328125,435923700,0.0,0 -2008-01-23,96.85399971511406,102.26484249536671,96.66347724578922,102.01335144042969,511913000,0.0,0 -2008-01-24,102.48586886022855,103.23272549777556,101.59422487808436,102.87454223632812,259949300,0.0,0 -2008-01-25,104.03289608025047,104.22341856993351,101.05313317932061,101.38844299316406,269603900,0.0,0 -2008-01-28,101.5561017560111,103.27843247181806,100.6415961488813,103.06504821777344,217934600,0.0,0 -2008-01-29,103.72043675436495,103.98716123377062,102.79068617554468,103.57563781738281,168968300,0.0,0 -2008-01-30,103.32417470409635,105.579954834622,102.57732967980073,102.8135757446289,334939200,0.0,0 -2008-01-31,101.6627879947622,105.57992961508732,101.51037234049672,104.68828582763672,343680800,0.0,0 -2008-02-01,105.12270088866806,106.39538984684923,104.8026244706139,106.37252807617188,206843600,0.0,0 -2008-02-04,106.09054693475132,106.15913223931614,104.89406013290605,105.03124237060547,124694300,0.0,0 -2008-02-05,103.59849699113694,103.83474298616756,101.86854976024898,102.2191162109375,286882500,0.0,0 -2008-02-06,102.562069106375,103.07266798859806,100.90833527418597,101.39607238769531,250792900,0.0,0 -2008-02-07,100.44344505643208,102.7220862770076,100.39009318495336,102.06668853759766,297368100,0.0,0 -2008-02-08,101.42653408342096,102.28769932434587,100.6720725835937,101.41130065917969,221643500,0.0,0 -2008-02-11,101.43415070684435,102.29530421972413,100.62632580121682,101.92950439453125,188576300,0.0,0 -2008-02-12,102.81357363559542,103.88049507698892,102.10482382707778,102.87454223632812,256654400,0.0,0 -2008-02-13,103.65185563380929,104.45205245118649,102.98884106185955,103.92620849609375,181967800,0.0,0 -2008-02-14,104.36822275548191,104.40632958104311,102.72210557123044,103.01170349121094,215207200,0.0,0 -2008-02-15,102.50107829980912,103.04978388716412,102.05144810778884,102.98881530761719,154110300,0.0,0 -2008-02-19,104.19290189599587,104.3224557460637,102.58489220734792,103.27839660644531,145190000,0.0,0 -2008-02-20,102.11243915316146,104.06338761362319,101.9371500876857,103.58326721191406,220085700,0.0,0 -2008-02-21,104.1471999484808,104.413924414375,102.17339006087784,102.72208404541016,201051200,0.0,0 -2008-02-22,102.85927572823765,103.52991856786683,101.2512655585407,103.35462951660156,205491000,0.0,0 -2008-02-25,103.29366650602644,104.90167676254949,102.71448233143526,104.6578140258789,190107000,0.0,0 -2008-02-26,104.21579901695726,105.89239455477025,104.02527653246557,105.44276428222656,212420700,0.0,0 -2008-02-27,104.83306988753093,106.03717315386625,104.71876107020086,105.33605194091797,168395800,0.0,0 -2008-02-28,104.58923982427176,105.13794557574185,104.06339584339865,104.30725860595703,170831100,0.0,0 -2008-02-29,103.33941424054268,103.40037121069744,101.19031474259472,101.98289489746094,252715200,0.0,0 -2008-03-03,101.46463798385109,101.97523676688793,100.77876179853422,101.7389907836914,189483500,0.0,0 -2008-03-04,100.77113084866109,101.66277451791159,99.82614694034473,101.35032653808594,282513100,0.0,0 -2008-03-05,101.67802651528298,102.70685246546205,100.85496808085338,101.99048614501953,270681400,0.0,0 -2008-03-06,101.34272805099357,101.52563384288655,99.4908548759322,99.8795166015625,247911700,0.0,0 -2008-03-07,98.89639338678253,100.39771117425865,97.98950469369555,98.85066986083984,326434600,0.0,0 -2008-03-10,98.94973540170213,99.0183206918482,97.23503337664624,97.54749298095703,235683600,0.0,0 -2008-03-11,99.62041333183718,101.14459344813999,98.27151067290919,101.05314636230469,341440600,0.0,0 -2008-03-12,101.15983462856857,101.9447864280972,99.95573098740549,100.10814666748047,229161100,0.0,0 -2008-03-13,98.77448129018144,101.0836129646321,98.00477460822125,100.32913970947266,351504200,0.0,0 -2008-03-14,101.1826664073715,101.2131448829239,97.37983410385173,98.77445983886719,484687800,0.0,0 -2008-03-17,96.45772019597342,98.50773811120818,96.07667523891985,97.77613830566406,405311100,0.0,0 -2008-03-18,99.54416084905473,101.88378199676364,99.05642386721436,101.83805847167969,334416600,0.0,0 -2008-03-19,102.22672884779497,102.61539047297316,99.10215590813877,99.3155517578125,345971600,0.0,0 -2008-03-20,99.6004509511651,101.79081883910935,98.99541284672017,101.1551513671875,245320700,0.642,0 -2008-03-24,102.09716316910671,104.01182007854501,102.04355885243437,103.17703247070312,208977300,0.0,0 -2008-03-25,103.28427337967337,103.81272066180524,102.4494856054184,103.27661895751953,192947200,0.0,0 -2008-03-26,102.97792651789615,103.46041217883901,101.9440069701872,102.01293182373047,196934300,0.0,0 -2008-03-27,102.77877735035679,102.96258861492996,101.36959271370982,101.69125366210938,225153200,0.0,0 -2008-03-28,101.85210574473571,102.13547126277649,100.37398435124393,100.71862030029297,180896100,0.0,0 -2008-03-31,100.5500996681855,101.6529437264548,100.39692947923393,101.07089233398438,166692100,0.0,0 -2008-04-01,102.32690852366385,104.80064159629215,102.25031758466599,104.62449645996094,254547300,0.0,0 -2008-04-02,104.96150760396601,105.43633883897533,104.14202873204748,104.69345092773438,210910800,0.0,0 -2008-04-03,104.12667513482778,105.26014841161805,103.93520950156629,104.95379638671875,175884800,0.0,0 -2008-04-04,105.01509627076884,105.65842986058182,104.24923352273548,104.83895111083984,204446800,0.0,0 -2008-04-07,105.58951977758556,106.12563318439356,104.72410260213073,104.89259338378906,154245500,0.0,0 -2008-04-08,104.30287249595715,104.86194915303304,104.11906118893708,104.78536987304688,148937300,0.0,0 -2008-04-09,104.62450976083767,104.77002555175103,103.30722490833712,104.02713775634766,195610600,0.0,0 -2008-04-10,103.71313847538885,104.6704669707895,103.3148865491833,104.17266082763672,192967800,0.0,0 -2008-04-11,103.00088017106569,103.48336573224258,101.8673949451885,102.15077209472656,222973300,0.0,0 -2008-04-14,102.00526124912254,102.2733061991693,101.51510955797674,101.8061294555664,160522000,0.0,0 -2008-04-15,102.30393909279218,102.38818445577554,101.34661073440716,102.04354858398438,172389200,0.0,0 -2008-04-16,103.03915887162157,104.85426135299511,103.02385003218195,104.80831146240234,189268900,0.0,0 -2008-04-17,104.17264915944352,105.11465702128626,103.89693811620448,104.96148681640625,179665700,0.0,0 -2008-04-18,106.40896549738069,106.88379663221654,105.88817278415074,106.0566635131836,218530600,0.0,0 -2008-04-21,105.86517838125641,106.43957530117272,105.5741585550708,106.11026000976562,118587400,0.0,0 -2008-04-22,105.83460080445494,105.92650061704383,104.84663107456055,105.64313507080078,162166000,0.0,0 -2008-04-23,105.75798247733196,106.28642963529263,105.01509468804491,105.47461700439453,193309000,0.0,0 -2008-04-24,105.75034702845237,107.02168220377598,104.95384309537299,105.93415832519531,229381300,0.0,0 -2008-04-25,106.76125445321897,107.13653137882638,105.6201265227325,106.91443634033203,190788100,0.0,0 -2008-04-28,107.128904687155,107.41227021661076,106.7459732425325,106.93743896484375,105610200,0.0,0 -2008-04-29,106.75362809020936,107.01401866891759,106.15625596910682,106.5162124633789,125514100,0.0,0 -2008-04-30,106.67702900728318,107.67265302872343,105.88819122314455,105.88819122314453,208395900,0.0,0 -2008-05-01,105.9801106613809,108.07856750488281,105.89586527587343,108.07856750488281,187279500,0.0,0 -2008-05-02,109.01289523424202,109.03587018062943,107.64966054661464,108.37722778320312,181585500,0.0,0 -2008-05-05,108.02498188906452,108.45386331278779,107.53483002107473,107.85649108886719,118504500,0.0,0 -2008-05-06,107.23613921294246,108.90571492305584,106.98340302371449,108.79084014892578,179339800,0.0,0 -2008-05-07,108.66825548976516,108.78313021761902,106.55447869429442,106.85316467285156,199267300,0.0,0 -2008-05-08,107.02165537110055,107.46585712891985,106.4395922537539,106.57745361328125,178321200,0.0,0 -2008-05-09,106.1485921763095,106.75361866591228,106.03370574092263,106.37834167480469,152588200,0.0,0 -2008-05-12,106.64636025319203,107.64963832546917,106.24810845445546,107.57305908203125,147865900,0.0,0 -2008-05-13,107.83346055700402,107.90238538897746,107.01398193419863,107.58837890625,159132200,0.0,0 -2008-05-14,108.04021638375909,108.90563296526042,107.57303984363783,107.81045532226562,181910800,0.0,0 -2008-05-15,108.01729969322763,109.23503060169288,107.85647502635337,109.15843963623047,166927000,0.0,0 -2008-05-16,109.41117347639616,109.41882789832368,108.45384485914019,109.25800323486328,204236800,0.0,0 -2008-05-19,109.37286748810592,110.51400729726753,108.98228165679573,109.55667877197266,165664400,0.0,0 -2008-05-20,108.95933613321111,109.0129404683119,107.98668681671376,108.66830444335938,178552100,0.0,0 -2008-05-21,108.60698020242702,108.84439575343626,106.4549080321348,106.83018493652344,252724800,0.0,0 -2008-05-22,106.7842419653087,107.35098463036383,106.4549265787641,106.84551239013672,170820400,0.0,0 -2008-05-23,106.49322229482374,106.96039905981789,105.32145317192898,105.4133529663086,181376400,0.0,0 -2008-05-27,105.53589185329761,106.45492483841596,105.329105633651,106.19453430175781,168322900,0.0,0 -2008-05-28,106.58514172192476,107.22080935112905,105.68908350325579,106.68470764160156,181288100,0.0,0 -2008-05-29,106.55449018267515,107.93303381010024,106.51619470721592,107.22078704833984,173927200,0.0,0 -2008-05-30,107.5807227114596,107.78750889010348,107.1748164594666,107.48882293701172,117362000,0.0,0 -2008-06-02,107.09060017855761,107.11357512840712,105.68906980704057,106.37834167480469,181069900,0.0,0 -2008-06-03,106.68468707731117,106.92975708893854,105.09934552100147,105.75798797607422,271965700,0.0,0 -2008-06-04,105.45927931677151,106.57744387199627,105.27547976905066,105.70436096191406,246637700,0.0,0 -2008-06-05,106.1332727456196,107.90241401916954,105.93415261615084,107.81816864013672,237867100,0.0,0 -2008-06-06,106.87618405655108,107.06764980667526,104.32585886255983,104.37946319580078,384276300,0.0,0 -2008-06-09,104.8159676236324,105.30611927499417,103.70546906668717,104.63215637207031,228263900,0.0,0 -2008-06-10,103.90458009277354,104.99976971447484,103.6595101315424,104.11136627197266,260234900,0.0,0 -2008-06-11,104.13438218952507,104.35647729447055,102.57201528815266,102.57968139648438,283890100,0.0,0 -2008-06-12,103.08516233821062,104.05779991403233,102.25802891301437,102.97027587890625,252791800,0.0,0 -2008-06-13,103.52166694150812,104.55558633272148,102.94726987664251,104.27220916748047,244726900,0.0,0 -2008-06-16,103.81271494857333,104.86959776853189,103.74379009455996,104.33349609375,185832500,0.0,0 -2008-06-17,104.97681230862626,105.0150960970541,103.67483629021278,103.82801818847656,191707700,0.0,0 -2008-06-18,103.15409739980443,103.78976514180984,102.40355487327999,102.8171157836914,265893000,0.0,0 -2008-06-19,102.74050226027877,103.5753016938055,102.24269605506603,102.9472885131836,304204900,0.0,0 -2008-06-20,102.24607754773191,102.43850091487742,100.99917788689797,101.2762680053711,289275700,0.669,0 -2008-06-23,101.66883486724556,101.77659151212632,101.07617919269815,101.17623138427734,165096400,0.0,0 -2008-06-24,100.86830237515706,101.93817551877666,100.20636571290837,100.97605895996094,267300600,0.0,0 -2008-06-25,101.38403087832837,102.67711034794806,101.0145812790929,101.45330047607422,287853900,0.0,0 -2008-06-26,100.49889926687455,101.15313178192547,98.5823579695299,98.69780731201172,297775000,0.0,0 -2008-06-27,98.73628964434906,99.1827133216972,97.78187126735666,98.15901947021484,303423400,0.0,0 -2008-06-30,98.43609562910879,99.22118624280216,97.98197930871943,98.50537109375,258842600,0.0,0 -2008-07-01,97.38161153880633,98.88251727162924,96.9274952274704,98.81324768066406,388622000,0.0,0 -2008-07-02,99.12881330593923,99.41360786602553,96.94288674763244,97.11991882324219,288064600,0.0,0 -2008-07-03,97.8357508003042,97.8357508003042,96.20399830707301,97.2199935913086,239352500,0.0,0 -2008-07-07,97.58944985529664,98.01277780153684,95.38042716344626,96.22708892822266,372427300,0.0,0 -2008-07-08,96.20399577600965,98.05126148210019,95.59593716300549,97.93580627441406,375973700,0.0,0 -2008-07-09,98.13591742719628,98.32064221575776,95.74217026565908,96.050048828125,336729400,0.0,0 -2008-07-10,95.77296098668124,96.81974460058152,95.11872269606741,96.44259643554688,436475700,0.0,0 -2008-07-11,95.41891921472784,96.9044281393919,94.2797700708849,95.31885528564453,481124600,0.0,0 -2008-07-14,96.41179372633353,96.59651849195619,94.21047020565759,94.45677185058594,322720800,0.0,0 -2008-07-15,93.74866934251982,95.04944734623247,92.37860999888363,93.12521362304688,502502500,0.0,0 -2008-07-16,93.47927670529779,95.88072262870382,93.20988514473035,95.41120910644531,371642900,0.0,0 -2008-07-17,96.31943611769785,97.18149488010165,95.51125566092584,96.36561584472656,375490600,0.0,0 -2008-07-18,97.11221828777356,97.30464164454838,96.32713357594194,96.96598052978516,267030100,0.0,0 -2008-07-21,97.37393290197737,97.5971447367196,96.35793764828956,97.0198745727539,222863000,0.0,0 -2008-07-22,96.32711945502744,98.36680791229492,96.09620911182962,98.1205062866211,296904200,0.0,0 -2008-07-23,98.43612093277247,99.40593070868502,98.17442790913476,98.6516342163086,311698400,0.0,0 -2008-07-24,98.78246873063524,98.8363529172714,96.33484884577891,96.60424041748047,248634500,0.0,0 -2008-07-25,96.8966945693773,97.35850940114213,96.34251442234184,96.58112335205078,219131000,0.0,0 -2008-07-28,96.60423840489221,97.02756633094795,94.99557584345239,95.16490936279297,205201300,0.0,0 -2008-07-29,95.4266227740838,97.2738828645725,95.16492387547672,97.19691467285156,261505600,0.0,0 -2008-07-30,97.83577014231992,98.9826180239973,97.19692293933943,98.9287338256836,354710000,0.0,0 -2008-07-31,98.05893371372078,98.95947939386168,97.46626654725146,97.62020874023438,277402100,0.0,0 -2008-08-01,97.84342766852991,97.96657555962456,96.56573379869195,97.10452270507812,248690900,0.0,0 -2008-08-04,97.0121599368991,97.08912810489667,96.02695329302254,96.2039794921875,188239600,0.0,0 -2008-08-05,96.99677332893863,98.95179554463124,96.1885986686277,98.79785919189453,251577600,0.0,0 -2008-08-06,98.53614499107834,99.52135152986872,98.120509881471,99.23655700683594,209555400,0.0,0 -2008-08-07,98.49000334459197,98.85945889764021,97.39703971994852,97.75879669189453,246312500,0.0,0 -2008-08-08,97.42783878582561,100.00630586777139,97.27389650849474,99.57527923583984,260811700,0.0,0 -2008-08-11,99.65221194102396,101.22238141187638,99.46748128491603,100.60663604736328,249425800,0.0,0 -2008-08-12,100.27566955648314,100.59893942137494,99.0826422669073,99.55986022949219,213200800,0.0,0 -2008-08-13,99.12883258851038,99.79076954769754,98.26677952337072,98.95951080322266,256393200,0.0,0 -2008-08-14,98.39762373625686,100.27567793699747,98.32835413326033,99.70610046386719,239555300,0.0,0 -2008-08-15,100.00627122328778,100.44500215747114,99.52137230264243,100.19100189208984,181000800,0.0,0 -2008-08-18,100.39112302690717,100.42961005291825,98.25908037207115,98.82095336914062,172275100,0.0,0 -2008-08-19,98.07434613141443,98.28216666865211,97.38931937382968,97.74337768554688,194673700,0.0,0 -2008-08-20,98.05125086811125,98.48227733303223,97.24307037408926,98.19749450683594,225498200,0.0,0 -2008-08-21,97.55865598368383,98.8594399426009,97.44320077847478,98.36683654785156,180609800,0.0,0 -2008-08-22,99.03645989428763,99.79075622558594,98.36683031662622,99.79075622558594,167715300,0.0,0 -2008-08-25,99.13652357436638,99.79075599222097,97.55864957550907,97.76646423339844,171936900,0.0,0 -2008-08-26,97.76646556408159,98.42070973543156,97.42780441801183,98.05125427246094,159117200,0.0,0 -2008-08-27,98.17439076960525,99.15959733437748,97.98196742891398,99.00566101074219,171032800,0.0,0 -2008-08-28,99.50599683918233,100.32187025124072,99.37515032447422,100.2064208984375,167537100,0.0,0 -2008-08-29,99.85235147784508,100.16792868531013,98.9133243083006,99.12883758544922,189195800,0.0,0 -2008-09-02,100.0832600952322,100.60665786206552,98.15132747200481,98.5130844116211,252364900,0.0,0 -2008-09-03,98.42838287353516,98.90559486160149,97.69717654448769,98.42838287353516,251947000,0.0,0 -2008-09-04,97.72800437565536,97.92812637032277,95.41122482842245,95.46510314941406,340042500,0.0,0 -2008-09-05,94.8955224143565,96.1732107066793,93.90261698905897,95.76527404785156,289503400,0.0,0 -2008-09-08,98.55155235176152,98.7055004502137,95.76526558972321,97.74337768554688,364075300,0.0,0 -2008-09-09,97.82804540707437,98.02816736690484,94.51836678803947,94.84163665771484,377326800,0.0,0 -2008-09-10,95.35732505930983,96.13471712073212,94.32593861165441,95.22647857666016,298916600,0.0,0 -2008-09-11,93.99497971730734,96.78126664790041,93.59473577738288,96.60424041748047,375369400,0.0,0 -2008-09-12,95.6652293214952,97.14303979519404,95.31117092919008,97.05067443847656,297851200,0.0,0 -2008-09-15,93.61780842819776,96.71197926070765,92.27854352353403,92.43247985839844,483607000,0.0,0 -2008-09-16,90.20806942562734,94.14890197895107,90.05413308551027,93.97956848144531,581744300,0.0,0 -2008-09-17,92.08613002511748,93.78715195830338,89.2844461501883,89.75395965576172,624095600,0.0,0 -2008-09-18,90.86231320020197,93.74096502977389,87.5911160708673,92.4170913696289,776114700,0.0,0 -2008-09-19,98.08463273059765,99.09102834978941,95.4757555298486,96.08733367919922,501087800,0.691,0 -2008-09-22,96.34279836557066,96.57504532606339,93.1765331814807,93.91197204589844,249966500,0.0,0 -2008-09-23,93.55587581102505,94.46162843638743,91.56631374797848,91.77533721923828,327470400,0.0,0 -2008-09-24,92.3946524722656,92.89785034288012,91.18698230781976,92.06951141357422,311818400,0.0,0 -2008-09-25,92.43334853876367,94.37646327348958,91.69016655519624,93.50941467285156,328253000,0.0,0 -2008-09-26,91.99209226683732,94.05906789694048,91.74436491685813,93.55587005615234,285917400,0.0,0 -2008-09-29,92.2320816241134,92.3869090116352,85.90728775263977,86.22468566894531,459562300,0.0,0 -2008-09-30,87.87363234119971,90.42058261353155,85.56666630794702,89.79351806640625,328154400,0.0,0 -2008-10-01,89.23610107278394,90.33539650641677,88.21422521945048,89.8476791381836,332783000,0.0,0 -2008-10-02,88.98839129896956,89.11225792681705,85.97695330828694,86.58853149414062,365337800,0.0,0 -2008-10-03,87.37044470324508,89.37548750492415,84.90865098255482,85.41958618164062,461798000,0.0,0 -2008-10-06,82.95005253323102,83.31390344683638,77.910341739934,81.0688705444336,610637500,0.0,0 -2008-10-07,82.7100296595078,83.08936670801111,77.14390550622296,77.43807983398438,540012100,0.0,0 -2008-10-08,75.4949656266967,79.10250078257738,74.94532085308748,75.48722839355469,725414800,0.0,0 -2008-10-09,77.15164718913833,77.89482908704639,69.86690651173471,70.21527099609375,534485200,0.0,0 -2008-10-10,67.16515162438063,72.7235402514695,64.70335820110088,68.51216888427734,871026300,0.0,0 -2008-10-13,72.66934673830329,78.45997619628906,69.63467908899304,78.45997619628906,455584000,0.0,0 -2008-10-14,81.05336410309856,81.695908922388,75.17757848086639,77.29874420166016,546268300,0.0,0 -2008-10-15,75.44854232854988,75.71175599527027,69.4488890498806,69.68887329101562,484627500,0.0,0 -2008-10-16,70.67204095879544,73.3660753341554,66.9948343514157,72.59192657470703,708811200,0.0,0 -2008-10-17,71.2139315762275,76.32331140723171,70.9507238742492,72.15839385986328,476649000,0.0,0 -2008-10-20,73.81507572189888,76.71813328232714,72.83964672783497,76.4936294555664,321294200,0.0,0 -2008-10-21,75.06919777255926,76.36202463907685,73.71443759468757,74.20989227294922,356502000,0.0,0 -2008-10-22,72.15068084034242,74.20991991269536,67.76125764669052,70.16886138916016,516168000,0.0,0 -2008-10-23,69.8978877076806,71.57004584936662,66.42969888945316,70.98169708251953,634666400,0.0,0 -2008-10-24,65.07493147331137,69.61144288453737,65.02848444586132,67.38189697265625,545812600,0.0,0 -2008-10-27,66.55357581313415,69.2940633781544,64.79625466275378,64.98979187011719,397288600,0.0,0 -2008-10-28,67.61415077375686,72.95577847093517,65.43879461887893,72.58419036865234,639939500,0.0,0 -2008-10-29,72.59189767288674,75.22400358831882,71.2990711768441,72.0577392578125,531270100,0.0,0 -2008-10-30,74.14796122080422,74.7363158840236,71.91841510069489,74.55052185058594,414582100,0.0,0 -2008-10-31,73.60606277949147,76.30784019760053,73.14157472203193,74.96082305908203,411394000,0.0,0 -2008-11-03,74.92211129500677,75.62658945693467,74.27956644745878,75.17758178710938,205419400,0.0,0 -2008-11-04,76.68714129732992,78.08061082860189,74.86789367492979,77.73224639892578,346793400,0.0,0 -2008-11-05,76.7955694002709,77.96453590646895,73.54414634540987,74.46538543701172,387844100,0.0,0 -2008-11-06,73.12606767601254,73.88473581592812,69.71981304469242,70.3391342163086,477721900,0.0,0 -2008-11-07,70.9507302696364,72.7699785521838,70.06045807417696,72.6615982055664,380391000,0.0,0 -2008-11-10,73.70670939047054,73.95443677980201,70.38561020124479,71.70940399169922,301773000,0.0,0 -2008-11-11,70.26171918631627,71.33004198507517,68.62826538642597,69.49530792236328,418498200,0.0,0 -2008-11-12,68.30314794780013,68.86053009579427,65.89554482273577,66.43744659423828,454330600,0.0,0 -2008-11-13,66.67742211578106,71.01265934696818,63.54986081329804,70.57913208007812,753141900,0.0,0 -2008-11-14,69.21663828270509,71.26812761803176,66.9793431000817,67.0567626953125,540352300,0.0,0 -2008-11-17,66.87096087523571,68.55860523447008,65.92650444658946,66.16648864746094,415254900,0.0,0 -2008-11-18,65.91877250093755,67.52126030498835,64.18468081639972,67.41287994384766,523811800,0.0,0 -2008-11-19,66.50711925948421,67.2503013206925,62.644112900590116,63.09312057495117,558327600,0.0,0 -2008-11-20,62.03252737810875,63.87500489107037,58.09985684027623,58.40951156616211,814180400,0.0,0 -2008-11-21,59.9655582840159,62.62863173270451,57.55021218592953,61.560302734375,718536500,0.0,0 -2008-11-24,63.41824893430696,67.34318191685078,62.21057906468881,65.82585144042969,523305300,0.0,0 -2008-11-25,67.58319460044778,67.7457651444994,64.8891540980389,66.31359100341797,454112400,0.0,0 -2008-11-26,65.26072703407354,69.04631308166478,65.21427410344283,68.8759994506836,370134200,0.0,0 -2008-11-28,68.6127857862151,69.7740086438109,68.49666822548342,69.7430419921875,118308100,0.0,0 -2008-12-01,67.74578215633257,67.77674882577854,63.371839027417145,63.56537628173828,369927100,0.0,0 -2008-12-02,64.61818611624292,66.18196372648544,63.511153355792764,66.01165008544922,469508400,0.0,0 -2008-12-03,64.56401645856957,67.99349616338812,64.36273610010772,67.59867858886719,519863500,0.0,0 -2008-12-04,66.62323800515539,68.16379823480644,64.82721318264797,66.0348892211914,444173800,0.0,0 -2008-12-05,64.75752973552187,68.4502158540768,63.66597740544816,68.07088470458984,471905300,0.0,0 -2008-12-08,69.93659166987361,71.51585574801001,68.1870198367115,70.4475326538086,412859300,0.0,0 -2008-12-09,69.95981207676623,71.32230937910298,68.88374598037228,69.28630065917969,370790000,0.0,0 -2008-12-10,69.92109251046442,70.72620766182908,68.8992167234024,69.7585220336914,396187400,0.0,0 -2008-12-11,69.31728063274151,70.44753711283036,67.63737924217504,68.07864379882812,365061000,0.0,0 -2008-12-12,66.22841976593601,68.95342043146078,65.95746301178781,68.89148712158203,415060400,0.0,0 -2008-12-15,68.91471533186765,69.01535844384955,66.80129266825061,67.93154907226562,256694200,0.0,0 -2008-12-16,71.12876892089844,71.23714924885932,68.26442155687239,71.12876892089844,377699500,0.0,0 -2008-12-17,70.32363486440666,71.55453383992412,69.71980008279871,70.43975830078125,281819800,0.0,0 -2008-12-18,70.75721107009424,70.96622867559458,68.28767417411521,69.1237564086914,374673300,0.0,0 -2008-12-19,69.5365764042206,70.72283785244521,68.74833743769788,68.82638549804688,301451300,0.719,0 -2008-12-22,69.13076543061132,69.2010015515836,66.71922405528524,67.94450378417969,243759500,0.0,0 -2008-12-23,68.31129287162246,68.62346723200727,66.96114724773689,67.24210357666016,221625200,0.0,0 -2008-12-24,67.46842805488923,67.79621442837971,67.11723559914427,67.63232421875,62061600,0.0,0 -2008-12-26,68.08497497720526,68.13180500726858,67.50745620976318,68.02254486083984,74767700,0.0,0 -2008-12-29,68.08497673243846,68.15521880222857,66.80506702336453,67.82743835449219,127795900,0.0,0 -2008-12-30,68.29568825094125,69.49755569312562,67.80401168800218,69.43511962890625,168256300,0.0,0 -2008-12-31,69.5209703824348,70.99598821909545,69.35708017327595,70.42626953125,193987200,0.0,0 -2009-01-02,70.58239057064816,72.92369050426028,70.12193182083628,72.54907989501953,227566300,0.0,0 -2009-01-05,72.29150721614579,73.09535821403452,71.7139884587796,72.46320343017578,240349700,0.0,0 -2009-01-06,73.0797470737174,73.71189591073356,72.33053209356419,72.94707489013672,328260900,0.0,0 -2009-01-07,71.79984110125814,72.00275536285962,70.39505921975825,70.7618637084961,280899200,0.0,0 -2009-01-08,70.36384213625543,71.0896391101751,69.98142567921857,71.05062103271484,263834400,0.0,0 -2009-01-09,71.14428021757722,71.26914641029214,66.61776611270149,69.52877807617188,330953600,0.0,0 -2009-01-12,69.33366037635875,69.38829639888604,67.43721402511325,67.858642578125,277858500,0.0,0 -2009-01-13,67.68695810522215,68.58445131033888,67.27332360514085,67.9835205078125,356432300,0.0,0 -2009-01-14,66.75825152417588,66.92214176910254,64.9008227941081,65.84514617919922,435491600,0.0,0 -2009-01-15,65.65001764366819,66.53190467650238,63.77697749418743,65.86853790283203,532647300,0.0,0 -2009-01-16,67.00798532589974,67.10943948368585,64.81497025679242,66.38363647460938,399237200,0.0,0 -2009-01-20,65.73588965179495,66.38364466874249,62.473678707303925,62.87950134277344,419855200,0.0,0 -2009-01-21,63.948698670473185,65.74369146795758,62.80146090738924,65.59541320800781,364360700,0.0,0 -2009-01-22,64.3233119022422,65.58761600641466,63.34777026593944,64.58085632324219,427940300,0.0,0 -2009-01-23,63.137044558597644,65.54858019260992,62.87950018382957,64.86180114746094,386800600,0.0,0 -2009-01-26,65.23639093013935,66.61776066007123,64.62765414917868,65.30663299560547,317978800,0.0,0 -2009-01-27,65.65781164003961,66.45385645853098,65.0100569183703,65.96998596191406,273789700,0.0,0 -2009-01-28,67.42940705490761,68.63907453664339,67.17186275627255,68.20203399658203,330007000,0.0,0 -2009-01-29,67.20309227775809,68.2800877731379,65.92318251655944,65.9856185913086,294392500,0.0,0 -2009-01-30,66.32121251303973,66.64899301100833,64.15940932689145,64.64328002929688,383383600,0.0,0 -2009-02-02,63.659926806786494,64.91642455592607,63.45701253654052,64.44816589355469,288233300,0.0,0 -2009-02-03,64.8539703992705,65.83731748406684,64.16719161620732,65.35344696044922,278385800,0.0,0 -2009-02-04,65.79049096323627,66.62555386601905,64.80714391511775,65.0334701538086,322989300,0.0,0 -2009-02-05,64.54181626316898,66.56314112829584,60.66306868110894,66.00122833251953,417679400,0.0,0 -2009-02-06,66.22754471923778,68.16301522086157,66.08706654135935,67.88206481933594,366101700,0.0,0 -2009-02-09,67.86645548948209,68.47519229944433,67.3669788272448,67.97571563720703,240075200,0.0,0 -2009-02-10,67.32796686513014,67.9210977407144,64.34671239792479,64.86180114746094,536212800,0.0,0 -2009-02-11,65.12715074345195,65.59541544542539,64.30769942346447,65.24421691894531,324442500,0.0,0 -2009-02-12,64.12817387542168,65.41588942981076,63.254092784815455,65.29102325439453,469302200,0.0,0 -2009-02-13,65.20518110504857,65.74367583916482,64.57302631091785,64.58863830566406,293998400,0.0,0 -2009-02-17,62.559533090735236,64.74474265007613,61.786899874915136,61.825923919677734,478910100,0.0,0 -2009-02-18,62.270766095417756,62.38783226692777,61.092310289553865,61.677635192871094,362964800,0.0,0 -2009-02-19,62.30978296560086,62.55172130312297,60.88939479947393,61.01426696777344,316867500,0.0,0 -2009-02-20,59.882624984335465,61.13911660023672,59.133404086200855,60.421119689941406,477176600,0.0,0 -2009-02-23,61.08451069376263,61.08451069376263,58.212515962247934,58.25934600830078,379641400,0.0,0 -2009-02-24,58.75881734455884,60.83476659576026,58.2983586525053,60.46796798706055,426260900,0.0,0 -2009-02-25,60.2025889422565,61.201542027119245,59.02413368391213,59.99187469482422,461985800,0.0,0 -2009-02-26,60.73330798401133,62.17710813229404,58.946115341608376,59.016357421875,363353900,0.0,0 -2009-02-27,57.75984283223122,59.07097039315789,57.60375267365023,57.69740676879883,470510900,0.0,0 -2009-03-02,56.59699395760929,57.68960125998963,54.91906790685213,55.09856414794922,426452600,0.0,0 -2009-03-03,55.88680787849536,55.95704399080639,54.34935390833129,54.684940338134766,443761000,0.0,0 -2009-03-04,55.59023704849973,56.87014650425405,54.68493204337915,55.98045349121094,462753100,0.0,0 -2009-03-05,54.70835749247988,55.98046721836403,53.20212141609634,53.69379806518555,485549400,0.0,0 -2009-03-06,54.16205555703648,54.981506697530584,52.3670571372712,53.787445068359375,490470000,0.0,0 -2009-03-09,53.03043559494792,54.63032609808504,52.85874528590967,53.15530776977539,379905300,0.0,0 -2009-03-10,54.247903694635475,56.47994272367879,54.13864354251994,56.3238525390625,406227900,0.0,0 -2009-03-11,56.97162645585002,57.55695138519095,56.05852099509412,56.690670013427734,356648300,0.0,0 -2009-03-12,56.67504645464248,59.11779959470231,56.167763815128815,58.922691345214844,409702700,0.0,0 -2009-03-13,59.32073331703166,60.07775452625878,58.32177970436489,59.38316345214844,337474700,0.0,0 -2009-03-16,60.06211497913464,60.8503538715086,59.16461595762731,59.20363998413086,360644900,0.0,0 -2009-03-17,59.3675524622782,61.154745179994016,58.883681741466695,61.01426696777344,356814300,0.0,0 -2009-03-18,60.725510907742354,63.137052811198146,60.14799195442014,62.38003158569336,473273200,0.0,0 -2009-03-19,63.16043984021523,63.21506990937508,61.41227167286904,61.60737991333008,428520400,0.0,0 -2009-03-20,61.906866155071235,62.02477021974584,60.154040918928416,60.29552459716797,371078200,0.561,0 -2009-03-23,61.89116069837233,64.68153173363942,61.553172144778166,64.62651062011719,419933300,0.0,0 -2009-03-24,63.856178176951836,64.73652156475383,63.282387698469606,63.353126525878906,330271000,0.0,0 -2009-03-25,63.84833632258416,65.0037794018796,62.142670259447975,64.02125549316406,441775100,0.0,0 -2009-03-26,64.65008797992618,65.4754106507509,63.91908978477758,65.32606506347656,422025200,0.0,0 -2009-03-27,64.49286108077737,64.87014684097431,63.91120270546279,64.14701080322266,322332300,0.0,0 -2009-03-30,62.724316017106936,62.77933710496839,61.27803799827192,61.93043518066406,324108500,0.0,0 -2009-03-31,62.535664346792906,63.730416482353284,62.13479900897396,62.50422286987305,364238300,0.0,0 -2009-04-01,61.72607166319377,63.9976661137855,61.568870260525834,63.714698791503906,377018300,0.0,0 -2009-04-02,65.30245499508743,66.50506301417184,63.769714463493735,65.57756042480469,476230700,0.0,0 -2009-04-03,65.6246969593539,66.24565242714164,64.98016189100466,66.22993469238281,284646300,0.0,0 -2009-04-06,65.50682264446584,66.24568244672585,64.68150626658425,65.71118927001953,264866600,0.0,0 -2009-04-07,64.65005404582627,64.96446280286558,64.06840174325488,64.1784439086914,258947800,0.0,0 -2009-04-08,64.5007329840878,65.19243371111321,64.09200564281413,64.87016296386719,230402800,0.0,0 -2009-04-09,66.55221926231575,67.45614220264198,66.28497571304187,67.44828033447266,269653500,0.0,0 -2009-04-13,66.74874707518846,68.02210030086314,66.48150343348227,67.46402740478516,224847500,0.0,0 -2009-04-14,66.835175071488,67.40897141235435,66.08845963852711,66.3006820678711,276598800,0.0,0 -2009-04-15,65.89982053839336,67.14173181779283,65.71903955280818,67.00811004638672,250726100,0.0,0 -2009-04-16,67.5425854368375,68.5015291862817,66.63080088921951,67.99061584472656,335202900,0.0,0 -2009-04-17,68.25004367601986,68.89457913387884,67.70768854795979,68.44654846191406,262649000,0.0,0 -2009-04-20,67.23606086847687,68.42295114736967,65.50681560182105,65.57756042480469,293690100,0.0,0 -2009-04-21,65.09808401964246,66.91378573068583,65.04306293739411,66.8587646484375,114090900,0.0,0 -2009-04-22,66.25354559402525,67.86488102774061,66.08062044574814,66.4500503540039,340395200,0.0,0 -2009-04-23,66.58370234724768,67.14177539905121,65.7347999599189,67.10247802734375,324903700,0.0,0 -2009-04-24,67.62119834676301,68.62730148016533,67.35395482557388,68.11639404296875,287703000,0.0,0 -2009-04-27,67.34609526601331,68.391501775107,67.23605310682953,67.47185516357422,289581600,0.0,0 -2009-04-28,66.78804094422821,68.06138799214854,66.62297766012186,67.25965118408203,247926300,0.0,0 -2009-04-29,68.0063847684058,69.45266334711187,67.83346556593708,68.69022369384766,311505700,0.0,0 -2009-04-30,69.60195770821964,69.9713814568137,68.32074339983171,68.71375274658203,301419800,0.0,0 -2009-05-01,68.72950004215534,69.33473200503816,68.16356542931234,69.08320617675781,236110300,0.0,0 -2009-05-04,69.60199820996058,71.48058350083727,69.46837039670329,71.43341827392578,287120000,0.0,0 -2009-05-05,71.18975830078125,71.47272569092607,70.615961649418,71.18975830078125,243036300,0.0,0 -2009-05-06,72.06221436735322,72.4709415645415,71.22117436817389,72.42378234863281,291941000,0.0,0 -2009-05-07,73.10765193309834,73.2176941480665,70.9618167475173,71.41770935058594,317728000,0.0,0 -2009-05-08,72.33733135281038,73.27269593019231,71.87358299730633,73.08405303955078,299081700,0.0,0 -2009-05-11,72.07795297364908,72.40022370058513,71.55918345792416,71.71638488769531,247923600,0.0,0 -2009-05-12,72.02288908054071,72.18009641375595,70.62377679758127,71.50411987304688,282431300,0.0,0 -2009-05-13,70.53731762627072,70.74954603701728,69.56265610125723,69.70413970947266,269619100,0.0,0 -2009-05-14,69.73563979843624,70.8360681210541,69.56271457671534,70.30157470703125,260098700,0.0,0 -2009-05-15,70.24654353523806,70.74173351094832,69.28759907703792,69.72776794433594,271502700,0.0,0 -2009-05-18,70.3879843006082,71.79495264263817,69.6176832559224,71.70849609375,241447400,0.0,0 -2009-05-19,71.66922191020059,72.29017771996908,71.37839268379768,71.62206268310547,206102200,0.0,0 -2009-05-20,72.27447235863569,72.94259356224805,71.06400791181531,71.14260864257812,285722200,0.0,0 -2009-05-21,70.31728576957606,70.58453544960126,69.37406500950777,70.12078094482422,258988400,0.0,0 -2009-05-22,70.31727062414994,70.74172167381484,69.70417666247289,69.97142028808594,166811900,0.0,0 -2009-05-26,69.45261469796381,71.96787220753527,69.4211732294883,71.76351165771484,236318500,0.0,0 -2009-05-27,71.87359524902764,72.11725927417714,70.37229574695712,70.48233795166016,246015800,0.0,0 -2009-05-28,71.1032833320062,71.7949779448122,70.03429693240179,71.46485137939453,289095000,0.0,0 -2009-05-29,71.85786499514654,73.64998759144153,71.27621253773584,72.7303466796875,258641500,0.0,0 -2009-06-01,73.62639293550059,74.80542134974331,73.43775006833935,74.49101257324219,276246800,0.0,0 -2009-06-02,74.20017273808362,74.96261190089012,74.06655098705598,74.55387878417969,230874500,0.0,0 -2009-06-03,73.91722655287354,73.9879653834991,72.91112320498357,73.61067962646484,235310500,0.0,0 -2009-06-04,73.88577909250478,74.41241033402686,73.33556824268297,74.3023681640625,210102300,0.0,0 -2009-06-05,75.05695376893142,75.19843743095063,73.72858571664743,74.31809997558594,284257900,0.0,0 -2009-06-08,73.7600190697953,74.75040467532858,73.13120747652974,74.01155090332031,238565100,0.0,0 -2009-06-09,74.42812017122134,74.78182622335896,73.90148303120108,74.3888168334961,225125500,0.0,0 -2009-06-10,75.04908956093422,75.05694543244947,73.24910550394212,74.20018768310547,296100400,0.0,0 -2009-06-11,74.34170677870209,75.54431534732356,74.32598303447688,74.53034973144531,275414200,0.0,0 -2009-06-12,74.2002097960638,74.78186229450455,73.88580093314843,74.73470306396484,184361800,0.0,0 -2009-06-15,73.85434213452226,73.90150135400708,72.6281544531258,73.02116394042969,224190500,0.0,0 -2009-06-16,73.28055024165648,73.32770946024058,71.98361775895496,72.03077697753906,227319000,0.0,0 -2009-06-17,71.99934221437532,72.57313874700685,71.39411019765512,71.96004486083984,223445200,0.0,0 -2009-06-18,72.07007476142485,72.84036992413631,71.7242245269064,72.48666381835938,211725100,0.0,0 -2009-06-19,73.18068611340965,73.27553732136346,72.34279558190681,72.75383758544922,215655600,0.518,0 -2009-06-22,72.04243598967173,72.08196135372346,70.54846889551865,70.57218170166016,251913600,0.0,0 -2009-06-23,70.72237496100152,71.04646004832209,70.23228815958905,70.62751770019531,188309800,0.0,0 -2009-06-24,71.2677793589122,71.99500012035669,70.82511826134873,71.23616027832031,211577700,0.0,0 -2009-06-25,70.8804227462917,72.8565692889945,70.80137809072771,72.78543090820312,279411000,0.0,0 -2009-06-26,72.5404246382822,72.91194135809775,72.14519511179316,72.59575653076172,167579000,0.0,0 -2009-06-29,72.80918290186327,73.37040809846495,72.40604710150402,73.27555084228516,168481300,0.0,0 -2009-06-30,73.29137329182743,73.56012650785556,72.14520390684794,72.6827163696289,228888200,0.0,0 -2009-07-01,72.99098588071364,73.69449998442867,72.88822837513564,72.98308563232422,173041100,0.0,0 -2009-07-02,72.03454048490971,73.00680789720006,70.9516153043159,70.99113464355469,212309900,0.0,0 -2009-07-06,70.30342771647058,71.08598046376598,70.08210015406168,70.98322296142578,174499600,0.0,0 -2009-07-07,70.91206163357624,70.99901258821437,69.56037774427753,69.60780334472656,197088900,0.0,0 -2009-07-08,70.02676989824182,70.19277161894371,68.76994280381636,69.5604019165039,248050500,0.0,0 -2009-07-09,70.04256704648202,70.27180086241084,69.48924820163532,69.69476318359375,163777600,0.0,0 -2009-07-10,69.32325703231719,69.94772041234658,69.04659756793049,69.52877807617188,173520300,0.0,0 -2009-07-13,69.80541493360371,71.27566878902024,69.23628363076865,71.2203369140625,217413500,0.0,0 -2009-07-14,71.44169637919416,71.68674281855324,70.9279027521512,71.62350463867188,181487400,0.0,0 -2009-07-15,72.57205416167824,73.91583836344358,71.67883747440777,73.71822357177734,220877900,0.0,0 -2009-07-16,73.51269653823293,74.70629147170382,73.37041365854286,73.59964752197266,231174500,0.0,0 -2009-07-17,74.35055191494868,74.55607288910944,73.93951599734913,74.4058837890625,138561700,0.0,0 -2009-07-20,74.8406687985227,75.32284933822557,74.45334552310912,75.19637298583984,164179400,0.0,0 -2009-07-21,75.78131433112776,75.8050271386621,74.63514504426624,75.54417419433594,217718300,0.0,0 -2009-07-22,75.06198452215854,75.98682008760755,75.00665263441518,75.52835845947266,196068100,0.0,0 -2009-07-23,75.57579962049111,77.52823468090902,75.51256144127906,77.19624328613281,258795500,0.0,0 -2009-07-24,76.83261019247283,77.57564356107464,76.4294804561119,77.51240539550781,154003100,0.0,0 -2009-07-27,77.37012909719301,77.78117117749906,76.94328048951701,77.74164581298828,159259400,0.0,0 -2009-07-28,77.19620384749713,77.75742883215021,76.72192377643559,77.37800598144531,186685200,0.0,0 -2009-07-29,77.02233591823307,77.53612950388005,76.65872545929821,77.1883316040039,194399300,0.0,0 -2009-07-30,78.12105945659873,78.9115184064037,77.9392512446254,77.99458312988281,225575400,0.0,0 -2009-07-31,77.97876196651733,78.62693794245526,77.76533471292878,78.10523223876953,207358000,0.0,0 -2009-08-03,78.92734847868293,79.46486096613809,78.5004997947575,79.39372253417969,175776900,0.0,0 -2009-08-04,79.03797994857338,79.70986877534816,78.8719843111859,79.59920501708984,176714600,0.0,0 -2009-08-05,79.6545624801267,79.72570693655622,78.71392023281017,79.37000274658203,184726400,0.0,0 -2009-08-06,79.73362949106092,79.85219355810325,78.587459920271,78.95897674560547,193203800,0.0,0 -2009-08-07,79.78893080954835,80.65052819693796,79.35417596131839,79.99444580078125,220640900,0.0,0 -2009-08-10,79.63084592362485,80.01026893503379,79.25932919160806,79.82846069335938,130898700,0.0,0 -2009-08-11,79.4727435746965,79.5280754599801,78.61904646112946,78.83247375488281,157301000,0.0,0 -2009-08-12,78.6981175516295,80.2790359823539,78.65859820929651,79.67829132080078,219052400,0.0,0 -2009-08-13,80.04187067919601,80.31853009073569,79.25141177152672,80.28691101074219,176449500,0.0,0 -2009-08-14,80.24740376490719,80.31064193916708,78.80876846947801,79.67037200927734,199616100,0.0,0 -2009-08-17,78.13687055421454,78.21591524767565,77.55193258573402,77.71002197265625,237667500,0.0,0 -2009-08-18,77.88392872700415,78.60324935259145,77.741645857697,78.32658386230469,173461500,0.0,0 -2009-08-19,77.71003115224903,79.2830490823406,77.63098644945066,79.01428985595703,192812800,0.0,0 -2009-08-20,79.11704631617023,80.01026893503379,78.94315038461214,79.82846069335938,174131300,0.0,0 -2009-08-21,80.48453328172415,81.52003261800019,80.32644389766124,81.39356231689453,224605000,0.0,0 -2009-08-24,81.72556353809965,82.16821869165618,81.09319386382676,81.38566589355469,191279000,0.0,0 -2009-08-25,81.70974260155458,82.41325057399796,81.36984501694641,81.54374694824219,215310600,0.0,0 -2009-08-26,81.2908056968516,81.92317534828237,81.01414623587212,81.5516586303711,194620700,0.0,0 -2009-08-27,81.50424086150754,81.98642141021728,80.57940513262803,81.73347473144531,196230100,0.0,0 -2009-08-28,82.38953097493376,82.4843821800182,81.15641103233263,81.71763610839844,147024400,0.0,0 -2009-08-31,80.91927907482163,81.085274718721,80.4608114693532,80.99041748046875,176051600,0.0,0 -2009-09-01,80.58727480671905,81.60696741478552,79.03797623320851,79.20397186279297,321276800,0.0,0 -2009-09-02,78.87199114857412,79.39369693763771,78.70599549679653,78.90361022949219,171805000,0.0,0 -2009-09-03,79.36208526107424,79.65455123308766,78.72180936651193,79.55970001220703,143572300,0.0,0 -2009-09-04,79.71780056534584,80.6979681796349,79.48066644920989,80.67425537109375,142687900,0.0,0 -2009-09-08,81.4172757681423,81.4568011298337,80.9350953029799,81.3698501586914,132909100,0.0,0 -2009-09-09,81.51213562051541,82.27097554957756,81.25918897749469,81.99431610107422,154612500,0.0,0 -2009-09-10,82.04963882020516,82.88752335080146,81.59117119637122,82.83219146728516,162902400,0.0,0 -2009-09-11,82.99027765414678,83.23532401236041,82.42905256267635,82.81637573242188,152360100,0.0,0 -2009-09-14,82.11287678911569,83.36180342668067,81.53584511009315,83.21952056884766,149593800,0.0,0 -2009-09-15,83.35389448205932,83.87560028766727,82.80848190341484,83.56732177734375,196795900,0.0,0 -2009-09-16,83.86772342645571,84.84789118690469,83.57525736987053,84.83208465576172,206406300,0.0,0 -2009-09-17,84.71350480252106,85.41701295645767,84.23923052345617,84.70560455322266,229170900,0.0,0 -2009-09-18,85.10108374660985,85.10902768147567,84.4736462000415,84.75956726074219,153799100,0.508,0 -2009-09-21,84.10037203325408,84.98196108628821,83.91770392140606,84.54513549804688,151892000,0.0,0 -2009-09-22,85.04552633587763,85.27585207436445,84.66429606472192,85.03758239746094,143126700,0.0,0 -2009-09-23,85.23614173563847,85.80004019602211,84.17982208503467,84.33072662353516,225947400,0.0,0 -2009-09-24,84.51338121395527,84.69604934599305,83.03612404610494,83.4014663696289,228636800,0.0,0 -2009-09-25,83.2188062416357,83.67945770718879,82.67078963401394,82.95671081542969,204059000,0.0,0 -2009-09-28,83.27443696163311,84.62462223674152,83.25855513791626,84.44194793701172,118285800,0.0,0 -2009-09-29,84.59282307625355,84.99787306424861,84.01303673150097,84.1877670288086,133733900,0.0,0 -2009-09-30,84.47370828536611,84.55312951405558,83.09175951592503,83.86215209960938,254383000,0.0,0 -2009-10-01,81.80508338319817,83.97331787106577,81.76536975389271,81.78125762939453,281840600,0.0,0 -2009-10-02,81.02673812625589,81.88450158852015,81.00291237332335,81.4000244140625,224748800,0.0,0 -2009-10-05,81.72564638606268,82.85344295071963,81.4873767751868,82.61517333984375,149875000,0.0,0 -2009-10-06,83.21086138101364,84.2751248910302,83.16320987112239,83.79859161376953,202491100,0.0,0 -2009-10-07,83.60797911747333,84.1162881816848,83.44913670077898,84.02892303466797,159200300,0.0,0 -2009-10-08,84.62462019906047,85.11703549530198,84.06865943112567,84.67227172851562,183305800,0.0,0 -2009-10-09,84.69605545123798,85.1884765625,84.47367367823219,85.1884765625,135008300,0.0,0 -2009-10-12,85.58560360082042,85.84769298810673,85.20437330925411,85.52206420898438,118031000,0.0,0 -2009-10-13,85.2917403611952,85.54589187476006,84.7913812730847,85.34733581542969,157692700,0.0,0 -2009-10-14,86.34803822937882,86.90399263122401,85.31554455801087,86.8166275024414,191421600,0.0,0 -2009-10-15,86.39570204780205,87.13433074951172,86.35599447598072,87.13433074951172,173873600,0.0,0 -2009-10-16,86.41160835213304,86.78488869221586,85.95890073257026,86.48308563232422,192069400,0.0,0 -2009-10-19,86.62603358087702,87.46790929705297,86.35600026038655,87.1978759765625,159530400,0.0,0 -2009-10-20,87.32495263362976,87.35672233031626,86.3162905164962,86.73722839355469,180921100,0.0,0 -2009-10-21,86.6021894631082,87.61085132093002,85.89533055704405,85.95886993408203,225379300,0.0,0 -2009-10-22,85.92711212079361,87.11050439929187,85.37909552213934,86.83252716064453,238444000,0.0,0 -2009-10-23,87.11841491698435,87.17401034338627,85.48230968675455,85.83971405029297,240033200,0.0,0 -2009-10-26,85.93503558546186,86.81662472262306,84.67222231622725,84.91049194335938,242028200,0.0,0 -2009-10-27,85.00582434502303,85.2917455726851,84.31485309438172,84.52134704589844,253266300,0.0,0 -2009-10-28,84.306921293894,84.56901680023073,82.87731494974037,82.92497253417969,248821400,0.0,0 -2009-10-29,83.54445848398947,84.87081164926775,83.34590240321904,84.70402526855469,198110600,0.0,0 -2009-10-30,84.42604151256691,84.68019303414385,82.15455963746348,82.24986267089844,325608100,0.0,0 -2009-11-02,82.70255769329246,83.71916968459941,81.86862598018236,82.85346221923828,254222900,0.0,0 -2009-11-03,82.39281551975878,83.23469727898724,82.23397310602873,83.11556243896484,228362600,0.0,0 -2009-11-04,83.79860493351059,84.44986859648968,83.11557157240442,83.33000946044922,247996700,0.0,0 -2009-11-05,83.91772378807879,84.88667212870216,83.74299351141663,84.86284637451172,180015300,0.0,0 -2009-11-06,84.39426442599512,85.29967952105712,84.2274780726363,85.08523559570312,170954100,0.0,0 -2009-11-09,85.73649464802142,87.07079146363355,85.6729613211442,87.02313995361328,159495700,0.0,0 -2009-11-10,86.81662733393183,87.3090484170736,86.54659407054501,87.03900909423828,171899800,0.0,0 -2009-11-11,87.61087552514394,88.01593159135508,87.0628649102761,87.48380279541016,169466200,0.0,0 -2009-11-12,87.36465959170877,87.81736713085147,86.37187936907573,86.5942611694336,157144500,0.0,0 -2009-11-13,86.81664496025924,87.43613886387362,86.3718813493463,87.06285858154297,150963000,0.0,0 -2009-11-16,87.66645325373422,88.70689093658048,87.61880174715081,88.3256607055664,210922200,0.0,0 -2009-11-17,88.09534423118396,88.46863056376984,87.76177153121556,88.42891693115234,147134100,0.0,0 -2009-11-18,88.36537602552538,88.50039266972884,87.8173594515302,88.3733139038086,156486800,0.0,0 -2009-11-19,87.76971922008418,88.4050949529669,86.673685931283,87.2217025756836,208734600,0.0,0 -2009-11-20,86.76896571750436,87.17402162482185,86.5783536679511,86.91192626953125,134196000,0.0,0 -2009-11-23,87.93650134103358,88.74660733075503,87.84119225894005,88.01592254638672,148010200,0.0,0 -2009-11-24,88.15885335951484,88.31769571656379,87.37257339106176,88.15090942382812,138420100,0.0,0 -2009-11-25,88.29389938168444,88.555994811628,88.01592213452439,88.46068572998047,109564800,0.0,0 -2009-11-27,86.09389915152164,87.61880806666423,86.00653401018488,87.02313995361328,126001800,0.0,0 -2009-11-30,86.9516589761678,87.52349525034458,85.87151369252976,87.31700134277344,160874800,0.0,0 -2009-12-01,88.09536189477954,88.68309226622365,87.94446339136623,88.39717102050781,159613700,0.0,0 -2009-12-02,88.38124762599388,88.96103382445453,88.0953264951706,88.357421875,132315100,0.0,0 -2009-12-03,88.59569726582346,89.09605624758181,87.59497324285799,87.66645050048828,167324900,0.0,0 -2009-12-04,88.82600271897674,89.25488437456949,87.39640325978202,88.16680145263672,274907800,0.0,0 -2009-12-07,88.08740082236008,88.57981590298694,87.75382208388316,88.03179931640625,127973800,0.0,0 -2009-12-08,87.39641771680213,87.97619789677923,86.78486178732355,87.05490112304688,169863700,0.0,0 -2009-12-09,87.03109963012712,87.50763299485243,86.58632990485975,87.38055419921875,155063400,0.0,0 -2009-12-10,87.92062209705549,88.2542008874115,87.72206603445197,87.87297058105469,138014600,0.0,0 -2009-12-11,88.24625396728516,88.4448100231814,87.84914185549266,88.24625396728516,124854000,0.0,0 -2009-12-14,88.8498764038086,88.95312338347647,88.26214604722044,88.8498764038086,107141500,0.0,0 -2009-12-15,88.52423954720348,88.8895819972289,88.15889709717807,88.43687438964844,120408800,0.0,0 -2009-12-16,88.7942873284496,89.05637676636643,88.37334332080519,88.5718994140625,155358200,0.0,0 -2009-12-17,87.9365035561869,88.1032899094648,87.42820055778292,87.50762176513672,183390100,0.0,0 -2009-12-18,87.99470646968112,88.07456135142097,87.26008785742282,88.00269317626953,174591200,0.59,0 -2009-12-21,88.44184325368121,89.19242895447383,88.44184325368121,88.89698791503906,118039600,0.0,0 -2009-12-22,89.08866770695442,89.4080690076515,88.9768781655214,89.2164306640625,91707500,0.0,0 -2009-12-23,89.43198675708813,89.51982223144734,89.03273681620826,89.39205932617188,111783100,0.0,0 -2009-12-24,89.58374943157015,89.91113138239815,89.4320324270963,89.81531524658203,39677500,0.0,0 -2009-12-28,90.1506577443217,90.2225198253294,89.68752624869896,90.00692749023438,87508500,0.0,0 -2009-12-29,90.23844071576353,90.25440802711432,89.87113163401116,89.87911224365234,80572500,0.0,0 -2009-12-30,89.61566172508381,89.95103027812264,89.56774758031268,89.84722137451172,73138400,0.0,0 -2009-12-31,90.0468484649655,90.07080858394082,88.94492058290737,88.98484802246094,90637900,0.0,0 -2010-01-04,89.72743648900011,90.54190371132576,89.04072608355636,90.4939956665039,118944600,0.0,0 -2010-01-05,90.43813274377356,90.77350135969087,90.11074474342124,90.73357391357422,111579900,0.0,0 -2010-01-06,90.64573785015858,91.02103390989214,90.57387575807662,90.79745483398438,116074400,0.0,0 -2010-01-07,90.62975809895723,91.29251462028039,90.37423831980605,91.18072509765625,131091100,0.0,0 -2010-01-08,90.9411506981738,91.52405829013841,90.75749913763154,91.484130859375,126402800,0.0,0 -2010-01-11,91.8914046994265,91.93132605427881,91.22066136193295,91.61193084716797,106375700,0.0,0 -2010-01-12,91.00504800968545,91.19668630819064,90.40617299629407,90.75751495361328,163333500,0.0,0 -2010-01-13,90.9890711182675,91.77959049200558,90.52594575559628,91.5240707397461,161822000,0.0,0 -2010-01-14,91.42024929087752,91.93927542978973,91.36435454312209,91.77159118652344,115718800,0.0,0 -2010-01-15,91.61193027215987,91.69975968911294,90.39021985354404,90.74156188964844,212283100,0.0,0 -2010-01-19,90.72556101282123,91.93129147794473,90.70160089843381,91.87539672851562,139172700,0.0,0 -2010-01-20,91.25259958034046,91.38834314882016,90.21455300175856,90.9411849975586,216490200,0.0,0 -2010-01-21,90.9651303105956,91.24460412835074,89.08066957934327,89.19245910644531,344859600,0.0,0 -2010-01-22,88.79321433906463,89.22440515157903,87.10837853337925,87.2042007446289,345942400,0.0,0 -2010-01-25,88.00267859673902,88.16238224168825,87.36388229315556,87.65133666992188,186937500,0.0,0 -2010-01-26,87.30798112324477,88.21028996065215,87.0684347948197,87.28402709960938,211168800,0.0,0 -2010-01-27,87.17224397016501,87.89888189796254,86.50150689103336,87.69925689697266,271863600,0.0,0 -2010-01-28,87.98674893289432,88.03465700084614,86.16616924583991,86.69317626953125,316104000,0.0,0 -2010-01-29,87.06841852149788,87.67528007247869,85.615149135883,85.75089263916016,310677600,0.0,0 -2010-02-01,86.35777975917824,87.09239832720125,85.83875351948329,87.08441162109375,187865000,0.0,0 -2010-02-02,87.2441116340362,88.30611200318852,86.94067773535994,88.138427734375,216327900,0.0,0 -2010-02-03,87.73917824192769,88.21828311796067,87.44373713938846,87.69925689697266,172730700,0.0,0 -2010-02-04,87.02051515160323,87.0604364883188,84.97635142075909,84.99232482910156,356715700,0.0,0 -2010-02-05,85.088182517931,85.34370234772373,83.5071554748614,85.16803741455078,493585800,0.0,0 -2010-02-08,85.23186999819436,85.7029880649405,84.48926491392042,84.55314636230469,224166900,0.0,0 -2010-02-09,85.54331279297136,86.35778629961106,84.85660222544072,85.61518096923828,337820500,0.0,0 -2010-02-10,85.47942760855416,85.91859895419353,84.72883565758336,85.4474868774414,240511500,0.0,0 -2010-02-11,85.33570641139762,86.43763433728586,84.8406341647725,86.34181213378906,223591600,0.0,0 -2010-02-12,85.43149311037732,86.31782838833956,85.04821661248937,86.2699203491211,304622100,0.0,0 -2010-02-16,86.92471019598945,87.71522348755957,86.09426946772454,87.62738800048828,159317500,0.0,0 -2010-02-17,88.05057552185399,88.16237110523227,87.62737157732114,88.04259490966797,168845100,0.0,0 -2010-02-18,87.89888898896285,88.7452971119787,87.86694825517915,88.5616455078125,193708600,0.0,0 -2010-02-19,88.33007532934323,89.08864791944806,88.12246361619185,88.74529266357422,222684900,0.0,0 -2010-02-22,89.07269080448727,89.09664483335074,88.49776974347407,88.76127624511719,132346900,0.0,0 -2010-02-23,88.52170836151801,88.7931954308039,87.45171521933668,87.68328094482422,207497000,0.0,0 -2010-02-24,87.94679632558237,88.6335068661453,87.72321728299389,88.48977661132812,176350700,0.0,0 -2010-02-25,87.22816997634956,88.43390712006519,86.98862354458686,88.37002563476562,259634700,0.0,0 -2010-02-26,88.44984208672895,88.72932195965072,87.922835255993,88.42588806152344,173589300,0.0,0 -2010-03-01,88.79321021810627,89.43201274597791,88.76925618942374,89.34417724609375,147709700,0.0,0 -2010-03-02,89.72745496399659,90.0228960906575,89.43200774526362,89.59170532226562,160992400,0.0,0 -2010-03-03,89.82324249951535,90.20652509129424,89.4479466116675,89.6715316772461,150785000,0.0,0 -2010-03-04,89.79134484150755,90.07082478450718,89.45597622039564,89.94306182861328,135770400,0.0,0 -2010-03-05,90.52597493193413,91.30051505192708,90.31037645431081,91.22865295410156,176118800,0.0,0 -2010-03-08,91.23663303188702,91.4442386941223,91.08491604696104,91.24461364746094,114631200,0.0,0 -2010-03-09,90.9730820931691,91.81948992831528,90.92517405482346,91.39628601074219,154556700,0.0,0 -2010-03-10,91.43622623453848,92.05106848361551,91.35637746209704,91.80353546142578,186088800,0.0,0 -2010-03-11,91.58796115380936,92.21079636357128,91.30848733075284,92.18683624267578,160791100,0.0,0 -2010-03-12,92.58607991464231,92.60205332747068,91.93929681274967,92.19481658935547,162074800,0.0,0 -2010-03-15,92.0351212806375,92.28265435692667,91.50810830161547,92.2187728881836,146816800,0.0,0 -2010-03-16,92.47426436378295,93.04119851817104,92.21874465910001,92.953369140625,168673000,0.0,0 -2010-03-17,93.23288450088313,93.8078056161807,92.96139126662682,93.5043716430664,177468100,0.0,0 -2010-03-18,93.51236144663837,93.6401183142268,93.08117059589009,93.45646667480469,196509100,0.0,0 -2010-03-19,92.98339080810547,94.04175112714037,92.62258240017763,92.98339080810547,226641100,0.48,0 -2010-03-22,92.45419608377226,93.64886491516208,92.39807115403411,93.48048400878906,184477800,0.0,0 -2010-03-23,93.61680633714941,94.21814757959767,93.31212285932692,94.13796997070312,182941600,0.0,0 -2010-03-24,93.7851903867636,94.15401231772694,92.6707049970465,93.68095397949219,196072600,0.0,0 -2010-03-25,94.31436706339065,94.74733353241038,93.416367972529,93.52861785888672,223396300,0.0,0 -2010-03-26,93.70499246485002,94.1459723202198,93.10365129348864,93.47247314453125,205808500,0.0,0 -2010-03-29,93.94552643922779,94.23417069446756,93.5606715103473,94.0657958984375,134513500,0.0,0 -2010-03-30,94.17804918622545,94.47471304743986,93.73706930930345,94.12994384765625,145772500,0.0,0 -2010-03-31,93.76915038190683,94.2261695225338,93.49654526374617,93.80924224853516,161078700,0.0,0 -2010-04-01,94.4506607055664,94.8114629804041,93.8894052459578,94.4506607055664,161215200,0.0,0 -2010-04-05,94.81146893401954,95.28452110705211,94.5468773200389,95.22038269042969,105847600,0.0,0 -2010-04-06,94.94778186784583,95.61326771502904,94.84355157691694,95.44489288330078,110384200,0.0,0 -2010-04-07,95.25243933450874,95.7014387630204,94.45866462275762,94.89965057373047,184576300,0.0,0 -2010-04-08,94.57093497109376,95.38876252698145,94.29031026746947,95.22840118408203,158704000,0.0,0 -2010-04-09,95.42885255213136,95.89389129164394,95.25246424277557,95.85380554199219,133006500,0.0,0 -2010-04-12,95.97403888503511,96.25466963060926,95.8617890336687,96.00611114501953,110279000,0.0,0 -2010-04-13,95.90990280676016,96.24665236902108,95.41279194069051,96.07827758789062,125043600,0.0,0 -2010-04-14,96.4310817614828,97.16873168945312,96.27874613528559,97.16873168945312,161609000,0.0,0 -2010-04-15,97.00837004647178,97.47340875419981,96.97629777864644,97.24890899658203,144615300,0.0,0 -2010-04-16,96.90413664005942,97.24890586161817,95.2123627990571,95.70145416259766,366786700,0.0,0 -2010-04-19,95.42082130057648,96.15846502211257,94.98785491172036,96.06224822998047,217947800,0.0,0 -2010-04-20,96.66357878985049,97.00033446670994,96.11034911810081,96.92015075683594,157708000,0.0,0 -2010-04-21,96.9762952741566,97.20080114313593,96.20657921770577,96.74378204345703,192910100,0.0,0 -2010-04-22,96.06225418919313,97.15268676569958,95.50902442230094,97.03241729736328,115360300,0.0,0 -2010-04-23,96.96827302679615,97.70591674954754,96.7197145093351,97.66582489013672,177335500,0.0,0 -2010-04-26,97.69786818874432,97.91435437792461,97.20076353552174,97.2969741821289,143457300,0.0,0 -2010-04-27,96.73577480170879,97.28900465587331,94.8114813562472,94.99589538574219,355853300,0.0,0 -2010-04-28,95.45290564308688,95.95803010209059,94.82750559082939,95.71749114990234,300674100,0.0,0 -2010-04-29,96.29478340430394,97.10459138250432,96.27073073160213,96.90414428710938,193775000,0.0,0 -2010-04-30,96.92015356821653,97.02438994721305,95.2363996037112,95.26045227050781,270000900,0.0,0 -2010-05-03,95.71748834253981,96.75981561523818,95.57316619889171,96.49522399902344,182747900,0.0,0 -2010-05-04,95.42084256338372,95.43687564039897,93.74510156733804,94.2261734008789,360353400,0.0,0 -2010-05-05,93.45643507868076,94.45065689831351,92.9833829544216,93.66490173339844,328973200,0.0,0 -2010-05-06,93.21591503656177,93.80923670118335,84.18777652670302,90.5539779663086,647356600,0.0,0 -2010-05-07,90.31342817047542,91.21944669260245,87.72365554342718,89.20696258544922,637558800,0.0,0 -2010-05-10,92.85509614485493,93.5286014620868,92.13349160682222,93.13572692871094,396159600,0.0,0 -2010-05-11,92.26177113102197,94.09786690679799,92.13348819549267,92.87113189697266,317849800,0.0,0 -2010-05-12,93.2399423777963,94.30632205888325,93.07958108149113,94.17001342773438,235607100,0.0,0 -2010-05-13,93.91345830878234,94.35444430623957,92.91924256206678,92.99942016601562,234452500,0.0,0 -2010-05-14,92.30190702800975,92.47028190212812,90.49788265467653,91.31570434570312,345601400,0.0,0 -2010-05-17,91.56421659570395,91.82078859351867,89.61587088452514,91.36376953125,325739800,0.0,0 -2010-05-18,92.10942587085852,92.38203703876589,89.82433073279864,90.1209945678711,360556800,0.0,0 -2010-05-19,89.61584733842291,90.41763538557306,88.48532937286302,89.60783386230469,394742700,0.0,0 -2010-05-20,87.69959931839496,88.10851305145451,86.16818689404896,86.22431182861328,530418300,0.0,0 -2010-05-21,84.91738879983717,87.69958863684383,84.47640285542893,87.48310852050781,500909400,0.0,0 -2010-05-24,87.01005748157242,87.70761545550242,86.28043336100643,86.36061096191406,269823000,0.0,0 -2010-05-25,84.27597626749566,86.48891388694697,83.69066806440478,86.44882202148438,396505200,0.0,0 -2010-05-26,86.97798183668435,87.77175039815812,85.67106322768976,85.9276351928711,349719300,0.0,0 -2010-05-27,87.54726863189546,88.8381483166688,87.21853248440785,88.80607604980469,300870500,0.0,0 -2010-05-28,88.70985781400258,88.77400234669302,87.27465601014012,87.69158935546875,297933500,0.0,0 -2010-06-01,86.87374410903631,88.15660391497201,86.08799515415735,86.21627807617188,277909400,0.0,0 -2010-06-02,86.65726510660264,88.46930192933033,86.2002461037507,88.46128845214844,240243700,0.0,0 -2010-06-03,88.71789349067438,89.046623575269,87.85998009120401,88.76599884033203,226618300,0.0,0 -2010-06-04,87.08221234027506,87.65950079285012,85.35836657693513,85.64701080322266,398475600,0.0,0 -2010-06-07,85.9517154109026,86.28045159640891,84.5165195349979,84.58065795898438,264609100,0.0,0 -2010-06-08,84.6447912790007,85.65504625807267,83.90714750508245,85.4866714477539,357774300,0.0,0 -2010-06-09,85.983763986446,86.81762444007998,84.66883184110428,85.0296401977539,268023300,0.0,0 -2010-06-10,86.48089760969755,87.61943567348031,85.02164296138385,87.51520538330078,317890600,0.0,0 -2010-06-11,86.74549951010997,87.99628760883522,86.68937455996615,87.9401626586914,214128200,0.0,0 -2010-06-14,88.61365783558195,89.09473580491908,87.71565867609498,87.8038558959961,207196100,0.0,0 -2010-06-15,88.42120103781261,89.88045530591313,88.2688593234925,89.80027770996094,238268700,0.0,0 -2010-06-16,89.33528294046108,90.13707152786533,89.15888847248632,89.76824951171875,216374000,0.0,0 -2010-06-17,90.02478649016105,90.06487835075326,89.03859033804699,89.91253662109375,263185800,0.0,0 -2010-06-18,90.09059173060568,90.33226896099696,89.72001506032818,90.0100326538086,174006600,0.531,0 -2010-06-21,91.12982358580625,91.1942671617272,89.25276691445609,89.75224304199219,213140700,0.0,0 -2010-06-22,89.75223306324625,90.14697681177817,88.1410270653459,88.2699203491211,239355400,0.0,0 -2010-06-23,88.32631148647594,88.64049616142475,87.39181521249729,87.99601745605469,254639900,0.0,0 -2010-06-24,87.56100699998908,87.67379094756151,86.3123196780973,86.53788757324219,268523600,0.0,0 -2010-06-25,86.7956659302658,87.34347621883796,86.01423003298136,86.90039825439453,238726500,0.0,0 -2010-06-28,87.0292662286061,87.262891769374,86.31228025526316,86.62646484375,169218600,0.0,0 -2010-06-29,85.41002075927246,86.61037356187646,83.420186665812,83.9518814086914,373649500,0.0,0 -2010-06-30,83.71825461178192,84.49163268334712,82.88042683102063,83.15433502197266,284101700,0.0,0 -2010-07-01,83.09794472659335,83.37184677676629,81.47062334125796,82.78376007080078,382924800,0.0,0 -2010-07-02,83.0657126479247,83.31544757151387,81.86536608924689,82.33261108398438,233385200,0.0,0 -2010-07-06,83.4926947255283,84.08078762197763,82.0748317195271,82.87238311767578,256935300,0.0,0 -2010-07-07,83.08183662959847,85.58726249509384,82.9932198063484,85.48253631591797,253769400,0.0,0 -2010-07-08,86.19951549501782,86.42508333721022,85.32141123130272,86.32841491699219,210842100,0.0,0 -2010-07-09,86.30425598666517,86.98096577179568,86.14313782872463,86.97290802001953,144999900,0.0,0 -2010-07-12,86.6828645191091,87.19844984598576,86.32034569892647,87.0292739868164,131283600,0.0,0 -2010-07-13,87.93156325972059,88.68882585358891,87.75432962724203,88.34242248535156,213025900,0.0,0 -2010-07-14,88.06045003637307,88.68076770426518,87.69793119397752,88.33435821533203,184426800,0.0,0 -2010-07-15,88.30216667699764,88.66468565616522,87.14209610963803,88.35855865478516,232337900,0.0,0 -2010-07-16,87.88324607662688,87.97992066881473,85.75645421860906,85.9256362915039,282693400,0.0,0 -2010-07-19,86.23979301104049,86.70703802246373,85.57114112810376,86.43313598632812,186709000,0.0,0 -2010-07-20,85.28919088691451,87.45625875367986,85.24890827885122,87.39181518554688,258162400,0.0,0 -2010-07-21,87.84294390102042,87.86711100542935,85.90143789666949,86.25590515136719,264527000,0.0,0 -2010-07-22,87.2790273457948,88.56799708164976,87.27097574098653,88.18130493164062,274781300,0.0,0 -2010-07-23,88.00407218072723,89.07552567239244,87.75433721134033,88.9466323852539,222020800,0.0,0 -2010-07-26,89.09968404795792,89.96167894900267,88.84994910304466,89.87306213378906,184445700,0.0,0 -2010-07-27,90.36447535499758,90.46114991515681,89.5105382521759,89.86500549316406,204855600,0.0,0 -2010-07-28,89.67971685458036,89.95362504357746,88.98689785132376,89.28497314453125,163056200,0.0,0 -2010-07-29,89.84085634235623,90.08253973445234,88.14103943066584,88.84996795654297,220149100,0.0,0 -2010-07-30,87.94766224605607,89.30913303844164,87.7946018829932,88.83382415771484,220070600,0.0,0 -2010-08-02,90.21947263095895,90.98479912105891,89.85695376203873,90.83979034423828,188263200,0.0,0 -2010-08-03,90.6142207519322,90.84784019416168,90.10668696972905,90.4047622680664,146657300,0.0,0 -2010-08-04,90.65450488009515,91.12175609660795,90.3564357028043,91.00897216796875,158171700,0.0,0 -2010-08-05,90.42893193545606,90.96063283735398,90.29198090785066,90.91229248046875,140473800,0.0,0 -2010-08-06,90.01805919771566,90.686711026877,89.35746511786981,90.54170227050781,239728300,0.0,0 -2010-08-09,90.9686706084633,91.17812906641386,90.48531013695283,91.02506256103516,120800400,0.0,0 -2010-08-10,90.2516934686404,91.01701992466923,89.71999874147677,90.53365325927734,242916300,0.0,0 -2010-08-11,89.13997680541853,89.17220166428099,87.90740514868028,88.05241394042969,273406900,0.0,0 -2010-08-12,86.72315449679029,87.82682654995916,86.6828718927825,87.51264190673828,239542600,0.0,0 -2010-08-13,87.2387581964807,87.77851078451788,87.15014136605504,87.25486755371094,158698500,0.0,0 -2010-08-16,86.65871630199298,87.49654417731307,86.34453161703085,87.21458435058594,147895300,0.0,0 -2010-08-17,87.96378973192542,88.93051083160458,87.71404863326906,88.28602600097656,172270300,0.0,0 -2010-08-18,88.24575044073899,88.92245398816542,87.73822278016917,88.44715118408203,182922100,0.0,0 -2010-08-19,87.98794300650833,88.20545306855338,86.54591318800689,86.90843200683594,265847600,0.0,0 -2010-08-20,86.65063194343955,86.95676492953746,85.99809566677632,86.62646484375,209714200,0.0,0 -2010-08-23,87.03734927231542,87.46431788094797,86.25591337518951,86.29619598388672,163490300,0.0,0 -2010-08-24,85.35362579470561,85.70809304165506,84.56413838537948,85.01527404785156,280677800,0.0,0 -2010-08-25,84.54804480110582,85.66783264376258,84.01634993912268,85.34559631347656,272234800,0.0,0 -2010-08-26,85.7483842270813,85.86116815398641,84.49163938315388,84.77360534667969,224439500,0.0,0 -2010-08-27,85.30531779955439,86.17537068418238,84.03246333644279,86.08675384521484,272649000,0.0,0 -2010-08-30,85.86119326900167,86.1270438085825,84.83002212352255,84.83807373046875,167238600,0.0,0 -2010-08-31,84.52388896415137,85.37783256284145,84.17747933125464,84.83807373046875,273933100,0.0,0 -2010-09-01,85.98201964377861,87.49655122524072,85.92562767389076,87.3757095336914,256828100,0.0,0 -2010-09-02,87.58515174391611,88.20546329715435,87.39986036521438,88.1893539428711,156112200,0.0,0 -2010-09-03,89.0513455722883,89.41386441987736,88.5760366637975,89.33330535888672,212197300,0.0,0 -2010-09-07,88.91437903641268,89.02716293136872,88.25378501755822,88.32628631591797,141973700,0.0,0 -2010-09-08,88.50354826576431,89.30109353458232,88.46326565626042,88.9466323852539,149924400,0.0,0 -2010-09-09,89.94558348165197,89.96975059021192,89.11581330768763,89.35749053955078,147017900,0.0,0 -2010-09-10,89.51860669870844,89.913350443901,89.31720595067466,89.80862426757812,127819000,0.0,0 -2010-09-13,90.6947798625826,90.99284901481008,90.33225485421421,90.80756378173828,178503500,0.0,0 -2010-09-14,90.63034849163859,91.26677567637597,90.29199666570646,90.75119018554688,209823600,0.0,0 -2010-09-15,90.4853351702058,91.20232139966114,90.21143308086941,91.09759521484375,168608400,0.0,0 -2010-09-16,90.81561735279253,91.12980199116242,90.50948431808993,91.07341003417969,199962900,0.0,0 -2010-09-17,91.5528626801122,91.64195389289245,90.85633475064795,91.10740661621094,195836900,0.602,0 -2010-09-20,91.42330626069426,92.70297488939453,91.13173603696085,92.50049591064453,214555200,0.0,0 -2010-09-21,92.57339710433024,93.01074628697381,91.93356275163315,92.31422424316406,268389100,0.0,0 -2010-09-22,92.16844403428483,92.68678978424022,91.60149912209262,91.86067199707031,191322400,0.0,0 -2010-09-23,91.10744629025925,92.06314738784917,90.85637431536335,91.11554718017578,202354300,0.0,0 -2010-09-24,92.1279049690535,93.05930920017823,92.04691464007875,92.99451446533203,209671800,0.0,0 -2010-09-27,93.02693345160283,93.13222028582139,92.45999483786072,92.54907989501953,128761800,0.0,0 -2010-09-28,92.67058314273477,93.17273326505008,91.66628907727257,92.87306213378906,209207500,0.0,0 -2010-09-29,92.63817918433082,93.06743980538519,92.34660896358386,92.71107482910156,179665800,0.0,0 -2010-09-30,93.18080371453661,93.78013960209903,91.9983213554048,92.43567657470703,287106700,0.0,0 -2010-10-01,93.13220574769176,93.23749874464131,92.27369707256369,92.8244400024414,174638700,0.0,0 -2010-10-04,92.63007974853882,93.01883590358955,91.66627789139747,92.1279296875,166153200,0.0,0 -2010-10-05,92.97832648358145,94.20939565062022,92.87303348604424,93.98262023925781,229634100,0.0,0 -2010-10-06,93.96643219256067,94.21751030253563,93.59387165101265,93.97453308105469,148626600,0.0,0 -2010-10-07,94.35519417003393,94.37949065626071,93.29420641034822,93.86114501953125,164860000,0.0,0 -2010-10-08,93.99073908466241,94.64676896132725,93.63437412490981,94.3875961303711,177760100,0.0,0 -2010-10-11,94.53339370025054,94.73587269423525,94.15273220289231,94.47669982910156,103098300,0.0,0 -2010-10-12,94.16890737056943,95.04361791778693,93.66676352911163,94.76824951171875,182210000,0.0,0 -2010-10-13,95.29466779734976,96.01549215173235,95.06788624995153,95.50524139404297,194347200,0.0,0 -2010-10-14,95.41617497239876,95.57816183404498,94.53336974830292,95.13270568847656,217764300,0.0,0 -2010-10-15,95.79683238340782,95.85352623783098,94.56576318200483,95.32707977294922,243705000,0.0,0 -2010-10-18,95.35948159314283,96.11270351086054,95.01121756281816,95.79683685302734,141204800,0.0,0 -2010-10-19,94.91404923147407,95.44859052882573,93.96644290302244,94.54148864746094,280604700,0.0,0 -2010-10-20,94.71154559469149,95.92641916597623,94.65485174186486,95.46476745605469,200051800,0.0,0 -2010-10-21,95.89401179608313,96.45284937876531,94.93021021936917,95.67533111572266,221585500,0.0,0 -2010-10-22,95.82114876487064,95.99933125267779,95.57007681159766,95.85354614257812,108212400,0.0,0 -2010-10-25,96.49337361737558,96.99552366753697,96.06411920668697,96.13700866699219,151145700,0.0,0 -2010-10-26,95.65106343485192,96.25039945113971,95.46478624426072,96.15321350097656,158982900,0.0,0 -2010-10-27,95.4809582965591,95.9831082388497,94.9707136465296,95.87781524658203,190024000,0.0,0 -2010-10-28,96.42858424497227,96.46908251129014,95.43239115689845,95.89404296875,168576000,0.0,0 -2010-10-29,95.79683737318396,96.15320229335823,95.62675580105541,95.9669189453125,144305500,0.0,0 -2010-11-01,96.43666860325033,96.98741156326057,95.44857039448195,95.99931335449219,174074800,0.0,0 -2010-11-02,96.72012949071951,96.987403149413,96.46095671903178,96.76062774658203,158345900,0.0,0 -2010-11-03,96.93069380526062,97.20606212777858,95.93449495443879,97.14936828613281,226702800,0.0,0 -2010-11-04,98.22658166105705,99.06889478675654,97.1655940006007,99.02030181884766,215039400,0.0,0 -2010-11-05,99.08511129462568,99.55486401410766,98.95552795979816,99.39288330078125,180654100,0.0,0 -2010-11-08,99.08512299437679,99.36859854492238,98.76116152947131,99.20661163330078,156107100,0.0,0 -2010-11-09,99.47384798277743,99.57913480134776,98.09699373737104,98.49385070800781,186621600,0.0,0 -2010-11-10,98.46957825529903,98.93933096817214,97.72445709781148,98.89073181152344,221387400,0.0,0 -2010-11-11,98.04030300796973,98.66393541026775,97.74063195902995,98.51815032958984,158017600,0.0,0 -2010-11-12,97.85404065603683,98.28329510255202,96.90644051797486,97.35189056396484,239068800,0.0,0 -2010-11-15,97.65965142580673,98.0403128513048,97.173703167813,97.21419525146484,163940800,0.0,0 -2010-11-16,96.61486395477681,96.77684465556894,95.23800328137321,95.69966125488281,299566200,0.0,0 -2010-11-17,95.74016723747563,96.14512522740448,95.45669788035916,95.7482681274414,172308900,0.0,0 -2010-11-18,96.67153778109139,97.50574995768416,96.66343689398053,97.15748596191406,197723700,0.0,0 -2010-11-19,97.1089303102453,97.46528915088547,96.58248367075545,97.42479705810547,156852900,0.0,0 -2010-11-22,96.93883022131479,97.38428023203791,96.19370294193696,97.34378814697266,181361000,0.0,0 -2010-11-23,96.19368969630517,96.39616863125326,95.5619564079338,95.93451690673828,222309000,0.0,0 -2010-11-24,96.54197464852697,97.37619323192915,96.52577904905216,97.35189056396484,140046100,0.0,0 -2010-11-26,96.50956485766348,97.03600515794848,96.2179946899414,96.2179946899414,76007800,0.0,0 -2010-11-29,95.9750274673561,96.76874771292405,95.35948969604836,96.50957489013672,223642300,0.0,0 -2010-11-30,95.55386634692906,96.51766192232411,95.41617596830089,95.9669189453125,233930700,0.0,0 -2010-12-01,97.3518908456806,98.19420414167362,97.3437961355038,98.00792694091797,221037200,0.0,0 -2010-12-02,98.16180429050637,99.33618604920474,98.10511042485665,99.26329040527344,191213600,0.0,0 -2010-12-03,98.92313401905096,99.64395872298925,98.89883752912448,99.53057098388672,151288900,0.0,0 -2010-12-06,99.31997706687713,99.65204552671642,99.21469023235534,99.4252700805664,103050500,0.0,0 -2010-12-07,100.3809942830932,100.4376881577709,99.42529312483401,99.48198699951172,206581000,0.0,0 -2010-12-08,99.60344925205104,99.9274106334309,99.14179748650075,99.84642028808594,138019200,0.0,0 -2010-12-09,100.40528822371492,100.44578031650207,99.74115734615198,100.2352066040039,123705100,0.0,0 -2010-12-10,100.54294664361102,100.91550716909545,100.21088437968307,100.81832122802734,117571700,0.0,0 -2010-12-13,101.28000387492253,101.4014863383085,100.85074318067223,100.88314056396484,133812700,0.0,0 -2010-12-14,101.03700850010152,101.42577085083961,100.66444792769349,100.97221374511719,147249600,0.0,0 -2010-12-15,100.78594134362686,101.18279842324345,100.34048510587581,100.51056671142578,160823100,0.0,0 -2010-12-16,100.57537371312027,101.16661513891344,100.22710957007367,101.09371948242188,185035200,0.0,0 -2010-12-17,101.02283820396016,101.33222247607084,100.81115095900945,101.20195770263672,141075300,0.653,0 -2010-12-20,101.4787881486908,101.69047542267943,100.94143579061297,101.44622039794922,119085500,0.0,0 -2010-12-21,101.76375282276412,102.15455963774085,101.66605577776762,102.08942413330078,94965500,0.0,0 -2010-12-22,102.1626954690601,102.4395120246806,102.10570345986393,102.40694427490234,78878100,0.0,0 -2010-12-23,102.2929818801979,102.4069659226853,102.00802177397941,102.26041412353516,70053700,0.0,0 -2010-12-27,101.87775754893164,102.39882926699472,101.80448475029692,102.30113220214844,58126000,0.0,0 -2010-12-28,102.50466200015487,102.54536703437878,102.17899066804344,102.44766998291016,55309100,0.0,0 -2010-12-29,102.5697821592864,102.74889546406247,102.50464666027888,102.52092742919922,58033100,0.0,0 -2010-12-30,102.42325929668596,102.69193243550247,102.20342847841403,102.35812377929688,76616900,0.0,0 -2010-12-31,102.20341829266066,102.48024110814323,102.04058572708094,102.38253784179688,91218900,0.0,0 -2011-01-03,103.16415615938526,103.88877171205556,102.34183733011962,103.44097900390625,138725200,0.0,0 -2011-01-04,103.66890930136293,103.70147704835219,102.74075025298812,103.38394927978516,137409700,0.0,0 -2011-01-05,103.058309495029,103.98646882685885,102.96060622422314,103.92133331298828,133975300,0.0,0 -2011-01-06,103.96203611823842,104.07602015242183,103.40839671863438,103.71778106689453,122519000,0.0,0 -2011-01-07,103.85618855006605,104.02716460000141,102.70820472573888,103.51423645019531,156034600,0.0,0 -2011-01-10,103.05827802155515,103.53050103540552,102.7488875309885,103.38394927978516,122401700,0.0,0 -2011-01-11,103.75846392383075,104.00271269668293,103.35951370167959,103.75032043457031,110287000,0.0,0 -2011-01-12,104.38541988336375,104.80064481079931,103.77478220135505,104.68666076660156,107929200,0.0,0 -2011-01-13,104.72734135351982,104.77618986911266,104.25511833057996,104.5156478881836,129048400,0.0,0 -2011-01-14,104.36913024534532,105.2972895597366,104.29585745236598,105.27286529541016,117677900,0.0,0 -2011-01-18,105.1751363486865,105.54966237125502,105.05301504965678,105.45196533203125,114401300,0.0,0 -2011-01-19,105.36241495771947,105.46824928519786,104.14115216532181,104.41796875,151958400,0.0,0 -2011-01-20,104.18184539649644,104.54007821775494,103.50607857045368,104.27954864501953,175745700,0.0,0 -2011-01-21,104.9308851173736,105.16699041718083,104.40166387735849,104.5156478881836,151462900,0.0,0 -2011-01-24,104.4505387311527,105.23215244770584,104.42611446719748,105.11003112792969,113715500,0.0,0 -2011-01-25,104.83318503274019,105.25655957792797,104.30397616875037,105.16699981689453,167552200,0.0,0 -2011-01-26,105.42754348809922,105.88347957935986,105.21585000049483,105.57408905029297,141281500,0.0,0 -2011-01-27,105.59851512151607,106.01375240150587,105.41125831439287,105.83463287353516,123302700,0.0,0 -2011-01-28,105.9567447754349,106.12772700908107,103.81546654393472,103.98644256591797,295637300,0.0,0 -2011-01-31,104.27140864477339,104.84946599554195,104.0108666555123,104.7680435180664,149249200,0.0,0 -2011-02-01,105.40310838535557,106.63250824949345,105.33797288997187,106.44525146484375,167194300,0.0,0 -2011-02-02,106.16843690011102,106.52667596163596,106.1114510985014,106.2417221069336,118323600,0.0,0 -2011-02-03,106.05447720480325,106.64068445390983,105.49270664579528,106.47785186767578,145886700,0.0,0 -2011-02-04,106.51853840031667,106.81977923610722,106.03002833165301,106.77906799316406,134634800,0.0,0 -2011-02-07,107.01518159328616,107.79678276480915,107.00703189101768,107.44669342041016,112439100,0.0,0 -2011-02-08,107.5444209499403,107.99221988522595,107.25131732169243,107.93523406982422,99072800,0.0,0 -2011-02-09,107.64213046974052,107.98408263223082,107.15362028142287,107.69097900390625,146436700,0.0,0 -2011-02-10,107.14548978872931,107.85381842567128,106.9012346790949,107.73169708251953,162708500,0.0,0 -2011-02-11,107.30829513381762,108.5132709657619,107.2838708713904,108.37486267089844,137710300,0.0,0 -2011-02-14,108.30972612389556,108.72495100450112,108.18760481293147,108.63539123535156,101690700,0.0,0 -2011-02-15,108.30159910557701,108.464431676733,107.73167889486666,108.29344940185547,119575400,0.0,0 -2011-02-16,108.65982051743688,109.10760690934748,108.43998974935182,108.97734832763672,130183500,0.0,0 -2011-02-17,108.65985408202906,109.44959278861528,108.5621445864054,109.30304718017578,109810500,0.0,0 -2011-02-18,109.40070434404082,109.66124633955292,109.14831205046731,109.53097534179688,130002400,0.0,0 -2011-02-22,108.38302154732695,109.55543607076308,107.03963715283237,107.3327407836914,233116400,0.0,0 -2011-02-23,107.26761578051288,107.52815788830223,106.01379105093821,106.67327117919922,227584000,0.0,0 -2011-02-24,106.55926912948443,107.0152053178386,105.59853579831295,106.59996795654297,260431400,0.0,0 -2011-02-25,107.04775323044045,107.80494263656531,106.98261772278138,107.73980712890625,141686900,0.0,0 -2011-02-28,108.13873520682999,108.54582272845187,107.7804962000707,108.40740203857422,141585500,0.0,0 -2011-03-01,108.74940271306303,108.84709977533053,106.56740641023026,106.59996795654297,258565500,0.0,0 -2011-03-02,106.45338539610341,107.32455864322932,106.12772035274938,106.82791137695312,200277400,0.0,0 -2011-03-03,107.79678772401056,108.79008242698457,107.78865044469435,108.66796112060547,176480100,0.0,0 -2011-03-04,108.5865199538924,108.79821341296795,107.14543909322074,107.85376739501953,277202300,0.0,0 -2011-03-07,108.17134207927774,108.41559717928858,106.44529423964059,107.00706481933594,216790400,0.0,0 -2011-03-08,107.17802505399975,108.28530384589844,106.71395164050497,107.94335174560547,174615000,0.0,0 -2011-03-09,107.73166471079152,108.12246531477838,107.14545759314763,107.78865051269531,153806000,0.0,0 -2011-03-10,106.6569461073766,106.80349167318212,105.6880756779055,105.7939224243164,301291800,0.0,0 -2011-03-11,105.45196763833593,106.90933567304538,105.42754337799582,106.52667236328125,225621800,0.0,0 -2011-03-14,105.83465824162465,106.23359621227917,105.0774687058305,105.88350677490234,234974100,0.0,0 -2011-03-15,103.06643820132302,105.29728277832024,102.99316541306264,104.67036437988281,359585400,0.0,0 -2011-03-16,104.33654006015018,104.67850455422584,101.99986136503645,102.73262023925781,468670300,0.0,0 -2011-03-17,104.21442030176837,104.53194811669225,103.48166141668887,104.09229278564453,254303700,0.0,0 -2011-03-18,105.35401813760552,105.38673360708057,104.26646584617933,104.47089385986328,230435400,0.553,0 -2011-03-21,105.77110425878321,106.3107849617073,105.64843991202237,106.09001159667969,153992600,0.0,0 -2011-03-22,106.07361518577797,106.21262474625671,105.6238710328782,105.72199249267578,129538600,0.0,0 -2011-03-23,105.42762017540096,106.3025781539243,104.92882774842232,106.0245590209961,148603100,0.0,0 -2011-03-24,106.6296495169757,107.19387284471152,106.03272319627226,107.03850555419922,159129800,0.0,0 -2011-03-25,107.31656273432199,107.83171297951155,107.0303584490952,107.3656234741211,155642800,0.0,0 -2011-03-28,107.59458285739115,107.8726020368965,107.07124749943436,107.10395050048828,109762400,0.0,0 -2011-03-29,107.0139925278071,107.8562351027581,106.66238226793155,107.82353210449219,129798800,0.0,0 -2011-03-30,108.38775216317836,108.8865570930308,108.23238485231154,108.5676498413086,135835000,0.0,0 -2011-03-31,108.42862578002074,108.72300262514634,108.30596148279571,108.42044067382812,132537100,0.0,0 -2011-04-01,109.09096241258757,109.38533923572176,108.61668794879945,108.87834930419922,153850100,0.0,0 -2011-04-04,109.10731705170463,109.30357244310251,108.65758538869075,108.96830749511719,100768900,0.0,0 -2011-04-05,108.75570231940684,109.43440481611921,108.70664159292329,108.95195770263672,120791500,0.0,0 -2011-04-06,109.47531547134926,109.57343694453431,108.85384620171537,109.29541778564453,120411600,0.0,0 -2011-04-07,109.09915111693066,109.55706791432686,108.47769438864964,109.01738739013672,170731500,0.0,0 -2011-04-08,109.49983559348507,109.56525406191982,108.1914911793843,108.6412353515625,147945400,0.0,0 -2011-04-11,108.75572230479816,109.12369029833977,108.05248931561506,108.31416320800781,121385400,0.0,0 -2011-04-12,107.70904381819645,107.92164448225475,107.11211745108491,107.5046157836914,161187400,0.0,0 -2011-04-13,108.0033992461572,108.08516295734928,107.08756582612081,107.49642181396484,162059000,0.0,0 -2011-04-14,106.87499113124684,107.74176414057378,106.52338084445532,107.5782241821289,161220400,0.0,0 -2011-04-15,107.77447543219779,108.24056491952562,107.45556815856342,107.97071838378906,170006700,0.0,0 -2011-04-18,106.78502841359085,107.96253592514125,105.90189777992805,106.760498046875,210759300,0.0,0 -2011-04-19,106.92407311081125,107.40653292841476,106.66241162078194,107.37381744384766,124258800,0.0,0 -2011-04-20,108.65759622242362,109.07462493376588,108.58399264500065,108.83749389648438,156133800,0.0,0 -2011-04-21,109.40171968902082,109.4426077981958,108.83750870068282,109.39354705810547,135935400,0.0,0 -2011-04-25,109.31173283578221,109.45892748217776,108.91923457421869,109.2790298461914,65757100,0.0,0 -2011-04-26,109.61429958474217,110.44018423920586,109.49982040018982,110.21939849853516,146600000,0.0,0 -2011-04-27,110.43203305825098,111.10255056162586,109.98228886112882,110.93901062011719,143031000,0.0,0 -2011-04-28,110.74270132768606,111.44593396361917,110.72635607559558,111.29875183105469,124791100,0.0,0 -2011-04-29,111.33968203989198,111.67494699396566,111.1924873743465,111.56045532226562,115094100,0.0,0 -2011-05-02,112.08380847549431,112.1737448322531,111.16796239857256,111.38874816894531,126278700,0.0,0 -2011-05-03,111.17614276448013,111.36421305410519,110.42383665138692,110.9880599975586,138375000,0.0,0 -2011-05-04,110.93895773800423,110.98801844934093,109.76145075675657,110.25208282470703,182678500,0.0,0 -2011-05-05,109.63887292019436,110.35027866894053,108.77209982923921,109.25454711914062,226900000,0.0,0 -2011-05-06,110.34210349665264,110.90632699913591,109.31177797827452,109.73699188232422,222787200,0.0,0 -2011-05-09,109.72878401383022,110.48107761645832,109.55705899014637,110.16217041015625,114104500,0.0,0 -2011-05-10,110.53014308781383,111.2987944583766,110.39113352805407,111.1025390625,114806900,0.0,0 -2011-05-11,110.93899462900787,110.9553523639902,109.42623479148462,109.93321228027344,193564200,0.0,0 -2011-05-12,109.63886879992131,110.68553956596305,109.07464530565042,110.45658111572266,171550700,0.0,0 -2011-05-13,110.51380119717795,110.66916851486079,109.2136417659872,109.60614013671875,157444900,0.0,0 -2011-05-16,109.21365185057907,110.07225225364074,108.73120459228802,108.91110229492188,141675400,0.0,0 -2011-05-17,108.50222365924209,109.04191665528174,108.03612175976326,108.89472198486328,192686200,0.0,0 -2011-05-18,108.9519491640659,109.98226177136186,108.71480570158613,109.86778259277344,135217900,0.0,0 -2011-05-19,110.22762768030351,110.41569802785018,109.52439455762867,110.1294937133789,119489500,0.0,0 -2011-05-20,109.84330100682719,110.12949284173303,109.05011903250781,109.25454711914062,182594900,0.0,0 -2011-05-23,107.92163445524126,108.5267458729821,107.60272725013499,107.98705291748047,168700000,0.0,0 -2011-05-24,108.2977934094678,108.53492443040415,107.69268195867325,107.89710998535156,147199600,0.0,0 -2011-05-25,107.46371529203742,108.70664109217657,107.43101230029757,108.25689697265625,151050100,0.0,0 -2011-05-26,107.96254779150732,108.95198510788798,107.75811972820789,108.75572967529297,164850000,0.0,0 -2011-05-27,109.05824268478456,109.4670986744281,108.72299025602075,109.17272186279297,120921900,0.0,0 -2011-05-31,110.203070223645,110.32572205553163,109.4425914340562,110.30936431884766,164731200,0.0,0 -2011-06-01,109.99046288222613,110.32572787680262,107.74175434547313,107.83170318603516,233094300,0.0,0 -2011-06-02,107.90530690989051,108.13426532666247,107.08759471361982,107.71722412109375,200466800,0.0,0 -2011-06-03,106.42523320390612,107.46373114631949,106.36799984059377,106.6460189819336,234690200,0.0,0 -2011-06-06,106.37614767535679,106.59693340192165,105.37853806915349,105.51754760742188,179951200,0.0,0 -2011-06-07,106.05728571756389,106.35984776140934,105.36223772991858,105.45218658447266,161660500,0.0,0 -2011-06-08,105.28860872756974,105.64023142952877,104.81433419777431,105.01058959960938,198696400,0.0,0 -2011-06-09,105.2968080890392,106.24534479974312,105.0433192923296,105.81195831298828,160964400,0.0,0 -2011-06-10,105.36222679679354,105.4276327957733,104.06206109502158,104.34008026123047,238629400,0.0,0 -2011-06-13,104.57718887311954,104.8633930686687,103.89031377655567,104.42182159423828,207599800,0.0,0 -2011-06-14,105.37856342569305,106.11451186556411,105.3376877990037,105.74654388427734,160570400,0.0,0 -2011-06-15,104.86342275498397,105.73019573802156,103.58778741093602,103.86580657958984,300958000,0.0,0 -2011-06-16,103.89850167622994,104.64262271219522,103.29339644431924,104.09475708007812,308032800,0.0,0 -2011-06-17,105.12853980575493,105.13675922242294,104.05202819662854,104.40538787841797,233284900,0.628,0 -2011-06-20,104.05202678773271,105.16140977893184,104.01915539108367,104.93952941894531,159479000,0.0,0 -2011-06-21,105.48189486170781,106.58305840296956,104.98061704976699,106.37761688232422,193157300,0.0,0 -2011-06-22,106.0489160558263,106.67345376797195,105.67089813850434,105.73664093017578,176703000,0.0,0 -2011-06-23,104.49579655977067,105.71200706622203,103.69868231549414,105.43260955810547,334286500,0.0,0 -2011-06-24,105.40794446121768,105.49011355029934,104.05202909981071,104.20816040039062,226129300,0.0,0 -2011-06-27,104.27389241689602,105.53940660251635,104.06845090669336,105.13674926757812,168904700,0.0,0 -2011-06-28,105.55585020782958,106.5255407001987,105.40793833258891,106.50910186767578,165556300,0.0,0 -2011-06-29,106.9939427692399,107.59382850598958,106.52554261672876,107.4212646484375,244295500,0.0,0 -2011-06-30,107.76639613375352,108.62102728345198,107.4130427614146,108.4484634399414,223322700,0.0,0 -2011-07-01,108.54705495656985,110.19881244420296,108.29230953328283,110.05088806152344,202385700,0.0,0 -2011-07-05,109.93590404229123,110.18243646127303,109.61541565936281,109.96055603027344,165936000,0.0,0 -2011-07-06,109.69754345663854,110.23168631956469,109.38526837662745,110.09198760986328,143331600,0.0,0 -2011-07-07,111.06990984578799,111.51365802559745,110.8398163393452,111.23426055908203,170464200,0.0,0 -2011-07-08,109.97692773389934,111.23422851011918,109.61534874533771,110.44532775878906,194100500,0.0,0 -2011-07-11,109.08943993700387,109.44279330934279,108.19371797817998,108.4484634399414,195918600,0.0,0 -2011-07-12,108.21839123778648,109.11411337276088,107.94720689121353,107.98007202148438,214675700,0.0,0 -2011-07-13,108.54707053324621,109.47567014804062,108.07867042237544,108.34162902832031,204062600,0.0,0 -2011-07-14,108.61283614541256,109.11411402434561,107.38840000463324,107.59384155273438,226111800,0.0,0 -2011-07-15,108.19374706358008,108.36631095348048,107.46237560549105,108.21839904785156,220012800,0.0,0 -2011-07-18,107.71711031174561,107.88146103675996,106.52555188144588,107.33087921142578,196872100,0.0,0 -2011-07-19,107.93077400247064,109.20451417109398,107.90612201862018,109.07302856445312,166554900,0.0,0 -2011-07-20,109.35242361787917,109.41815387302842,108.81826811633346,109.00727081298828,137145400,0.0,0 -2011-07-21,109.62359069417786,110.79050956902417,109.02370496325092,110.51932525634766,245246300,0.0,0 -2011-07-22,110.54396539460379,110.70831608612423,109.91941521201053,110.59326934814453,126019400,0.0,0 -2011-07-25,109.54145459314832,110.51935853475906,109.42640780820348,109.97698974609375,136653800,0.0,0 -2011-07-26,109.9030129571861,110.08380251914532,109.31955345465286,109.56608581542969,131278200,0.0,0 -2011-07-27,108.9579766843797,108.99085435407707,107.18295869543184,107.32266998291016,249020100,0.0,0 -2011-07-28,107.32265108792404,108.2841158612082,106.83779958892723,107.0103759765625,207939900,0.0,0 -2011-07-29,105.93388192377606,107.28157796568648,105.161419703567,106.27902221679688,307038400,0.0,0 -2011-08-01,107.5198623511609,107.61848279954724,104.79981884883917,105.8270263671875,325790900,0.0,0 -2011-08-02,105.0299099459369,105.59693048946012,103.12341308593751,103.1234130859375,346653800,0.0,0 -2011-08-03,103.26312428986317,103.79726720379146,101.51275860036888,103.68222045898438,370830800,0.0,0 -2011-08-04,102.24411993907026,102.40847689394245,98.66121982735437,98.82557678222656,520721800,0.0,0 -2011-08-05,100.05824061830045,100.31298609928487,96.03158553367028,98.67767333984375,655619200,0.0,0 -2011-08-08,96.07265465550336,98.71052244976273,92.05421362490263,92.2514419555664,702263900,0.0,0 -2011-08-09,93.73885887616558,96.67256360551052,90.61614518500488,96.54108428955078,717828700,0.0,0 -2011-08-10,94.71676955018332,95.55496827647775,91.99671929172467,92.276123046875,662607400,0.0,0 -2011-08-11,93.07322108551764,97.72441349378184,92.30075902124277,96.41780853271484,487979700,0.0,0 -2011-08-12,97.29714070254174,97.96276946446356,96.37676009935733,97.06704711914062,313731600,0.0,0 -2011-08-15,97.9462885892643,99.22002208906405,97.79015104771344,99.12141418457031,258810600,0.0,0 -2011-08-16,98.17641647500167,99.17897231931353,97.22316460022023,98.2750244140625,294095200,0.0,0 -2011-08-17,98.81737750986811,99.59805282851818,97.56007632929915,98.34075164794922,238201100,0.0,0 -2011-08-18,95.7357538166127,98.37362233251507,93.18006065951285,94.1004409790039,512956300,0.0,0 -2011-08-19,92.82668556940071,95.22624075786362,92.44867396597795,92.563720703125,428281300,0.0,0 -2011-08-22,94.64281592443005,94.69212616191876,92.37474562647162,92.63771057128906,275090600,0.0,0 -2011-08-23,92.98284332322443,95.79328211792497,92.514436857441,95.68645477294922,331136600,0.0,0 -2011-08-24,95.48100412993472,97.16562085492123,95.25912377437707,97.03414154052734,246869700,0.0,0 -2011-08-25,97.56827730685366,98.11885900730404,95.21802610868205,95.55494689941406,312365400,0.0,0 -2011-08-26,95.07013772049736,97.38751825209931,93.55808458812182,96.94376373291016,314495900,0.0,0 -2011-08-29,98.25034139066918,99.78704607455086,97.0176923851874,99.72952270507812,190977200,0.0,0 -2011-08-30,99.29400794316038,100.60883256279878,98.00383530551858,99.99250793457031,241315700,0.0,0 -2011-08-31,100.63347852243993,101.49633545815392,99.68023308134042,100.4362564086914,301828400,0.0,0 -2011-09-01,100.49379016857397,101.40595115956711,99.25292144098877,99.38440704345703,254585900,0.0,0 -2011-09-02,97.313552666107,99.32688363371865,96.50000587717845,96.84514617919922,255517200,0.0,0 -2011-09-06,94.00180699000512,96.27810219387524,93.99358757498247,96.13839721679688,285130500,0.0,0 -2011-09-07,97.5929415142168,98.89132715704075,97.26423382797142,98.8502426147461,209803200,0.0,0 -2011-09-08,98.25857888216926,99.3844007713337,97.60116345047444,97.82304382324219,250568200,0.0,0 -2011-09-09,96.70542710132924,97.83946197084288,94.7331873664373,95.25911712646484,380195100,0.0,0 -2011-09-12,94.06757469345901,95.94941997931805,93.72243440598574,95.87545776367188,305793500,0.0,0 -2011-09-13,96.18773109888737,97.11632459842096,95.50566368448696,96.75474548339844,272514700,0.0,0 -2011-09-14,97.24777597017423,99.26932565074223,95.91651919221044,98.09420013427734,319389500,0.0,0 -2011-09-15,99.14608700634966,99.81993499690094,98.11887932138868,99.78706359863281,326777200,0.0,0 -2011-09-16,100.18766689726777,100.74935906934088,99.3864288854268,100.37764739990234,284528300,0.625,0 -2011-09-19,98.73390385868723,99.89033000668185,98.06483147608282,99.3781967163086,241517000,0.0,0 -2011-09-20,99.79944013369496,100.76587915466106,99.1303679348744,99.26252746582031,218932200,0.0,0 -2011-09-21,99.31210863693623,99.61773114585503,96.18150086726396,96.33843994140625,316251300,0.0,0 -2011-09-22,93.54650400201828,94.33948023334327,91.93577201682922,93.22435760498047,513911300,0.0,0 -2011-09-23,92.60482827867801,94.29816678144553,92.53048342193496,93.7860336303711,307242500,0.0,0 -2011-09-26,94.66986722735248,96.14843932496494,93.3234609561698,96.01627349853516,260673700,0.0,0 -2011-09-27,97.90787414300468,98.75867134109308,96.51190225370541,97.09011840820312,311753900,0.0,0 -2011-09-28,97.28835197674296,97.87482371684526,94.96724453602603,95.107666015625,286696800,0.0,0 -2011-09-29,96.68536977309181,97.16445522710043,94.10819240118728,95.8593521118164,298108900,0.0,0 -2011-09-30,94.53769371022003,95.36371115216869,93.39779190905553,93.46387481689453,288392300,0.0,0 -2011-10-03,92.9187261482416,94.1247111929839,90.70499853545057,90.80412292480469,365136800,0.0,0 -2011-10-04,89.4989918550771,92.99304853547078,88.7390572931141,92.7947998046875,459177500,0.0,0 -2011-10-05,93.02611144148204,94.76074727273948,92.16705231603707,94.512939453125,284108000,0.0,0 -2011-10-06,94.46337826621455,96.36322136810396,93.76126452926046,96.22279357910156,257800800,0.0,0 -2011-10-07,96.7844760935523,96.85055901090448,95.04157853597535,95.5784912109375,312657900,0.0,0 -2011-10-10,97.20575746277706,98.8164893621009,97.1974955216995,98.77519226074219,230666300,0.0,0 -2011-10-11,98.18869316656989,99.15513203728257,98.0895688072611,98.87428283691406,209088000,0.0,0 -2011-10-12,99.61771902722822,100.88978685845292,99.39469705320178,99.74162292480469,281544900,0.0,0 -2011-10-13,99.15515146479532,99.84074757148146,98.39521680057202,99.54338073730469,212538800,0.0,0 -2011-10-14,100.69981909225915,101.26976701004249,100.13812681388245,101.24498748779297,211397600,0.0,0 -2011-10-17,100.76586655195082,101.2284406074422,99.06427262850137,99.31208038330078,202311600,0.0,0 -2011-10-18,99.23774998339691,102.01316951234739,98.46129145386494,101.25323486328125,318857900,0.0,0 -2011-10-19,101.08803349375547,101.66624960568586,99.7085855886597,100.05551147460938,226601300,0.0,0 -2011-10-20,100.30332219107301,100.85675249691388,98.97343328765486,100.4933090209961,262075600,0.0,0 -2011-10-21,101.67452094623681,102.5253245150969,101.36889841466065,102.40142059326172,278999400,0.0,0 -2011-10-24,102.56660132216902,103.91301400988725,102.4757388842734,103.65694427490234,203215600,0.0,0 -2011-10-25,103.16130255919123,103.21086158173227,101.41840551036157,101.64143371582031,268596800,0.0,0 -2011-10-26,102.71528544032913,103.06221131356597,100.9476083124873,102.67398834228516,289053800,0.0,0 -2011-10-27,105.42463973219604,106.90321221281025,104.58210442750735,106.25066375732422,393220200,0.0,0 -2011-10-28,105.73025262430988,106.43237262475452,105.56505162539007,106.22586822509766,225906500,0.0,0 -2011-10-31,105.03639912513638,106.24237774088385,103.51652349181106,103.66520690917969,228146700,0.0,0 -2011-11-01,100.79891892205772,102.0214276483729,100.37764821408248,100.77413940429688,416565800,0.0,0 -2011-11-02,102.28575581188626,102.7565855682904,101.42669679454973,102.41791534423828,244717600,0.0,0 -2011-11-03,103.47520621813658,104.49121048426863,102.09575854873258,104.28470611572266,291174800,0.0,0 -2011-11-04,103.44217725971224,103.83040020213684,102.43443487253573,103.648681640625,249401600,0.0,0 -2011-11-07,103.57435400441754,104.40037166538396,102.59139097122282,104.2929916381836,196617200,0.0,0 -2011-11-08,104.83817299554175,105.74679757669732,103.83869225828604,105.63114929199219,224426300,0.0,0 -2011-11-09,103.16134497322358,103.91302406820756,101.4845301330465,101.73233795166016,337982000,0.0,0 -2011-11-10,103.07876588792045,103.20266982297507,101.61671079790469,102.69053649902344,231866500,0.0,0 -2011-11-11,103.93779409065966,104.89597121089984,103.90475263106323,104.6233901977539,189924400,0.0,0 -2011-11-14,104.23515960292683,105.27593720795016,103.18611375559172,103.63216400146484,159258300,0.0,0 -2011-11-15,103.39262331386132,104.69773265749937,103.02091790807236,104.14430236816406,184709400,0.0,0 -2011-11-16,103.09526077811664,104.35906677304713,102.34358799440598,102.49227142333984,235782500,0.0,0 -2011-11-17,102.30228976972586,102.5583595393054,100.13812745260005,100.86502075195312,331219600,0.0,0 -2011-11-18,101.18716343459845,101.3936678497711,100.33636625240948,100.75763702392578,215580400,0.0,0 -2011-11-21,99.28729908370106,99.4112029667383,98.00697576435175,98.84125518798828,229611600,0.0,0 -2011-11-22,98.62650401873714,99.20471382566036,97.89960448773165,98.45304107666016,216494900,0.0,0 -2011-11-23,97.52790519565806,98.45304724672827,96.2806167602539,96.2806167602539,224329100,0.0,0 -2011-11-25,96.13191841075157,97.22226130437397,95.9832350024388,96.098876953125,99557000,0.0,0 -2011-11-28,98.74213925814621,99.27078998906305,98.14740561036129,98.88256072998047,210686000,0.0,0 -2011-11-29,99.16341400146484,99.94812818958572,98.79996424594911,99.16341400146484,199241500,0.0,0 -2011-11-30,102.00490827248801,103.4339214459256,99.1221087087718,103.24393463134766,324439500,0.0,0 -2011-12-01,103.12830785894866,103.78086259359884,102.78138193987334,103.22743225097656,176954800,0.0,0 -2011-12-02,104.17733477186414,104.49121916941887,103.07046798740971,103.13655090332031,221109700,0.0,0 -2011-12-05,104.77205263926973,105.05290185549853,103.61563322542511,104.25992584228516,225263900,0.0,0 -2011-12-06,104.25168823432942,104.99510538960212,103.87998280770039,104.2929916381836,178842100,0.0,0 -2011-12-07,103.94604434012912,105.11899403794335,103.22741311270879,104.68120574951172,237802500,0.0,0 -2011-12-08,103.99561818390836,104.22690210599579,102.13707856454468,102.38488006591797,240862800,0.0,0 -2011-12-09,102.84743161257008,104.3838245311339,102.75656919142824,104.11949920654297,209111400,0.0,0 -2011-12-12,103.21092949380025,103.22745338019455,101.73236306433522,102.59967803955078,215826100,0.0,0 -2011-12-13,103.1365230479207,103.72299458870049,101.14581828236179,101.64143371582031,245159800,0.0,0 -2011-12-14,101.23671374892237,101.624943024296,100.33635760136573,100.55937957763672,238618800,0.0,0 -2011-12-15,101.62495004284877,101.76537153027343,100.76589093422778,100.92283630371094,199109200,0.0,0 -2011-12-16,101.60446625747416,102.20296550106893,100.8313975997671,101.07245635986328,220481400,0.77,0 -2011-12-19,101.46315763586144,101.67928608303225,99.77570810068075,99.99183654785156,183903000,0.0,0 -2011-12-20,101.5628948606091,103.19215644553279,100.05832298892588,103.01759338378906,225418100,0.0,0 -2011-12-21,103.0176022975239,103.37504279068399,102.03671952619965,103.21710205078125,194230900,0.0,0 -2011-12-22,103.59945863288054,104.23952946277674,103.26696122418706,104.13146209716797,119465400,0.0,0 -2011-12-23,104.46397918199679,105.09573575472086,104.24785710715575,105.06248474121094,92187200,0.0,0 -2011-12-27,104.87958240358803,105.419900146199,104.78814372367013,105.14558410644531,86075700,0.0,0 -2011-12-28,105.16225819895007,105.17888053794108,103.68262269139026,103.7657470703125,119107100,0.0,0 -2011-12-29,104.1065412114843,104.94611188229779,103.79066608853643,104.83805084228516,123507200,0.0,0 -2011-12-30,104.75493580840326,105.01262962234586,104.32268524169922,104.32268524169922,95599000,0.0,0 -2012-01-03,106.2013274306267,106.71670872051925,105.92701127591049,105.98519897460938,193697900,0.0,0 -2012-01-04,105.73579246824193,106.24285928812462,105.32847868147746,106.15142059326172,127186500,0.0,0 -2012-01-05,105.57788001385367,106.59200752219506,105.09574976665293,106.43406677246094,173895000,0.0,0 -2012-01-06,106.56706574392865,106.58369442309598,105.81062573337087,106.15975189208984,148050000,0.0,0 -2012-01-09,106.40080601493293,106.55042606008847,105.91036784385933,106.41743469238281,99530200,0.0,0 -2012-01-10,107.55627868025881,107.77240080534185,107.19052378860472,107.34015655517578,115282000,0.0,0 -2012-01-11,107.00761633775517,107.53961984445027,106.8330596236637,107.39830780029297,111540700,0.0,0 -2012-01-12,107.70587662929499,107.81393131492239,106.84967103663088,107.65599060058594,118983700,0.0,0 -2012-01-13,106.93281133319678,107.27362946483764,106.16805704919892,107.09906005859375,179836200,0.0,0 -2012-01-17,108.1298284845244,108.32933457651292,107.14893938720425,107.51469421386719,132209200,0.0,0 -2012-01-18,107.48974468191003,108.76156574180708,107.29855929409884,108.70338439941406,163395200,0.0,0 -2012-01-19,109.07746381111946,109.3684086193403,108.72833765163394,109.27696990966797,126328900,0.0,0 -2012-01-20,109.09407268437697,109.68425750732422,108.82806460127253,109.68425750732422,138230200,0.0,0 -2012-01-23,109.31850351875161,109.93363770236795,108.87793872641952,109.4016342163086,129295800,0.0,0 -2012-01-24,108.72833765163394,109.3102145841098,108.56208891149309,109.27696990966797,103083300,0.0,0 -2012-01-25,109.11071692380148,110.4490403139801,108.6867807010617,110.19135284423828,198613200,0.0,0 -2012-01-26,110.68178513357577,110.88959923460312,109.19384175116511,109.6260986328125,184880500,0.0,0 -2012-01-27,109.09410264011576,109.76741834490726,109.01927991872014,109.57623291015625,135259100,0.0,0 -2012-01-30,108.48724469284438,109.26031957301176,108.11318193068232,109.2021255493164,147311800,0.0,0 -2012-01-31,109.74243735960182,109.87542870001572,108.62854451040702,109.16056060791016,157212000,0.0,0 -2012-02-01,109.96691386918971,110.67348694891929,109.83392247255443,110.11654663085938,166234500,0.0,0 -2012-02-02,110.3326496430373,110.57372108287998,109.9004054818798,110.29108428955078,113090400,0.0,0 -2012-02-03,111.38836160587252,111.90373653353787,111.19717617850739,111.83723449707031,160598500,0.0,0 -2012-02-06,111.37171392539327,111.81227874335829,111.24703055576707,111.76240539550781,107694500,0.0,0 -2012-02-07,111.5296386618722,112.23621147903891,111.0890739070618,112.04501342773438,135528100,0.0,0 -2012-02-08,112.10322448886068,112.40247726252016,111.64603098966361,112.3775405883789,139361400,0.0,0 -2012-02-09,112.560409953082,112.71002998724909,111.85383707894573,112.51884460449219,148602900,0.0,0 -2012-02-10,111.52137330975117,111.77906079121632,111.25536514567915,111.6876220703125,167907500,0.0,0 -2012-02-13,112.48559993599847,112.65184864639419,112.00346979707638,112.51884460449219,115841900,0.0,0 -2012-02-14,112.21959986284135,112.44404261407708,111.59615764138111,112.3775405883789,165329500,0.0,0 -2012-02-15,112.74329400228768,112.90954272250791,111.62940094048719,111.85384368896484,195195100,0.0,0 -2012-02-16,111.86213652363499,113.1921386382827,111.6626304986553,113.09239196777344,186567800,0.0,0 -2012-02-17,113.48312419471293,113.574562903586,113.01762265280117,113.39168548583984,129869400,0.0,0 -2012-02-21,113.65766564192663,113.92367373521654,113.09241746963156,113.44154357910156,134042300,0.0,0 -2012-02-22,113.26697579757437,113.50804722105156,112.87628433246104,113.07579040527344,124455300,0.0,0 -2012-02-23,113.01759145811545,113.65764955959484,112.63520804910867,113.57453155517578,137704300,0.0,0 -2012-02-24,113.82389068603516,114.04833338257517,113.57452400539223,113.82389068603516,105539100,0.0,0 -2012-02-27,113.0674973154985,114.32268989368795,112.88461989563436,114.01512908935547,145728900,0.0,0 -2012-02-28,114.04838053735357,114.48063743579247,113.82393774801479,114.3476333618164,129355900,0.0,0 -2012-02-29,114.51386422489514,114.87131103237573,113.49973051617994,113.89874267578125,185934700,0.0,0 -2012-03-01,114.13982643354214,114.70508744151911,113.82394490896772,114.48895263671875,145023500,0.0,0 -2012-03-02,114.41411712767243,114.56374985799187,113.88211360069201,114.13980102539062,120638300,0.0,0 -2012-03-05,113.96526887824409,114.04838691615302,113.28363247396736,113.67432403564453,140765000,0.0,0 -2012-03-06,112.51056926264141,112.57705861892626,111.68762078398495,112.01181030273438,202129900,0.0,0 -2012-03-07,112.26949437880678,112.97606743660057,112.1614269811763,112.7931900024414,143692200,0.0,0 -2012-03-08,113.48311534697554,114.14812293384446,113.25036459416613,113.91535949707031,116968900,0.0,0 -2012-03-09,114.13147616394527,114.65515893276982,113.9901641352807,114.35591888427734,122836800,0.0,0 -2012-03-12,114.33930587958561,114.51386259846291,113.95692240305648,114.36424255371094,104003500,0.0,0 -2012-03-13,114.97938801473278,116.48396002583353,114.78818991580061,116.42576599121094,184090500,0.0,0 -2012-03-14,116.4590118923263,116.74994398492093,115.94362436482064,116.30107116699219,145163600,0.0,0 -2012-03-15,116.47565205712684,117.02428436931032,116.17639922175103,116.97441101074219,165118500,0.0,0 -2012-03-16,117.18645971472641,117.28664354847612,116.88589547392569,117.13636779785156,152893500,0.614,0 -2012-03-19,117.06120040049153,117.95453575175367,116.97770540038925,117.59553527832031,125291100,0.0,0 -2012-03-20,116.92763232312653,117.39517382406389,116.58532060295796,117.25324249267578,121729700,0.0,0 -2012-03-21,117.32006555120942,117.42859380873664,116.81912082266322,117.06124877929688,122388400,0.0,0 -2012-03-22,116.20125499326683,116.51017633477863,115.83391007667261,116.21795654296875,135216700,0.0,0 -2012-03-23,116.31816055644482,116.72725300373766,115.67528461699108,116.59366607666016,120521000,0.0,0 -2012-03-26,117.42857079022858,118.2300796508789,117.38683602065868,118.2300796508789,120164000,0.0,0 -2012-03-27,118.33863830687712,118.41377619079019,117.78760167907522,117.86273956298828,119868500,0.0,0 -2012-03-28,117.80431101373759,117.98798990180298,116.58535227835402,117.2783203125,148562100,0.0,0 -2012-03-29,116.5853011140216,117.29497039188091,116.1261040953802,117.07788848876953,164963700,0.0,0 -2012-03-30,117.65401137138376,117.76255236031011,116.92765303516016,117.56217193603516,135486800,0.0,0 -2012-04-02,117.4202349172735,118.73103290953497,117.18646413666862,118.42211151123047,151741100,0.0,0 -2012-04-03,118.25513200997075,118.45551242086836,117.24489828857911,117.9378662109375,155806700,0.0,0 -2012-04-04,117.06958434708798,117.16976818941095,116.3348688645791,116.7690200805664,146896000,0.0,0 -2012-04-05,116.36822660502114,117.05283715991722,116.26803006220757,116.71052551269531,137439400,0.0,0 -2012-04-09,115.24113213303329,116.75229760180858,115.08249925141813,115.39976501464844,127555900,0.0,0 -2012-04-10,115.17432953477906,115.49993967769618,113.34589844780245,113.46278381347656,235360300,0.0,0 -2012-04-11,114.62330259529476,114.83202738989142,114.1724626443682,114.38118743896484,154133000,0.0,0 -2012-04-12,114.4897708075925,115.96753372811763,114.4062757630131,115.87569427490234,154321500,0.0,0 -2012-04-13,115.60850257636397,115.9007224157755,114.38954404055379,114.49808502197266,169246700,0.0,0 -2012-04-16,115.08250790784845,115.24948520753735,114.03053945421223,114.42294311523438,147825300,0.0,0 -2012-04-17,115.08252567972832,116.35157628453233,114.96564027794068,116.11780548095703,147877600,0.0,0 -2012-04-18,115.60017302165274,116.11780655728853,115.53337954293305,115.72540283203125,123884200,0.0,0 -2012-04-19,115.74209940741433,116.17623789108566,114.43965847729454,114.98233795166016,198666700,0.0,0 -2012-04-20,115.49162036020505,115.90907000537044,115.10756108124993,115.17435455322266,143199600,0.0,0 -2012-04-23,113.99714675348487,114.30606816262922,113.49621480870508,114.20587158203125,171844900,0.0,0 -2012-04-24,114.30606004230849,114.93223448346409,114.21422061469028,114.6400146484375,137484200,0.0,0 -2012-04-25,115.75878772777942,116.25973242471242,115.65860388421395,116.20964050292969,150252200,0.0,0 -2012-04-26,115.95915226675324,117.15306477705788,115.89235880034344,117.01947784423828,136291600,0.0,0 -2012-04-27,117.37012732707721,117.54544901934564,116.71890699097949,117.21149444580078,130725000,0.0,0 -2012-04-30,116.97773942358653,117.06123444797349,116.46010594313319,116.77735900878906,115092200,0.0,0 -2012-05-01,116.71055867812551,118.27182891345191,116.57698448232944,117.50372314453125,138832200,0.0,0 -2012-05-02,116.81910119969741,117.26995392756666,116.43505467993629,117.15306854248047,121081000,0.0,0 -2012-05-03,117.16975696805255,117.2615963945707,116.04265063120424,116.25971984863281,143759700,0.0,0 -2012-05-04,115.65023775718662,116.30145809716413,114.31439397594787,114.38118743896484,193927300,0.0,0 -2012-05-07,113.97207573419888,114.84872236925598,113.93034096930784,114.46467590332031,127765900,0.0,0 -2012-05-08,113.78005157476865,114.18915673167739,112.64458825679402,114.00547790527344,213377700,0.0,0 -2012-05-09,112.79490383351437,114.05559722724517,112.28561473599744,113.32923889160156,220752500,0.0,0 -2012-05-10,114.11402738233006,114.25597147062004,113.30418680177851,113.56300354003906,150600000,0.0,0 -2012-05-11,112.85334536695476,114.27267173613141,112.80325344350372,113.220703125,153032400,0.0,0 -2012-05-14,112.13533059496574,113.22070230692434,111.80137594800303,111.96835327148438,163910000,0.0,0 -2012-05-15,111.89322345774377,112.55278839191337,111.15016348603437,111.32548522949219,207629300,0.0,0 -2012-05-16,111.8264121770658,112.33570125006904,110.87462750254933,110.89967346191406,207265500,0.0,0 -2012-05-17,110.92469629170336,111.05828320682836,109.19644901859084,109.25489807128906,247992900,0.0,0 -2012-05-18,109.68069349425181,109.87272946463654,108.16118371797225,108.31981658935547,319615900,0.0,0 -2012-05-21,108.67048274331168,110.22339576223317,108.49514830620973,110.18164825439453,177861100,0.0,0 -2012-05-22,110.46551954333643,111.23362533720477,109.65566623837216,110.3736801147461,197531200,0.0,0 -2012-05-23,109.58055080396754,110.59078472535471,108.52858210248903,110.4321517944336,204958400,0.0,0 -2012-05-24,110.73271073348677,110.90803247212514,109.72247686106387,110.64921569824219,167357600,0.0,0 -2012-05-25,110.60746764394604,110.91638908120908,110.0230406187309,110.29021453857422,135465600,0.0,0 -2012-05-29,111.17516717164946,111.81803030593206,109.51371337703705,111.62600708007812,152883500,0.0,0 -2012-05-30,110.67426976847479,111.61771022972559,109.78093397577345,110.00634765625,162370400,0.0,0 -2012-05-31,109.96459398312004,110.58241132065383,108.8207732628447,109.76421356201172,196186000,0.0,0 -2012-06-01,108.04430296233892,109.78923914293614,107.00067901611328,107.00067901611328,253240900,0.0,0 -2012-06-04,107.192722495644,107.48494235158519,106.14909835235767,106.95060729980469,202545800,0.0,0 -2012-06-05,106.7418677969647,107.91907242672274,106.6834251042785,107.76045227050781,164149400,0.0,0 -2012-06-06,108.51184985725533,110.23174016798059,108.47844675516413,110.18164825439453,184202800,0.0,0 -2012-06-07,111.43403751418889,111.48412944596343,110.02305531794745,110.24848175048828,184772700,0.0,0 -2012-06-08,109.96457459392671,111.15012987911817,109.61390573392973,111.12508392333984,143915400,0.0,0 -2012-06-11,112.01845199517328,112.08524547653147,109.6055932166573,109.7141342163086,169756100,0.0,0 -2012-06-12,110.03136750195695,111.04994561491408,109.50538963281144,110.97480773925781,181931800,0.0,0 -2012-06-13,110.64917553715853,111.34214331159784,109.88941430667758,110.26512908935547,172223900,0.0,0 -2012-06-14,110.49054279832997,111.87647834879942,110.18997861557212,111.4339828491211,230615500,0.0,0 -2012-06-15,111.93587805476706,112.6743876337005,111.7008959879843,112.57368469238281,169444500,0.688,0 -2012-06-18,112.10368931253721,113.06879304599761,111.85191922385577,112.7918472290039,131360900,0.0,0 -2012-06-19,113.36253515013851,114.34442704201564,112.76667965734491,113.88285064697266,137382600,0.0,0 -2012-06-20,113.89125794542448,114.21855526240611,112.68277160408911,113.69822692871094,206451800,0.0,0 -2012-06-21,113.83249037559399,113.94998137564158,111.05465738438699,111.14697265625,205272200,0.0,0 -2012-06-22,111.7260525055019,112.2128046474157,111.29803946739906,112.00299835205078,130029200,0.0,0 -2012-06-25,110.81968491931745,110.86164873842189,109.81261728860927,110.2070541381836,146375700,0.0,0 -2012-06-26,110.52596612865622,111.09664597440499,109.87975908151115,110.76094818115234,141634000,0.0,0 -2012-06-27,111.13020175344258,111.97781461303003,110.75255296671789,111.7596206665039,108088000,0.0,0 -2012-06-28,111.02111456400068,111.60858254629109,110.17350158083337,111.44072723388672,169242100,0.0,0 -2012-06-29,113.46324129361,114.36121837906984,113.16952016519187,114.21855163574219,212250900,0.0,0 -2012-07-02,114.53750736168124,114.68017417833265,113.7318579493478,114.56268310546875,129524500,0.0,0 -2012-07-03,114.53745365019151,115.40185459638575,114.41996263242416,115.31793975830078,80450000,0.0,0 -2012-07-05,114.88996088209635,115.64527148292136,114.37803283691076,114.79764556884766,126177500,0.0,0 -2012-07-06,113.68986931561508,113.94163950415346,113.16955365004915,113.70665740966797,151192100,0.0,0 -2012-07-09,113.61431630891455,113.7737711523095,113.04363649074111,113.56396484375,103780500,0.0,0 -2012-07-10,114.1430319538739,114.32766254999783,112.18763540806128,112.57368469238281,167884800,0.0,0 -2012-07-11,112.63243318647847,112.95973054136941,111.93587465056966,112.59046936035156,141733400,0.0,0 -2012-07-12,111.93586944578936,112.64920323453096,111.28127476644474,112.04496002197266,143583200,0.0,0 -2012-07-13,112.33866979744334,114.0422956865576,112.32188171027555,113.9248046875,129642600,0.0,0 -2012-07-16,113.66465807657914,113.99195537778557,113.21146931764117,113.65625762939453,97525200,0.0,0 -2012-07-17,114.10943217072594,114.67171142296834,112.91773412492826,114.43672943115234,138860300,0.0,0 -2012-07-18,114.16821439558778,115.51098011798982,114.10108763447437,115.28438568115234,113349700,0.0,0 -2012-07-19,115.51934726164446,115.96413560707573,115.15009891879562,115.58648681640625,129847300,0.0,0 -2012-07-20,114.9318925174113,115.10813544989739,114.40318933107454,114.52906799316406,142904500,0.0,0 -2012-07-23,112.85059520344633,114.45351814650509,112.32187931617733,113.37091064453125,145210900,0.0,0 -2012-07-24,113.4548932241988,113.5052447034579,111.64216313748464,112.3974609375,173301200,0.0,0 -2012-07-25,112.63242899793455,112.92615015877641,111.82676710516888,112.42262268066406,129122300,0.0,0 -2012-07-26,114.04230832009195,114.52067281567835,113.51359236298275,114.27729034423828,156526500,0.0,0 -2012-07-27,114.8815659530768,116.71108400720945,114.25214689508152,116.38377380371094,236768900,0.0,0 -2012-07-30,116.24950747023354,116.93766564808203,116.03970111756846,116.38377380371094,106782000,0.0,0 -2012-07-31,116.22427372944183,116.54317051856087,115.5696792602539,115.5696792602539,120575900,0.0,0 -2012-08-01,116.40051925865774,116.42569498731032,115.3095240633304,115.46897888183594,138293800,0.0,0 -2012-08-02,114.59617234987027,115.45218540583653,113.7821231035649,114.67169952392578,199556600,0.0,0 -2012-08-03,116.28304979638884,117.18941461245068,114.70530221225587,116.946044921875,157825000,0.0,0 -2012-08-06,117.25652742142935,117.63417614058282,117.12224833733444,117.17259979248047,86326200,0.0,0 -2012-08-07,117.64258223838318,118.26361349558805,117.51670358076882,117.76008605957031,109545100,0.0,0 -2012-08-08,117.365659703505,118.03702967832669,117.33208352142707,117.90276336669922,89754700,0.0,0 -2012-08-09,117.73486745640217,118.2384075693906,117.61737647077915,118.00342559814453,90291700,0.0,0 -2012-08-10,117.52510462991646,118.23845125260323,117.33208639770166,118.19648742675781,99792700,0.0,0 -2012-08-13,117.9950743341687,118.19648019700846,117.52509744123371,118.13774108886719,79426900,0.0,0 -2012-08-14,118.5741077858652,118.64964777647911,117.80202223677468,118.15449523925781,102379400,0.0,0 -2012-08-15,118.02866053252659,118.49023706672384,117.95313331935706,118.288818359375,71085900,0.0,0 -2012-08-16,118.45663167702175,119.30425732328695,118.16291054080612,119.16159057617188,112014200,0.0,0 -2012-08-17,119.36301637933163,119.42176829973106,119.05250710168441,119.32105255126953,90813700,0.0,0 -2012-08-20,119.15319432347704,119.35461298826245,118.8258969966762,119.32943725585938,78255700,0.0,0 -2012-08-21,119.62313087994801,120.08470721040793,118.70837866493693,118.96853637695312,105581100,0.0,0 -2012-08-22,118.6664883023571,119.21199259620556,118.38955513822725,119.01897430419922,133243500,0.0,0 -2012-08-23,118.7252034843578,118.73359112730391,117.86080245512558,118.0454330444336,111466400,0.0,0 -2012-08-24,117.75169082276142,119.02731676713044,117.67616362485057,118.75875854492188,99481200,0.0,0 -2012-08-27,119.07765420833769,119.23710903329953,118.61607782175321,118.78392028808594,68785900,0.0,0 -2012-08-28,118.48179706026733,119.03568871637938,118.30556695504205,118.66642761230469,75689600,0.0,0 -2012-08-29,118.76715899284142,119.07766823240433,118.4314612150807,118.75875854492188,65421300,0.0,0 -2012-08-30,118.24683599684882,118.28041217892675,117.65099322336036,117.90276336669922,96589900,0.0,0 -2012-08-31,118.5740792365682,119.01888020999044,117.79360623446254,118.4649887084961,151970400,0.0,0 -2012-09-04,118.36429889562838,118.71678469587705,117.60061380991198,118.35591125488281,120226200,0.0,0 -2012-09-05,118.40631872045095,118.72522849745505,118.02028217461232,118.25526428222656,100660300,0.0,0 -2012-09-06,118.96855991656776,120.66379840861217,118.96017227455717,120.65541076660156,158272500,0.0,0 -2012-09-07,120.8567927940281,121.17570240540655,120.74770224715478,121.12535095214844,107272100,0.0,0 -2012-09-10,121.0079052385629,121.21771157601638,120.39527431875625,120.43722534179688,86458500,0.0,0 -2012-09-11,120.51276851419524,121.15896283048393,120.47919232820759,120.7729263305664,88760000,0.0,0 -2012-09-12,121.17575073242188,121.31002986503492,120.76452569088052,121.17575073242188,87640900,0.0,0 -2012-09-13,121.15891493112585,123.3996442513348,120.84001812369439,123.0219955444336,225470200,0.0,0 -2012-09-14,123.26543661821336,124.29768032296136,123.1647208577171,123.56755828857422,169777000,0.0,0 -2012-09-17,123.31573827191602,123.5255445469654,122.8373738181352,123.14789581298828,119427800,0.0,0 -2012-09-18,122.93810486572332,123.2066502723779,122.73668620656208,123.04719543457031,98326600,0.0,0 -2012-09-19,123.18986893508001,123.50877862817545,122.87097204754565,123.11434173583984,128318300,0.0,0 -2012-09-20,122.55208465410402,123.18989135078952,122.21639961491447,123.12276458740234,154009800,0.0,0 -2012-09-21,123.72090740551309,123.74621752697459,123.02063067260434,123.07125091552734,108737500,0.779,0 -2012-09-24,122.46382086701884,123.16409782073383,122.3710127677573,122.88567352294922,95682000,0.0,0 -2012-09-25,123.14720339491429,123.38343979556063,121.5441559631932,121.57791137695312,133165200,0.0,0 -2012-09-26,121.55260256867763,121.58634510888433,120.6076440819504,120.89450073242188,146502200,0.0,0 -2012-09-27,121.4007066899368,122.31190959543835,121.08009468413549,122.03348541259766,111830300,0.0,0 -2012-09-28,121.56948697079609,121.96602947012782,121.03796141499097,121.46824645996094,150696100,0.0,0 -2012-10-01,121.93227721385388,122.91941073764333,121.50197932923217,121.78884887695312,135911200,0.0,0 -2012-10-02,122.26972931754617,122.46377786644008,121.3500939402697,121.91537475585938,113422200,0.0,0 -2012-10-03,122.24444665339091,122.70004179962547,121.60323533211863,122.4131851196289,121283100,0.0,0 -2012-10-04,122.87720640520098,123.46779738194249,122.70846796526308,123.2906265258789,124311600,0.0,0 -2012-10-05,123.94872884720102,124.1596551383954,122.92783993329584,123.299072265625,124842100,0.0,0 -2012-10-08,122.84351574811005,123.28223330245157,122.59883396403191,122.87725830078125,78415400,0.0,0 -2012-10-09,122.78442968920386,122.88567020423815,121.62011227062531,121.66230010986328,148872900,0.0,0 -2012-10-10,121.64539204318147,121.76352311313698,120.72575659732249,120.88606262207031,124247500,0.0,0 -2012-10-11,121.72977467674481,121.90695841791438,120.92825738098982,120.95356750488281,123601500,0.0,0 -2012-10-12,121.03794784450976,121.45135513335012,120.29548318450722,120.5570297241211,124181900,0.0,0 -2012-10-15,120.8438730707595,121.68757813726411,120.45577594956124,121.56102752685547,107689100,0.0,0 -2012-10-16,122.1347349012874,122.87719944365148,122.05037212305686,122.7928237915039,108815500,0.0,0 -2012-10-17,122.87723566323619,123.45096194433508,122.69161947293252,123.3497085571289,128834100,0.0,0 -2012-10-18,123.02909088134764,123.61968191026232,122.61567072414793,123.02909088134766,148108500,0.0,0 -2012-10-19,122.80124159718596,122.80967401094667,120.69197950473658,120.97883605957031,185645200,0.0,0 -2012-10-22,120.7764093260967,121.21513968963046,120.04238983000698,120.99578094482422,125578600,0.0,0 -2012-10-23,119.6880277203142,119.85676619615094,118.81901233953054,119.3167953491211,192056300,0.0,0 -2012-10-24,119.74703262493362,119.8904737897908,118.79365482350673,118.9792709350586,120179400,0.0,0 -2012-10-25,119.82302936673113,120.04238810839139,118.5996592661531,119.32523345947266,134457400,0.0,0 -2012-10-26,119.21551786412167,119.67111289497169,118.44774323217861,119.25770568847656,146023500,0.0,0 -2012-10-31,119.67955818419256,119.8314189031704,118.69241201517063,119.25770568847656,103438500,0.0,0 -2012-11-01,119.51080721615247,120.65824652261904,119.40113432122726,120.50638580322266,100995600,0.0,0 -2012-11-02,121.22355690951149,121.25731232689341,119.30835537547631,119.43490600585938,137702200,0.0,0 -2012-11-05,119.25771290843223,119.94954434885388,118.90334546153059,119.6795654296875,98378500,0.0,0 -2012-11-06,120.04237028783378,121.08856933089285,119.91581966218594,120.61609649658203,107068100,0.0,0 -2012-11-07,119.51929769073777,119.53616252776892,117.32565872807737,117.88250732421875,264304500,0.0,0 -2012-11-08,117.86562650375578,118.46466292451413,116.37226453492748,116.46507263183594,181517300,0.0,0 -2012-11-09,116.11070667527598,117.64625633424284,116.05165400511869,116.56631469726562,201055300,0.0,0 -2012-11-12,116.92908996801286,117.11470612508346,116.39756449450931,116.65911102294922,97677500,0.0,0 -2012-11-13,116.0431935983942,117.4859349815425,115.89133286012496,116.25411987304688,123018300,0.0,0 -2012-11-14,116.60847369683651,116.79407695280506,114.42326796299132,114.684814453125,191505000,0.0,0 -2012-11-15,114.72702774328783,115.15732562804028,114.0520610187879,114.49079132080078,178128400,0.0,0 -2012-11-16,114.65954877504082,115.28389535155262,113.64710495755486,115.05609130859375,239483900,0.0,0 -2012-11-19,116.34694552198098,117.40157705453672,115.08983297448339,117.38471221923828,151495800,0.0,0 -2012-11-20,117.1990739748662,117.62935892452921,116.49879722421785,117.43531036376953,119807400,0.0,0 -2012-11-21,117.53654489747001,117.75591644313607,117.30030852053503,117.6546630859375,81710800,0.0,0 -2012-11-23,118.22838456901624,119.29988063891497,118.15244133561082,119.25770568847656,65409200,0.0,0 -2012-11-26,118.66714120974746,119.2661776166481,118.27904399450543,119.00463104248047,100124400,0.0,0 -2012-11-27,118.88652165891416,119.29149661438359,118.32124063021763,118.39717102050781,128646200,0.0,0 -2012-11-28,117.91621598378153,119.41800997891632,117.27500476500053,119.35052490234375,177086500,0.0,0 -2012-11-29,119.7976909835983,120.23640836344747,119.27458507082727,119.90736389160156,151085900,0.0,0 -2012-11-30,119.92424580602722,120.16048221082512,119.51927095121918,119.93267822265625,136568300,0.0,0 -2012-12-03,120.48108615271235,120.58232664110739,119.24927109026298,119.34207916259766,124656300,0.0,0 -2012-12-04,119.33366114445097,119.6964481875828,118.85274301871526,119.17335510253906,127512200,0.0,0 -2012-12-05,119.27461442301978,119.94114882098971,118.43090912102731,119.3843002319336,147300500,0.0,0 -2012-12-06,119.27460017655794,119.83988111650375,119.09742929369715,119.78926086425781,103220600,0.0,0 -2012-12-07,120.25327905998714,120.38827495695654,119.52769220617326,120.15203857421875,108726400,0.0,0 -2012-12-10,119.9833618521277,120.48957741525355,119.93272870928357,120.20272064208984,98840700,0.0,0 -2012-12-11,120.70042986474242,121.58632265007223,120.64137721180899,121.02104187011719,152570400,0.0,0 -2012-12-12,121.49354406787572,121.95758448458547,120.91138544272341,121.08012390136719,145880100,0.0,0 -2012-12-13,121.00417543161537,121.35009758815482,120.0339197840622,120.33765411376953,135715000,0.0,0 -2012-12-14,120.07612672756062,120.29548543852654,119.70489439060344,119.89051055908203,137701700,0.0,0 -2012-12-17,120.20268018323708,121.36699747220128,120.16892476603601,121.29949951171875,143238200,0.0,0 -2012-12-18,121.4935270067035,122.75908457969,121.31634328196175,122.64939880371094,177762800,0.0,0 -2012-12-19,122.78439623467172,122.82658406241495,121.69602237283445,121.73819732666016,150895400,0.0,0 -2012-12-20,121.81415063578797,122.4553618991705,121.47666085986143,122.43848419189453,168487000,0.0,0 -2012-12-21,120.8002767714874,122.43167800379047,120.60485194275552,121.32707977294922,245883800,1.022,0 -2012-12-24,121.06369783558785,121.1316745185716,120.81729370434498,120.9532470703125,53874600,0.0,0 -2012-12-26,121.19969687188484,121.25918135387047,120.10360323815611,120.44347381591797,106947700,0.0,0 -2012-12-27,120.47742256088112,120.72383968244985,118.88850795062692,120.28199768066406,167920600,0.0,0 -2012-12-28,119.50026732037898,120.16302374131229,118.84600312245344,118.9819564819336,148806700,0.0,0 -2012-12-31,118.66756671779832,121.13165958739943,118.5655952232788,121.00421142578125,243935200,0.0,0 -2013-01-02,123.29836600986344,124.18203682718861,122.97548005486739,124.1055679321289,192059000,0.0,0 -2013-01-03,124.04609985864309,124.36897285729549,123.49379423805142,123.8251724243164,144761800,0.0,0 -2013-01-04,124.02907193570266,124.57287223489902,123.77416270931208,124.36894226074219,116817700,0.0,0 -2013-01-07,123.9271781000424,124.14809263007554,123.57029721773166,124.02913665771484,110002500,0.0,0 -2013-01-08,123.80822511884921,123.97816037415171,123.18794263741312,123.67227172851562,121265100,0.0,0 -2013-01-09,123.94412337192104,124.32649377278616,123.74869854307796,123.98661041259766,90745600,0.0,0 -2013-01-10,124.67487079700045,124.98075937390578,124.0291117858563,124.9722671508789,130735400,0.0,0 -2013-01-11,124.93830354643886,125.0317698839927,124.57294338660151,124.96380615234375,113917300,0.0,0 -2013-01-14,124.81083038458623,124.96378116328381,124.41996769700496,124.8788070678711,89567200,0.0,0 -2013-01-15,124.30103659908877,125.08276213057326,124.22456767706178,124.96380615234375,93172600,0.0,0 -2013-01-16,124.70887306193406,125.1422098362331,124.57291969403471,124.94678497314453,104849500,0.0,0 -2013-01-17,125.49904277380965,126.11081979385847,125.03171130499135,125.75395202636719,133833500,0.0,0 -2013-01-18,125.728489475854,126.17033141109862,125.2696501293853,126.03437805175781,169906000,0.0,0 -2013-01-22,126.03435760691417,126.71411132812499,125.73696130225198,126.714111328125,111797300,0.0,0 -2013-01-23,126.7141051250376,127.02848585755275,126.48468550024975,126.91802215576172,104596100,0.0,0 -2013-01-24,126.73109894725383,127.57229565692032,126.61214301545267,126.9520263671875,146426400,0.0,0 -2013-01-25,127.35145920059473,127.6658401489258,126.91810928237395,127.66584014892578,147211600,0.0,0 -2013-01-28,127.6997140880467,127.73370889791988,127.0369579129526,127.5127944946289,113357700,0.0,0 -2013-01-29,127.25789188622086,128.17555732210647,127.17291782234888,128.0141143798828,105694400,0.0,0 -2013-01-30,127.99711033094175,128.2520195441059,127.39382562529396,127.5127944946289,137447700,0.0,0 -2013-01-31,127.35992173138648,127.77627413322413,127.11351755248933,127.19847869873047,108975800,0.0,0 -2013-02-01,128.00567728501704,128.65994162223598,127.78476275274838,128.5070037841797,131173000,0.0,0 -2013-02-04,127.72523873720253,128.53244058061475,126.96900318696004,127.06246948242188,159073600,0.0,0 -2013-02-05,127.75078590472053,128.71092595575442,127.69979365813737,128.34556579589844,113912400,0.0,0 -2013-02-06,127.89521010137898,128.52397175306646,127.80174377947583,128.4390106201172,138762800,0.0,0 -2013-02-07,128.4814804793323,128.60043642172752,127.3343951335586,128.26905822753906,162490000,0.0,0 -2013-02-08,128.48998212858336,129.0592722493282,128.48998212858336,128.98280334472656,103133700,0.0,0 -2013-02-11,128.93181627487797,129.06775666420356,128.6344199324507,128.95730590820312,73775000,0.0,0 -2013-02-12,128.96580872095888,129.40765067691905,128.82136313184674,129.16973876953125,65392700,0.0,0 -2013-02-13,129.4331479302927,129.67105985168314,128.914837037572,129.2801971435547,82322600,0.0,0 -2013-02-14,128.88936415202073,129.55212072849162,128.7449185356339,129.399169921875,80834300,0.0,0 -2013-02-15,129.5180790218717,129.65403236405254,128.7703615703258,129.24618530273438,215226500,0.0,0 -2013-02-19,129.46712842645644,130.24034864543884,129.28870096990798,130.21485900878906,95105400,0.0,0 -2013-02-20,130.12134616483766,130.16383319913965,128.52392702468472,128.59190368652344,160574800,0.0,0 -2013-02-21,128.2690581351041,128.65990781759587,127.4023717181491,127.81021881103516,183257000,0.0,0 -2013-02-22,128.4304754080036,129.0592498779297,127.8696905729063,129.0592498779297,106356600,0.0,0 -2013-02-25,129.6880592380169,129.883484112441,126.60368347167967,126.60368347167969,245824800,0.0,0 -2013-02-26,127.21541241373204,127.62325942106473,126.37421580621216,127.47032165527344,186596200,0.0,0 -2013-02-27,127.3598879599458,129.433131263868,127.24942423737146,129.07626342773438,150781900,0.0,0 -2013-02-28,129.0677083395837,129.89190741577372,128.65136917433887,128.82130432128906,126866000,0.0,0 -2013-03-01,128.379498952862,129.4416101288541,127.80171669642999,129.24618530273438,170634800,0.0,0 -2013-03-04,128.94882097351046,129.93446350097656,128.74490387488132,129.93446350097656,99010200,0.0,0 -2013-03-05,130.56324587891032,131.44691694420823,130.54624846460797,131.09854125976562,121431900,0.0,0 -2013-03-06,131.56586874691124,131.6338454367394,130.98808633121374,131.2769775390625,94469900,0.0,0 -2013-03-07,131.44691588669076,131.68482782409646,131.2939780548849,131.514892578125,86101400,0.0,0 -2013-03-08,132.09268100701928,132.2541110547811,131.41292709375085,132.07568359375,123477800,0.0,0 -2013-03-11,131.9736934403287,132.58545755401846,131.81225046600076,132.57696533203125,83746800,0.0,0 -2013-03-12,132.48351683411178,132.63646760700803,131.8802448613338,132.2795867919922,105755800,0.0,0 -2013-03-13,132.3475662643578,132.65345485137635,131.89723208975084,132.46652221679688,92550900,0.0,0 -2013-03-14,132.81486988871413,133.23122211720766,132.47499951479298,133.17173767089844,126329900,0.0,0 -2013-03-15,133.01299891048646,133.17514689845436,132.55211887722143,132.9959259033203,138601100,0.694,0 -2013-03-18,131.7242585812972,132.8337696750472,131.60477357260962,132.26194763183594,126704300,0.0,0 -2013-03-19,132.5435457168981,132.72276665366158,131.08411011740156,131.95465087890625,167567300,0.0,0 -2013-03-20,132.7313920560421,133.09837716961218,132.50948196607638,132.87648010253906,113759300,0.0,0 -2013-03-21,132.08277622308773,132.83383264682402,131.51949692818096,131.74139404296875,128605000,0.0,0 -2013-03-22,132.15953578861115,132.7996368408203,132.057110764693,132.7996368408203,111163600,0.0,0 -2013-03-25,133.1495624683059,133.3714725237475,131.7328152252639,132.24488830566406,151322300,0.0,0 -2013-03-26,132.79113644098754,133.33735565991722,132.64604838533833,133.30322265625,86856600,0.0,0 -2013-03-27,132.50949032488256,133.34589867800412,132.28759324381198,133.30322265625,99950600,0.0,0 -2013-03-28,133.21786219550572,133.86650643024004,132.92768610254177,133.71287536621094,102932800,0.0,0 -2013-04-01,133.64461354351718,133.91772968232866,132.85942417877555,133.18374633789062,99194100,0.0,0 -2013-04-02,133.6616625446891,134.17374870151374,133.45682547738087,133.8408966064453,101504300,0.0,0 -2013-04-03,133.91767402948938,134.02008602753668,132.13392894142493,132.4838409423828,154167400,0.0,0 -2013-04-04,132.65456255311975,133.28613374020298,132.36438647644306,133.0215606689453,131885000,0.0,0 -2013-04-05,131.39141587566786,132.58627907019016,131.23779786341095,132.4241180419922,159666000,0.0,0 -2013-04-08,132.51806523912927,133.32885775603563,132.07425794727342,133.32032775878906,86571200,0.0,0 -2013-04-09,133.5677608994966,134.26761100950992,133.12395383029033,133.7811279296875,101922200,0.0,0 -2013-04-10,134.13961259962494,135.59050612074486,134.10547959732844,135.41981506347656,135711100,0.0,0 -2013-04-11,135.44536716440595,136.30737816740128,135.30880916075884,135.8635711669922,110142500,0.0,0 -2013-04-12,135.42827568338703,135.73552463823475,134.77964478888015,135.53070068359375,116359900,0.0,0 -2013-04-15,134.8479611927825,134.9589162142532,132.37290888637472,132.3899688720703,217259000,0.0,0 -2013-04-16,133.38849792569928,134.41266986673554,133.0641889657325,134.34439086914062,147507800,0.0,0 -2013-04-17,133.38861757812555,133.4142336202281,131.67315017698735,132.38153076171875,226834800,0.0,0 -2013-04-18,132.60332035397752,132.63746636714544,131.0500153499332,131.55355834960938,167583200,0.0,0 -2013-04-19,131.86080957358513,132.75695360244933,131.5364875574714,132.69720458984375,149687600,0.0,0 -2013-04-22,132.95329594216142,133.6019271873377,132.074224600545,133.28614807128906,106553500,0.0,0 -2013-04-23,133.95179155070085,134.78818649156332,133.2860875944999,134.66017150878906,166141300,0.0,0 -2013-04-24,134.7029141869496,135.10404536204715,134.45540106722027,134.74559020996094,96781200,0.0,0 -2013-04-25,135.13814752125762,135.9318796948375,134.93332349418475,135.29177856445312,131060600,0.0,0 -2013-04-26,135.12960441285395,135.36004444383656,134.61751834909023,135.05279541015625,95918800,0.0,0 -2013-04-29,135.41978467340425,136.25617978234536,135.20641764397817,135.9574737548828,88572800,0.0,0 -2013-04-30,135.9318720234848,136.31593007546283,135.36857993504228,136.2817840576172,116010700,0.0,0 -2013-05-01,135.98308878753105,136.05136780573508,134.93332655628737,135.08694458007812,138874200,0.0,0 -2013-05-02,135.4283506138329,136.46105304044264,135.3003355713535,136.34156799316406,96407600,0.0,0 -2013-05-03,137.5278575826486,138.1594287037838,136.367140374251,137.72415161132812,144202300,0.0,0 -2013-05-06,137.82658137822852,138.2703754563188,137.76683235451006,138.07408142089844,66882100,0.0,0 -2013-05-07,138.37277949602188,138.81657350676764,137.98017846186633,138.77391052246094,90359200,0.0,0 -2013-05-08,138.62031308369163,139.44817833687043,138.54350406586485,139.40550231933594,97419200,0.0,0 -2013-05-09,139.34576102305218,139.71274610955396,138.66298383709753,139.01290893554688,106738600,0.0,0 -2013-05-10,139.10677018079915,139.58471023270238,138.69709611669091,139.46522521972656,103203000,0.0,0 -2013-05-13,139.28595015587447,139.806566063946,138.96164122927405,139.5761260986328,81843200,0.0,0 -2013-05-14,139.68714630695584,141.1209797326209,139.68714630695584,141.0185546875,119000900,0.0,0 -2013-05-15,140.78809366008565,142.05975270550303,140.74541765349224,141.77810668945312,120718500,0.0,0 -2013-05-16,141.48795484334184,141.98296795400407,140.89905971129727,141.1124267578125,109913600,0.0,0 -2013-05-17,141.63305854026027,142.56333584048392,141.44529447672434,142.47799682617188,129801000,0.0,0 -2013-05-20,142.34140638293707,143.02418345651492,142.1963183710213,142.46942138671875,85071200,0.0,0 -2013-05-21,142.59746162103826,143.21195973757605,142.1024485258889,142.6742706298828,95804200,0.0,0 -2013-05-22,142.81937542711958,144.29588489261843,140.9673508666494,141.61598205566406,244031800,0.0,0 -2013-05-23,140.1053306757658,141.59889989611773,139.91756664623318,141.206298828125,211064400,0.0,0 -2013-05-24,140.36994272779896,141.14660206375268,139.95173854161763,141.08685302734375,151573900,0.0,0 -2013-05-28,142.5633208216793,143.1948919709372,141.5135585936955,141.9317626953125,143679800,0.0,0 -2013-05-29,141.18073134675348,141.50505348352445,140.25898397610962,141.01004028320312,160363400,0.0,0 -2013-05-30,141.120993754714,142.1792861506126,141.01003870434658,141.53065490722656,107793800,0.0,0 -2013-05-31,141.13801632939766,141.9402784316058,139.22625610921455,139.49935913085938,176850100,0.0,0 -2013-06-03,139.82369472712534,140.36138385259952,138.8251385104526,140.2675018310547,168390700,0.0,0 -2013-06-04,140.3443025881466,140.90759468428584,138.8848663432204,139.59324645996094,157631500,0.0,0 -2013-06-05,139.192068138453,139.47371409090502,137.51927844998679,137.63876342773438,211737800,0.0,0 -2013-06-06,137.57909740158178,138.89344597237675,136.7683050619555,138.88490295410156,200225500,0.0,0 -2013-06-07,139.8407581748285,140.77956530318926,139.23479007304107,140.65155029296875,188337800,0.0,0 -2013-06-10,141.08681435060862,141.1636233567409,140.2845522255197,140.65155029296875,105667100,0.0,0 -2013-06-11,139.37132881502248,140.42962081412134,138.8933888124761,139.2006378173828,159505400,0.0,0 -2013-06-12,140.15652911309758,140.3016171239236,137.92044691124232,138.0484619140625,177361500,0.0,0 -2013-06-13,137.97161880733825,140.39546442944203,137.6643698536289,140.14796447753906,163587800,0.0,0 -2013-06-14,139.9943586678199,140.54057768122235,139.0384786500878,139.26890563964844,141197500,0.0,0 -2013-06-17,140.21627128505543,141.01000340618256,139.3030671712915,140.34429931640625,136295600,0.0,0 -2013-06-18,140.42109433738167,141.6671633948316,140.41256434529708,141.45379638671875,114695600,0.0,0 -2013-06-19,141.33432336999056,141.5818233903215,139.43962313542355,139.49935913085938,206149500,0.0,0 -2013-06-20,138.14233802007078,139.51642209080495,135.68434588785342,136.0428009033203,321255900,0.0,0 -2013-06-21,136.96859740979673,137.07155132460528,135.10677326347604,136.47955322265625,271956800,0.839,0 -2013-06-24,135.0553351941176,135.93046990557767,133.61391451275344,134.75503540039062,222329000,0.0,0 -2013-06-25,135.97333909535422,137.36328247072734,135.0638778929277,136.05056762695312,162262200,0.0,0 -2013-06-26,137.1659388517193,137.706473122511,136.63399280224223,137.39759826660156,134848000,0.0,0 -2013-06-27,138.22123439146375,138.83898394899634,138.0925289300959,138.20407104492188,129483700,0.0,0 -2013-06-28,137.8180150955773,138.47865354595322,137.1573635534006,137.63783264160156,160402900,0.0,0 -2013-07-01,138.35856277640627,139.40530481554723,138.20413186496086,138.44436645507812,131954800,0.0,0 -2013-07-02,138.2384147801725,139.2508429780779,137.70646874759527,138.31564331054688,154863700,0.0,0 -2013-07-03,137.68926632120096,138.79607299385276,137.4661951980277,138.3756561279297,75216400,0.0,0 -2013-07-05,139.39672778351817,139.92009879595173,138.39288759740202,139.86862182617188,122416900,0.0,0 -2013-07-08,140.58928847196466,141.04401904071122,139.92006185789236,140.66650390625,108092500,0.0,0 -2013-07-09,141.5501885952184,141.85048821483977,140.9410273213069,141.67889404296875,119298000,0.0,0 -2013-07-10,141.5416501493302,142.21087675731476,141.2499386723727,141.73040771484375,121410100,0.0,0 -2013-07-11,143.377752290954,143.8067444271735,141.72183583741017,143.660888671875,135592200,0.0,0 -2013-07-12,143.6179628419337,144.08126851124186,143.39489168142217,143.72091674804688,104212700,0.0,0 -2013-07-15,144.11561916398747,144.47597098708334,143.8667963937873,144.27005004882812,69450600,0.0,0 -2013-07-16,144.36439200780708,144.45019564980421,143.3434019821083,143.7294921875,88702100,0.0,0 -2013-07-17,144.2786321568449,144.55318030441754,143.90969212791236,144.09844970703125,92873900,0.0,0 -2013-07-18,144.40730260238456,145.2309731034876,144.31292382717274,144.8877716064453,103620100,0.0,0 -2013-07-19,144.5874876774384,145.19664907169175,144.4073052517107,145.14517211914062,103831700,0.0,0 -2013-07-22,145.3511140277873,145.63425039953708,145.00791247484787,145.42832946777344,79428600,0.0,0 -2013-07-23,145.68574914654394,145.71148763038846,145.04226086682584,145.11947631835938,80829700,0.0,0 -2013-07-24,145.67710897669951,145.73717414545297,144.29575410514659,144.5874786376953,112914000,0.0,0 -2013-07-25,144.3301085462538,145.06797550940425,144.08987400865138,144.93927001953125,111088600,0.0,0 -2013-07-26,144.3300532968082,145.1365602608005,143.72946718270603,145.09365844726562,107814600,0.0,0 -2013-07-29,144.72475265892322,145.0507908084501,144.23570852643135,144.6475372314453,79695000,0.0,0 -2013-07-30,145.0851175040837,145.23954835903953,144.3043488259001,144.6475372314453,85209600,0.0,0 -2013-07-31,144.9479126156556,145.72868170165762,144.56182218480134,144.75057983398438,142388700,0.0,0 -2013-08-01,145.84873615210742,146.55227637525593,145.77150762577918,146.423583984375,110438400,0.0,0 -2013-08-02,146.09753076694182,146.68954188765642,145.9001980906364,146.67237854003906,91116700,0.0,0 -2013-08-05,146.3464024512219,146.68101583682568,146.15764483967075,146.4579315185547,54072700,0.0,0 -2013-08-06,146.17473486536744,146.4921978585935,145.29960058167788,145.62562561035156,87495000,0.0,0 -2013-08-07,145.1623968173373,145.36830471456483,144.6132872717235,145.15380859375,84854700,0.0,0 -2013-08-08,145.8401303132917,146.01172451100337,144.93924441055776,145.68569946289062,102181300,0.0,0 -2013-08-09,145.49697925106017,145.9431347530284,144.75911222895175,145.26531982421875,91757700,0.0,0 -2013-08-12,144.53597414669835,145.26525260960975,144.46733386340162,145.09365844726562,68593300,0.0,0 -2013-08-13,145.3510803697125,145.77148417348707,144.49309632374306,145.52267456054688,80806000,0.0,0 -2013-08-14,145.4540161511214,145.6856754803858,144.74188791063898,144.77621459960938,79829200,0.0,0 -2013-08-15,143.6351417688328,143.65229202717921,142.50259631155694,142.7514190673828,152931800,0.0,0 -2013-08-16,142.47688902299961,142.9659464207414,141.99641984811143,142.27955627441406,130868200,0.0,0 -2013-08-19,142.11651353442582,142.6055708509288,141.36148318970587,141.3700714111328,96437600,0.0,0 -2013-08-20,141.6016989259217,142.59696368596204,141.44726806236332,142.0650177001953,89294400,0.0,0 -2013-08-21,141.67033412624545,142.45110281979856,140.8724151763364,141.1898651123047,159530500,0.0,0 -2013-08-22,141.48161791951625,142.68280389720988,141.47304278883223,142.4768829345703,101471400,0.0,0 -2013-08-23,142.8972843339676,143.1375188848245,142.22805764346617,142.95733642578125,90888900,0.0,0 -2013-08-26,143.10315565112418,143.54073590060966,142.33097523360524,142.42535400390625,89702100,0.0,0 -2013-08-27,141.018289979151,142.42538360677483,140.03161334148695,140.1345672607422,158619400,0.0,0 -2013-08-28,140.07453492507787,141.1298652121032,139.89436552413454,140.63223266601562,108113000,0.0,0 -2013-08-29,140.32330914788915,141.60169714167827,140.19460367141303,140.85525512695312,119200500,0.0,0 -2013-08-30,141.14697029927393,141.1641336482812,139.9972746599763,140.4091033935547,134928900,0.0,0 -2013-09-03,141.76470985306008,142.0650095262109,140.4519952211465,141.04400634765625,142375100,0.0,0 -2013-09-04,141.0783159368212,142.4510957010197,140.82093118635734,142.2108612060547,97389400,0.0,0 -2013-09-05,142.29667346794224,142.7685542669009,142.1937064697231,142.39105224609375,63090500,0.0,0 -2013-09-06,142.8629398878228,143.26619349634873,141.12123306452978,142.45968627929688,159756500,0.0,0 -2013-09-09,142.81148528420482,143.90970414983926,142.81148528420482,143.82391357421875,87559300,0.0,0 -2013-09-10,144.69043892802003,144.91351008224868,144.36440077877947,144.8877716064453,105847200,0.0,0 -2013-09-11,144.6904811586644,145.34254455566406,144.4416714560126,145.34254455566406,94545900,0.0,0 -2013-09-12,145.29106478446195,145.47982238633446,144.7591186838042,144.95645141601562,83209000,0.0,0 -2013-09-13,145.11081551304838,145.39395175997467,144.77620232882546,145.28240966796875,72727800,0.0,0 -2013-09-16,146.85256750740552,146.9212078091367,145.89161637496812,146.12327575683594,106299200,0.0,0 -2013-09-17,146.2519892334123,146.80967371426527,146.2519892334123,146.77536010742188,82523300,0.0,0 -2013-09-18,146.72387178907866,148.87742048722902,146.3549448710145,148.4741668701172,203460600,0.0,0 -2013-09-19,148.87743347646472,148.94607378874903,148.0795013014852,148.22535705566406,146616900,0.0,0 -2013-09-20,148.5771448977787,148.5771448977787,147.06835362052905,147.18905639648438,132867100,0.838,0 -2013-09-23,146.99072820181772,147.12866480308577,146.04234006777727,146.50790405273438,104616500,0.0,0 -2013-09-24,146.48203745696424,147.0252063925584,145.88715380799522,146.1630401611328,106333100,0.0,0 -2013-09-25,146.25789036855488,146.55102375273214,145.61126564460366,145.74058532714844,117306500,0.0,0 -2013-09-26,145.98201279192446,146.71484629653384,145.74922419370057,146.30101013183594,77146900,0.0,0 -2013-09-27,145.56813631472124,145.82678880275918,145.24913903432125,145.6284942626953,99141800,0.0,0 -2013-09-30,144.39562244856486,145.30951671159139,144.11110596289555,144.85256958007812,143937000,0.0,0 -2013-10-01,144.9646552895872,146.1372020981331,144.81808858297083,145.99925231933594,127160000,0.0,0 -2013-10-02,145.14575666158618,145.99929320121413,144.69742633276402,145.86134338378906,113350000,0.0,0 -2013-10-03,145.52504464236634,145.65437748131566,143.84382298093544,144.5163116455078,176698000,0.0,0 -2013-10-04,144.6284131817001,145.75784905753508,144.4387355217995,145.6112823486328,96878000,0.0,0 -2013-10-07,144.3438611015148,145.23189121953888,144.19729442875095,144.35247802734375,96295000,0.0,0 -2013-10-08,144.3266222382282,144.51629986206373,142.56780891020455,142.6712646484375,178015000,0.0,0 -2013-10-09,142.94718039103319,143.29204166138078,141.85222546647756,142.77474975585938,168973000,0.0,0 -2013-10-10,144.23178439303123,145.93025293700728,144.18005652356806,145.8526611328125,195955000,0.0,0 -2013-10-11,145.62848776399198,146.84414519459295,145.50778502904228,146.7924041748047,105040000,0.0,0 -2013-10-14,145.88717899027398,147.49942591154138,145.77509315104493,147.37872314453125,112106000,0.0,0 -2013-10-15,147.0079533439402,147.5597391831557,146.11130628856952,146.30960083007812,155485000,0.0,0 -2013-10-16,147.18905164313205,148.43057338750208,147.12007675115422,148.3529815673828,161676000,0.0,0 -2013-10-17,147.74943894042372,149.4306737990629,147.72357500311674,149.34445190429688,129389000,0.0,0 -2013-10-18,149.89624805932576,150.45665094672037,149.5944845626579,150.3531951904297,138316000,0.0,0 -2013-10-21,150.4048968314776,150.6635493326992,150.02554158376762,150.36178588867188,104104000,0.0,0 -2013-10-22,150.80155222930307,151.68095262549593,150.38770281926455,151.23263549804688,126663000,0.0,0 -2013-10-23,150.71528856728565,150.78426344985368,149.9824551067762,150.5083770751953,105484000,0.0,0 -2013-10-24,150.81015876276052,151.19813104676211,150.45666734940988,151.00845336914062,70350000,0.0,0 -2013-10-25,151.31879537592044,151.74126157883677,151.02566200521755,151.69815063476562,93625000,0.0,0 -2013-10-28,151.6464874758886,152.14654569815386,151.4826737118822,151.9396209716797,84979000,0.0,0 -2013-10-29,152.28446641649697,152.81038848462325,152.06892480096352,152.75003051757812,87401000,0.0,0 -2013-10-30,152.93110453134088,153.04317723532267,151.44817703536316,151.9913330078125,140002000,0.0,0 -2013-10-31,151.87059158219958,152.50859939324127,151.33605267508685,151.56021118164062,133795000,0.0,0 -2013-11-01,151.75853430060283,152.2672093222351,151.0687985454392,151.92234802246094,142805000,0.0,0 -2013-11-04,152.3361911449237,152.51723873107784,151.72404718101313,152.45689392089844,85677000,0.0,0 -2013-11-05,151.86195898028998,152.38788090596364,151.3705310709036,151.9740447998047,85825000,0.0,0 -2013-11-06,152.629327739104,153.03454702875624,152.20686143663661,152.75003051757812,87348000,0.0,0 -2013-11-07,153.03454546199947,153.15524823923784,150.67220464391642,150.8187713623047,157000000,0.0,0 -2013-11-08,150.7670589142892,152.87074738061517,150.74982505550236,152.85350036621094,136713000,0.0,0 -2013-11-11,152.706874471305,153.0603657737471,152.52582693486258,152.8793182373047,62614000,0.0,0 -2013-11-12,152.55167694404716,152.91378513949184,152.06023593590794,152.5689239501953,83990000,0.0,0 -2013-11-13,151.81884490077158,153.83631067667463,151.81884490077158,153.793212890625,103844000,0.0,0 -2013-11-14,153.9311712389284,154.68988182143758,153.68114879093653,154.56056213378906,103435000,0.0,0 -2013-11-15,154.81056603815782,155.2933770216988,154.6122714889776,155.2330322265625,102818000,0.0,0 -2013-11-18,155.49168655595776,155.62100622963754,154.34500387875005,154.6898651123047,104796000,0.0,0 -2013-11-19,154.61227494222956,155.07783892801027,154.08635300435185,154.35362243652344,93891000,0.0,0 -2013-11-20,154.66401050127925,155.12957451020517,153.4483528729538,153.87081909179688,124909000,0.0,0 -2013-11-21,154.3018664565485,155.23300741409213,154.20702766025394,155.1123046875,92841000,0.0,0 -2013-11-22,155.1726907801231,155.9055374152665,154.99164321984517,155.88829040527344,81296000,0.0,0 -2013-11-25,156.16422228790125,156.19870315941512,155.50896732858723,155.73313903808594,79486000,0.0,0 -2013-11-26,155.8107238839834,156.24180711448236,155.54345438597267,155.77622985839844,86994000,0.0,0 -2013-11-27,155.9400370071319,156.25904753005153,155.7503593412712,156.15557861328125,58800000,0.0,0 -2013-11-29,156.32800831030846,156.69873352472294,155.8796781264181,156.05210876464844,55870900,0.0,0 -2013-12-02,156.1297380450339,156.42287151593024,155.40552129730727,155.6469268798828,99726000,0.0,0 -2013-12-03,155.13820945736802,155.52618166957416,154.47433770854434,154.97439575195312,116563000,0.0,0 -2013-12-04,154.41398226411766,155.60376275583613,153.76735756980494,154.95713806152344,123033000,0.0,0 -2013-12-05,154.6812200879679,154.9657364739936,154.12943432114508,154.2760009765625,106934000,0.0,0 -2013-12-06,155.76762334369957,156.14697871679914,155.31929307043492,156.0004119873047,127728000,0.0,0 -2013-12-09,156.45731535154994,156.6297459758404,156.19004591055773,156.39695739746094,70124000,0.0,0 -2013-12-10,156.03481184674447,156.3624391635213,155.74167853994953,155.83651733398438,80976000,0.0,0 -2013-12-11,155.89691926839222,155.92278320537244,153.89668710449624,154.08636474609375,130591000,0.0,0 -2013-12-12,154.0173978610192,154.20707551246744,153.25868725522625,153.57769775390625,115565000,0.0,0 -2013-12-13,153.89668439391096,154.03463415911835,153.26730667912116,153.56044006347656,107808000,0.0,0 -2013-12-16,154.28460651076747,155.02606981353293,154.24149557868392,154.51739501953125,96195000,0.0,0 -2013-12-17,154.6553918342987,154.6812557703028,153.68113972609854,154.0260009765625,89886000,0.0,0 -2013-12-18,154.25878794501284,156.68147306659336,152.87932980195168,156.65560913085938,234906000,0.0,0 -2013-12-19,156.2072962326373,156.6556264275496,155.8020901428608,156.47457885742188,136531200,0.0,0 -2013-12-20,156.6306280267186,157.757532070563,156.52661059182307,157.38478088378906,197087000,0.98,0 -2013-12-23,158.15625184056626,158.32095490702832,157.82685893467928,158.22560119628906,85598000,0.0,0 -2013-12-24,158.2342720614063,158.6416912778043,158.2256083516881,158.5723419189453,45368800,0.0,0 -2013-12-26,158.9277301069253,159.4651842558558,158.91040268992194,159.3784942626953,63365000,0.0,0 -2013-12-27,159.58652374237,159.65585985638813,159.20510898009834,159.36981201171875,61814000,0.0,0 -2013-12-30,159.38720385233032,159.51723887950288,159.1358239630602,159.3438720703125,56857000,0.0,0 -2013-12-31,159.56055097760975,160.09799194335938,159.43917968646687,160.09799194335938,86119900,0.0,0 -2014-01-02,159.48254611251372,159.56057241402198,158.18227529653302,158.56369018554688,119636900,0.0,0 -2014-01-03,158.83236823093742,159.1531106074955,158.3122679798491,158.5376434326172,81390600,0.0,0 -2014-01-06,159.05775787743835,159.1184302882682,157.83550045534432,158.07821655273438,108028200,0.0,0 -2014-01-07,158.71101771569892,159.3178079842856,158.58965966198159,159.04908752441406,86144200,0.0,0 -2014-01-08,159.02306985786834,159.35247593053714,158.53763768364152,159.08375549316406,96582300,0.0,0 -2014-01-09,159.59523967969113,159.61258032738783,158.45967195577347,159.1878204345703,90683400,0.0,0 -2014-01-10,159.4565280069509,159.69058043721964,158.64168958286072,159.62123107910156,102026400,0.0,0 -2014-01-13,159.2138207624594,159.6559080647445,157.19406523567753,157.49746704101562,149892000,0.0,0 -2014-01-14,158.01755291506058,159.30049615363913,157.72282807391568,159.21380615234375,105016100,0.0,0 -2014-01-15,159.58654348761326,160.3146918182051,159.2484736750502,160.0719757080078,98525800,0.0,0 -2014-01-16,159.74257931545316,160.07198543947663,159.35250077370983,159.8639373779297,72290600,0.0,0 -2014-01-17,159.58657596936214,159.8899645553415,158.91043620661668,159.1878204345703,107848700,0.0,0 -2014-01-21,160.10664795995692,160.16733360261262,158.67635561406948,159.6558837890625,88621200,0.0,0 -2014-01-22,159.92465182214045,159.99400118847797,159.42187883647287,159.75994873046875,61270900,0.0,0 -2014-01-23,158.9537464093919,158.97975076388784,157.61014400364525,158.4509735107422,132496900,0.0,0 -2014-01-24,157.41939704608595,157.47140574201453,155.0182275101261,155.0702362060547,208677100,0.0,0 -2014-01-27,155.21764145203127,155.6163969426606,153.53595594174456,154.3074493408203,180843100,0.0,0 -2014-01-28,154.42009813027215,155.42564365597184,154.40275748961298,155.2262725830078,110463200,0.0,0 -2014-01-29,153.9346921517463,154.77553480157243,153.3279018959531,153.73532104492188,216597300,0.0,0 -2014-01-30,155.01825671151326,155.86776310144975,154.52414755304744,155.364990234375,118938100,0.0,0 -2014-01-31,153.4405939894748,155.4170042366209,153.36258092822766,154.45480346679688,194677900,0.0,0 -2014-02-03,154.27279702518896,154.61953061388587,150.68405015131782,150.97877502441406,254837100,0.0,0 -2014-02-04,151.65490106866653,152.4263944767415,150.92675266201906,152.03631591796875,165012400,0.0,0 -2014-02-05,151.50754171753593,152.18368142699532,150.5800221579596,151.84561157226562,164230500,0.0,0 -2014-02-06,152.2010031357346,153.84800720214844,151.88893767580993,153.84800720214844,132877600,0.0,0 -2014-02-07,154.56744430806694,155.919723143018,154.06467159791274,155.75502014160156,170787200,0.0,0 -2014-02-10,155.7724324500626,156.09317489456154,155.3476858057577,156.04115295410156,92218800,0.0,0 -2014-02-11,156.17123071886044,158.14764173219558,156.06720003649852,157.74888610839844,117814100,0.0,0 -2014-02-12,157.98288465019687,158.4856575575901,157.5147930340926,157.8268585205078,94717700,0.0,0 -2014-02-13,156.76065145351123,158.80641146568325,156.75198774285934,158.64170837402344,100542200,0.0,0 -2014-02-14,158.4943102658408,159.81192147558738,158.34694785568382,159.51719665527344,96498400,0.0,0 -2014-02-18,159.65586381483254,159.92459747821178,159.19643599262383,159.7078857421875,80460900,0.0,0 -2014-02-19,159.29183381725628,160.32338408284775,158.52034035234945,158.65037536621094,126524300,0.0,0 -2014-02-20,158.86710877984945,159.95066790761365,158.28632267461265,159.5865936279297,104998100,0.0,0 -2014-02-21,159.8899121405332,160.27132690259367,159.32646693196574,159.40447998046875,118116400,0.0,0 -2014-02-24,159.74254798393585,161.36354747772688,159.6731986436889,160.28866577148438,114063900,0.0,0 -2014-02-25,160.4186736710535,160.87810143687935,159.6991892085068,160.22796630859375,117085000,0.0,0 -2014-02-26,160.46206982387537,160.8868296715277,159.78593013302458,160.2366943359375,98677200,0.0,0 -2014-02-27,160.00261961498305,161.1208464164754,159.82057592648943,161.0775146484375,93880800,0.0,0 -2014-02-28,161.05151602751403,162.2304286062886,160.4100576422494,161.4849395751953,150842000,0.0,0 -2014-03-03,160.06331934929221,160.7567997077357,159.28316221294187,160.34938049316406,167748500,0.0,0 -2014-03-04,161.91837851286223,162.94992877071618,161.8837104456489,162.6031951904297,167545900,0.0,0 -2014-03-05,162.7418703488279,163.02793146331067,162.49047729653864,162.7505340576172,88376900,0.0,0 -2014-03-06,163.14930859910788,163.49604216973304,162.77655744499984,163.123291015625,82516500,0.0,0 -2014-03-07,163.79939986131592,163.79939986131592,162.47311190453487,163.19259643554688,114513500,0.0,0 -2014-03-10,162.9412692057497,163.16664471596545,162.16977572700367,163.10597229003906,74939200,0.0,0 -2014-03-11,163.3486649507053,163.5827173645562,161.9270362766007,162.29977416992188,99009100,0.0,0 -2014-03-12,161.51091303620066,162.4037642423566,161.14682571581537,162.34307861328125,104824400,0.0,0 -2014-03-13,162.8285519982557,162.95858699621067,160.07198463849542,160.52273559570312,155014300,0.0,0 -2014-03-14,160.23667876000653,161.06018079296413,159.88126830327624,160.0719757080078,153919600,0.0,0 -2014-03-17,160.8781207863634,161.9010070559501,160.8087714437614,161.51959228515625,98359500,0.0,0 -2014-03-18,161.8489986593391,162.88921234625144,161.67561868899148,162.67250061035156,101804600,0.0,0 -2014-03-19,162.6898538204827,162.91524252685645,160.77412917852638,161.80567932128906,176267300,0.0,0 -2014-03-20,161.4502634792607,162.871892115849,161.16420236477794,162.7505340576172,117241000,0.0,0 -2014-03-21,163.4339562797549,164.5745352369767,161.97121943298285,162.11923217773438,163128000,0.825,0 -2014-03-24,162.67651976263596,162.87678409305997,160.74362488182211,161.44886779785156,121411000,0.0,0 -2014-03-25,162.26729561864326,162.76358552263576,161.30956345007817,162.21505737304688,103852000,0.0,0 -2014-03-26,162.85068724842247,163.11189182984398,161.0048645677665,161.04840087890625,119843000,0.0,0 -2014-03-27,160.85681406440716,161.3705078734613,160.1167368046138,160.70880126953125,142383000,0.0,0 -2014-03-28,161.17025395283937,162.31083327594453,161.0744794066405,161.50111389160156,101642000,0.0,0 -2014-03-31,162.52849834704392,163.07702649060855,161.52722988967398,162.82452392578125,99745000,0.0,0 -2014-04-01,163.35566182946104,163.9999646236155,162.8158488279953,163.90419006347656,89193000,0.0,0 -2014-04-02,164.11316631683655,164.67039653178213,163.8084254382532,164.45272827148438,78774000,0.0,0 -2014-04-03,164.70523454931694,164.74877086293787,163.730084894213,164.23507690429688,77435000,0.0,0 -2014-04-04,165.13181320183423,165.16663426967926,162.03222003251207,162.2934112548828,169381000,0.0,0 -2014-04-07,161.90166216838574,162.17156870685392,160.16903118192843,160.4998779296875,140803000,0.0,0 -2014-04-08,160.43018472134767,161.42275130180928,159.8468354691427,161.16156005859375,112660000,0.0,0 -2014-04-09,161.59690139144422,162.94643380686136,161.12673056862857,162.89419555664062,100254000,0.0,0 -2014-04-10,162.8854941950368,162.9638515706493,159.27219354529078,159.4724578857422,172959000,0.0,0 -2014-04-11,158.58438641890945,159.69884684337393,157.86172620398983,158.03585815429688,167251000,0.0,0 -2014-04-14,159.2721685584186,159.65526671519808,157.97487459208483,159.2808837890625,132382000,0.0,0 -2014-04-15,159.61174664523307,160.49112140465616,158.0358176920895,160.3779296875,157093000,0.0,0 -2014-04-16,161.48373272162672,162.06708207476117,160.7697744388727,162.05838012695312,105197000,0.0,0 -2014-04-17,161.84067536953657,162.73746735195715,161.5620536775078,162.2847137451172,105255000,0.0,0 -2014-04-21,162.328236014586,162.90288324066643,162.12798499413216,162.8506317138672,68329000,0.0,0 -2014-04-22,163.0160662587153,164.03475185068058,162.92900694908988,163.59071350097656,85790000,0.0,0 -2014-04-23,163.52975375998676,163.61681305920277,163.07700023366576,163.2075958251953,73869000,0.0,0 -2014-04-24,164.00862485578892,164.02604203104298,162.75485394573985,163.53846740722656,88170000,0.0,0 -2014-04-25,163.00741046208063,163.10318502957205,161.83199652336012,162.1976776123047,100380000,0.0,0 -2014-04-28,162.85936228030954,163.41659238326275,161.03965917844502,162.7113494873047,135121000,0.0,0 -2014-04-29,163.23374090638984,163.72131557286545,162.88547708331106,163.4688262939453,84098000,0.0,0 -2014-04-30,163.1989396841499,164.12185088441177,162.97255621474727,163.9564208984375,101508000,0.0,0 -2014-05-01,163.87801087535303,164.41782367897733,163.45137648465635,163.97378540039062,93019000,0.0,0 -2014-05-02,163.95639195370308,164.67905196803582,163.49493643503712,163.7387237548828,98122000,0.0,0 -2014-05-05,162.93770966221106,164.16536151680512,162.48495607962218,164.0521697998047,75883000,0.0,0 -2014-05-06,163.68647411846956,163.79966582530872,162.58943125553014,162.62425231933594,85454000,0.0,0 -2014-05-07,163.17281391779613,163.66038862372048,161.95386386757087,163.58203125,106500000,0.0,0 -2014-05-08,163.43404562234056,164.60074433198588,162.8855173442673,163.4079132080078,93618000,0.0,0 -2014-05-09,163.43403463091886,163.72134502967455,162.66783813979524,163.65170288085938,83679000,0.0,0 -2014-05-12,164.38300713599,165.32333523891103,163.68646632433615,165.24496459960938,86940000,0.0,0 -2014-05-13,165.46269329723074,165.7935532930085,165.22762114957206,165.39305114746094,66454000,0.0,0 -2014-05-14,165.24502360825477,165.3233942755424,164.37435060412474,164.60943603515625,72367000,0.0,0 -2014-05-15,164.27855324537805,164.31338760054211,162.3630755591853,163.16409301757812,154956000,0.0,0 -2014-05-16,163.2598554830975,163.79968166487612,162.57202980177385,163.7300262451172,97458000,0.0,0 -2014-05-19,163.41658615632596,164.4613908988746,163.2685733689611,164.33079528808594,63839000,0.0,0 -2014-05-20,164.25242514118938,164.2698423178191,162.87677375194232,163.29469299316406,111644000,0.0,0 -2014-05-21,163.76485715401142,164.7487217497908,163.73873803070876,164.6703643798828,89093000,0.0,0 -2014-05-22,164.71388276076212,165.41042369520076,164.43527435823464,165.07086181640625,61549000,0.0,0 -2014-05-23,165.21888262029736,165.84576816315888,165.0708698234292,165.7325897216797,61092800,0.0,0 -2014-05-27,166.35076634858626,166.80351998793395,166.25499179575147,166.75128173828125,72010000,0.0,0 -2014-05-28,166.7512882136446,167.0124927574659,166.35077280839664,166.62939453125,66723000,0.0,0 -2014-05-29,167.01248878064902,167.51746739182025,166.5858542597192,167.49134826660156,64377000,0.0,0 -2014-05-30,167.33460929498474,167.8657202399154,167.19529845703934,167.76123046875,76316000,0.0,0 -2014-06-02,167.99632560670435,168.03115996092072,167.143069924192,167.95278930664062,64656000,0.0,0 -2014-06-03,167.5435646781874,167.9527819671595,167.38684994730258,167.86572265625,65047000,0.0,0 -2014-06-04,167.578391024842,168.3010510264717,167.40425912592926,168.2052764892578,55529000,0.0,0 -2014-06-05,168.396843703351,169.4764695641546,167.77866011739596,169.30233764648438,92103000,0.0,0 -2014-06-06,169.66799368516268,170.15556829703687,169.58963632655684,170.11204528808594,78696000,0.0,0 -2014-06-09,170.08597329817184,170.69544173189396,169.92924525212015,170.28622436523438,65119000,0.0,0 -2014-06-10,170.07726641198357,170.33847097794435,169.7115853338049,170.30364990234375,57129000,0.0,0 -2014-06-11,169.69416118934703,169.8857103108297,169.32848012617526,169.71157836914062,68772000,0.0,0 -2014-06-12,169.5112914681576,169.60706600865353,168.13562683440355,168.510009765625,106350000,0.0,0 -2014-06-13,168.8408650276042,169.18914211317065,168.30105215174171,169.02371215820312,82017000,0.0,0 -2014-06-16,168.81474584353128,169.5199886740967,168.61449482278846,169.1630096435547,87424000,0.0,0 -2014-06-17,168.92799666874762,169.75513344729657,168.74514947423486,169.63323974609375,84834000,0.0,0 -2014-06-18,169.63320946124188,170.97403993509263,169.2588131909066,170.87826538085938,105267000,0.0,0 -2014-06-19,171.02625740264455,171.17428347472,170.47774255452222,171.06979370117188,85929000,0.0,0 -2014-06-20,171.49587762773947,171.55712319006173,171.207177153384,171.41714477539062,100587000,0.937,0 -2014-06-23,171.4608727641738,171.51336132722233,171.0496945548605,171.36463928222656,70611000,0.0,0 -2014-06-24,171.05841348116908,171.90701405869765,170.13982371333088,170.33229064941406,96237000,0.0,0 -2014-06-25,169.97361541743444,171.27713916758432,169.9386274938598,171.1021728515625,82782000,0.0,0 -2014-06-26,171.12842785593938,171.1459284933606,169.83366101734728,170.97970581054688,84312000,0.0,0 -2014-06-27,170.5772539317837,171.36462234692377,170.49852109499182,171.3121337890625,71445100,0.0,0 -2014-06-30,171.20715451457832,171.6183327101316,171.05843247455954,171.2246551513672,70201200,0.0,0 -2014-07-01,171.64461970477512,172.8956552395457,171.58338748438828,172.37074279785156,90470000,0.0,0 -2014-07-02,172.38820386874747,172.764380797987,172.30947102528918,172.54566955566406,52475000,0.0,0 -2014-07-03,173.03556697885034,173.4729894290562,172.9043455834181,173.39425659179688,52938800,0.0,0 -2014-07-07,173.061816757744,173.20178179016563,172.53690450167497,172.7906036376953,61696000,0.0,0 -2014-07-08,172.47564880201168,172.53689434621322,171.25961505113116,171.67955017089844,108143000,0.0,0 -2014-07-09,172.10824337631053,172.60691140565763,171.74081009898478,172.44943237304688,72992000,0.0,0 -2014-07-10,170.78722205333875,172.22196715207775,170.6472436658828,171.7670440673828,99040000,0.0,0 -2014-07-11,171.6621220714192,172.12578894830924,171.27718805685132,172.00331115722656,64243000,0.0,0 -2014-07-14,172.87809789121178,173.09680911199672,172.72937586296686,172.86935424804688,58658000,0.0,0 -2014-07-15,172.97434839628394,173.30679375631874,171.78455870408422,172.54566955566406,111307000,0.0,0 -2014-07-16,173.31554758535015,173.44676899882526,172.71190238521325,173.184326171875,79986400,0.0,0 -2014-07-17,172.65061089624126,173.30674443648684,170.97089728603012,171.2158660888672,145398000,0.0,0 -2014-07-18,171.77580183010045,173.14055774123176,171.67956835692715,172.96559143066406,124330000,0.0,0 -2014-07-21,172.4231954912684,172.7818851401475,171.84579459537645,172.64190673828125,67592000,0.0,0 -2014-07-22,173.22803392486685,173.70920128990807,173.10555617277214,173.39425659179688,67678000,0.0,0 -2014-07-23,173.65671231167815,173.9629133657111,173.3067796919894,173.77919006347656,65612000,0.0,0 -2014-07-24,173.945458190345,174.14666883179837,173.61301277040502,173.78797912597656,56888000,0.0,0 -2014-07-25,173.29805363281824,173.44677569018612,172.63317619710136,172.974365234375,76837000,0.0,0 -2014-07-28,173.0093102544575,173.29801065271477,172.01198767369274,173.0443115234375,69259000,0.0,0 -2014-07-29,173.3680168302947,173.6129723408671,172.27446067628435,172.3007049560547,80466000,0.0,0 -2014-07-30,172.91307926206161,173.1405474664673,171.60956897897444,172.32693481445312,104222000,0.0,0 -2014-07-31,171.12842593895826,171.27714798268477,168.81883574100985,168.92381286621094,183479000,0.0,0 -2014-08-01,168.46014237003283,169.5099536460377,167.59405440813924,168.40765380859375,189261000,0.0,0 -2014-08-04,168.73129661699642,169.98233150620268,168.01393088920898,169.62364196777344,91340000,0.0,0 -2014-08-05,168.93253383492748,169.3699562287527,167.36655418955286,167.97894287109375,152690000,0.0,0 -2014-08-06,167.19162184160945,168.74884481181655,167.16537756057815,168.03147888183594,94818000,0.0,0 -2014-08-07,168.79260025802176,168.9588229476378,166.70172119463857,167.12164306640625,135733000,0.0,0 -2014-08-08,167.49777019574162,169.1687134080961,167.05159086050372,169.05499267578125,117014000,0.0,0 -2014-08-11,169.69364388644925,170.2972889234957,169.46618904818664,169.544921875,74544000,0.0,0 -2014-08-12,169.37876606772267,169.85117656409008,168.79262143280795,169.30877685546875,73632000,0.0,0 -2014-08-13,169.9736055961555,170.64723989044404,169.68491853339103,170.45477294921875,69047000,0.0,0 -2014-08-14,170.73479022729137,171.2596893310547,170.57731115180763,171.2596893310547,57371000,0.0,0 -2014-08-15,171.8807888656661,172.03825454954202,169.99112056470733,171.2246551513672,139951000,0.0,0 -2014-08-18,172.1695127239857,172.7381566807739,172.07327923216715,172.659423828125,75424000,0.0,0 -2014-08-19,173.0793336730441,173.69172251057722,172.7294010084753,173.5605010986328,59135000,0.0,0 -2014-08-20,173.32427603805294,174.23412225531817,173.28928811388482,174.02415466308594,72763000,0.0,0 -2014-08-21,174.17289602274266,174.75904059837012,174.03291761239333,174.53158569335938,67791000,0.0,0 -2014-08-22,174.39158211536602,174.69778317518075,173.86668317592168,174.26036071777344,76107000,0.0,0 -2014-08-25,175.09146285633727,175.48514040299906,174.22536157442195,175.14395141601562,63855000,0.0,0 -2014-08-26,175.25772094726562,175.68639985306427,175.21397602233486,175.25772094726562,47298000,0.0,0 -2014-08-27,175.34519459012796,175.46768571756843,174.91652904039296,175.18772888183594,47874000,0.0,0 -2014-08-28,174.61027859054448,175.2051800238713,174.43531229218,175.09144592285156,58330000,0.0,0 -2014-08-29,175.36266543933226,175.60762095444727,174.81152220486067,175.5901336669922,65907000,0.0,0 -2014-09-02,175.81756918920388,175.84381346657156,174.84649087938078,175.50262451171875,72426000,0.0,0 -2014-09-03,176.17627037219944,176.20251465129417,175.16144708362395,175.40640258789062,57462000,0.0,0 -2014-09-04,175.7038378847157,176.35122788459378,174.6715273609602,175.15269470214844,85236000,0.0,0 -2014-09-05,175.11770711085612,176.01005267248158,174.45282977323618,175.9400634765625,102177000,0.0,0 -2014-09-08,175.77385364659747,176.02756616897415,174.96899785833813,175.4851531982422,64146000,0.0,0 -2014-09-09,175.3277081960939,175.4501859750839,174.01544055731105,174.37413024902344,88591000,0.0,0 -2014-09-10,174.47026847333962,175.1439026160249,173.89288112867987,175.03018188476562,67251000,0.0,0 -2014-09-11,174.3303409059333,175.25767433187352,174.19910616604705,175.23143005371094,66774400,0.0,0 -2014-09-12,175.0564711105336,175.07395839732845,173.7092024791942,174.20787048339844,117409300,0.0,0 -2014-09-15,174.23410865782478,174.37408704172213,173.55173072266692,174.07662963867188,76401000,0.0,0 -2014-09-16,173.75294507540275,175.70384545290008,173.65671160302,175.38890075683594,116201000,0.0,0 -2014-09-17,175.6426379671993,176.43873678812594,174.75029231127291,175.62513732910156,151266000,0.0,0 -2014-09-18,176.15878370190583,176.58746254289406,175.9313288153615,176.56121826171875,94990000,0.0,0 -2014-09-19,177.1228321250041,177.45681801701582,176.04173335156048,176.402099609375,121649000,0.939,0 -2014-09-22,176.0944963171194,176.12086327379262,174.67061359939837,175.03976440429688,125553000,0.0,0 -2014-09-23,174.40696215093143,175.13647937754587,173.98507735641704,174.03781127929688,111393000,0.0,0 -2014-09-24,174.06417758550907,175.5144275166241,173.6071413146205,175.40016174316406,107276000,0.0,0 -2014-09-25,174.9430815347523,174.95187946131455,172.50844311928304,172.56996154785156,150300000,0.0,0 -2014-09-26,172.88639771142653,174.37179902688828,172.64029714677338,173.9411163330078,103547000,0.0,0 -2014-09-29,172.44689564082222,173.93229665600572,172.31506086907845,173.62466430664062,95112000,0.0,0 -2014-09-30,173.7565633382491,174.29271391936075,172.80731246948704,173.1676788330078,131302000,0.0,0 -2014-10-01,172.886333631927,172.94786545602125,170.43411341036736,170.8208465576172,177798000,0.0,0 -2014-10-02,170.67146192613635,171.44492842895224,169.0630239027852,170.84725952148438,157285000,0.0,0 -2014-10-03,171.98986489469002,173.09733074437906,171.46251233855236,172.72817993164062,121569000,0.0,0 -2014-10-06,173.44888463711106,173.6774161293545,171.90196515883437,172.5260009765625,104778000,0.0,0 -2014-10-07,171.63827437594577,172.02500758076067,169.82767201837765,169.86282348632812,147913000,0.0,0 -2014-10-08,169.9594996145528,173.0797186637452,169.0717809129453,172.8336181640625,186461000,0.0,0 -2014-10-09,172.56119406225375,172.79851010780578,169.26519002976616,169.40582275390625,210705000,0.0,0 -2014-10-10,169.3618851256389,170.20565470619084,167.42823197444207,167.47216796875,221909000,0.0,0 -2014-10-13,167.40186797482144,168.00832150405148,164.6244317824606,164.7211151123047,230939000,0.0,0 -2014-10-14,165.60884713569914,166.83936355878265,164.39591315891766,164.97601318359375,215847000,0.0,0 -2014-10-15,162.74347381871698,164.96717646484166,159.89572193537032,163.85971069335938,380715000,0.0,0 -2014-10-16,160.89773085542532,164.87051814153358,160.74831361518895,163.7191162109375,270391000,0.0,0 -2014-10-17,165.60880633811485,166.7777906169013,164.90565618549854,165.6527557373047,214625000,0.0,0 -2014-10-20,165.353868875412,167.392988919118,165.30113497768664,167.2611541748047,130011000,0.0,0 -2014-10-21,168.47414764834363,170.68906620059525,168.29836344198483,170.57481384277344,154949000,0.0,0 -2014-10-22,170.87367652372166,171.3131438178551,169.29159158254575,169.36190795898438,151822000,0.0,0 -2014-10-23,171.05820570427966,172.4469236418531,170.74178878924425,171.33067321777344,154944000,0.0,0 -2014-10-24,171.6119573780827,172.70184096241798,170.94397200079868,172.6490936279297,117927000,0.0,0 -2014-10-27,172.0338096934931,172.66664347311442,171.41855835538163,172.41175842285156,82954000,0.0,0 -2014-10-28,172.99188646855953,174.39817355453513,172.91277217461473,174.38938903808594,106736000,0.0,0 -2014-10-29,174.51240461717464,175.0133901846486,172.974269621526,174.12567138671875,142557000,0.0,0 -2014-10-30,173.65985877156368,175.7429289888218,173.50164360704528,175.241943359375,113330000,0.0,0 -2014-10-31,177.3513632108021,177.38652809347087,176.46364438977716,177.2458953857422,146903000,0.0,0 -2014-11-03,177.47440985187404,177.94024395167048,176.93825939964967,177.3425750732422,93600000,0.0,0 -2014-11-04,176.86799054696812,177.19320544072139,175.83963876304634,176.7273712158203,93343000,0.0,0 -2014-11-05,178.0193968976568,178.0633463072156,177.06136146206845,177.84361267089844,91709000,0.0,0 -2014-11-06,177.88751098142973,178.6521795244862,177.22831026183678,178.5554962158203,107089000,0.0,0 -2014-11-07,178.57308643520338,178.95103517153206,178.08088535563695,178.72250366210938,89540000,0.0,0 -2014-11-10,178.75765116163322,179.33773757100977,178.53791760023344,179.28500366210938,66319000,0.0,0 -2014-11-11,179.35534759566596,179.5750811908604,178.99498128079327,179.4608154296875,54499400,0.0,0 -2014-11-12,178.73132077750012,179.51357187968134,178.6961558900566,179.26747131347656,90120300,0.0,0 -2014-11-13,179.44322227843716,180.0321066250263,178.60823741122783,179.46958923339844,85357900,0.0,0 -2014-11-14,179.39049953671676,179.7332833840262,179.05650020445043,179.5135498046875,80417500,0.0,0 -2014-11-17,179.17077141450352,179.81238973050222,178.99497381649948,179.6278076171875,80441000,0.0,0 -2014-11-18,179.6893147268201,180.9901336218714,179.6893147268201,180.66493225097656,76068100,0.0,0 -2014-11-19,180.45402458552203,180.6649736755766,179.5663056447247,180.3749237060547,82373000,0.0,0 -2014-11-20,179.53109982491003,180.805565179305,179.46078347759038,180.69129943847656,72840300,0.0,0 -2014-11-21,182.50190962361515,182.6776937937191,181.04287552995459,181.6581268310547,142327300,0.0,0 -2014-11-24,182.08880685114576,182.2821734589446,181.86028877441632,182.16790771484375,65880800,0.0,0 -2014-11-25,182.41396119328053,182.63369469898342,181.76355859973998,182.03602600097656,79108300,0.0,0 -2014-11-26,182.19429022924956,182.6073904346931,181.96577213300412,182.50192260742188,62167800,0.0,0 -2014-11-28,182.37010234225167,182.70408829235774,181.86031878406664,182.11520385742188,57890100,0.0,0 -2014-12-01,181.41206585367394,181.53511614071567,180.51556236728783,180.84954833984375,103968400,0.0,0 -2014-12-02,180.89345886648033,182.23822721851195,180.86709191145795,182.01849365234375,74507200,0.0,0 -2014-12-03,182.20308115712615,182.95016725738955,182.02729698126848,182.72164916992188,68952000,0.0,0 -2014-12-04,182.41403474614265,183.05566649777103,181.6757330880359,182.51951599121094,91316600,0.0,0 -2014-12-05,182.70404786096276,183.23141374554743,182.42279589093008,182.8183135986328,91025500,0.0,0 -2014-12-08,182.39643676864097,182.92378926439483,180.99892113793376,181.5966033935547,108588200,0.0,0 -2014-12-09,179.62781942752324,181.58785280005353,179.2235171130685,181.4735870361328,125180100,0.0,0 -2014-12-10,180.9813853941813,181.0429038281708,178.36215122778034,178.56431579589844,159856400,0.0,0 -2014-12-11,179.197121757817,181.22745775866963,179.04770454254563,179.46958923339844,159012800,0.0,0 -2014-12-12,178.10724204638643,179.14439147123935,176.53395558549556,176.56910705566406,202330200,0.0,0 -2014-12-15,177.52717026053162,178.01058687345667,174.71458280097897,175.356201171875,189965800,0.0,0 -2014-12-16,174.53877450700466,177.89629655141383,173.90594073952394,173.94989013671875,259543800,0.0,0 -2014-12-17,174.41574623433104,177.84358506418343,174.28389802612148,177.36016845703125,253910100,0.0,0 -2014-12-18,179.95305797621052,187.1866853014342,179.23232523421115,181.74607849121094,257633900,0.0,0 -2014-12-19,182.43982772099722,183.23524273364654,181.71513064955693,182.51937866210938,245084600,1.135,0 -2014-12-22,182.72265163435333,183.35897827148438,182.46635977365997,183.35897827148438,148318900,0.0,0 -2014-12-23,183.97763361978187,184.03065859432255,183.2971149803499,183.6064453125,122167900,0.0,0 -2014-12-24,183.84503080838414,184.12783510565887,183.57989250841186,183.62408447265625,42963400,0.0,0 -2014-12-26,184.10130538305003,184.57855697540413,184.0482804251202,184.21620178222656,57326700,0.0,0 -2014-12-29,184.02179139564953,184.68463042522214,183.9510869475691,184.46368408203125,79643900,0.0,0 -2014-12-30,184.01295604546385,184.15435145432343,183.39429550403872,183.47384643554688,73540800,0.0,0 -2014-12-31,183.81854446375002,183.99529886036586,181.52069685125588,181.65325927734375,130333800,0.0,0 -2015-01-02,182.39560654248885,182.83749917313892,180.45126817923415,181.55599975585938,121465900,0.0,0 -2015-01-05,180.4424472411213,180.619201608096,177.95017965014245,178.2771759033203,169632600,0.0,0 -2015-01-06,178.6042058065925,179.16099497868427,175.74958245991652,176.5980224609375,209151400,0.0,0 -2015-01-07,178.01204606886992,179.16096973704055,177.53480790681152,178.79861450195312,125346700,0.0,0 -2015-01-08,180.3010663129865,182.20121313773186,180.28340031339516,181.97142028808594,147217800,0.0,0 -2015-01-09,182.41330155481515,182.43098103879146,179.85916228278802,180.51316833496094,158567300,0.0,0 -2015-01-12,180.65459216028688,180.82251355819676,178.45396147301386,179.09912109375,144396100,0.0,0 -2015-01-13,180.39823759851498,181.60018605584017,177.20777236524324,178.59532165527344,214553300,0.0,0 -2015-01-14,176.44773418781983,177.72923369343965,175.4932579070525,177.51712036132812,192991100,0.0,0 -2015-01-15,178.19770278675892,178.5335321595903,175.76729227842014,175.89102172851562,176613900,0.0,0 -2015-01-16,175.67005050129694,178.365599017747,175.47561660925814,178.1976776123047,211879600,0.0,0 -2015-01-20,178.8781663059206,179.16098411761473,176.90732856559814,178.5776824951172,130991100,0.0,0 -2015-01-21,178.08277382061274,179.99175368965058,177.5878561106021,179.47915649414062,122942700,0.0,0 -2015-01-22,180.28342055422902,182.2896040582488,178.81633329368356,182.14820861816406,174356000,0.0,0 -2015-01-23,181.87418735534405,182.14817215086734,181.00808145917418,181.1494903564453,117516800,0.0,0 -2015-01-26,180.91970741391694,181.67091688042524,180.15965146307096,181.57369995117188,92009700,0.0,0 -2015-01-27,179.3819014844234,180.39824917426026,178.29484935309014,179.1786346435547,134044600,0.0,0 -2015-01-28,180.44242106788712,180.54847097559062,176.67750100400335,176.88076782226562,168514300,0.0,0 -2015-01-29,177.0929131493273,178.78977940691001,175.59046726253013,178.51580810546875,173585400,0.0,0 -2015-01-30,177.26084491615913,178.67489347096566,175.9881917769867,176.27099609375,197729700,0.0,0 -2015-02-02,176.80125945018233,178.5511506423575,174.86576740352925,178.4539337158203,163107000,0.0,0 -2015-02-03,179.4084449547592,181.04345341874222,179.01074420245124,181.03460693359375,124212900,0.0,0 -2015-02-04,180.22148544893372,181.51181783958313,179.8591302621529,180.34521484375,134306700,0.0,0 -2015-02-05,181.05229816132285,182.32495143964832,180.97276070280165,182.16586303710938,97953200,0.0,0 -2015-02-06,182.5546971937018,183.15567823578175,181.10528977932475,181.66207885742188,125672000,0.0,0 -2015-02-09,180.9727131627887,181.74160203083946,180.415924128411,180.84898376464844,87219000,0.0,0 -2015-02-10,181.95373610419506,183.04962133115373,180.89318287288802,182.77565002441406,96164200,0.0,0 -2015-02-11,182.59889430482568,183.34127077288542,181.90954280084244,182.88169860839844,91087800,0.0,0 -2015-02-12,183.7301440224893,184.70231334481315,182.9170630826797,184.64044189453125,97545900,0.0,0 -2015-02-13,184.77303257067362,185.4535376652261,184.49904775029347,185.4005126953125,93670400,0.0,0 -2015-02-17,185.06469076737062,185.87778534935833,184.79956588791532,185.6921844482422,76968200,0.0,0 -2015-02-18,185.29442818212908,185.7893457896392,185.01161041469848,185.70980834960938,80652900,0.0,0 -2015-02-19,185.0735045814912,185.9661229842385,184.92326268115718,185.57725524902344,91462500,0.0,0 -2015-02-20,185.13534040242294,186.77034859750808,184.47250142909007,186.69081115722656,140896400,0.0,0 -2015-02-23,186.42565034554133,186.66427612304688,186.0191032240686,186.66427612304688,74411100,0.0,0 -2015-02-24,186.58479642348306,187.40672397727874,186.2666330938681,187.19461059570312,72472300,0.0,0 -2015-02-25,187.06201968512238,187.57461683177726,186.67315195417765,187.03550720214844,73061700,0.0,0 -2015-02-26,186.938274082731,187.10619545068937,186.16937168657924,186.81454467773438,72697900,0.0,0 -2015-02-27,186.70850107033414,186.99131887488767,186.12521294805904,186.17823791503906,108076000,0.0,0 -2015-03-02,186.28428196575194,187.4155261772486,186.2312570004466,187.35366821289062,87491400,0.0,0 -2015-03-03,186.8940928462341,187.40668997792363,185.6656317241573,186.5847625732422,110325800,0.0,0 -2015-03-04,185.94842914459434,186.02798007374324,184.76416003186395,185.79818725585938,114497200,0.0,0 -2015-03-05,186.1428449759381,186.30193333333983,185.46234004322574,186.00144958496094,76873000,0.0,0 -2015-03-06,185.08230305159216,185.54187515160763,183.03192831047147,183.38543701171875,188128000,0.0,0 -2015-03-09,183.59758044880058,184.52554431320985,183.4296590682329,184.14552307128906,89818900,0.0,0 -2015-03-10,182.6872528521385,182.77562328482273,181.11410271864597,181.15829467773438,157121300,0.0,0 -2015-03-11,181.43228482025785,181.6178856735416,180.6457164144248,180.73410034179688,110145700,0.0,0 -2015-03-12,181.4057614834359,183.10262760058242,181.35273652438337,183.0319366455078,93993500,0.0,0 -2015-03-13,182.74032163957494,183.7655025146578,180.80482934250708,181.90956115722656,162410900,0.0,0 -2015-03-16,182.6872913886299,184.43718275661706,181.9360683821403,184.3399658203125,136099200,0.0,0 -2015-03-17,183.55336638670838,184.19852586315218,182.92587290625423,183.7919921875,94510400,0.0,0 -2015-03-18,183.288222395353,186.7173134795371,182.60770397714793,186.00144958496094,228808500,0.0,0 -2015-03-19,185.55960817376888,186.01033392073563,184.7376806522455,185.15306091308594,117917300,0.0,0 -2015-03-20,186.16599057948412,187.32891550125507,185.970688763311,186.7873992919922,177715100,0.931,0 -2015-03-23,186.7962613444875,187.4087975933937,186.4234161376953,186.4234161376953,71784500,0.0,0 -2015-03-24,186.29027698700864,186.7785179939738,185.3048954518938,185.37591552734375,77805300,0.0,0 -2015-03-25,185.59782317798872,185.84638663572758,182.61504813943003,182.659423828125,159521700,0.0,0 -2015-03-26,181.94923200790535,183.20092159963303,181.20352816380384,182.22442626953125,153067200,0.0,0 -2015-03-27,182.10016170988445,182.82809367576567,181.89597394208653,182.6416778564453,118939000,0.0,0 -2015-03-30,183.7423908779664,185.18939542184984,183.72464602706688,184.86981201171875,96180400,0.0,0 -2015-03-31,183.99101408959874,184.7367179862329,183.19206207725972,183.2541961669922,126768700,0.0,0 -2015-04-01,183.218676278573,183.24530710685443,181.54973879591978,182.60614013671875,137303600,0.0,0 -2015-04-02,182.53513130924128,183.74244526647433,182.3398295260351,183.26307678222656,86900900,0.0,0 -2015-04-06,182.31320162775236,185.0474130909143,182.17117504779364,184.4970245361328,114368200,0.0,0 -2015-04-07,184.52362850740138,185.32258039293092,183.9732400327339,184.0087432861328,81236300,0.0,0 -2015-04-08,184.24842468854428,185.1006381826992,183.83119036793263,184.63014221191406,89351900,0.0,0 -2015-04-09,184.45263039356934,185.69544759569945,183.9288726830782,185.44688415527344,85548900,0.0,0 -2015-04-10,185.7131721194387,186.50325152788568,185.50012552118997,186.4588623046875,72722900,0.0,0 -2015-04-13,186.30798347046144,186.98266727541596,185.56229315037737,185.6155548095703,74436600,0.0,0 -2015-04-14,185.4024951887549,186.16594385700154,184.73669740408715,185.97064208984375,75099900,0.0,0 -2015-04-15,186.46777115013552,187.34661556709474,186.37899269284898,186.8050994873047,99529300,0.0,0 -2015-04-16,186.44997551571444,187.29331650875937,186.23691538818903,186.75180053710938,68934900,0.0,0 -2015-04-17,185.4823888415409,185.73982469313188,183.7690621544331,184.60353088378906,191113200,0.0,0 -2015-04-20,185.58893799802362,186.64533947078144,185.50017307456127,186.29025268554688,92189500,0.0,0 -2015-04-21,187.01817816628983,187.1868491244129,185.74873008473963,186.0683135986328,72559800,0.0,0 -2015-04-22,186.43221829277292,187.17792197096406,185.44683720850534,186.9826202392578,78264600,0.0,0 -2015-04-23,186.5565391395403,188.1455841496264,186.4322574247032,187.45315551757812,102585900,0.0,0 -2015-04-24,187.8970695693499,188.17226389905613,187.40881501066403,187.88818359375,61327400,0.0,0 -2015-04-27,188.4918061732637,188.62496032114433,186.90276109820005,187.10694885253906,79358100,0.0,0 -2015-04-28,187.08031260979124,187.75498284901738,185.82860947316524,187.70172119140625,86863500,0.0,0 -2015-04-29,186.7518559100351,187.56856632250938,186.06831319638215,186.92941284179688,125684900,0.0,0 -2015-04-30,186.31684988348817,186.7340842309511,184.31057062657524,185.0562744140625,161304900,0.0,0 -2015-05-01,185.89076550538798,187.10696552909238,185.78424217758922,187.0625762939453,103399700,0.0,0 -2015-05-04,187.51530880441484,188.21662348157002,187.3999130510505,187.59521484375,70927200,0.0,0 -2015-05-05,187.3377543337993,187.71948546811242,185.2959716054213,185.44688415527344,113326200,0.0,0 -2015-05-06,186.03279527035096,186.36125120988459,183.54714724328235,184.68344116210938,135060200,0.0,0 -2015-05-07,184.57691633721032,185.87300877396697,184.2218295701029,185.42025756835938,88244900,0.0,0 -2015-05-08,187.20458416919553,188.0745561362556,187.11580571419964,187.8614959716797,155877300,0.0,0 -2015-05-11,187.81710129399687,188.10116793901042,186.88498179357643,186.96487426757812,75708100,0.0,0 -2015-05-12,186.0771532464985,186.98264196729488,185.1982953516941,186.40560913085938,119727600,0.0,0 -2015-05-13,186.84062283098206,187.50642064687264,186.19258341630874,186.44114685058594,94667900,0.0,0 -2015-05-14,187.52419999422665,188.4829505816829,187.2312472970295,188.3852996826172,95934000,0.0,0 -2015-05-15,188.58949279785156,188.74040537594195,188.07460741899925,188.58949279785156,76510100,0.0,0 -2015-05-18,188.41187492226445,189.44163172730137,188.34085487610864,189.17532348632812,74549700,0.0,0 -2015-05-19,189.29964957293294,189.59260225239194,188.8113951071679,189.11322021484375,72114600,0.0,0 -2015-05-20,189.2197495021233,189.77902404833225,188.64273009892122,188.98007202148438,76857500,0.0,0 -2015-05-21,188.82912502268363,189.75235860855855,188.65156811959739,189.53042602539062,64764600,0.0,0 -2015-05-22,189.12204291143522,189.56590802801318,189.00664719219304,189.07766723632812,57433500,0.0,0 -2015-05-26,188.5539234520122,189.00667460582957,186.600919364942,187.04478454589844,124308600,0.0,0 -2015-05-27,187.53304510697544,189.0688148906794,187.09805233891768,188.82025146484375,93214000,0.0,0 -2015-05-28,188.49179896440938,188.72260399380588,187.87039038857964,188.60720825195312,74974600,0.0,0 -2015-05-29,188.53616950042544,188.5805451798276,187.15131236997397,187.4353790283203,124919600,0.0,0 -2015-06-01,188.14559460526365,188.5006813686194,186.97378390394454,187.817138671875,93338800,0.0,0 -2015-06-02,187.32885121926932,188.36749404212787,186.66305349597363,187.63067626953125,91531000,0.0,0 -2015-06-03,188.19884293275484,188.79362066937074,187.60406519613895,188.12782287597656,87820900,0.0,0 -2015-06-04,187.37324123370215,188.07454221176198,186.20143073382772,186.5387725830078,151882800,0.0,0 -2015-06-05,186.37896218822863,186.9382365770778,185.51786276711013,186.21917724609375,121704700,0.0,0 -2015-06-08,186.1037908472177,186.26358935467644,184.99412790485792,185.0740203857422,89063300,0.0,0 -2015-06-09,185.04737854003906,185.62441136312054,184.37270838770635,185.04737854003906,105034700,0.0,0 -2015-06-10,185.8640928423478,187.67507028149953,185.80195876368015,187.26670837402344,134551300,0.0,0 -2015-06-11,187.7372241561911,188.27874025054797,187.48866072393133,187.87039184570312,73876400,0.0,0 -2015-06-12,186.99152544683128,187.73721571006263,186.13929832764507,186.4322509765625,135382400,0.0,0 -2015-06-15,185.21609686690726,185.93515642745814,184.4615204996394,185.63333129882812,124384200,0.0,0 -2015-06-16,185.47351402620146,186.73410316916377,185.2870982196913,186.64532470703125,85308200,0.0,0 -2015-06-17,186.9471435546875,187.59519650606612,185.85523894456085,186.9471435546875,126708600,0.0,0 -2015-06-18,187.58628599633553,189.3883774069883,186.9826358991879,188.89125061035156,165867900,0.0,0 -2015-06-19,188.6326046547824,188.71288576273048,187.6513457412561,188.05276489257812,130478700,1.03,0 -2015-06-22,189.03401909936647,189.6406056042019,188.79316216993772,189.01617431640625,70696000,0.0,0 -2015-06-23,189.23918894208396,189.50680624366848,188.73072831949662,189.1499786376953,68476800,0.0,0 -2015-06-24,188.86453023676555,189.26594938350522,187.74946948938788,187.77622985839844,92307300,0.0,0 -2015-06-25,188.31142139786755,188.4452232123219,187.1249954005852,187.2052764892578,97107400,0.0,0 -2015-06-26,187.58885166492388,187.84755332288097,186.58084623084974,187.1696014404297,104174800,0.0,0 -2015-06-29,185.5906902629151,187.17853547931085,183.16431729169875,183.24459838867188,202621300,0.0,0 -2015-06-30,184.88595176324122,184.93948610249572,183.1196996920479,183.628173828125,182925100,0.0,0 -2015-07-01,185.30521921048344,185.57283646563764,184.26152416580322,185.1000518798828,135979900,0.0,0 -2015-07-02,185.60852276700766,185.7869297301184,184.48453303741036,184.93055725097656,104373700,0.0,0 -2015-07-06,183.55682300773188,185.23386492593252,183.34272646831954,184.40426635742188,117975400,0.0,0 -2015-07-07,184.6183954482234,185.69776669723686,182.07605133931472,185.56396484375,173820200,0.0,0 -2015-07-08,184.13667016007486,184.43996341693537,182.200926333762,182.45069885253906,164020100,0.0,0 -2015-07-09,184.6896990180671,184.96624546218837,182.66475890357447,182.7807159423828,144113100,0.0,0 -2015-07-10,184.9126924772094,185.52820800322976,182.82530265020074,185.08218383789062,129456900,0.0,0 -2015-07-13,186.429188122527,187.2409417699399,186.384582983057,187.12498474121094,106069400,0.0,0 -2015-07-14,187.08042384749658,188.26685005779308,187.01797391515328,187.93678283691406,81709600,0.0,0 -2015-07-15,187.98135480638032,188.4719841468315,187.3658392348669,187.8743133544922,97914100,0.0,0 -2015-07-16,188.9982938164098,189.3818817138672,188.7396057644891,189.3818817138672,106683300,0.0,0 -2015-07-17,189.37298905890717,189.6049304035684,188.9358939615033,189.54248046875,89030000,0.0,0 -2015-07-20,189.7833560551174,190.16693045271498,189.30165575857444,189.640625,70446800,0.0,0 -2015-07-21,189.49786761050785,189.77441409570983,188.57014310433513,188.8912811279297,77965000,0.0,0 -2015-07-22,188.1598035693376,188.90913473377833,188.12412761464108,188.55230712890625,88667900,0.0,0 -2015-07-23,188.69502413185342,188.80206559956042,187.10717884015776,187.49075317382812,90509100,0.0,0 -2015-07-24,187.59778031636847,187.66021662603956,185.18925237057218,185.5460662841797,117755000,0.0,0 -2015-07-27,184.60049022594563,185.14464027127536,183.99389026171323,184.4666748046875,132361100,0.0,0 -2015-07-28,185.3587662670103,186.88417530994846,184.47564689461854,186.73252868652344,123544800,0.0,0 -2015-07-29,186.86632434588861,188.25791789042032,186.71467772747994,188.01707458496094,105791300,0.0,0 -2015-07-30,187.47292073382906,188.24008300755406,186.81279992708605,188.06167602539062,91304400,0.0,0 -2015-07-31,188.59691293507862,188.62367330408918,187.47293660189123,187.77622985839844,103266900,0.0,0 -2015-08-03,187.740537269032,187.8029735873307,186.12591801616236,187.14285278320312,113965700,0.0,0 -2015-08-04,187.06262443182655,187.55325397786666,186.25978598309473,186.7771759033203,81820800,0.0,0 -2015-08-05,187.73160834193635,188.4987706137232,187.08933231850645,187.39263916015625,85786800,0.0,0 -2015-08-06,187.58890030985788,187.70487098739994,185.23389247242358,185.85833740234375,116030800,0.0,0 -2015-08-07,185.6888162738569,185.84937846791814,184.53806610343256,185.50148010253906,117858000,0.0,0 -2015-08-10,186.6879337419811,187.92788077690892,186.6879337419811,187.83868408203125,80270700,0.0,0 -2015-08-11,186.41138559644753,186.85740986908837,185.3320008675612,186.14376831054688,126081400,0.0,0 -2015-08-12,184.75218499292907,186.56304255124505,183.19109995220282,186.36679077148438,172123700,0.0,0 -2015-08-13,186.19729349937572,186.92877985756306,185.55501744248275,186.13485717773438,89383300,0.0,0 -2015-08-14,185.92970258693666,186.8931168089661,185.7780559424852,186.81283569335938,72786500,0.0,0 -2015-08-17,186.1794491581296,187.8564910888672,185.68881975887925,187.8564910888672,79072600,0.0,0 -2015-08-18,187.56210463363902,187.9367633541171,187.0625596730016,187.3123321533203,71692700,0.0,0 -2015-08-19,186.51842580241407,187.33910882512302,184.96627005555212,185.83155822753906,172946000,0.0,0 -2015-08-20,184.21693865450573,185.80478396298048,181.88869142621266,181.95114135742188,194327900,0.0,0 -2015-08-21,179.9529524501096,181.92438575957192,176.19743562972423,176.47396850585938,346588500,0.0,0 -2015-08-24,167.25013968318524,176.16169343634675,162.70960351810018,169.04315185546875,507244300,0.0,0 -2015-08-25,174.33301397165675,174.350858751072,166.74168691409398,167.0539093017578,369833100,0.0,0 -2015-08-26,171.34469345427576,173.76213749169557,168.03518722228392,173.4677734375,339257000,0.0,0 -2015-08-27,175.7514202980704,177.89233150945438,174.13681449784428,177.75852966308594,274143900,0.0,0 -2015-08-28,177.07161359091958,178.26695522193327,176.55422386744078,177.7674102783203,160414400,0.0,0 -2015-08-31,176.72374133849303,177.63363468388292,175.7424824746823,176.33123779296875,163298800,0.0,0 -2015-09-01,172.27238770151322,173.74427577160054,170.14039250748863,171.06813049316406,256000400,0.0,0 -2015-09-02,173.61049006465763,174.359821215713,171.64798591345212,174.31521606445312,160269300,0.0,0 -2015-09-03,175.07344083242262,176.67021533486965,173.91378862450787,174.44009399414062,152087800,0.0,0 -2015-09-04,172.03154762976882,172.93251165678208,170.92540266058523,171.7996063232422,207081000,0.0,0 -2015-09-08,174.78796778540791,176.27768699801533,174.101086697868,176.1171112060547,116025700,0.0,0 -2015-09-09,177.80312318382866,177.93692502144376,173.36964080977356,173.7621307373047,149347700,0.0,0 -2015-09-10,173.5569221427634,175.92977398423787,173.2803893363467,174.70767211914062,158611100,0.0,0 -2015-09-11,174.28844614517988,175.57299820621853,173.5301994500561,175.5016326904297,119691200,0.0,0 -2015-09-14,175.68900716977862,175.74252791953887,174.3330891923203,174.85047912597656,79452000,0.0,0 -2015-09-15,175.3856545522402,177.50873431885316,174.80582847708646,177.03594970703125,113806200,0.0,0 -2015-09-16,177.3570937999926,178.77544768417215,176.99135063894096,178.5702667236328,99581600,0.0,0 -2015-09-17,178.42752490084771,180.9876994548875,177.7674041842296,178.1688232421875,276046600,0.0,0 -2015-09-18,175.4904255623797,178.15357043229227,174.8179110528676,175.2572784423828,223657500,1.033,0 -2015-09-21,176.1450110562196,177.25689300912651,175.04209103823044,176.16294860839844,105726200,0.0,0 -2015-09-22,173.84951957678396,174.36959919893715,172.66588726105354,173.8764190673828,153890900,0.0,0 -2015-09-23,174.0557700316083,174.5579121467511,172.97974925882417,173.5984649658203,92790600,0.0,0 -2015-09-24,172.29823430426646,173.46392900904328,170.8725066421756,172.9707489013672,159378800,0.0,0 -2015-09-25,174.53099606641823,174.85380363580893,171.99337255638636,172.92593383789062,155054800,0.0,0 -2015-09-28,171.96646326752386,172.08303684165608,168.25418327222124,168.58595275878906,178515900,0.0,0 -2015-09-29,168.8191272382607,170.1372571447069,167.617557242473,168.6846160888672,159045600,0.0,0 -2015-09-30,170.70213691629692,172.01130470723925,169.8682253696026,171.83197021484375,163452000,0.0,0 -2015-10-01,172.2354821686396,172.60312678422238,170.20897633839846,172.2803192138672,131079000,0.0,0 -2015-10-02,170.1641406397944,174.88070503287256,169.5812864119139,174.8538055419922,211003300,0.0,0 -2015-10-05,176.16298237975752,178.20742588508696,176.0464087917624,177.96531677246094,126320800,0.0,0 -2015-10-06,177.82182566631266,178.42260373631257,176.64716901583932,177.3555450439453,110274500,0.0,0 -2015-10-07,178.3508783063748,179.18480358064522,177.07758555425613,178.80819702148438,124307300,0.0,0 -2015-10-08,178.395706499925,180.72709595886258,178.07289893918514,180.42222595214844,153055200,0.0,0 -2015-10-09,180.57465271567418,181.04091964030957,179.8573010931697,180.52981567382812,107069200,0.0,0 -2015-10-12,180.6105059740731,180.91537595309202,180.15320100554473,180.70018005371094,56395600,0.0,0 -2015-10-13,179.92007035390938,181.2740751958258,179.38206687382657,179.5614013671875,88038700,0.0,0 -2015-10-14,179.49862690829417,180.11734254086184,178.386744839188,178.70057678222656,99106200,0.0,0 -2015-10-15,179.40897650314977,181.45341994416637,179.01443238605088,181.4444580078125,134142200,0.0,0 -2015-10-16,181.87484983356063,182.28731779225413,181.0588621695069,182.26939392089844,114580100,0.0,0 -2015-10-19,181.57892681423306,182.35903930664062,181.24715735099645,182.35903930664062,76523900,0.0,0 -2015-10-20,181.89279730469673,182.7805078672634,181.62378871390413,182.1259307861328,78448500,0.0,0 -2015-10-21,182.5742563515676,182.73565328114367,180.8167455851235,180.99609375,102038000,0.0,0 -2015-10-22,182.00934398169804,184.27795876042939,180.99609797468412,184.0537872314453,174911700,0.0,0 -2015-10-23,185.8381861298773,186.4658636360668,184.9863371084527,186.07131958007812,144442300,0.0,0 -2015-10-26,185.8830375061629,185.94579842045076,185.21948484424502,185.61402893066406,69033000,0.0,0 -2015-10-27,184.89664272299322,185.61399422943043,184.52899819462215,185.2553253173828,77905800,0.0,0 -2015-10-28,185.61401375386885,187.389448315241,184.90563778924218,187.362548828125,135906700,0.0,0 -2015-10-29,186.82453838762012,187.6494878869989,186.69900289167862,187.25494384765625,90525500,0.0,0 -2015-10-30,187.46122738174677,187.8019725389982,186.27760863301765,186.44796752929688,131076900,0.0,0 -2015-11-02,186.7976752792624,188.86004258817184,186.6631641401951,188.65380859375,86270800,0.0,0 -2015-11-03,188.27718951879288,189.79259128141618,188.03508043043993,189.20077514648438,95246100,0.0,0 -2015-11-04,189.5146162711785,189.64911371576972,188.05301352234747,188.62689208984375,96224500,0.0,0 -2015-11-05,188.68965439400714,189.18283449838384,187.48809827232165,188.43858337402344,78408700,0.0,0 -2015-11-06,188.07095566577598,188.59103526812876,186.92319850277144,188.33995056152344,110471500,0.0,0 -2015-11-09,187.6853786458651,187.84678927007397,185.56919875324286,186.58245849609375,131008700,0.0,0 -2015-11-10,186.07132477365505,187.0487230059419,185.78439234096396,187.01284790039062,75874600,0.0,0 -2015-11-11,187.29984025125347,187.35363924051708,186.2058818617359,186.27761840820312,67846000,0.0,0 -2015-11-12,185.1657180949844,185.6678602260665,183.65929170173817,183.67721557617188,121315200,0.0,0 -2015-11-13,183.23777437105744,183.52470675243467,181.52510096938306,181.61476135253906,153577100,0.0,0 -2015-11-16,181.41754881749716,184.43937677100922,181.2919996211818,184.37660217285156,117645200,0.0,0 -2015-11-17,184.70837655509422,185.64988602385512,183.71305442661355,184.24209594726562,121123700,0.0,0 -2015-11-18,184.75325568726714,187.3177792809766,184.70843231415253,187.16534423828125,121342500,0.0,0 -2015-11-19,187.03976767709744,187.4522493416172,186.69006061292984,187.00390625,88220500,0.0,0 -2015-11-20,187.8108992751457,188.41167731503947,187.28185776450934,187.68536376953125,94011500,0.0,0 -2015-11-23,187.74809372433256,188.2860970423309,186.97694332109498,187.47012329101562,64931200,0.0,0 -2015-11-24,186.394131926838,188.1516426719366,185.9816639902572,187.7212371826172,98874400,0.0,0 -2015-11-25,187.85573286360486,188.0709424474217,187.4163517608577,187.6943359375,51980100,0.0,0 -2015-11-27,187.7929723215217,188.12475549835642,187.28186833493828,187.9095458984375,37317800,0.0,0 -2015-11-30,188.07994080154376,188.20547632915918,187.0128819756397,187.12945556640625,112822700,0.0,0 -2015-12-01,187.80190602953996,189.0393370088237,187.50599802656598,188.91378784179688,97858400,0.0,0 -2015-12-02,188.86001727357402,189.20076236139002,186.71693809554208,186.98594665527344,108441300,0.0,0 -2015-12-03,187.25497584606038,187.54190830766828,183.59649460472937,184.36764526367188,166224200,0.0,0 -2015-12-04,184.36761188069732,188.27716344892212,184.36761188069732,187.96331787109375,192913900,0.0,0 -2015-12-07,187.61362890751502,188.0619719576,185.79335721875742,186.82455444335938,102027100,0.0,0 -2015-12-08,185.15671541085413,186.7707394248966,184.52006226754423,185.56918334960938,103372400,0.0,0 -2015-12-09,184.88770744976412,187.1204470615536,183.08535964862062,184.12551879882812,162401500,0.0,0 -2015-12-10,184.1972630212913,185.99959721536771,183.94619199969887,184.60076904296875,116128900,0.0,0 -2015-12-11,182.34109392236323,183.04946980464106,180.69118137110607,181.02296447753906,211173300,0.0,0 -2015-12-14,181.1933845747444,182.07213320791828,179.2924005522099,181.9376220703125,182385200,0.0,0 -2015-12-15,183.55164138448043,184.81597206546107,181.9107041845076,183.84754943847656,154069600,0.0,0 -2015-12-16,185.0490955313282,186.86040518114456,183.6413055493875,186.53759765625,197017000,0.0,0 -2015-12-17,186.86940793961597,186.94114448108007,183.6772070973677,183.6951446533203,173092500,0.0,0 -2015-12-18,182.90315469608456,183.04746787389934,180.25120564042027,180.42259216308594,251393500,1.212,0 -2015-12-21,181.6763829995144,182.10033474088843,180.4857065095857,181.91090393066406,99094300,0.0,0 -2015-12-22,182.8580536027336,183.8773437181067,181.80268863329886,183.56163024902344,111026200,0.0,0 -2015-12-23,184.6350552987451,185.87985120871417,184.53583223688125,185.83474731445312,110987200,0.0,0 -2015-12-24,185.56407322751878,186.11430751322962,185.2934637197678,185.52798461914062,48539600,0.0,0 -2015-12-28,184.78842768777233,185.14923137319573,183.95856820027586,185.10414123535156,65899900,0.0,0 -2015-12-29,186.27668346320857,187.43127129811595,186.24060861501826,187.07948303222656,92640700,0.0,0 -2015-12-30,186.81793405118543,186.90814183253948,185.6001978217806,185.7535400390625,63317700,0.0,0 -2015-12-31,185.03192793771692,185.71746026099777,183.89537048339844,183.89537048339844,114877900,0.0,0 -2016-01-04,180.84654452628428,181.3336307509781,179.1326930579565,181.32461547851562,222353500,0.0,0 -2016-01-05,181.6673614720795,182.11837281015693,180.44963911753467,181.63128662109375,110845800,0.0,0 -2016-01-06,178.90716814616727,180.4586481970175,178.23968019753227,179.34014892578125,152112600,0.0,0 -2016-01-07,176.19212476684282,178.09539352900015,174.622600054445,175.03753662109375,213436100,0.0,0 -2016-01-08,176.0658292930585,176.66116763340523,172.8095264944518,173.1162109375,209817200,0.0,0 -2016-01-11,174.0994279869478,174.4602453907335,171.2219866130003,173.28761291503906,187941300,0.0,0 -2016-01-12,174.83007219592437,175.48854501052335,172.41264384497606,174.6857452392578,172330500,0.0,0 -2016-01-13,175.39828074460243,175.76811329056085,169.92301106541012,170.32891845703125,221168900,0.0,0 -2016-01-14,170.97839384444538,174.32489012486312,169.27357160859012,173.1251983642578,240795600,0.0,0 -2016-01-15,168.4707921704715,170.26580862668783,167.34326373766234,169.4088897705078,324846400,0.0,0 -2016-01-19,171.34823551722386,171.4835334146369,167.9566214084824,169.63438415527344,195244400,0.0,0 -2016-01-20,166.90129099495172,169.12928859109388,163.28418423124316,167.46054077148438,286547800,0.0,0 -2016-01-21,167.96566878149386,170.36503892813295,166.54948640301694,168.3986358642578,195772900,0.0,0 -2016-01-22,171.18581570741685,172.0697938351839,170.37400102572036,171.8533172607422,168319600,0.0,0 -2016-01-25,171.31213415189563,171.51959550041295,169.0480623331233,169.25552368164062,130371700,0.0,0 -2016-01-26,169.95908300871542,171.86235109261398,169.59827950316281,171.56468200683594,141036800,0.0,0 -2016-01-27,171.00545511011688,172.79145609774028,168.73235418528594,169.69752502441406,185681700,0.0,0 -2016-01-28,171.34825257502632,171.56472923243751,168.8225860496186,170.58152770996094,143798800,0.0,0 -2016-01-29,171.40235642640252,174.88416458104794,171.2760737996029,174.73983764648438,210529300,0.0,0 -2016-02-01,173.66643572057532,175.5155850763762,173.0440378324681,174.67669677734375,136061600,0.0,0 -2016-02-02,173.15226896428445,173.16128423556646,170.9693621020189,171.52862548828125,182564900,0.0,0 -2016-02-03,172.6561841162047,172.98992814134976,168.76846812550562,172.5569610595703,205054900,0.0,0 -2016-02-04,172.02473037052522,173.864850357156,171.34821344306548,172.82752990722656,139531800,0.0,0 -2016-02-05,172.27735936179477,172.89072832980156,168.85868484847762,169.5352020263672,180788300,0.0,0 -2016-02-08,167.56876521658145,167.88446491157592,164.88975662639464,167.2530517578125,191526700,0.0,0 -2016-02-09,165.39488979163542,168.6241328453558,165.25056285024826,167.26206970214844,184513100,0.0,0 -2016-02-10,168.14605741859546,169.88695464998878,166.9824404098319,167.1177520751953,148214100,0.0,0 -2016-02-11,164.47479206612036,166.0623605977062,163.34726385392455,164.94384765625,219058900,0.0,0 -2016-02-12,166.83813657442326,168.36254348482467,165.9361138099425,168.34451293945312,127632400,0.0,0 -2016-02-16,170.27482933537536,171.21292688953235,169.24652400549547,171.1858673095703,120250700,0.0,0 -2016-02-17,172.4306821548529,174.37905471071892,172.29537047627085,173.98216247558594,136009500,0.0,0 -2016-02-18,174.27079231047975,174.3339405095059,172.93580249615044,173.26954650878906,102343000,0.0,0 -2016-02-19,172.43968920925423,173.3507272410061,171.79023172158605,173.18836975097656,114793000,0.0,0 -2016-02-22,174.87513991014606,175.8493260971047,174.80297644106307,175.69598388671875,103640300,0.0,0 -2016-02-23,174.9923964708022,175.28105032932407,173.35070861894934,173.4770050048828,111455300,0.0,0 -2016-02-24,171.9525912944066,174.56845161264454,170.77094376243485,174.27078247070312,150812200,0.0,0 -2016-02-25,174.74885659220644,176.3905445388885,173.9370416548242,176.3815155029297,110728300,0.0,0 -2016-02-26,177.31062004607872,177.40982933617843,175.80422992895134,175.97561645507812,129833700,0.0,0 -2016-02-29,175.99365802463012,177.0039190928836,174.3880586423952,174.59552001953125,125918100,0.0,0 -2016-03-01,175.9034313218632,178.78991480140053,175.39830084207617,178.69970703125,141799700,0.0,0 -2016-03-02,178.36598921279614,179.55665212350164,177.92399309627973,179.50253295898438,102415000,0.0,0 -2016-03-03,179.3130778202312,180.22412951206374,178.69970902045443,180.20608520507812,95172200,0.0,0 -2016-03-04,180.41354106350482,181.62226184677988,179.52956274097963,180.79238891601562,129293600,0.0,0 -2016-03-07,179.80922380580932,181.36973317844874,179.7280450561416,180.93675231933594,100219000,0.0,0 -2016-03-08,179.7911767327321,180.33238211121068,178.78993095420606,178.9613037109375,123974900,0.0,0 -2016-03-09,179.82724682975754,180.215109985887,178.98835855782943,179.8452911376953,94801200,0.0,0 -2016-03-10,180.36845513093976,181.36970082288457,178.04123504253369,179.98959350585938,156838700,0.0,0 -2016-03-11,181.54107005520683,182.9392078882842,179.97155948353026,182.89410400390625,137964500,0.0,0 -2016-03-14,182.3529072686317,183.14667786321039,182.0011189759571,182.6595916748047,73612000,0.0,0 -2016-03-15,181.63130224821865,182.68666721846807,181.3516773967562,182.3619384765625,93169100,0.0,0 -2016-03-16,181.84777150438822,183.8502628858349,181.80266761919336,183.4172821044922,129303200,0.0,0 -2016-03-17,183.32709114746606,185.12210743963055,182.90313939437402,184.58090209960938,134278500,0.0,0 -2016-03-18,185.1158512992479,185.66892365704285,184.78038594337298,185.30625915527344,138372400,1.05,0 -2016-03-21,185.02518661306155,185.8139894901758,184.78038047249512,185.56918334960938,72926700,0.0,0 -2016-03-22,184.74407867608133,186.07689192938363,184.5718220606558,185.46942138671875,97471900,0.0,0 -2016-03-23,185.06143960935648,185.26090922131763,184.06409154955077,184.2454376220703,81052500,0.0,0 -2016-03-24,183.1484075028291,184.20015415338742,182.91267690933162,184.16387939453125,84360900,0.0,0 -2016-03-28,184.6080941012368,184.83476284730506,183.79209214929594,184.2726287841797,62408200,0.0,0 -2016-03-29,183.83738911700087,186.0950144925768,183.51098561492677,185.97714233398438,92922900,0.0,0 -2016-03-30,187.04706553899658,187.56386312330096,186.40332016634855,186.79319763183594,86365300,0.0,0 -2016-03-31,186.69349229822242,187.14682989469145,186.16761902614647,186.3398895263672,94584100,0.0,0 -2016-04-01,185.2790292376063,187.80864622901598,184.94355010268492,187.6091766357422,114423500,0.0,0 -2016-04-04,187.52759741878307,187.74520440568978,186.67532068018548,187.00172424316406,63497000,0.0,0 -2016-04-05,185.56915700358346,187.01076682769454,184.86195166214173,185.1339569091797,99662200,0.0,0 -2016-04-06,185.13398697369678,187.21934242662002,184.9435791194208,187.15586853027344,91839800,0.0,0 -2016-04-07,185.99529671094314,186.37609852572066,184.13661031626398,184.91635131835938,113859000,0.0,0 -2016-04-08,186.176673391506,186.6390865757597,184.84385980412475,185.41506958007812,95040600,0.0,0 -2016-04-11,186.09504955067646,186.83852971453715,184.88010833314914,184.97984313964844,83757500,0.0,0 -2016-04-12,185.16118504566057,187.00173434234597,184.68971013261512,186.70252990722656,115350600,0.0,0 -2016-04-13,187.68171919309174,188.67906719613865,187.53664787723727,188.58839416503906,96336400,0.0,0 -2016-04-14,188.65187000582225,189.13240663914567,188.22573165781662,188.5974578857422,65212900,0.0,0 -2016-04-15,188.59749320301455,188.7425645475366,188.04442080744354,188.3889617919922,75761600,0.0,0 -2016-04-18,188.38894060171467,189.7489531754116,187.68173517017334,189.7126922607422,82531000,0.0,0 -2016-04-19,190.16600819685476,190.58307092464096,189.44066547294108,190.31106567382812,88316100,0.0,0 -2016-04-20,190.3564068357279,191.23588266179726,189.84867106702023,190.49241638183594,81100300,0.0,0 -2016-04-21,190.51055098928958,190.62842316914612,189.1777376016113,189.46788024902344,85695000,0.0,0 -2016-04-22,189.0870784003672,189.75800906167265,188.50680694030584,189.46788024902344,99251700,0.0,0 -2016-04-25,188.8241376962211,189.18681601086314,188.17133056462853,189.1414794921875,66166500,0.0,0 -2016-04-26,189.53135823119524,189.96657224347345,188.91482579928078,189.4225616455078,75864200,0.0,0 -2016-04-27,189.01454524983333,190.22948647379724,188.63374339611764,189.8124237060547,77329400,0.0,0 -2016-04-28,189.00547226298383,190.1841386682067,187.64545979409996,188.0897216796875,97216200,0.0,0 -2016-04-29,187.4278519544672,187.79959201459124,185.89556903302702,187.07424926757812,142424100,0.0,0 -2016-05-02,187.60920665689184,188.7516122818258,187.1468073447251,188.56121826171875,62188000,0.0,0 -2016-05-03,187.24653907619475,187.50040698866937,186.12225701040458,186.92013549804688,106422100,0.0,0 -2016-05-04,185.85930260952566,186.63904361835762,185.34249124837297,185.87742614746094,92243800,0.0,0 -2016-05-05,186.37615208921946,186.7569540134374,185.38787944055156,185.84121704101562,67619200,0.0,0 -2016-05-06,185.01611053799982,186.56653097796246,184.85291566787927,186.5211944580078,89315000,0.0,0 -2016-05-09,186.3852099025949,187.1377380729329,186.19480204128,186.6753387451172,74374900,0.0,0 -2016-05-10,187.4278550665887,189.01453629931734,187.35531940720676,188.99639892578125,77472200,0.0,0 -2016-05-11,188.50682402935024,189.0780199367348,187.22840881347656,187.22840881347656,81727000,0.0,0 -2016-05-12,187.94467534558802,188.1260214333548,186.20386081418684,187.28280639648438,89586300,0.0,0 -2016-05-13,186.96546194139555,187.55479517042704,185.30624499480237,185.65077209472656,96474600,0.0,0 -2016-05-16,185.83212785889765,187.99000511117453,185.7686539635077,187.48226928710938,77486800,0.0,0 -2016-05-17,187.1921387557031,187.5004049553989,185.17024341324094,185.73239135742188,114924900,0.0,0 -2016-05-18,185.36062010998907,187.04703598957477,184.62621564924004,185.78675842285156,120062100,0.0,0 -2016-05-19,185.0160714429008,185.45127150602517,183.85552871806217,185.14300537109375,115430500,0.0,0 -2016-05-20,185.79582597558962,186.86570959270975,185.74142769306448,186.31263732910156,104990400,0.0,0 -2016-05-23,186.3307988411046,186.63000329073247,185.85933773996163,186.05880737304688,58682600,0.0,0 -2016-05-24,186.92917535030088,188.80599916327978,186.90197620820803,188.47052001953125,93537800,0.0,0 -2016-05-25,189.19585961346212,190.1932075895051,188.470516888591,189.74893188476562,76621400,0.0,0 -2016-05-26,189.89403302824044,190.13883917622934,189.4678946292377,189.80335998535156,55280700,0.0,0 -2016-05-27,189.97562412723744,190.6284312769169,189.92122583722474,190.61936950683594,64211200,0.0,0 -2016-05-31,190.90949047241386,191.02736265243112,189.65827451457807,190.25668334960938,109879400,0.0,0 -2016-06-01,189.60390644390043,190.83698522407275,189.39537502928187,190.64659118652344,69936200,0.0,0 -2016-06-02,190.22044248283598,191.24497595831798,189.71270663042642,191.2268524169922,63044700,0.0,0 -2016-06-03,190.62842524550265,191.02736447265818,189.36814750595025,190.65562438964844,101757100,0.0,0 -2016-06-06,191.03643111227976,192.00658006075346,190.86416063845633,191.6257781982422,64887000,0.0,0 -2016-06-07,191.78897680733198,192.5233814018369,191.76177766196244,191.9249725341797,60974800,0.0,0 -2016-06-08,192.07004619869264,192.68659244900542,191.93405047019328,192.5505828857422,66170900,0.0,0 -2016-06-09,191.77084723881916,192.41459265321924,191.48071839814554,192.28765869140625,73786900,0.0,0 -2016-06-10,190.81882791880346,191.18149238742905,189.88493993430535,190.46522521972656,113829200,0.0,0 -2016-06-13,189.82147648714314,190.73721324666062,188.90573972762564,188.99639892578125,117751200,0.0,0 -2016-06-14,188.58838813742798,189.2593325757388,187.6091775388507,188.62464904785156,125059300,0.0,0 -2016-06-15,188.62465450729138,189.82147210733527,188.1622552535083,188.36172485351562,109124500,0.0,0 -2016-06-16,188.36172458957907,189.10520470397583,186.4033033384853,188.92385864257812,149533100,0.0,0 -2016-06-17,188.81271976557395,188.84006036102093,187.5185472564732,188.22032165527344,117055700,1.078,0 -2016-06-20,190.31649890399362,191.03649100772446,189.34130476260674,189.4324493408203,82789600,0.0,0 -2016-06-21,189.8426054970614,190.40766304251466,189.3686786391292,189.97019958496094,72461700,0.0,0 -2016-06-22,190.16156232953944,190.93624957306923,189.50536026381587,189.66030883789062,95560500,0.0,0 -2016-06-23,191.2187369375745,192.18480812898474,190.72659244812874,192.130126953125,102731400,0.0,0 -2016-06-24,185.5863833798353,192.1666214705794,184.75701499372502,185.23094177246094,333444400,0.0,0 -2016-06-27,183.72712478484172,183.73624758602958,181.0476356969278,181.91346740722656,230775800,0.0,0 -2016-06-28,183.626896246281,185.22182911924747,183.29879521328806,185.19448852539062,159382400,0.0,0 -2016-06-29,186.68913331737346,188.59393517215221,186.57977096221555,188.34786987304688,137328600,0.0,0 -2016-06-30,188.84913860564131,190.9726651867812,188.25672672840665,190.91798400878906,165021900,0.0,0 -2016-07-01,190.917986391582,191.83849925496665,190.74482005464,191.31900024414062,106055300,0.0,0 -2016-07-05,190.43495458322576,190.55343974386506,189.30483978343784,189.94281005859375,109803700,0.0,0 -2016-07-06,189.4142161279304,191.2096558320299,188.71244182009053,191.08206176757812,96021500,0.0,0 -2016-07-07,191.27341601721096,191.98429910901294,190.14330131060677,190.9635467529297,85593800,0.0,0 -2016-07-08,192.3489280107625,194.07145514311568,192.1028487327942,193.80714416503906,133971000,0.0,0 -2016-07-11,194.29926222455012,195.10128998160437,194.08052358897817,194.4906463623047,73633900,0.0,0 -2016-07-12,195.5205076401306,196.22228187726893,194.51797301564724,195.90328979492188,101275600,0.0,0 -2016-07-13,196.34987276036705,196.35898165436677,195.3564609527697,195.87594604492188,87324100,0.0,0 -2016-07-14,197.22480786098433,197.47088707660217,196.5503881016962,196.96961975097656,91230900,0.0,0 -2016-07-15,197.57114005865645,197.7807558827841,196.23139553103692,196.705322265625,107155400,0.0,0 -2016-07-18,196.83288817535967,197.40706829272625,196.55946840962764,197.23390197753906,58725900,0.0,0 -2016-07-19,196.78733754115123,197.0698662310236,196.52304054612145,197.03341674804688,54345700,0.0,0 -2016-07-20,197.03345309843544,198.1088868785107,197.03345309843544,197.85369873046875,58159500,0.0,0 -2016-07-21,197.7351998876485,197.9721563044442,196.632411760676,197.10633850097656,67777300,0.0,0 -2016-07-22,197.23391632346141,198.04505287988385,196.9513876441143,197.99037170410156,62787500,0.0,0 -2016-07-25,197.77165934634488,197.8263405293855,196.83292861958796,197.45266723632812,55873100,0.0,0 -2016-07-26,197.34333153159898,197.92662074068517,196.64155713877736,197.54383850097656,70080500,0.0,0 -2016-07-27,197.94481242010363,198.01772529483716,196.51392332853732,197.3341827392578,84083900,0.0,0 -2016-07-28,197.12456496140192,197.87191154270187,196.6324204104971,197.56204223632812,65035700,0.0,0 -2016-07-29,197.27953549718262,198.26382470863678,196.9787750521839,197.88104248046875,79519400,0.0,0 -2016-08-01,197.94481223999884,198.36404388594906,197.23392909537986,197.7169647216797,73311400,0.0,0 -2016-08-02,197.4526550200528,197.61671246573434,195.55697589394978,196.45013427734375,92295500,0.0,0 -2016-08-03,196.38635216487268,197.08812649761018,196.06737394578147,197.02432250976562,53993600,0.0,0 -2016-08-04,197.14275742005938,197.5711118081413,195.26529635231677,197.23390197753906,46585500,0.0,0 -2016-08-05,197.23397142927044,198.89269448716996,197.23397142927044,198.8471221923828,71892200,0.0,0 -2016-08-08,199.04757007433898,199.1569463307008,198.44606324453378,198.7285919189453,39906500,0.0,0 -2016-08-09,198.80156380430753,199.37573021714758,198.50080334676662,198.8471221923828,51251700,0.0,0 -2016-08-10,198.96556437175178,199.0475861406128,197.98126142529307,198.3549346923828,57941100,0.0,0 -2016-08-11,198.91999482470172,199.5397473136135,198.637466123597,199.2754364013672,72504300,0.0,0 -2016-08-12,198.9473158672615,199.33011190302128,198.67390999415835,199.10226440429688,61313500,0.0,0 -2016-08-15,199.4941773990051,200.05012591339272,199.48506850446768,199.67645263671875,49813500,0.0,0 -2016-08-16,199.229910898129,199.30280987901,198.64662170410156,198.64662170410156,53213600,0.0,0 -2016-08-17,198.68306899765048,199.16610470423632,197.78991047126496,199.02027893066406,75134300,0.0,0 -2016-08-18,198.99291589406232,199.5032921372939,198.8744446347683,199.4668426513672,52989300,0.0,0 -2016-08-19,198.96560081375313,199.36661474391187,198.44611560911036,199.1752166748047,75443000,0.0,0 -2016-08-22,198.91999744024176,199.41215588007074,198.5281063739976,199.16607666015625,61368800,0.0,0 -2016-08-23,199.82226573469845,200.14125781052724,199.50327365886963,199.56707763671875,53399200,0.0,0 -2016-08-24,199.4121587651755,199.51241223350158,198.09975478225593,198.5463409423828,71728900,0.0,0 -2016-08-25,198.13618714299866,198.85619305512273,197.9721436126665,198.40960693359375,69224800,0.0,0 -2016-08-26,198.61014522609884,199.70381065453046,197.08812530296902,198.0359649658203,122506300,0.0,0 -2016-08-29,198.1726920981532,199.29369817734198,198.13622870151468,199.01116943359375,70502200,0.0,0 -2016-08-30,198.92000085631932,199.2207612621111,198.09064642342508,198.68304443359375,58114500,0.0,0 -2016-08-31,198.3276120574558,198.45520612280484,197.28862783769668,198.1179962158203,85269500,0.0,0 -2016-09-01,198.10887401586817,198.43697502872106,196.88761454285867,198.12710571289062,97844200,0.0,0 -2016-09-02,199.0385106290384,199.475974043044,198.4096491488803,199.02027893066406,79293900,0.0,0 -2016-09-06,199.32100474599315,199.70378690508696,198.55544042780554,199.62176513671875,56702100,0.0,0 -2016-09-07,199.44861149023205,199.79494418765836,198.9564669419981,199.60354614257812,76554900,0.0,0 -2016-09-08,199.24808060634433,199.5397320863308,198.81972618680672,199.1478271484375,74102900,0.0,0 -2016-09-09,197.744333962618,197.79901514986395,194.35396128625985,194.3813018798828,221589100,0.0,0 -2016-09-12,193.5701323201806,197.5984746756418,193.49721944731644,197.1701202392578,168110900,0.0,0 -2016-09-13,195.8030318726744,196.08556055858222,193.6703825273945,194.335693359375,182828800,0.0,0 -2016-09-14,194.3903809181141,195.6754442478582,193.67038888544477,194.26278686523438,134185500,0.0,0 -2016-09-15,194.0896229325381,196.61416325110227,193.8982249112066,196.20404052734375,134427900,0.0,0 -2016-09-16,195.54637756576983,195.73874243827822,194.7128337234547,195.44561767578125,155236400,1.082,0 -2016-09-19,196.14175541881957,196.8287507637486,195.13415665547464,195.48223876953125,80250500,0.0,0 -2016-09-20,196.39827892039568,196.56315113315597,195.45480620882012,195.49143981933594,69665300,0.0,0 -2016-09-21,196.24254722986987,197.88217023672362,195.50974928063889,197.6898193359375,110284400,0.0,0 -2016-09-22,198.7706594371377,199.2561350447954,198.5050273785342,198.93553161621094,76678700,0.0,0 -2016-09-23,198.5141763173653,198.6607386789955,197.74474488424093,197.8455047607422,73630900,0.0,0 -2016-09-26,196.9569972626846,197.1493481399109,196.03183454902648,196.24252319335938,89827300,0.0,0 -2016-09-27,196.06850076206618,197.56156134269472,195.6746161096721,197.4608154296875,78494800,0.0,0 -2016-09-28,197.69894994420642,198.60578886732677,196.6730413275539,198.44090270996094,87411000,0.0,0 -2016-09-29,198.2210641336691,198.65158236259504,196.05931808221698,196.6455535888672,128070600,0.0,0 -2016-09-30,197.53408165464003,198.88059382427252,197.26844956985659,198.12948608398438,117202900,0.0,0 -2016-10-03,197.68981530865145,197.89132111305057,196.97532718941181,197.65316772460938,83512100,0.0,0 -2016-10-04,197.7722360910982,198.01038946109588,196.0135295711475,196.6455535888672,119948100,0.0,0 -2016-10-05,197.31425780310428,197.97377455750353,197.24097661073122,197.51577758789062,72816000,0.0,0 -2016-10-06,197.27760686144757,197.89132111305057,196.70054019388667,197.65316772460938,62927400,0.0,0 -2016-10-07,197.9463071685825,198.12950317150424,196.19675529021785,196.975341796875,89788300,0.0,0 -2016-10-10,198.00125122070312,198.49588179293522,197.84553392987158,198.00125122070312,51855000,0.0,0 -2016-10-11,197.54325638284965,197.61653757525792,194.72199337144323,195.50057983398438,130367400,0.0,0 -2016-10-12,195.64712079549247,196.31580636163778,195.11584269142597,195.75704956054688,73866100,0.0,0 -2016-10-13,194.33730393485746,195.64716873718143,193.467112369619,195.1158905029297,101357000,0.0,0 -2016-10-14,196.16011702510184,196.65476163115133,195.13420817842103,195.21664428710938,93346200,0.0,0 -2016-10-17,195.18913567998035,195.4639366437999,194.34642297403587,194.53878784179688,58275700,0.0,0 -2016-10-18,196.24252517725375,196.30663747799179,195.35401003491322,195.75704956054688,76869700,0.0,0 -2016-10-19,196.04102141871482,196.60893317285775,195.65630565200914,196.2791748046875,66519200,0.0,0 -2016-10-20,195.90364239357092,196.50820181916717,195.20749194943457,195.91281127929688,73639800,0.0,0 -2016-10-21,195.91280351633918,196.0959995231202,194.88688071983847,196.00439453125,89089100,0.0,0 -2016-10-24,196.93869629343288,197.23182106197237,196.46237553303533,196.8379364013672,60146600,0.0,0 -2016-10-25,196.64555760443105,196.9203585612442,196.00436468983852,196.17840576171875,66542300,0.0,0 -2016-10-26,195.29905501703485,196.4073997490557,195.04256388009176,195.78453063964844,75705500,0.0,0 -2016-10-27,196.55399078513526,196.59062439463725,195.1799998089872,195.26243591308594,77220200,0.0,0 -2016-10-28,195.23493517115,195.9585641870337,193.9250706307031,194.68533325195312,140623200,0.0,0 -2016-10-31,195.04258724352553,195.2807546184397,194.52047795421106,194.69451904296875,61272500,0.0,0 -2016-11-01,195.04255038822777,195.09752175609958,191.9923033767502,193.28384399414062,122781800,0.0,0 -2016-11-02,192.95408816819548,193.3662965593096,191.65337865085272,192.12054443359375,103330800,0.0,0 -2016-11-03,192.34953904444993,192.57853748962916,190.9480696781095,191.24118041992188,88939300,0.0,0 -2016-11-04,191.3602759186551,192.25794596572615,190.87480030300753,191.030517578125,109122100,0.0,0 -2016-11-07,191.03051635119542,195.28072717019882,191.03051635119542,195.24407958984375,109794900,0.0,0 -2016-11-08,194.8227491725293,196.7280182042583,194.53879329350937,196.1234588623047,106772100,0.0,0 -2016-11-09,194.52962938766777,198.86229080559676,194.50215068750825,198.20277404785156,258429000,0.0,0 -2016-11-10,199.04550040798787,199.97064932284817,197.14023119723134,198.69741821289062,172113300,0.0,0 -2016-11-11,197.9279301051796,198.49584175447785,197.23177989065417,198.23936462402344,100552700,0.0,0 -2016-11-14,198.79815062743512,199.0179941903057,197.59820088113426,198.39511108398438,94580000,0.0,0 -2016-11-15,198.80732346556889,199.9431610107422,198.58749385982858,199.9431610107422,91652600,0.0,0 -2016-11-16,199.28362531167426,199.8149034366837,199.15538672818627,199.5675811767578,65617700,0.0,0 -2016-11-17,199.73246937136992,200.65761816329163,199.6133856937223,200.593505859375,69797200,0.0,0 -2016-11-18,200.6667918147694,200.84998780068594,199.95230370886662,200.14466857910156,86265800,0.0,0 -2016-11-21,200.75838152666032,201.68353033460485,200.60266424078597,201.6560516357422,72402600,0.0,0 -2016-11-22,201.98583098674277,202.24230818741586,201.27135678815927,202.04995727539062,67429000,0.0,0 -2016-11-23,201.5003495566162,202.21482373488064,201.28967485711738,202.1598663330078,56620200,0.0,0 -2016-11-25,202.52623398974964,202.94758330902928,202.4437839255809,202.91094970703125,37872300,0.0,0 -2016-11-28,202.58122666395943,202.87433746186176,201.84842870376582,201.95834350585938,76572500,0.0,0 -2016-11-29,201.9949815610216,202.83769428354938,201.6743781007635,202.3522186279297,69886700,0.0,0 -2016-11-30,203.0117527429098,203.18579383755178,201.8026339655817,201.86676025390625,113291800,0.0,0 -2016-12-01,202.18734401282134,202.18734401282134,200.7400718706578,201.1248016357422,79040500,0.0,0 -2016-12-02,201.21635473733312,201.74763281426033,200.84079392790863,201.2255096435547,74840300,0.0,0 -2016-12-05,202.11405839366898,202.80105385248766,201.90338369984627,202.43466186523438,67837800,0.0,0 -2016-12-06,202.63616304192306,203.1124837631754,202.12320871609302,203.07583618164062,59877400,0.0,0 -2016-12-07,202.91094573680033,205.7963205484342,202.7827071668823,205.73220825195312,110738100,0.0,0 -2016-12-08,205.70477400138785,206.7398376904497,205.4208041280414,206.2360382080078,99714400,0.0,0 -2016-12-09,206.4741505103396,207.5000590176819,206.43750293396016,207.4817352294922,88005800,0.0,0 -2016-12-12,207.38100005277735,207.89396834448286,206.79476455349925,207.2436065673828,102016100,0.0,0 -2016-12-13,207.94894346453006,209.15804816993847,207.93061967259658,208.62677001953125,110477500,0.0,0 -2016-12-14,208.30616306429593,209.05727074708116,206.43752779861762,206.90469360351562,142501800,0.0,0 -2016-12-15,207.16120130229385,208.67258577435922,206.91387901482463,207.756591796875,124972600,0.0,0 -2016-12-16,208.24397182507101,208.308476066505,207.00930890566215,207.3502197265625,156420200,1.329,0 -2016-12-19,207.54372318311616,208.25319955858097,207.38708809802304,207.8017120361328,90341100,0.0,0 -2016-12-20,208.3729843104508,208.75998167392422,208.12421836736792,208.60333251953125,89838800,0.0,0 -2016-12-21,208.46512227610816,208.64939802536853,208.02285766601562,208.02285766601562,67909000,0.0,0 -2016-12-22,207.86622072377529,207.99521515304184,207.23966631066745,207.66351318359375,56219100,0.0,0 -2016-12-23,207.7095873945111,207.97679919934507,207.50689389823143,207.96759033203125,36697800,0.0,0 -2016-12-27,208.25320282734322,208.9073838365685,208.2347710348847,208.48355102539062,42672500,0.0,0 -2016-12-28,208.7599632146933,208.77838094706715,206.64075706667234,206.76052856445312,64095000,0.0,0 -2016-12-29,206.83421850585037,207.21199286726767,206.2445277711676,206.71444702148438,48696100,0.0,0 -2016-12-30,207.06457650368367,207.15672139695974,205.22179111285647,205.9589080810547,108998300,0.0,0 -2017-01-03,207.35022557689848,208.07813374826495,206.2814206343511,207.53451538085938,91366500,0.0,0 -2017-01-04,207.88466742054965,208.92584592569528,207.87545855265415,208.7692108154297,78744400,0.0,0 -2017-01-05,208.48356101115988,208.76919054092542,207.75565279723583,208.60333251953125,78379000,0.0,0 -2017-01-06,208.72311597693596,209.84721635063045,208.1426339969337,209.34967041015625,71559900,0.0,0 -2017-01-09,209.07326962959166,209.22069587017174,208.62178204006773,208.6586456298828,46939700,0.0,0 -2017-01-10,208.67706336545024,209.57081561737425,208.24400757083384,208.6586456298828,63771900,0.0,0 -2017-01-11,208.56650097297006,209.2483367919922,207.85702449122246,209.2483367919922,74650000,0.0,0 -2017-01-12,208.69545809710038,208.92580628484552,207.27651944669887,208.7230987548828,72113200,0.0,0 -2017-01-13,208.9074027803031,209.5247343199293,208.87055325138283,209.20225524902344,62717900,0.0,0 -2017-01-17,208.5204149888571,208.953470755918,208.05050969366602,208.4651336669922,61240800,0.0,0 -2017-01-18,208.7322978540288,208.9718689560498,208.14260708336752,208.92579650878906,54793300,0.0,0 -2017-01-19,209.0087593649998,209.1561856013392,207.69117428348324,208.15187072753906,66608800,0.0,0 -2017-01-20,208.8797471321585,209.44179730840878,208.20713431774047,208.91661071777344,129168600,0.0,0 -2017-01-23,208.91661830236131,208.98110849052094,207.56216969827983,208.37298583984375,75061600,0.0,0 -2017-01-24,208.60333224298657,210.15127951681143,208.483560734774,209.70901489257812,95555300,0.0,0 -2017-01-25,210.72251815509182,211.52413940429688,210.54745128389374,211.52413940429688,84437700,0.0,0 -2017-01-26,211.36752974417786,211.65317335857483,211.00818706856765,211.30303955078125,59970700,0.0,0 -2017-01-27,211.3859527192419,211.54258782162984,210.77783004455492,210.97132873535156,59711100,0.0,0 -2017-01-30,210.23420768398182,210.26184834510104,208.61256130784938,209.66294860839844,79737300,0.0,0 -2017-01-31,209.13773188521188,209.70900497631143,208.52962332789812,209.64450073242188,75880800,0.0,0 -2017-02-01,209.64454077571943,210.62121507179907,209.1009222991228,209.7274627685547,79117700,0.0,0 -2017-02-02,209.72742900382377,210.16970767975624,208.990325995947,209.8656463623047,69657600,0.0,0 -2017-02-03,210.8331264237251,211.50573929229716,210.50142442317704,211.31224060058594,80563200,0.0,0 -2017-02-06,210.87916692334446,211.30301380003874,210.57510561036912,210.9344482421875,57790100,0.0,0 -2017-02-07,211.34909491014534,211.60708378154052,210.7409722592828,210.9436798095703,57931200,0.0,0 -2017-02-08,210.9436873574584,211.35831133994088,210.36320534637915,211.22010803222656,51566200,0.0,0 -2017-02-09,211.22009550773245,212.79566935960875,211.22009550773245,212.4731903076172,65955200,0.0,0 -2017-02-10,212.8417250084183,213.55120136172758,212.4915912648277,213.31163024902344,66015900,0.0,0 -2017-02-13,213.83686239241283,214.749046409731,213.8092217306159,214.47262573242188,55182100,0.0,0 -2017-02-14,214.27912622748715,215.3387364765951,213.91057469869983,215.3295135498047,71109000,0.0,0 -2017-02-15,215.0991494621056,216.65630556614258,215.04386814279943,216.45359802246094,86785800,0.0,0 -2017-02-16,216.48123403285507,216.67473270560956,215.46771039555233,216.26931762695312,84722400,0.0,0 -2017-02-17,215.55985744971176,216.6102447509766,215.54142565583993,216.61024475097656,77204100,0.0,0 -2017-02-21,217.00645530008907,218.0844832838891,216.99723237330045,217.90020751953125,88946100,0.0,0 -2017-02-22,217.46713848351314,217.94625262976808,217.29207159727667,217.70669555664062,62115200,0.0,0 -2017-02-23,218.2595105024733,218.2779282340231,217.04326537200654,217.85409545898438,74615900,0.0,0 -2017-02-24,216.9511572681623,218.17659733346724,216.9050848157862,218.1305389404297,82381600,0.0,0 -2017-02-27,218.0383950581075,218.65572655669396,217.77119732801492,218.47145080566406,56515400,0.0,0 -2017-02-28,218.06606593769996,218.324054836762,217.4671661490217,217.8817901611328,96961900,0.0,0 -2017-03-01,219.6508440725056,221.42913960914888,219.63241227862855,220.93157958984375,149158200,0.0,0 -2017-03-02,220.7288891614283,220.7381120887521,219.485017135333,219.54029846191406,70246000,0.0,0 -2017-03-03,219.44814908548577,219.8535642179627,219.04273395300885,219.67849731445312,81974300,0.0,0 -2017-03-06,218.8308085604558,219.40206764591971,218.3793209888004,219.02430725097656,55391500,0.0,0 -2017-03-07,218.7018191937027,219.07959365674967,218.14897783025154,218.3701171875,65103700,0.0,0 -2017-03-08,218.6833917721527,218.9598124594059,217.81728017978472,217.96470642089844,78168800,0.0,0 -2017-03-09,218.09367790038775,218.59123789691535,217.20914866553224,218.24110412597656,90683900,0.0,0 -2017-03-10,219.2638635139542,219.30993596955645,217.99233684789783,219.00587463378906,81991700,0.0,0 -2017-03-13,218.94138816821987,219.1625275374696,218.59126840775028,219.116455078125,57256800,0.0,0 -2017-03-14,218.5359429672041,218.59123834641764,217.62377307059225,218.2779541015625,59880800,0.0,0 -2017-03-15,218.88611914890907,220.61834249041812,218.6373391019316,220.16685485839844,96081800,0.0,0 -2017-03-16,220.31423134836308,220.39715332551137,219.3836296885762,219.73374938964844,78344000,0.0,0 -2017-03-17,220.01415994131068,220.21774935775255,219.34786987304688,219.34786987304688,89002100,1.033,0 -2017-03-20,219.34786854121472,219.65325266402334,218.6908415270988,219.10726928710938,52537000,0.0,0 -2017-03-21,219.75502722717718,219.88458283220447,216.15521707266674,216.2940216064453,131809300,0.0,0 -2017-03-22,216.33107469309152,217.10840842792925,215.66478460022955,216.8030242919922,97569200,0.0,0 -2017-03-23,216.80299031284127,217.7839133178987,216.1737242795975,216.57164001464844,100410300,0.0,0 -2017-03-24,216.89553446982254,217.50628852093547,215.58146648030458,216.41432189941406,112504900,0.0,0 -2017-03-27,214.62833018785219,216.46988397369446,214.33220909594777,216.1922607421875,87454500,0.0,0 -2017-03-28,215.86838152083203,218.21889478747067,215.7480748241778,217.7654571533203,93483900,0.0,0 -2017-03-29,217.4600499962033,218.21887169660118,217.2194366283554,217.96900939941406,61950400,0.0,0 -2017-03-30,217.90426414831828,218.87593842173166,217.71918669635966,218.6630859375,56737900,0.0,0 -2017-03-31,218.30216725017291,218.86666262409867,218.09857782872405,218.15411376953125,73733100,0.0,0 -2017-04-03,218.2096437488043,218.42248210809402,216.46063574132722,217.7747039794922,85546500,0.0,0 -2017-04-04,217.46930924689244,218.0060437046005,217.0621304085964,217.9134979248047,56466200,0.0,0 -2017-04-05,218.63529117096923,219.68099908734345,217.04359991368375,217.2657012939453,108800600,0.0,0 -2017-04-06,217.41377946151954,218.43171239946065,216.94181577055778,217.87648010253906,69135800,0.0,0 -2017-04-07,217.60814604855634,218.39474293189463,217.1361963908567,217.6544189453125,74412300,0.0,0 -2017-04-10,217.80246278644645,218.63531835510184,217.21945541608528,217.78395080566406,67615300,0.0,0 -2017-04-11,217.37674320534515,217.6358544200174,215.9331195463133,217.52481079101562,88045300,0.0,0 -2017-04-12,217.2286810745368,217.43227046658666,216.33104078790845,216.57164001464844,81864400,0.0,0 -2017-04-13,216.2107776570659,216.9973744775353,215.16506958007815,215.16506958007812,92880400,0.0,0 -2017-04-17,215.720303724691,217.0713958740234,215.50746537339677,217.07139587402344,68405400,0.0,0 -2017-04-18,216.28479469525533,216.99735765569454,215.69253842333922,216.42359924316406,83225800,0.0,0 -2017-04-19,217.0251309643337,217.42304677140794,215.78508240998832,216.02569580078125,68699900,0.0,0 -2017-04-20,216.68272093742806,218.25591453233895,216.3403269557259,217.78395080566406,92572200,0.0,0 -2017-04-21,217.70064247705065,217.75616429236592,216.66419760315958,217.08987426757812,110389800,0.0,0 -2017-04-24,219.4866811809167,219.69953365407332,217.06213420925127,219.47743225097656,119209900,0.0,0 -2017-04-25,220.16222980836716,221.12464096105631,220.06968402884746,220.75448608398438,76698300,0.0,0 -2017-04-26,220.71747159352046,221.66138492720557,220.5694181086417,220.6156768798828,84702500,0.0,0 -2017-04-27,220.95803671600947,221.12460214530228,220.2269618994441,220.80072021484375,57410300,0.0,0 -2017-04-28,221.07838661714058,221.10614752922461,220.18074613841114,220.31956481933594,63532800,0.0,0 -2017-05-01,220.87477111816403,221.32822282953674,220.4305824567294,220.87477111816406,66882500,0.0,0 -2017-05-02,221.02283959496654,221.15239521009008,220.52312911391945,220.95806884765625,57375700,0.0,0 -2017-05-03,220.95807154912833,221.05986625657732,219.967885383897,220.689697265625,73137700,0.0,0 -2017-05-04,221.01357636752843,221.096859088898,220.04190225671059,220.94879150390625,61462700,0.0,0 -2017-05-05,221.34676070169766,221.83722230868707,220.87479695574226,221.81871032714844,62001300,0.0,0 -2017-05-08,221.8649294444963,222.02224594818242,221.32819507400777,221.78164672851562,48385700,0.0,0 -2017-05-09,222.05929183098806,222.27213016634607,221.20791024855086,221.5780792236328,51363200,0.0,0 -2017-05-10,221.53180926932876,221.97599792480472,221.3097078813394,221.9759979248047,54293800,0.0,0 -2017-05-11,221.49479158278226,221.6983809844325,220.36580094472265,221.52255249023438,62358300,0.0,0 -2017-05-12,221.254199495768,221.56883255747576,220.86553263356956,221.15240478515625,53912700,0.0,0 -2017-05-15,221.60586232888966,222.50350276768467,221.58735034815567,222.3739471435547,61918900,0.0,0 -2017-05-16,222.68855630318257,222.71631721105084,221.75390612377802,222.1703338623047,51241800,0.0,0 -2017-05-17,222.17033848003365,222.17033848003365,218.16334928856594,218.22813415527344,172174100,0.0,0 -2017-05-18,218.14484402968165,220.01415860543295,217.86722081617353,219.10726928710938,107047700,0.0,0 -2017-05-19,219.62548983997357,221.24494209739845,219.56996802185662,220.53237915039062,115011400,0.0,0 -2017-05-22,221.07834126283424,221.827928096839,221.0043215927984,221.652099609375,61010600,0.0,0 -2017-05-23,222.05007810691316,222.31845243852638,221.64289920412466,222.1426239013672,48341700,0.0,0 -2017-05-24,222.39243696750214,222.77184075147343,222.03151692141535,222.66079711914062,46927700,0.0,0 -2017-05-25,223.20678365124468,224.02114128795776,222.9846963834159,223.72500610351562,64071700,0.0,0 -2017-05-26,223.52144131327853,223.85458637221754,223.43815857866986,223.67877197265625,46629900,0.0,0 -2017-05-30,223.33632267756465,223.7527503943192,223.16975723906356,223.48439025878906,35201900,0.0,0 -2017-05-31,223.79904891793456,223.83607287773313,222.6885701725338,223.42889404296875,91796000,0.0,0 -2017-06-01,223.91935073379014,225.22416993848083,223.61396661436237,225.20565795898438,68962000,0.0,0 -2017-06-02,225.26117974714742,226.12181028733056,224.9465466982646,225.95523071289062,88666100,0.0,0 -2017-06-05,225.77016904948638,226.0755531902048,225.5758285524823,225.78868103027344,44698800,0.0,0 -2017-06-06,225.18714131786197,225.77939756496073,224.98355190912463,225.0668487548828,50375400,0.0,0 -2017-06-07,225.4277698054155,225.72389088857585,224.7152068270396,225.48329162597656,54144300,0.0,0 -2017-06-08,225.58506564611804,226.10328808449418,225.02981925042795,225.5943145751953,65950700,0.0,0 -2017-06-09,225.88121928508372,226.73258685958172,223.90086089819604,225.251953125,132256400,0.0,0 -2017-06-12,224.9928196165358,225.2611797769707,224.29876865070074,225.20565795898438,86108100,0.0,0 -2017-06-13,225.77939926070667,226.36240658287895,225.40924439867837,226.306884765625,60067000,0.0,0 -2017-06-14,226.59376382106464,226.60301275072177,225.14087699620723,226.02001953125,78602300,0.0,0 -2017-06-15,224.57635445954017,225.71460809227287,224.28023342081002,225.58505249023438,66464900,0.0,0 -2017-06-16,225.75526227663872,225.81105485536722,224.6951607127056,225.63436889648438,84553100,1.183,0 -2017-06-19,226.51775677636132,227.57785821054088,226.41546566475796,227.5127716064453,65123800,0.0,0 -2017-06-20,227.13152268351755,227.14081671650064,225.9598360062995,225.97842407226562,56906400,0.0,0 -2017-06-21,226.39688794219975,226.5177671237167,225.42047532126705,225.92262268066406,55977600,0.0,0 -2017-06-22,225.93194236149753,226.4619860350015,225.63436303019716,225.82034301757812,44148100,0.0,0 -2017-06-23,225.88549034676652,226.44343042383784,225.4763257766277,226.09007263183594,66986800,0.0,0 -2017-06-26,226.8060482296963,227.25241722371163,226.01562974118386,226.23880004882812,56700500,0.0,0 -2017-06-27,226.006344088686,226.32252570502956,224.39759647318624,224.41619873046875,82247700,0.0,0 -2017-06-28,225.50418455783512,226.63868097199685,225.2531037607694,226.4248046875,70042600,0.0,0 -2017-06-29,226.58288333411193,226.6386759123556,223.14220384523404,224.43478393554688,106949700,0.0,0 -2017-06-30,225.29958049006345,225.69945091995535,224.64864347678173,224.8532257080078,86820700,0.0,0 -2017-07-03,225.8575378053664,226.32249484848396,225.23449707031247,225.2344970703125,39153800,0.0,0 -2017-07-05,225.62507686758218,225.97843459912318,224.76024932151574,225.75526428222656,54427600,0.0,0 -2017-07-06,224.93693851874397,225.0671259352617,223.49556871413506,223.69085693359375,65400800,0.0,0 -2017-07-07,224.3046081727054,225.29960897824336,223.70015543515694,225.1415252685547,57972300,0.0,0 -2017-07-10,225.14151295428414,225.7831559810436,224.81603732960906,225.38328552246094,36663300,0.0,0 -2017-07-11,225.3832743324044,225.5506662477265,223.9698151264288,225.2158966064453,50354600,0.0,0 -2017-07-12,226.24812458601576,227.08504167875878,226.24812458601576,226.90835571289062,59610400,0.0,0 -2017-07-13,226.91763660328232,227.41048992798923,226.67584986253823,227.2895965576172,39471600,0.0,0 -2017-07-14,227.28961462410624,228.73098439310814,227.18732350012897,228.34971618652344,60262700,0.0,0 -2017-07-17,228.26601528562048,228.67517975996066,228.13582787918162,228.32180786132812,33531900,0.0,0 -2017-07-18,227.88474910635324,228.4984958232694,227.5220831716961,228.4427032470703,42742500,0.0,0 -2017-07-19,228.77745612288953,229.6887679091839,228.76814790129654,229.67947387695312,51034300,0.0,0 -2017-07-20,229.94914651793073,230.07933391813134,229.1959184032441,229.78176879882812,47135200,0.0,0 -2017-07-21,229.16802974551246,229.60509050131296,228.92624300183215,229.5771942138672,82340800,0.0,0 -2017-07-24,229.49351286039624,229.67019882383323,229.01926173961604,229.52142333984375,46622300,0.0,0 -2017-07-25,230.32110298695463,230.4327023207005,229.83755790171725,230.07933044433594,54915600,0.0,0 -2017-07-26,230.3862329062459,230.423423229288,229.80969066822735,230.08865356445312,47575400,0.0,0 -2017-07-27,230.58148432988924,230.618674646997,228.46126733134113,229.87474060058594,70766600,0.0,0 -2017-07-28,229.36331578270577,229.74458398360395,228.8797706487196,229.6051025390625,50088400,0.0,0 -2017-07-31,230.03286981279695,230.13516094179317,229.25174529466094,229.4749298095703,65838700,0.0,0 -2017-08-01,230.11656131451667,230.15375163711772,229.42841973680441,229.9863739013672,55050400,0.0,0 -2017-08-02,230.125872975999,230.24676636509986,229.10296165906445,230.0979766845703,47211200,0.0,0 -2017-08-03,229.97707277636133,230.00496906651404,229.35403198067712,229.651611328125,40856000,0.0,0 -2017-08-04,230.17234422014488,230.42341081321965,229.66088862235014,230.07005310058594,60191800,0.0,0 -2017-08-07,230.14442979080576,230.49778747558594,230.0328304577617,230.49778747558594,31995000,0.0,0 -2017-08-08,230.16305105220871,231.46493940350865,229.53071622765825,229.93057250976562,61719400,0.0,0 -2017-08-09,229.19591756413442,229.97704196247835,228.8146493973133,229.92124938964844,62632600,0.0,0 -2017-08-10,229.02856993309942,229.16806558150455,226.62009547790743,226.67588806152344,120479500,0.0,0 -2017-08-11,226.9176372230312,227.64296906851905,226.66655644901994,227.0106201171875,74869900,0.0,0 -2017-08-14,228.3776164851961,229.49351064585244,228.34042616189868,229.2610321044922,73291900,0.0,0 -2017-08-15,229.6701998371381,229.68880209373046,228.9076775892235,229.23313903808594,55242700,0.0,0 -2017-08-16,229.7910487649315,230.21881544761274,229.17730212039987,229.63296508789062,56715500,0.0,0 -2017-08-17,228.98206399583665,229.31683365535224,226.05282592773438,226.05282592773438,128490400,0.0,0 -2017-08-18,225.87616519628193,227.07576252242086,225.22522806046038,225.69949340820312,136748000,0.0,0 -2017-08-21,225.6343870775425,226.1551367734544,224.88115882528675,225.87615966796875,65469700,0.0,0 -2017-08-22,226.49917861936152,228.4054911263406,226.4805763647939,228.2381134033203,63140100,0.0,0 -2017-08-23,227.20590828107356,227.8754475520897,227.04782459060624,227.41978454589844,50203800,0.0,0 -2017-08-24,227.82897874057178,227.99635648548616,226.6665859918954,226.8897705078125,50741700,0.0,0 -2017-08-25,227.73595192683305,228.39619716524385,227.26170085543112,227.41978454589844,64445900,0.0,0 -2017-08-28,227.98703233928012,228.01492862641467,226.9827234343485,227.42909240722656,40565600,0.0,0 -2017-08-29,226.02489908569956,227.96841597908443,225.9040057226884,227.689453125,51135700,0.0,0 -2017-08-30,227.6708813398968,229.0564585211772,227.47559312654118,228.76817321777344,62030800,0.0,0 -2017-08-31,229.42841709728853,230.4048297793581,228.80537632800085,230.1444549560547,103803900,0.0,0 -2017-09-01,230.5443044995088,230.92557269014037,230.31182597195735,230.46990966796875,62007000,0.0,0 -2017-09-05,229.93056085734972,230.17234761043036,227.78246150433495,228.81466674804688,91398800,0.0,0 -2017-09-06,229.5399868447489,229.9491513080063,228.97273869148074,229.5957794189453,57916900,0.0,0 -2017-09-07,229.92124296428082,229.93984521782508,229.13081036631303,229.56787109375,58034700,0.0,0 -2017-09-08,229.26101326383346,229.79107110546943,229.03784296366078,229.2982177734375,63832800,0.0,0 -2017-09-11,230.65586932588508,231.82757008268229,230.63728126166055,231.74388122558594,71364800,0.0,0 -2017-09-12,232.1344774264823,232.56222999611163,231.93918920861563,232.52503967285156,56896000,0.0,0 -2017-09-13,232.21815342551363,232.67381644903875,232.09726005023157,232.63661193847656,59228000,0.0,0 -2017-09-14,232.29255648280696,232.77611581169498,232.10657649150042,232.56222534179688,95446300,0.0,0 -2017-09-15,232.40801017652922,232.96871899866971,232.29587126405718,232.87527465820312,95432400,1.235,0 -2017-09-18,233.2678372026058,233.74444196725437,232.95944085143879,233.37063598632812,46235200,0.0,0 -2017-09-19,233.63226751940573,233.6976913990065,233.25846159528746,233.60423278808594,47108100,0.0,0 -2017-09-20,233.69766313165005,233.80980204336515,232.62294627518608,233.6883087158203,59574100,0.0,0 -2017-09-21,233.52011918362464,233.61356353099757,232.87529186969152,233.06219482421875,48211400,0.0,0 -2017-09-22,232.74445149485138,233.28648002880314,232.71641676549015,233.10891723632812,51214000,0.0,0 -2017-09-25,232.83792176347734,233.2117419628827,231.83798303210585,232.63232421875,57064400,0.0,0 -2017-09-26,233.0902584607343,233.35192548147893,232.52019510987282,232.77252197265625,54082000,0.0,0 -2017-09-27,233.52013262101323,234.0901959339822,232.57624911769778,233.6790008544922,81001400,0.0,0 -2017-09-28,233.3799176641849,234.04343951281425,233.28647332226083,233.95933532714844,44778800,0.0,0 -2017-09-29,233.94999276037387,234.86584147216777,233.7537496505475,234.78172302246094,85578000,0.0,0 -2017-10-02,235.02470846462023,235.8003692626953,234.8377912527227,235.8003692626953,59023000,0.0,0 -2017-10-03,235.80038376562914,236.33305792491203,235.71626531118903,236.30502319335938,66810200,0.0,0 -2017-10-04,236.14617360034632,236.84707046404924,236.0246802475072,236.5854034423828,55953600,0.0,0 -2017-10-05,236.94048514306184,238.0058476720466,236.62274869366564,237.98716735839844,63522800,0.0,0 -2017-10-06,237.51057371437525,238.0245675900582,237.2302263819872,237.71617126464844,80646000,0.0,0 -2017-10-09,237.95914135668872,238.02455097641948,237.04329260220243,237.3236541748047,35803100,0.0,0 -2017-10-10,237.9310877535545,238.35162294921162,237.35167008019192,237.94976806640625,43057400,0.0,0 -2017-10-11,237.8469894445286,238.3236083984375,237.66944089871959,238.3236083984375,47674300,0.0,0 -2017-10-12,237.9871851754151,238.36099111330384,237.71616374963065,237.96849060058594,47065100,0.0,0 -2017-10-13,238.43572624203958,238.55721957402423,237.96846174644418,238.25816345214844,54800400,0.0,0 -2017-10-16,238.50120846297813,238.78155582109488,238.13674263764213,238.57595825195312,38221700,0.0,0 -2017-10-17,238.51984851658435,238.7908699300593,238.28621625106962,238.744140625,31561000,0.0,0 -2017-10-18,239.14599159685903,239.19272090393488,238.77218565981732,238.977783203125,40888300,0.0,0 -2017-10-19,238.1460424594612,239.0805715209857,237.69747250266835,239.04318237304688,61903800,0.0,0 -2017-10-20,239.89364731556768,240.3048424284513,239.0245277797394,240.2767791748047,89176400,0.0,0 -2017-10-23,240.6225528910853,240.6505876221325,239.25811992795218,239.34222412109375,63915300,0.0,0 -2017-10-24,239.8001302317138,240.0150536340724,239.37958080347872,239.7627410888672,66935900,0.0,0 -2017-10-25,239.407688687196,239.52918204890275,237.3704218326984,238.57595825195312,103715300,0.0,0 -2017-10-26,239.2300780207738,239.5197654623771,238.7534591161147,238.88429260253906,69798000,0.0,0 -2017-10-27,239.67868690255747,241.0057308146074,238.89368585639093,240.83749389648438,85562500,0.0,0 -2017-10-30,239.6787016208863,240.73472416484339,239.62263215125736,239.94036865234375,54285700,0.0,0 -2017-10-31,240.34218268092147,240.58516936872127,239.99641148339975,240.31414794921875,60304800,0.0,0 -2017-11-01,241.14587452401665,241.5103260089506,240.23938024053385,240.63186645507812,54202700,0.0,0 -2017-11-02,240.55716390377216,240.8749004082477,239.41703714321034,240.72537231445312,56449500,0.0,0 -2017-11-03,240.8935665021668,241.5757830424058,240.45433666494344,241.52906799316406,59589700,0.0,0 -2017-11-06,241.3888239808228,242.04300567425335,241.3140742110747,241.90283203125,49652600,0.0,0 -2017-11-07,242.0149969743612,242.3701225878709,241.19260682087858,241.73464965820312,57502200,0.0,0 -2017-11-08,241.54771399415912,242.24861075061065,241.24865786671157,242.1457977294922,50469600,0.0,0 -2017-11-09,240.85618469313172,241.47297729454743,239.57585592560244,241.2673797607422,95085500,0.0,0 -2017-11-10,240.8561796706427,241.3795136513134,240.51973438617554,241.19259643554688,59984700,0.0,0 -2017-11-13,240.46367597870244,241.65987206372978,240.4262868299179,241.41688537597656,50228600,0.0,0 -2017-11-14,240.55712976793382,240.96832483762705,239.72538521164773,240.85618591308594,61315200,0.0,0 -2017-11-15,239.81887369096484,240.3795969030712,238.89369892870812,239.65066528320312,80811500,0.0,0 -2017-11-16,240.6599394642663,242.08044204263393,240.61322441400597,241.68792724609375,67777000,0.0,0 -2017-11-17,241.31410644051672,241.65987764595926,240.89355693577772,240.9776611328125,75756800,0.0,0 -2017-11-20,241.23934017243474,241.59443725134525,240.97764466234855,241.3888397216797,48075500,0.0,0 -2017-11-21,242.211188794364,243.16442650653028,241.35143819386843,242.96815490722656,69176800,0.0,0 -2017-11-22,242.9775737633426,243.11774742890833,242.575733082199,242.7532958984375,45033400,0.0,0 -2017-11-24,243.2765889601457,243.42611701808946,243.12706090220192,243.31394958496094,27856500,0.0,0 -2017-11-27,243.360743805758,243.6784803001964,242.9775834249322,243.19253540039062,52274900,0.0,0 -2017-11-28,243.68779492018598,245.6876721808213,243.58498189843007,245.65963745117188,98971700,0.0,0 -2017-11-29,245.79977246814792,246.3698499075053,245.03348047164513,245.51007080078125,77512100,0.0,0 -2017-11-30,246.49139980467874,248.63145086846012,246.407295609643,247.65956115722656,127894400,0.0,0 -2017-12-01,247.42591414972864,247.93989370641074,243.68779804422226,247.14553833007812,164390900,0.0,0 -2017-12-04,248.87437771390643,249.3322877277396,246.79036809502054,246.84646606445312,94040600,0.0,0 -2017-12-05,245.95867919921878,247.79034797485966,245.8185055591678,245.95867919921875,77994500,0.0,0 -2017-12-06,246.06150159356244,246.46337078301818,245.51013284473353,246.00543212890625,75898600,0.0,0 -2017-12-07,246.78109741210938,247.11751418662706,245.72507500393363,246.78109741210938,77218600,0.0,0 -2017-12-08,247.79975540367448,248.13617221176807,246.74373289010293,248.12684631347656,76563900,0.0,0 -2017-12-11,248.87438964843753,248.9398135162817,248.09874320826376,248.8743896484375,83077500,0.0,0 -2017-12-12,249.7154850327602,249.81829805249106,248.9118037855992,249.3136444091797,85195800,0.0,0 -2017-12-13,249.57536155546492,250.04262614738627,249.19220116774133,249.28565979003906,102905400,0.0,0 -2017-12-14,249.60335268697494,249.7248460227816,248.21091357255617,248.26698303222656,100666700,0.0,0 -2017-12-15,249.33876490997034,250.83225786483908,249.2824088486803,250.3344268798828,144610300,1.351,0 -2017-12-18,251.82792341594453,252.29757637693461,251.71521129176162,251.92185974121094,83653600,0.0,0 -2017-12-19,252.184818686571,252.23177250804937,250.87916993156654,250.95433044433594,82382900,0.0,0 -2017-12-20,251.98760622481396,252.0439622906749,250.50351538156917,250.8228759765625,76751500,0.0,0 -2017-12-21,251.48976993332587,252.10034174294054,251.0764730154374,251.3394775390625,67032300,0.0,0 -2017-12-22,251.35823097927522,251.39581123654818,250.70070547101642,251.27369689941406,78720900,0.0,0 -2017-12-26,250.84159744492004,251.20794045901084,250.6913337454002,250.97311401367188,45244400,0.0,0 -2017-12-27,251.15160141880014,251.4803641976131,250.80406284260965,251.09524536132812,57751000,0.0,0 -2017-12-28,251.63065138615755,251.65882941431278,251.21735452965527,251.61184692382812,45116100,0.0,0 -2017-12-29,252.2318157192037,252.2505915179443,250.456542449274,250.6631622314453,96007400,0.0,0 -2018-01-02,251.5836972364917,252.49482513660018,251.17040033343164,252.45724487304688,86655700,0.0,0 -2018-01-03,252.63564014700154,254.21369542808614,252.63564014700154,254.0540008544922,90070400,0.0,0 -2018-01-04,254.73972676872154,255.6414522479233,254.11978154385278,255.12481689453125,80636400,0.0,0 -2018-01-05,255.97024046336935,256.9565001170275,255.44423149270648,256.82501220703125,83524000,0.0,0 -2018-01-08,256.7217166946166,257.46377644972046,256.4117583320432,257.2947082519531,57319200,0.0,0 -2018-01-09,257.74552809498925,258.5439438271603,257.44494333329226,257.8770446777344,57254000,0.0,0 -2018-01-10,257.0691860944629,257.7642918292214,256.35533322990386,257.48248291015625,69574300,0.0,0 -2018-01-11,258.074330866831,259.3611755371094,257.89586042850897,259.3611755371094,62361500,0.0,0 -2018-01-12,259.6429211864067,261.2303216122462,259.3235319897239,261.0518798828125,90816100,0.0,0 -2018-01-16,262.39512921758836,263.090206464505,259.417516671256,260.1595764160156,106555100,0.0,0 -2018-01-17,261.15522201339274,263.0526096393548,260.1595600381972,262.6393127441406,113258800,0.0,0 -2018-01-18,262.5172671136594,262.9681156751701,261.67186873013486,262.1979064941406,100728000,0.0,0 -2018-01-19,262.8178165194634,263.39080810546875,262.1978997633,263.39080810546875,140920100,0.0,0 -2018-01-22,263.1653084278166,265.5323486328125,263.1089237098848,265.5323486328125,91322400,0.0,0 -2018-01-23,265.5793727947124,266.4059666721469,265.2318341565512,266.09600830078125,97084700,0.0,0 -2018-01-24,266.78159758552147,267.42034725433757,264.7339180162442,265.9925842285156,134816100,0.0,0 -2018-01-25,266.91313403935317,267.016443915851,265.25994669268687,266.1053161621094,84587300,0.0,0 -2018-01-26,266.9977091050005,269.2332617923168,266.72530236053257,269.186279296875,107743100,0.0,0 -2018-01-29,268.57571311563606,269.0453660376717,267.2325126382955,267.4015808105469,90118300,0.0,0 -2018-01-30,265.447796306411,267.45789559656896,264.1515498551619,264.6587829589844,131796400,0.0,0 -2018-01-31,265.5699536297908,266.10533617931765,263.6443594356234,264.7903137207031,108364800,0.0,0 -2018-02-01,264.0106891087455,265.8798984850807,263.6443460859636,264.4897155761719,90102500,0.0,0 -2018-02-02,263.08080462236194,263.2217234507566,258.6942617757541,258.7318420410156,173174800,0.0,0 -2018-02-05,256.85317424093245,259.10750235558675,247.32859997494617,247.9109649658203,294681800,0.0,0 -2018-02-06,244.1632158203398,253.3308519995773,242.99848549654493,252.79544067382812,355026800,0.0,0 -2018-02-07,252.2036377847434,255.8293448516331,251.33946373153887,251.4240264892578,167376100,0.0,0 -2018-02-08,251.74339314913797,251.89368553878015,241.95581268058982,241.99339294433594,246449500,0.0,0 -2018-02-09,244.97095344483455,247.61040058182144,237.569233116286,245.62847900390625,283565300,0.0,0 -2018-02-12,247.8170515810126,250.8040659912252,245.77877392439447,249.23541259765625,143736000,0.0,0 -2018-02-13,247.94860244971437,250.43775758133938,247.32865705866635,249.8553924560547,81223600,0.0,0 -2018-02-14,248.2679140259629,253.61256632810446,248.25851179504255,253.22744750976562,120735700,0.0,0 -2018-02-15,255.0873006624809,256.4680814307785,252.45722704102167,256.45867919921875,111200300,0.0,0 -2018-02-16,255.79181290448813,258.6097308615769,255.74483040599705,256.5338439941406,160420100,0.0,0 -2018-02-20,255.51931842382416,257.0597934708936,254.08218190568448,254.92755126953125,86369700,0.0,0 -2018-02-21,255.39728290345278,258.04613257075914,253.55625126175474,253.65956115722656,98883700,0.0,0 -2018-02-22,254.6458526675474,256.4774820988182,253.2744739858968,253.9883270263672,110511300,0.0,0 -2018-02-23,255.29392531100206,258.03668212890625,254.78669216482183,258.03668212890625,92766400,0.0,0 -2018-02-26,259.18264744685814,261.13641960063,258.55332860147087,261.0330810546875,86491400,0.0,0 -2018-02-27,261.2303200757955,261.99118413949054,257.70792335549834,257.773681640625,99099200,0.0,0 -2018-02-28,258.947846744237,259.4269019319032,254.82430869965955,255.16244506835938,121907800,0.0,0 -2018-03-01,254.93702645422067,256.5902140675167,249.8553779220551,251.45220947265625,176855100,0.0,0 -2018-03-02,249.66752109772355,253.3496131974965,248.74701957178831,252.74844360351562,139083200,0.0,0 -2018-03-05,251.48036639661152,256.3271880389615,251.36762561534218,255.66966247558594,97307400,0.0,0 -2018-03-06,256.71230250167145,256.79686526195917,254.72097849093726,256.31781005859375,79213200,0.0,0 -2018-03-07,254.00712866132972,256.59959360160894,253.8004802048621,256.223876953125,87063500,0.0,0 -2018-03-08,256.9471096886463,257.59523303322356,255.88571813271258,257.4637451171875,66901200,0.0,0 -2018-03-09,258.9666907613724,261.94427490234375,258.62852567568166,261.94427490234375,113625300,0.0,0 -2018-03-12,262.25422833664305,262.92112752373566,261.2021816112836,261.615478515625,71924800,0.0,0 -2018-03-13,262.8553736301339,263.3907849013609,259.27662025356216,259.92474365234375,91968900,0.0,0 -2018-03-14,260.9485481339414,261.14579433027967,257.99914275864154,258.59088134765625,105895100,0.0,0 -2018-03-15,259.13567568020386,259.82135053396223,257.7736708724498,258.30908203125,83433000,0.0,0 -2018-03-16,258.8721422604331,259.7114865179347,258.5326516260384,258.5892333984375,100343700,1.097,0 -2018-03-19,257.7875818666391,258.7777915176182,253.32686093324932,255.09039306640625,109208400,0.0,0 -2018-03-20,255.51482158587115,256.2032715203458,254.7980807653303,255.52426147460938,59757300,0.0,0 -2018-03-21,255.4770870560948,257.71215336291647,254.80751690893788,255.03384399414062,78709600,0.0,0 -2018-03-22,252.65732054694445,253.56265765818537,248.3663444017155,248.65872192382812,148785900,0.0,0 -2018-03-23,249.13025750697088,249.47918802192677,243.15118174962296,243.35865783691406,183534800,0.0,0 -2018-03-26,247.206384550016,250.3184969986603,244.64123880089716,250.01670837402344,141956100,0.0,0 -2018-03-27,251.01638362912723,251.58220131801085,244.10368013508082,245.76348876953125,129941400,0.0,0 -2018-03-28,245.90489688938678,247.6873086141939,243.85842736610547,245.03726196289062,146452300,0.0,0 -2018-03-29,246.25389295793318,250.1582079975496,245.0467671603021,248.16831970214844,111601600,0.0,0 -2018-04-02,247.60246351187467,248.14945900607196,240.17109830878528,242.8116912841797,186286300,0.0,0 -2018-04-03,244.13195926642123,246.43304702425775,242.21753276413833,245.9237823486328,119956900,0.0,0 -2018-04-04,242.13268721716878,249.3094202370602,241.99123278592464,248.5549774169922,123715300,0.0,0 -2018-04-05,250.4316848609161,251.4596542621857,249.2717296912882,250.51658630371094,82652600,0.0,0 -2018-04-06,248.42292550568595,250.01668356482836,243.3114855111418,244.93356323242188,179521200,0.0,0 -2018-04-09,246.48966234375035,249.76210923973022,245.1410820996817,246.14073181152344,105442900,0.0,0 -2018-04-10,249.22449339327704,250.89374165948544,248.00795676892136,250.0543975830078,103529000,0.0,0 -2018-04-11,248.47010880908607,250.51657888161122,248.394676037909,248.7436065673828,91140200,0.0,0 -2018-04-12,250.1581692473971,251.79909797964058,249.96954417880016,250.79000854492188,68890500,0.0,0 -2018-04-13,252.18573981753076,252.3083432246695,248.97931535914205,250.0543975830078,85079200,0.0,0 -2018-04-16,251.7991213110607,252.93081426721432,250.9220750991227,252.11032104492188,63405300,0.0,0 -2018-04-17,253.99642662077366,255.4487590266592,253.44945998434986,254.80747985839844,64682000,0.0,0 -2018-04-18,255.27903310440803,255.85429066601094,254.50571057687802,254.99612426757812,57303900,0.0,0 -2018-04-19,254.29825500655448,254.5151709857109,252.47814085585657,253.58154296875,77655900,0.0,0 -2018-04-20,253.50612724637944,253.7418942654159,250.48829789065093,251.43136596679688,99953100,0.0,0 -2018-04-23,252.044304276164,252.63844158282023,250.2430413614337,251.39358520507812,65558000,0.0,0 -2018-04-24,252.48756793002545,252.7233348944758,246.4047687351475,248.00799560546875,112885500,0.0,0 -2018-04-25,247.94199213862666,249.09253616076074,245.99927451143864,248.62100219726562,103840900,0.0,0 -2018-04-26,249.71492380993885,252.03486247436817,249.2433899231767,251.14837646484375,67731900,0.0,0 -2018-04-27,251.79913798373786,252.1197776271779,250.38453608495283,251.38418579101562,57053600,0.0,0 -2018-04-30,252.04431850126892,252.6384558414576,249.37542041441284,249.4508819580078,82182300,0.0,0 -2018-05-01,248.84731422024163,250.0072980610463,247.1875055596068,249.89413452148438,74203400,0.0,0 -2018-05-02,249.6866529023294,250.55425920649748,247.80051720439405,248.21546936035156,86368900,0.0,0 -2018-05-03,247.3289969189489,248.36634858590102,244.30172823511725,247.66848754882812,136311500,0.0,0 -2018-05-04,246.63108796341086,251.60107383517024,246.28215747195105,250.87489318847656,91222100,0.0,0 -2018-05-07,251.69540296216377,252.76104550247842,250.95978237906377,251.72369384765625,55304900,0.0,0 -2018-05-08,251.32759267061377,252.11032635600384,250.05444526315705,251.72369384765625,67499200,0.0,0 -2018-05-09,252.440378517887,254.50569927547275,251.88397197673143,254.15676879882812,59666100,0.0,0 -2018-05-10,254.9489779972091,256.8822845009802,254.83581445165157,256.5333251953125,72063900,0.0,0 -2018-05-11,256.66532099244984,257.5989488505708,256.11832558334237,257.3160400390625,59871500,0.0,0 -2018-05-14,257.7781502631647,258.4760112289423,256.8539334537778,257.43865966796875,54790600,0.0,0 -2018-05-15,256.1277433596827,256.1465943521081,254.65656010655533,255.6656494140625,87036100,0.0,0 -2018-05-16,255.7034624690711,257.23122797127826,255.67514280110765,256.7408142089844,53942600,0.0,0 -2018-05-17,256.45784309716765,257.6744085382115,255.69396053143265,256.52386474609375,56536400,0.0,0 -2018-05-18,256.1560966245063,256.5427579144543,255.5053774755874,255.88259887695312,64368000,0.0,0 -2018-05-21,257.46693642538276,258.3817132968891,257.05198431170083,257.8064270019531,58025900,0.0,0 -2018-05-22,258.36286907731903,258.63636682173416,256.74079116792393,257.0897216796875,52966400,0.0,0 -2018-05-23,255.73173536255726,257.82534725386137,255.5619612674744,257.7970275878906,64694200,0.0,0 -2018-05-24,257.3727038667467,257.6650526585709,255.36396432410407,257.2689514160156,76043800,0.0,0 -2018-05-25,256.65594482421875,257.32551503111546,256.1183891687528,256.65594482421875,56374800,0.0,0 -2018-05-29,254.92065181028653,255.73170507471858,252.515840459752,253.70408630371094,115908600,0.0,0 -2018-05-30,255.0998622617287,257.5612556210216,255.02442949438026,257.0897216796875,69678400,0.0,0 -2018-05-31,256.65595102590277,256.9765906930116,254.87356746754014,255.5148468017578,93519900,0.0,0 -2018-06-01,256.90115377555287,258.34404664436283,256.82569221971323,258.0234069824219,71258400,0.0,0 -2018-06-04,258.9004590902947,259.5228874292055,258.64584108898407,259.2493896484375,45385200,0.0,0 -2018-06-05,259.3907871055433,259.8434700107928,258.57032272364665,259.4379577636719,51135000,0.0,0 -2018-06-06,260.0887603493244,261.72024974924744,259.42860116474503,261.6070861816406,62732200,0.0,0 -2018-06-07,262.125700261802,262.43689996510585,260.6073461743888,261.5787048339844,72969400,0.0,0 -2018-06-08,261.08834330003305,262.4086325505472,260.90915810387605,262.35205078125,72139700,0.0,0 -2018-06-11,262.5878557321449,263.46490206327104,262.46525228754956,262.7010192871094,58892500,0.0,0 -2018-06-12,263.14422047498095,263.4271293173209,262.3520469362823,263.0404968261719,72329000,0.0,0 -2018-06-13,263.2950728227908,263.5685705135631,261.984194908522,262.20111083984375,79070600,0.0,0 -2018-06-14,263.1253554395657,263.42711526191204,262.22942951971424,262.8612976074219,77097600,0.0,0 -2018-06-15,262.0238707852297,262.8859195079813,260.8397426895891,262.52593994140625,120041600,1.246,0 -2018-06-18,260.9724325580346,262.1186897319672,260.4609098736572,261.9860534667969,52917600,0.0,0 -2018-06-19,259.5608882730972,261.218667669002,259.11565493610766,260.9818420410156,97531500,0.0,0 -2018-06-20,261.7113032661489,262.13760103959976,261.06714437641386,261.4271240234375,53785500,0.0,0 -2018-06-21,261.4176307545717,261.4365953095443,259.257781872622,259.78826904296875,71061400,0.0,0 -2018-06-22,261.1334635698961,261.25661754536884,260.0251067000251,260.2619323730469,54898500,0.0,0 -2018-06-25,259.03038391085323,259.2008914133857,254.91909475221448,256.7189636230469,137854200,0.0,0 -2018-06-26,257.32528423066486,258.1967864066226,256.5200712635398,257.2873840332031,68547400,0.0,0 -2018-06-27,257.91259868359197,259.4377418958524,254.9948906723592,255.15594482421875,105110700,0.0,0 -2018-06-28,255.0991172346457,257.42947346281045,254.3412578328253,256.61480712890625,76650500,0.0,0 -2018-06-29,257.7799836592947,259.2388377849321,256.86109896390633,256.9842529296875,97592500,0.0,0 -2018-07-02,255.3075532470118,257.7042278589193,255.05176302269956,257.53369140625,63554800,0.0,0 -2018-07-03,258.4904241098698,258.5946424159564,256.16955037035814,256.6242370605469,42187100,0.0,0 -2018-07-05,257.82733567883093,258.7840915511452,256.68107883226014,258.7177734375,56925900,0.0,0 -2018-07-06,258.74618808907,261.30388719059295,258.3388260911773,260.9060363769531,66493700,0.0,0 -2018-07-09,261.9765372472968,263.3122374577844,261.92918367837746,263.2554016113281,50550400,0.0,0 -2018-07-10,263.73852871615793,264.3069160760963,263.42590265896996,264.20269775390625,51966800,0.0,0 -2018-07-11,262.54489795536466,263.38801110780474,261.94809271880683,262.2701721191406,77054700,0.0,0 -2018-07-12,263.6153623520419,264.7047545435547,262.97120353211545,264.6479187011719,60124700,0.0,0 -2018-07-13,264.45847235029044,265.1784026482786,263.9753387864636,264.8563232421875,48216000,0.0,0 -2018-07-16,264.9037760644464,265.0553190763022,264.1459164582273,264.61956787109375,48201000,0.0,0 -2018-07-17,263.79530604870826,266.1067262662139,263.7384702162594,265.6899108886719,52315500,0.0,0 -2018-07-18,265.7752500050585,266.3625730685969,265.30159864292955,266.2489013671875,44593500,0.0,0 -2018-07-19,265.53838194388493,265.9457151120528,264.7331689749013,265.2447204589844,61412100,0.0,0 -2018-07-20,265.0268125945942,265.69941819088643,264.77105132448344,264.9415588378906,82337700,0.0,0 -2018-07-23,264.72369670491526,265.6520346582302,264.35423485129775,265.4341735839844,47047600,0.0,0 -2018-07-24,266.9404033796979,267.66981596238264,265.8415289125571,266.7698669433594,68026900,0.0,0 -2018-07-25,266.50463351946524,269.384441580567,266.4572799521904,269.0434265136719,78882900,0.0,0 -2018-07-26,268.2760840868909,269.13810393617183,268.17186577725715,268.40869140625,57919500,0.0,0 -2018-07-27,268.7591761127074,268.86339441745554,265.6046716025263,266.5898742675781,76768700,0.0,0 -2018-07-30,266.6751161371915,266.8456236362242,264.63839278658924,265.19732666015625,63742500,0.0,0 -2018-07-31,266.01202124587087,267.1582492130158,265.6046880960544,266.5046081542969,68570500,0.0,0 -2018-08-01,266.7225411665967,267.26251063426787,265.3679052678852,266.0594177246094,53853300,0.0,0 -2018-08-02,264.6668511331677,267.688719417706,264.44896115755745,267.5087585449219,63426400,0.0,0 -2018-08-03,267.6413477485271,268.71180409335796,267.4518757034795,268.65496826171875,53935400,0.0,0 -2018-08-06,268.69289525293163,269.9717305017449,268.2760798397526,269.64019775390625,39400900,0.0,0 -2018-08-07,270.3507092644034,270.93803226474944,270.2085907435479,270.5306701660156,43196600,0.0,0 -2018-08-08,270.35067246651136,270.84325935570075,269.9243747807325,270.4169616699219,42114600,0.0,0 -2018-08-09,270.4833280343667,270.90014351856667,269.9054872868158,270.0475769042969,35717000,0.0,0 -2018-08-10,268.5128849786356,269.0907255836419,267.48029989110233,268.2381591796875,77076000,0.0,0 -2018-08-13,268.53193109511426,269.1855723525781,266.92150474003785,267.234130859375,65732900,0.0,0 -2018-08-14,268.0109013277421,269.1950297412848,267.5940858134242,268.9392395019531,43842000,0.0,0 -2018-08-15,267.49929550823066,267.6508673835508,265.39628271590516,266.930908203125,102925400,0.0,0 -2018-08-16,268.4655056803192,270.0190955582242,268.42760548809866,269.0907287597656,69967900,0.0,0 -2018-08-17,268.8728932928818,270.5117372534515,268.43714217683345,270.0380859375,65618500,0.0,0 -2018-08-20,270.5212917616694,270.90020713372695,270.0381580393969,270.61602783203125,39807500,0.0,0 -2018-08-21,271.16532608268176,272.16946436608066,270.6537746654899,271.2505798339844,67272000,0.0,0 -2018-08-22,270.8148737588763,271.64850467805775,270.53066563131097,271.089599609375,44993300,0.0,0 -2018-08-23,270.90012178511057,271.81900645027997,270.3885702938357,270.7296142578125,49204900,0.0,0 -2018-08-24,271.3453575568359,272.5105501557723,271.2885217139006,272.3589782714844,57487400,0.0,0 -2018-08-27,273.63777687114805,274.62297953192177,273.46726936525755,274.5093078613281,57072400,0.0,0 -2018-08-28,275.00194901581557,275.1156496182281,274.14938240870964,274.6419982910156,46943500,0.0,0 -2018-08-29,274.86930470267504,276.3660299752042,274.6135434277622,276.1197509765625,61485500,0.0,0 -2018-08-30,275.6082407093951,276.0060916409441,274.367276526306,275.001953125,61229500,0.0,0 -2018-08-31,274.56618019895035,275.4850648709136,274.0451753363384,275.01141357421875,66140800,0.0,0 -2018-09-04,274.56619868505857,274.9166960427102,273.4673241336153,274.53778076171875,57594400,0.0,0 -2018-09-05,274.1588993574415,274.37678940289226,272.71900954799935,273.7989196777344,72452400,0.0,0 -2018-09-06,273.9125211828548,274.2346005744175,271.8758265221431,272.9747009277344,65909900,0.0,0 -2018-09-07,271.8568604713126,273.4862217995113,271.6010703144328,272.4441833496094,73524800,0.0,0 -2018-09-10,273.524105226036,273.80831331192377,272.70943898045397,272.9178466796875,50210900,0.0,0 -2018-09-11,272.22637893945426,274.2914919377701,271.8569459102878,273.8178405761719,50530500,0.0,0 -2018-09-12,273.82728891226344,274.52828358953417,273.04104046959884,273.8841247558594,59810800,0.0,0 -2018-09-13,275.0208626791768,275.7029216205252,274.7177189476047,275.50396728515625,51034200,0.0,0 -2018-09-14,275.7218685989875,275.92079403315785,274.71773024257527,275.5513610839844,55079900,0.0,0 -2018-09-17,275.494516870808,275.53238815562725,273.7988373203701,274.0924987792969,68244000,0.0,0 -2018-09-18,274.3199051947097,276.2145106088601,274.29148726987285,275.579833984375,61930400,0.0,0 -2018-09-19,275.6366484626871,276.3187075152735,275.504012217476,275.87347412109375,49080600,0.0,0 -2018-09-20,277.2186624439872,278.45014432999403,275.892415601355,278.1091003417969,100360600,0.0,0 -2018-09-21,278.9017361269234,279.025447573832,277.68370109929776,277.85498046875,105479700,1.323,0 -2018-09-24,277.23649237937843,277.38875040988796,276.3134480345863,276.9319763183594,53409600,0.0,0 -2018-09-25,277.41728855417085,277.53147481352426,276.41812974457997,276.675048828125,44370000,0.0,0 -2018-09-26,276.8273360360989,278.0929392643458,275.39994956321414,275.8471984863281,79739700,0.0,0 -2018-09-27,276.35150615927785,277.7788924545558,276.0565153148069,276.6179504394531,59249500,0.0,0 -2018-09-28,275.95181262185326,277.1793729110245,275.9137698980281,276.646484375,70091400,0.0,0 -2018-10-01,277.9692462189158,278.74955784548604,276.89397273186654,277.607666015625,62078900,0.0,0 -2018-10-02,277.44580078125,278.20706177622185,277.0461489229757,277.44580078125,47258200,0.0,0 -2018-10-03,278.5686957071096,279.01594456753077,277.21745296472955,277.59808349609375,64694600,0.0,0 -2018-10-04,277.0841642341223,277.141257351085,273.73457553125917,275.42840576171875,111545900,0.0,0 -2018-10-05,275.66637220533363,276.21828215375035,272.36435059603497,273.88690185546875,105951700,0.0,0 -2018-10-08,273.15415827123036,274.2675324102647,271.67920398130224,273.88690185546875,87742200,0.0,0 -2018-10-09,273.47768645681543,274.8764970158451,272.8876757831772,273.4871826171875,74339000,0.0,0 -2018-10-10,272.9447972544705,273.0209407868302,264.4280763674668,264.8277282714844,214731000,0.0,0 -2018-10-11,263.666792577964,265.3986949776344,257.27210059785347,258.9945068359375,274840500,0.0,0 -2018-10-12,263.3718046893944,263.67632074907874,259.1848105096094,262.5915222167969,183186500,0.0,0 -2018-10-15,262.21083975205954,263.6287298261871,261.02135122289894,261.11651611328125,102263700,0.0,0 -2018-10-16,263.2100164445791,267.22573071134497,262.7056744910864,266.8260498046875,118255800,0.0,0 -2018-10-17,266.86413202547834,267.53975337676724,264.12354581598606,266.8736572265625,110626000,0.0,0 -2018-10-18,265.8744561567684,266.5120347523592,261.65891600221784,263.0196838378906,134557500,0.0,0 -2018-10-19,263.7143882731102,265.77932424407066,262.1347439091973,262.8769836425781,139901600,0.0,0 -2018-10-22,263.59064733527987,263.9332060784192,261.12603068798535,261.6969909667969,82415800,0.0,0 -2018-10-23,257.8335401090627,261.56375980036256,255.60679207099736,260.36474609375,146352700,0.0,0 -2018-10-24,260.0983123839023,260.5075185170506,251.88610780121797,252.47608947753906,177806700,0.0,0 -2018-10-25,254.43636786349072,258.6519083882269,253.34204418653923,257.0056457519531,138061500,0.0,0 -2018-10-26,253.04705987839,257.88112877664105,249.5927803335767,252.485595703125,201574600,0.0,0 -2018-10-29,255.78762285393796,257.1674412780049,247.27090170477362,251.08676147460938,160749100,0.0,0 -2018-10-30,250.90595042654493,255.1405119174707,250.3825580803099,254.8074493408203,157116000,0.0,0 -2018-10-31,257.5480435782613,260.00316409875865,257.0437016171121,257.5290222167969,128296300,0.0,0 -2018-11-01,258.4520807935222,260.47897395144594,257.2911387943487,260.2696228027344,99495000,0.0,0 -2018-11-02,261.44959383416773,261.9063679102785,256.53938141691094,258.7280578613281,122634100,0.0,0 -2018-11-05,259.25143354944635,260.74543826916215,258.2142029275498,260.15545654296875,65622500,0.0,0 -2018-11-06,260.08881437037195,261.97294564065544,260.02219604216555,261.8016662597656,60085900,0.0,0 -2018-11-07,264.12355511337756,267.4921948678116,263.66678104896755,267.40655517578125,102752100,0.0,0 -2018-11-08,266.55006928391316,267.60635011036896,265.70316862293026,266.92120361328125,65584900,0.0,0 -2018-11-09,265.5224138251396,265.72223978750543,262.8103738891742,264.31390380859375,98812600,0.0,0 -2018-11-12,263.77143606714867,264.02835510907425,258.82315266827226,259.3750915527344,99673600,0.0,0 -2018-11-13,259.8699690598935,262.0015234950888,258.1190451966615,258.88983154296875,98176600,0.0,0 -2018-11-14,260.8881958481174,261.3163943802081,255.45461929017623,257.1199035644531,125335900,0.0,0 -2018-11-15,255.76860848443548,260.298190594918,254.08430299624575,259.8033447265625,135101400,0.0,0 -2018-11-16,258.6329151208357,261.4496162308513,258.0809761015564,260.47900390625,126668000,0.0,0 -2018-11-19,259.83185582829185,260.14589705423094,255.09295223908876,256.0730895996094,103061700,0.0,0 -2018-11-20,252.51413082668293,254.07475374028675,250.4111232060196,251.33416748046875,136021300,0.0,0 -2018-11-21,252.9899150591185,254.21747531710076,252.18108611479917,252.19058227539062,75563700,0.0,0 -2018-11-23,250.4396682931685,252.0002911975722,250.33500724092676,250.50628662109375,42807900,0.0,0 -2018-11-26,252.91379978392172,254.7884348076081,252.49509751181182,254.550537109375,79981400,0.0,0 -2018-11-27,253.44670756270426,255.406982421875,252.79963274400197,255.406982421875,75502400,0.0,0 -2018-11-28,256.5488473503677,261.2877502441406,255.3403086282671,261.2877502441406,127629600,0.0,0 -2018-11-29,260.4599098083278,262.21083339597953,259.24187474560284,260.71685791015625,82346400,0.0,0 -2018-11-30,260.5550761583832,262.90550660982746,260.2125174076853,262.3059997558594,98204200,0.0,0 -2018-12-03,266.7118356819919,266.8260219176039,264.0759399424706,265.7792663574219,103176300,0.0,0 -2018-12-04,264.89433225542723,265.35110630409986,256.83435683811877,257.16741943359375,177986000,0.0,0 -2018-12-06,253.04703260791402,256.9009636449156,249.73548624409517,256.7772521972656,204185400,0.0,0 -2018-12-07,256.41561445764427,258.0904233339721,249.91626294275747,250.81076049804688,161018900,0.0,0 -2018-12-10,250.62046539728848,252.32382106767847,246.10040907839223,251.28659057617188,151445900,0.0,0 -2018-12-11,254.7028188743406,254.90264482822292,249.77358507691653,251.3437042236328,121504400,0.0,0 -2018-12-12,254.52199023156635,255.97792298133731,252.5236437608375,252.60928344726562,97976700,0.0,0 -2018-12-13,253.61800540724468,254.54104977531532,251.33419297250566,252.523681640625,96662700,0.0,0 -2018-12-14,250.2303222897306,251.24853147094504,247.27088867957713,247.86087036132812,116961100,0.0,0 -2018-12-17,246.84269696709998,248.0321857044513,241.25686250293845,242.99827575683594,165492300,0.0,0 -2018-12-18,244.7491787445236,245.46287188350615,241.01892982706548,242.73179626464844,134515100,0.0,0 -2018-12-19,242.81743388946367,246.84265909342508,237.2791827285689,239.09671020507812,214992800,0.0,0 -2018-12-20,237.76449365928315,239.43928835126567,232.80669887317052,235.2047119140625,252053400,0.0,0 -2018-12-21,236.16666393602424,239.00939419688098,229.69633517303475,230.38548278808594,255345600,1.435,0 -2018-12-24,228.79661129786385,230.5194803161667,224.23102665550738,224.2980194091797,147311600,0.0,0 -2018-12-26,225.85817458752993,235.63064575195312,223.7428714573097,235.63064575195312,218485400,0.0,0 -2018-12-27,232.1753537373754,237.6502258319504,228.72004952253798,237.43966674804688,186267300,0.0,0 -2018-12-28,238.88492862988585,240.62692987785215,235.88905160626118,237.1333465576172,153100200,0.0,0 -2018-12-31,238.86579913764763,239.4688069164015,236.86536377008522,239.2103729248047,144299400,0.0,0 -2019-01-02,235.4392106947059,240.4451041817098,235.41049742922849,239.459228515625,126925200,0.0,0 -2019-01-03,237.59277955392207,237.91822088545382,233.2281881936798,233.74505615234375,144140700,0.0,0 -2019-01-04,236.98018179367654,242.26364088062783,236.57818153558114,241.57449340820312,142628800,0.0,0 -2019-01-07,241.86168117845273,244.981977764934,240.9045333734677,243.47926330566406,103139100,0.0,0 -2019-01-08,245.81472062206598,246.2837137267129,243.11555664423815,245.766845703125,102512600,0.0,0 -2019-01-09,246.52298642682496,247.81514180610665,245.21169860697478,246.91542053222656,95006600,0.0,0 -2019-01-10,245.27868510731494,248.05440766503858,244.5512434899057,247.78640747070312,96823900,0.0,0 -2019-01-11,246.63781629855066,247.9108391175615,246.015676125495,247.8821258544922,73858100,0.0,0 -2019-01-14,245.85297452878564,247.23126972359336,245.42227553655405,246.36984252929688,70908200,0.0,0 -2019-01-15,246.77186801290523,249.52845852034474,246.76228718716834,249.19345092773438,85208300,0.0,0 -2019-01-16,249.6528369634964,250.7439994287581,249.43271167336331,249.7964324951172,77636700,0.0,0 -2019-01-17,248.86803582383982,252.6104876330233,248.8201609027464,251.6916046142578,96118400,0.0,0 -2019-01-18,253.6250505785959,255.53934629950356,251.72988729935685,255.04161071777344,127900300,0.0,0 -2019-01-22,253.47188642470468,253.70159254866857,249.8730013634961,251.59585571289062,115531200,0.0,0 -2019-01-23,252.69660899547807,253.44318313758697,249.49015790968755,252.122314453125,86030300,0.0,0 -2019-01-24,251.93084464082634,252.8784407517212,250.8492630374783,252.2562713623047,59204100,0.0,0 -2019-01-25,254.22803433720534,255.27135123085978,252.3616134974653,254.39076232910156,96883400,0.0,0 -2019-01-28,252.1031963805989,252.52431457850338,250.57175391698402,252.45733642578125,85613700,0.0,0 -2019-01-29,252.6104691956002,253.21344779474546,251.23217397788625,252.122314453125,66136300,0.0,0 -2019-01-30,253.73987865991322,257.0133076627899,252.92629721668223,256.11358642578125,92473700,0.0,0 -2019-01-31,256.0466335735802,258.8797830626155,255.81689822431517,258.3629150390625,104012100,0.0,0 -2019-02-01,258.5734750027893,259.57849772908514,257.645040458792,258.4873352050781,85782500,0.0,0 -2019-02-04,258.53514861645584,260.37288503422957,257.8172878487024,260.3058776855469,60744800,0.0,0 -2019-02-05,260.7653431698527,261.72249094856755,260.229342750559,261.3970642089844,79552800,0.0,0 -2019-02-06,261.10033462867875,261.62675419056745,260.26762078613507,261.0524597167969,58347800,0.0,0 -2019-02-07,259.3296715305057,259.9135177674354,256.7932352695599,258.56396484375,95482000,0.0,0 -2019-02-08,257.23346539526455,258.98503297707174,256.35287658698064,258.8797607421875,75788900,0.0,0 -2019-02-11,259.5784774736161,259.8560292857496,258.4586017710917,259.0233154296875,68021400,0.0,0 -2019-02-12,260.74624656798267,262.75623381168,260.66965837765287,262.354248046875,72270200,0.0,0 -2019-02-13,263.24434877687287,264.1057759238559,262.79448815869495,263.2060546875,65277200,0.0,0 -2019-02-14,262.047897916947,263.828207840147,261.1768899988532,262.6221923828125,83234400,0.0,0 -2019-02-15,264.51731083190424,265.5223333914358,264.29718556644576,265.4840393066406,97088700,0.0,0 -2019-02-19,264.6322242003087,266.64221113603867,264.6226433755264,265.9435119628906,59120800,0.0,0 -2019-02-20,265.90522818267647,266.96767743059445,265.369227751057,266.4795227050781,76610800,0.0,0 -2019-02-21,265.79998266462525,266.1828359814348,264.5078271617869,265.531982421875,64214700,0.0,0 -2019-02-22,266.19236289004516,267.3887976501705,265.5127961250472,267.1782531738281,78114600,0.0,0 -2019-02-25,268.7001316218115,269.2552645331776,267.45582186694276,267.5419616699219,69030700,0.0,0 -2019-02-26,267.1686949841882,268.2885416570369,266.9485404541329,267.3505554199219,56844100,0.0,0 -2019-02-27,266.5847131987411,267.6088680418371,265.5893008222929,267.235595703125,56921600,0.0,0 -2019-02-28,267.0058541312073,267.4748762613514,266.39329493015765,266.73785400390625,69268300,0.0,0 -2019-03-01,268.4225443066167,268.84369169570243,266.87196946945306,268.4034118652344,78880500,0.0,0 -2019-03-04,269.53283038150784,269.7912497758602,264.9767974377411,267.4270935058594,106494600,0.0,0 -2019-03-05,267.5610900161118,267.7716636893495,266.47950838583773,267.0633544921875,59114600,0.0,0 -2019-03-06,267.1877861633369,267.19736698789654,265.10121109749514,265.4457702636719,75039800,0.0,0 -2019-03-07,264.96720648273003,265.1203536323618,262.3254984786213,263.2252197265625,94885100,0.0,0 -2019-03-08,261.2438765860361,262.8805908553177,260.746170333751,262.69873046875,85795800,0.0,0 -2019-03-11,263.4644865379852,266.6804888049993,263.4357732752379,266.5082092285156,65098900,0.0,0 -2019-03-12,267.10167806164407,268.0684067422764,266.90068519086117,267.51324462890625,79667500,0.0,0 -2019-03-13,268.4608633784869,270.27943861338633,268.28855455067617,269.28399658203125,80639200,0.0,0 -2019-03-14,269.31262612593144,269.7624866840099,268.6426403054882,269.11163330078125,67518400,0.0,0 -2019-03-15,269.7010445171483,271.30650584699924,269.4991369685932,270.4412841796875,81309000,1.233,0 -2019-03-18,270.6719818484837,271.73911098437446,270.4316408845485,271.4218444824219,62199800,0.0,0 -2019-03-19,272.556264833853,273.3734006065709,270.53739495276596,271.4891357421875,90268100,0.0,0 -2019-03-20,271.2583831439158,272.54660697194726,269.48947746937296,270.67193603515625,84609200,0.0,0 -2019-03-21,269.79718162518776,274.1617526502856,269.74909582661724,273.7291564941406,79550400,0.0,0 -2019-03-22,272.27746147500653,272.8350395800324,268.3935434388242,268.4608459472656,122659300,0.0,0 -2019-03-25,268.09549690844057,269.36450406484147,266.9130383050378,268.2589416503906,85575200,0.0,0 -2019-03-26,270.1336239599952,271.2776493103295,268.75888066959243,270.25860595703125,68125900,0.0,0 -2019-03-27,270.2489809719605,270.87389095261307,267.191850934216,268.84539794921875,72224700,0.0,0 -2019-03-28,269.51833353241517,270.34509227601876,268.28778910434676,269.8644104003906,56238500,0.0,0 -2019-03-29,271.47949031516185,271.9120863467743,270.2777857743111,271.5660095214844,82186800,0.0,0 -2019-04-01,273.7003045687803,275.1038875993006,273.4118778056892,274.78662109375,77617900,0.0,0 -2019-04-02,274.98850611967634,275.1711675864662,274.0751987857271,274.92120361328125,40070400,0.0,0 -2019-04-03,276.21907292977625,276.6420753777573,274.709724619412,275.3538513183594,68243200,0.0,0 -2019-04-04,275.6999024959871,276.3536228224006,274.9596629503565,276.0844421386719,48997500,0.0,0 -2019-04-05,276.7958348276566,277.4783948039126,276.4881914186293,277.42071533203125,58621700,0.0,0 -2019-04-08,276.9688893622849,277.74759161649195,276.2670832955647,277.6322326660156,53566300,0.0,0 -2019-04-09,276.6036048331425,276.94968173258246,275.62302427956666,276.2094421386719,66142300,0.0,0 -2019-04-10,276.65168331558147,277.24775358734485,276.2094641293685,277.151611328125,52601500,0.0,0 -2019-04-11,277.6706562605371,277.68027928556677,276.4689516714645,277.0746154785156,55093100,0.0,0 -2019-04-12,278.79551908226824,279.2473612694931,277.12275535609353,278.9493408203125,69727800,0.0,0 -2019-04-15,279.0261897161201,279.1319549786872,277.91100430823144,278.7666320800781,49596700,0.0,0 -2019-04-16,279.70882648362607,279.7665059680806,278.31483715281604,278.9493408203125,52153200,0.0,0 -2019-04-17,280.14136517015675,280.17020490647,277.82447522468806,278.2667236328125,58268300,0.0,0 -2019-04-18,278.8916838452604,279.10318508482953,277.5073174296259,278.81475830078125,68708500,0.0,0 -2019-04-22,277.99754662236097,279.2184679269359,277.9014043872272,279.0550231933594,40160100,0.0,0 -2019-04-23,279.449197080177,281.8141729380963,279.19926246494794,281.564208984375,52246600,0.0,0 -2019-04-24,281.47769239195253,281.8333922749499,280.7855093385414,280.9393310546875,50392900,0.0,0 -2019-04-25,280.8335683715407,281.46807195454295,279.49728804162885,280.7662658691406,57770900,0.0,0 -2019-04-26,280.81437673349615,282.1506571827283,279.98758859509377,282.0737609863281,50916400,0.0,0 -2019-04-29,282.16985334542716,283.0735375204498,282.0737111130543,282.51593017578125,57197700,0.0,0 -2019-04-30,282.1506346413811,282.9677996880134,280.6413158669583,282.66015625,81111700,0.0,0 -2019-05-01,283.3330941841242,283.5542183839843,280.52589943618574,280.5355224609375,71671900,0.0,0 -2019-05-02,280.4105524477085,281.3911621825119,278.33400336861405,279.92987060546875,65030200,0.0,0 -2019-05-03,281.50655780967924,282.9678202386277,280.0452660422356,282.6697998046875,56543700,0.0,0 -2019-05-06,278.0744552494673,281.97758959491284,277.7379720806891,281.50653076171875,107198100,0.0,0 -2019-05-07,278.9396483307083,279.5741518401981,274.7673337426898,276.805419921875,144729900,0.0,0 -2019-05-08,276.4209289550781,278.24751428566935,275.78642533400006,276.4209289550781,91568300,0.0,0 -2019-05-09,274.2098209474803,276.2286615576348,272.3543664234438,275.5845642089844,103471100,0.0,0 -2019-05-10,274.58470992306155,277.77644461827794,271.3929752278452,276.9689025878906,112429300,0.0,0 -2019-05-13,271.5083603829732,272.5369972876065,269.11454471999303,270.00860595703125,127290500,0.0,0 -2019-05-14,271.09494361687257,274.08480001564584,270.9603679532788,272.4504699707031,77003200,0.0,0 -2019-05-15,270.7104227180747,274.72891626875247,270.4892984872836,274.0463562011719,73956400,0.0,0 -2019-05-16,274.7961988326606,278.03598983515224,274.7193026449407,276.5843505859375,76749600,0.0,0 -2019-05-17,274.12326660892603,277.4495768824048,274.1040205577922,274.79620361328125,100353000,0.0,0 -2019-05-20,273.08501450610294,274.9115999642539,272.1813301346551,272.9792785644531,62877600,0.0,0 -2019-05-21,274.7865580406231,275.8440639189726,274.5173773932696,275.4403076171875,46847100,0.0,0 -2019-05-22,274.42129140197,275.61337305161527,274.0848082251689,274.5943298339844,49482500,0.0,0 -2019-05-23,272.2197466641695,272.2678031133935,269.72981822143987,271.2391662597656,98733800,0.0,0 -2019-05-24,272.77736272853485,273.21961121241867,271.1911182825919,271.8544616699219,55268100,0.0,0 -2019-05-28,272.15246158841455,273.17150482053455,269.30683309146593,269.3260498046875,70029400,0.0,0 -2019-05-29,268.1339942202538,268.5665903485203,266.01898202040724,267.5187072753906,104972900,0.0,0 -2019-05-30,268.3262553314227,269.22034602664155,267.0764940374135,268.2493591308594,62523800,0.0,0 -2019-05-31,265.52870370341145,266.41314200452194,264.60577329551876,264.6346130371094,86862800,0.0,0 -2019-06-03,264.6730697571849,265.865151500352,262.5388409127597,263.961669921875,96428000,0.0,0 -2019-06-04,266.4131217742404,269.83557442942526,265.9324398918999,269.6913757324219,77231900,0.0,0 -2019-06-05,271.42180199185174,272.05630555793005,269.4894808944912,272.0274658203125,71169700,0.0,0 -2019-06-06,272.34474739341385,274.5174095377128,271.6525643136698,273.79638671875,69430400,0.0,0 -2019-06-07,274.88273852554335,277.6899336715843,274.7000770602961,276.5362854003906,74272200,0.0,0 -2019-06-10,278.1898375030219,279.58382674536915,277.70915560407394,277.8052978515625,60799100,0.0,0 -2019-06-11,279.7472227517949,280.1413854022035,277.04579301421944,277.73797607421875,58641300,0.0,0 -2019-06-12,277.48811321174674,278.08415418506223,276.6997877070712,277.2477722167969,47096300,0.0,0 -2019-06-13,278.2186851854904,278.77629263374473,277.46882257526556,278.3917236328125,48945200,0.0,0 -2019-06-14,278.08404541015625,278.7281425951175,277.2668804792677,278.08404541015625,52324700,0.0,0 -2019-06-17,278.3340180285061,279.0069843784834,278.0071578791863,278.1898193359375,39205700,0.0,0 -2019-06-18,280.1318256671139,282.22759181388574,279.7472566572323,281.102783203125,85434800,0.0,0 -2019-06-19,281.2469387768916,282.3044447148806,280.20867888954274,281.73724365234375,78674400,0.0,0 -2019-06-20,284.6021143919417,284.86167202582806,281.80454253942315,284.4290466308594,116570000,0.0,0 -2019-06-21,284.1411843262233,285.4839638134585,283.78375445151715,284.0155944824219,83309500,1.432,0 -2019-06-24,284.2378064343848,284.5758966669855,283.5036069965851,283.6678466796875,47582700,0.0,0 -2019-06-25,283.72574743233315,283.7547274313945,280.76966960336415,280.8855895996094,82028700,0.0,0 -2019-06-26,281.8420593203217,282.3830391493823,280.4896097476704,280.60552978515625,51584900,0.0,0 -2019-06-27,281.4170055295921,282.1415352605181,281.01128538974,281.6005554199219,40355200,0.0,0 -2019-06-28,282.64381100372145,283.5808704550865,282.0931909196206,283.049560546875,59350900,0.0,0 -2019-07-01,286.6045818202502,286.8364513393953,284.3343831779401,285.6192321777344,78705600,0.0,0 -2019-07-02,285.57096906729413,286.42108868307724,284.6725592611014,286.3631286621094,61504500,0.0,0 -2019-07-03,287.0876389855511,288.67195806801135,286.933069113029,288.6526184082031,40898900,0.0,0 -2019-07-05,287.338807980658,288.4980672406781,285.9573784906519,288.32415771484375,51677300,0.0,0 -2019-07-08,286.92341803824655,288.1309674835694,286.16023853407,286.7398681640625,45841800,0.0,0 -2019-07-09,285.503270015843,287.41600900904666,285.44531001490833,287.09722900390625,41101300,0.0,0 -2019-07-10,288.2372151183228,289.4834143895366,287.6672553229459,288.46905517578125,58448500,0.0,0 -2019-07-11,289.15490298169675,289.40605316346466,288.0729436354258,289.1452331542969,50826100,0.0,0 -2019-07-12,289.666968960172,290.5170884928908,289.338519046383,290.4397888183594,40326000,0.0,0 -2019-07-15,290.90341556446316,290.90341556446316,289.99533627508634,290.53631591796875,33900000,0.0,0 -2019-07-16,290.43978440713744,290.66198411655574,289.27088480799637,289.5993347167969,36650100,0.0,0 -2019-07-17,289.57034996920976,289.74423000963634,287.62860107421875,287.62860107421875,36036300,0.0,0 -2019-07-18,287.0972900918945,289.08732916390125,286.6239401564831,288.68157958984375,51392600,0.0,0 -2019-07-19,289.85043944733525,289.87941944705017,286.894361551699,287.0779113769531,58678600,0.0,0 -2019-07-22,287.50295191080164,288.36274106863976,286.95233189636804,287.7831115722656,43638100,0.0,0 -2019-07-23,288.98106051646994,289.8408203125,288.0922912344716,289.8408203125,44564500,0.0,0 -2019-07-24,289.0293833641376,291.2029724121094,288.93277351021226,291.2029724121094,47213200,0.0,0 -2019-07-25,290.7199386479644,290.7778986612956,288.9520697978015,289.8118591308594,55394100,0.0,0 -2019-07-26,290.54605168992583,291.9661309451017,290.4107920079157,291.75360107421875,45084100,0.0,0 -2019-07-29,291.62800908335413,291.7535989353897,290.6329895772387,291.2222595214844,38126500,0.0,0 -2019-07-30,289.72489565609453,290.9421147998836,289.3191461144367,290.50738525390625,45849000,0.0,0 -2019-07-31,290.76826135215026,290.97115090268795,285.1749130718072,287.32916259765625,104245200,0.0,0 -2019-08-01,287.4933659008826,290.65230450544846,283.97696791114663,284.82708740234375,142646600,0.0,0 -2019-08-02,283.87076505099037,284.1315851506143,281.02093621067814,282.6825256347656,116749700,0.0,0 -2019-08-05,278.3062824864292,278.4222024954732,272.15261632982345,274.1813049316406,178745400,0.0,0 -2019-08-06,276.2003845748526,278.2580536324012,274.62573531555716,278.02618408203125,120711700,0.0,0 -2019-08-07,274.74163104006794,279.01153865053277,272.4617919450118,278.1903991699219,140572300,0.0,0 -2019-08-08,279.7843708377664,283.6485290527344,279.1951008609538,283.6485290527344,87713900,0.0,0 -2019-08-09,282.64382031745004,283.28140991702446,279.8133316941948,281.7164306640625,93730000,0.0,0 -2019-08-12,280.1128786612584,281.7068383582442,277.27271942756283,278.2870788574219,65527600,0.0,0 -2019-08-13,277.968190113332,284.16050670834215,277.6010903945074,282.6148376464844,94299800,0.0,0 -2019-08-14,278.28700361148674,278.93423356183854,274.12337575833106,274.25860595703125,135622100,0.0,0 -2019-08-15,275.20532346621155,275.93952288281275,272.79989466765204,274.9831237792969,99556600,0.0,0 -2019-08-16,276.75101834832657,279.5042074555195,275.041109492615,279.04052734375,83018300,0.0,0 -2019-08-19,282.26711353600103,283.1268734511641,281.54258383483693,282.40234375,53571800,0.0,0 -2019-08-20,281.86131195859923,282.43127168067616,280.1031426136708,280.2383728027344,51596400,0.0,0 -2019-08-21,282.54719899532097,282.91426918321156,281.8129996571772,282.5182189941406,49524700,0.0,0 -2019-08-22,283.271814701557,283.9480247619198,280.53790600653656,282.43133544921875,51666400,0.0,0 -2019-08-23,281.04022800145475,282.8177371670491,273.84322183949206,275.1763610839844,149161500,0.0,0 -2019-08-26,277.51420182344737,278.21942138671875,275.8815924436203,278.21942138671875,72423800,0.0,0 -2019-08-27,279.70708501813897,280.10316475078696,276.31627693996677,277.12774658203125,66668900,0.0,0 -2019-08-28,276.4225539984372,279.2530426304842,275.5627647008117,279.07916259765625,59696700,0.0,0 -2019-08-29,281.8130799386555,283.20417935396387,280.7407606271434,282.64385986328125,57899400,0.0,0 -2019-08-30,284.2280980261434,284.24740819986994,281.5231996453738,282.5182189941406,62901200,0.0,0 -2019-09-03,280.7020922918931,281.67777209693855,279.44622330820584,280.8663024902344,69101400,0.0,0 -2019-09-04,283.1848914563834,284.07363146980936,282.38306199691425,284.0543212890625,46887300,0.0,0 -2019-09-05,286.7108825553499,288.6815819851615,284.01562399131217,287.7059020996094,83258100,0.0,0 -2019-09-06,288.0440195761339,288.61397936098126,287.319489928774,287.9280700683594,49584300,0.0,0 -2019-09-09,288.98107755855284,289.0776579260698,287.0683086864936,288.072998046875,51260300,0.0,0 -2019-09-10,287.2614713455819,288.0729704118953,285.91869186533137,288.0053405761719,57947100,0.0,0 -2019-09-11,288.33382602227385,290.140315266005,287.6382763661711,290.0533752441406,68821100,0.0,0 -2019-09-12,291.0193945994959,292.1882941205827,290.2079249705541,291.05804443359375,72908700,0.0,0 -2019-09-13,291.53143786612355,291.9082074721536,290.4687883786936,290.8648681640625,62104800,0.0,0 -2019-09-16,289.6572299669717,290.9130987881379,289.2804899574484,289.96636962890625,58191200,0.0,0 -2019-09-17,289.7538932247927,290.79720292472376,289.5703433592477,290.70062255859375,41475500,0.0,0 -2019-09-18,290.28521226846715,290.990431746691,288.1116233010123,290.87451171875,73375800,0.0,0 -2019-09-19,291.2898931475013,292.3525425079359,290.4977336714331,290.85516357421875,76560500,0.0,0 -2019-09-20,291.4995547909268,291.80043723394266,288.63659570498044,289.4809265136719,89565000,1.384,0 -2019-09-23,288.7724603931848,290.1796982012936,288.5007213922715,289.4129943847656,43476800,0.0,0 -2019-09-24,290.5776403597218,290.99494858447133,286.113330874898,287.1420593261719,94869400,0.0,0 -2019-09-25,287.22935846482795,289.3159288980625,285.64743769748725,288.84039306640625,71854000,0.0,0 -2019-09-26,288.8501286771311,289.0733248835009,286.7344442549152,288.23870849609375,56179700,0.0,0 -2019-09-27,289.0441920107594,289.16067696588664,285.02633477007737,286.6858825683594,84746600,0.0,0 -2019-09-30,287.2390532897808,288.7724311502273,287.19054010848146,288.01544189453125,51662400,0.0,0 -2019-10-01,288.956883496958,289.65564522085214,284.3567194247659,284.5896301269531,88242400,0.0,0 -2019-10-02,282.9009638579745,282.9106783428572,278.1843445087715,279.56243896484375,122539500,0.0,0 -2019-10-03,279.319791605972,281.8819275557079,276.4180041202906,281.8528137207031,85278800,0.0,0 -2019-10-04,282.55160153158243,285.93863965907235,282.49334423885097,285.6669006347656,65091200,0.0,0 -2019-10-07,284.8128356532726,286.5500401627699,284.1334733305576,284.434326171875,59610500,0.0,0 -2019-10-08,282.4545516085538,283.2406548606821,279.9797568815328,280.0185852050781,95708100,0.0,0 -2019-10-09,282.173084662263,283.6773489330484,281.5034367953117,282.677734375,62359400,0.0,0 -2019-10-10,282.59040095362633,285.5310170380697,282.4157179269859,284.5896301269531,55296300,0.0,0 -2019-10-11,287.53023302517585,289.92737094969397,287.4040928121642,287.5399475097656,98720400,0.0,0 -2019-10-14,287.2002670756926,287.9184577385611,286.85090103753856,287.2196960449219,40394800,0.0,0 -2019-10-15,288.33571215298,290.8590194436506,288.20954234584764,290.0632019042969,46754500,0.0,0 -2019-10-16,289.56829827214807,290.3350021422327,289.13159071374434,289.597412109375,48928200,0.0,0 -2019-10-17,290.83967144572597,291.38314952211533,289.7138869682526,290.45147705078125,45736600,0.0,0 -2019-10-18,289.8788458917521,290.56789307079663,288.26781123436433,289.1800842285156,64304000,0.0,0 -2019-10-21,290.5872791065818,291.35395319390096,290.12142819414095,291.14044189453125,39048600,0.0,0 -2019-10-22,291.71305551325656,292.0236228114956,290.09233610697805,290.18939208984375,48594700,0.0,0 -2019-10-23,289.91768256836036,291.09198011357694,289.6944567468189,291.03375244140625,34352200,0.0,0 -2019-10-24,292.0333865633568,292.1886702386739,290.6261486177117,291.5093078613281,35453100,0.0,0 -2019-10-25,290.89785631371734,293.2853092450756,290.8396286439115,292.7030029296875,45205400,0.0,0 -2019-10-28,294.00347820998826,294.8866373823915,293.9743643742972,294.35284423828125,42147000,0.0,0 -2019-10-29,294.0617060797125,295.2554325785126,293.9258217681756,294.2655029296875,44284900,0.0,0 -2019-10-30,294.4790140135277,295.5659700355771,293.08149066594126,295.1680908203125,49643900,0.0,0 -2019-10-31,295.1584069196841,295.1584069196841,292.8292109714703,294.3819885253906,69053800,0.0,0 -2019-11-01,295.9251186917832,297.15764405272404,295.7504060375914,297.109130859375,71141500,0.0,0 -2019-11-04,298.7686158431431,298.9141850097797,297.9048560922195,298.3027648925781,60606900,0.0,0 -2019-11-05,298.5163173974508,298.8365992255703,297.6622720122413,297.97283935546875,42933200,0.0,0 -2019-11-06,297.9727988164596,298.33187930063093,297.0314120472923,298.0407409667969,46487100,0.0,0 -2019-11-07,299.4674071932418,300.5155349144175,298.58424800485733,299.0888977050781,54272300,0.0,0 -2019-11-08,298.7201528891124,299.88476576815606,297.9728778890831,299.8265380859375,49032100,0.0,0 -2019-11-11,298.35128682749047,299.43824268546336,298.20568805370857,299.25384521484375,35797300,0.0,0 -2019-11-12,299.64204893143045,300.8454601524283,299.0597426699607,299.8846740722656,46484600,0.0,0 -2019-11-13,298.8268827699311,300.40880369564786,298.5842575878845,299.9817810058594,53917700,0.0,0 -2019-11-14,299.6808662523742,300.5057976244147,299.00150403602885,300.4184265136719,51219900,0.0,0 -2019-11-15,301.84510276783465,302.64092041233437,301.10754241052257,302.5924072265625,62023600,0.0,0 -2019-11-18,302.34007693530185,303.0679524483824,301.8548265932481,302.81561279296875,49228000,0.0,0 -2019-11-19,303.4561164069796,303.46583089039353,302.03919384294215,302.7282409667969,67804700,0.0,0 -2019-11-20,302.0974734068222,302.65066594455396,299.9429605490591,301.6025085449219,79406200,0.0,0 -2019-11-21,301.71895328315674,301.83540861929174,300.26320234681185,301.1172180175781,54664700,0.0,0 -2019-11-22,301.91306558439044,302.0586347689982,300.70965416852397,301.7868957519531,44850200,0.0,0 -2019-11-25,302.77681291238076,304.12579345703125,302.77681291238076,304.12579345703125,48647200,0.0,0 -2019-11-26,304.1646614162385,305.0089923793893,303.8249802103884,304.81488037109375,37569000,0.0,0 -2019-11-27,305.32922346238297,306.173583984375,305.0963127624305,306.173583984375,44444600,0.0,0 -2019-11-29,305.57181892784405,305.8338730571358,304.79543025862466,305.0380554199219,36592700,0.0,0 -2019-12-02,305.3097975411219,305.3777396951396,301.9907018863052,302.44683837890625,75767800,0.0,0 -2019-12-03,299.54498196217486,300.5057976244147,298.0698318222537,300.4184265136719,73941700,0.0,0 -2019-12-04,301.53459533038347,302.9126898117725,301.16580030604956,302.27215576171875,49080000,0.0,0 -2019-12-05,303.01943926110926,303.0388386125524,301.4180894384676,302.81561279296875,40709000,0.0,0 -2019-12-06,304.85363701720723,306.0085350751507,304.8439225338763,305.5815124511719,48927000,0.0,0 -2019-12-09,305.16423606783917,305.8823970932093,304.5431014165664,304.6207580566406,34838500,0.0,0 -2019-12-10,304.5625509210281,305.2709975098249,303.5823356909772,304.2810974121094,52649800,0.0,0 -2019-12-11,304.766326944773,305.4165754311216,304.1937350984247,305.14483642578125,53429100,0.0,0 -2019-12-12,305.1545082586967,308.60948815787896,304.90219823279847,307.7748718261719,96389600,0.0,0 -2019-12-13,307.52252191409787,309.2694407731909,306.69759047652576,307.9592590332031,81503900,0.0,0 -2019-12-16,309.8032121802673,310.7057706576791,307.89132475517164,310.074951171875,82749700,0.0,0 -2019-12-17,310.48262553564956,310.8028777644826,310.055602818983,310.1429443359375,61097700,0.0,0 -2019-12-18,310.5602318491834,310.8028570303156,310.10409532396164,310.1623229980469,48133000,0.0,0 -2019-12-19,310.3661285523784,311.511341948578,310.09438952659076,311.4336853027344,85310500,0.0,0 -2019-12-20,312.53569922400766,314.0083696681147,311.4921806621476,312.7990417480469,147142100,1.57,0 -2019-12-23,313.63779298464004,313.69630693742494,313.12089989307583,313.2769470214844,52990000,0.0,0 -2019-12-24,313.5207073793926,313.569459076759,312.9647951672985,313.2866516113281,20270000,0.0,0 -2019-12-26,313.6962668706267,314.9641384659025,313.6865343883699,314.9543762207031,30911200,0.0,0 -2019-12-27,315.73461760294754,315.79313155485687,314.3107285015766,314.8763732910156,42528800,0.0,0 -2019-12-30,314.96419554614846,315.1104804359374,312.62351825773356,313.1404113769531,49729100,0.0,0 -2019-12-31,312.6039632218848,314.1644045254851,312.23335506394864,313.90106201171875,57077300,0.0,0 -2020-01-02,315.5395710117542,316.8561944138564,314.55453658093904,316.836669921875,59151200,0.0,0 -2020-01-03,313.21840980262533,315.63709546887657,313.1598958541869,314.4375,77709700,0.0,0 -2020-01-06,312.56498417602006,315.7248864067375,312.4381940253842,315.6371154785156,55653900,0.0,0 -2020-01-07,315.0323802310774,315.539540766859,314.27166919034727,314.74957275390625,40496400,0.0,0 -2020-01-08,314.9543925082585,317.72416186034826,314.69107974074103,316.42706298828125,68296000,0.0,0 -2020-01-09,318.0947580215935,318.6506702727343,317.4705695789913,318.5726318359375,48473300,0.0,0 -2020-01-10,319.1968304540203,319.362610052348,317.15851523113554,317.6558837890625,53029300,0.0,0 -2020-01-13,318.3190929559576,319.85024761946664,317.8607138525909,319.84051513671875,47086800,0.0,0 -2020-01-14,319.3723694840367,320.4939264945821,318.75794327259655,319.3528747558594,62832800,0.0,0 -2020-01-15,319.25535988638006,320.8840477893748,319.16758896013596,320.0745849609375,72056600,0.0,0 -2020-01-16,321.5472273791711,322.73706054687494,321.3034093510592,322.737060546875,54050300,0.0,0 -2020-01-17,323.49782011891654,323.9659317548105,322.6688327464245,323.74163818359375,95846000,0.0,0 -2020-01-21,322.71759357128826,323.9659409071848,322.6395848840845,323.1076965332031,77742400,0.0,0 -2020-01-22,324.02440645438094,324.71687110349563,322.9808878838227,323.14666748046875,48914900,0.0,0 -2020-01-23,322.4542298964593,323.95615731397476,321.2643967044174,323.51727294921875,51963000,0.0,0 -2020-01-24,324.2194594582929,324.3072303757813,319.2650605207031,320.64019775390625,87578400,0.0,0 -2020-01-27,315.042200675856,317.0805161517614,314.6813546974684,315.5005798339844,84062500,0.0,0 -2020-01-28,317.0219674492371,319.7429851221418,315.59807842252127,318.8067321777344,63834000,0.0,0 -2020-01-29,320.2599152462082,320.50373331558217,318.32886542210485,318.5434265136719,53888900,0.0,0 -2020-01-30,316.33929840143503,319.8015328088434,315.5395977619218,319.57720947265625,75491800,0.0,0 -2020-01-31,318.9140270918076,319.07983647070193,312.79908077324035,313.77435302734375,113845600,0.0,0 -2020-02-03,315.35427572075275,318.0947882555932,315.2274855711142,316.105224609375,69083000,0.0,0 -2020-02-04,319.9575552394076,321.84958567331864,319.61620401954724,320.9230651855469,62573200,0.0,0 -2020-02-05,324.053712794015,324.8534432142078,322.4933009417682,324.6291198730469,65951100,0.0,0 -2020-02-06,325.653159346524,325.92623438498094,324.57059167635555,325.721435546875,50359700,0.0,0 -2020-02-07,324.5900765545177,325.7311282762224,323.4002433686814,323.98541259765625,64139400,0.0,0 -2020-02-10,323.039412117659,326.4723594470727,323.00039289717,326.4040832519531,42070000,0.0,0 -2020-02-11,327.8474960800644,328.68621585964496,326.40408255450296,326.9697570800781,54864500,0.0,0 -2020-02-12,328.5008877335763,329.30061799718834,328.1107848468936,329.0763244628906,43992700,0.0,0 -2020-02-13,327.5548812726131,329.75900579694235,327.26231154059735,328.7252197265625,54501900,0.0,0 -2020-02-14,329.164151675128,329.3787127639399,327.8865473890494,329.2519226074219,64582200,0.0,0 -2020-02-18,328.1888507951302,329.3201700954118,326.92097910344364,328.4034118652344,57226200,0.0,0 -2020-02-19,329.4371840063319,330.695263635711,329.13485202442433,329.97357177734375,48814700,0.0,0 -2020-02-20,329.38845515779536,330.2662240330376,325.42885202391244,328.6180114746094,74163400,0.0,0 -2020-02-21,327.17457738667963,327.50616637454027,324.3560263454442,325.2337951660156,113788200,0.0,0 -2020-02-24,315.1494933624793,325.3118136760255,313.2964522523599,314.4472961425781,161088400,0.0,0 -2020-02-25,315.9296538422726,316.5830694973033,303.98257033791947,304.9188232421875,218913200,0.0,0 -2020-02-26,306.41102470176514,310.2438374553743,303.0170963605923,303.79730224609375,194773800,0.0,0 -2020-02-27,297.9066245449798,303.85579065130236,290.1532287597656,290.1532287597656,284353500,0.0,0 -2020-02-28,281.56111756937986,290.52387215358164,278.47925361023044,288.9341735839844,384975800,0.0,0 -2020-03-02,290.83590301482917,301.5151450909119,287.1786324083549,301.4468688964844,238703600,0.0,0 -2020-03-03,301.8467655472551,306.0794435992073,290.2117745870386,292.81573486328125,300139100,0.0,0 -2020-03-04,298.5503020251495,305.3577122608297,295.82928442739245,305.1236267089844,176613400,0.0,0 -2020-03-05,297.43850929537945,300.8421995549829,292.5914055474934,294.9808044433594,186366800,0.0,0 -2020-03-06,285.90104084879493,291.39182812320394,283.0532626783845,290.1044616699219,228667200,0.0,0 -2020-03-09,268.492432237462,277.16261662330606,266.6882024275362,267.44891357421875,309417300,0.0,0 -2020-03-10,277.6014670083577,281.38549778517665,266.7369214428988,281.2879943847656,276444100,0.0,0 -2020-03-11,273.758935928018,274.9682640091057,264.18175516929153,267.57568359375,255316300,0.0,0 -2020-03-12,249.66967400090172,260.06607884139885,241.55540245276575,241.9747772216797,392220700,0.0,0 -2020-03-13,256.58438229563063,264.76693106596025,242.37467281903318,262.66033935546875,329566100,0.0,0 -2020-03-16,235.21613576084084,250.54741552524558,231.490603794865,233.91903686523438,297240000,0.0,0 -2020-03-17,238.98067634934978,249.83547480715055,231.20777115893475,246.54879760742188,262070500,0.0,0 -2020-03-18,230.40805220603943,242.22834624920506,222.38156634256302,234.06532287597656,327597100,0.0,0 -2020-03-19,233.33387599706967,241.26284381810098,226.4777135584991,234.56271362304688,289322000,0.0,0 -2020-03-20,237.92364496010447,239.826801082704,224.16011688045532,224.4544219970703,347158800,1.406,0 -2020-03-23,223.85601299878326,225.3177040006051,214.11460469212452,218.7155303955078,326025200,0.0,0 -2020-03-24,229.9676729198424,239.46382903257478,229.3594533334805,238.5318603515625,235494500,0.0,0 -2020-03-25,240.21920557699968,251.48117794658793,235.19645397764472,242.1027374267578,299430300,0.0,0 -2020-03-26,244.78090362795456,257.8086621669641,244.3198290786598,256.23907470703125,257632800,0.0,0 -2020-03-27,248.45965119980093,255.85643751605994,246.28181442618262,248.60679626464844,224341200,0.0,0 -2020-03-30,250.8434921781517,257.44566523559473,248.7147087396888,256.68048095703125,171369500,0.0,0 -2020-03-31,255.6112113367011,258.32859026596213,251.35364405269925,252.85458374023438,194881100,0.0,0 -2020-04-01,243.27012876564996,252.76628497690646,239.26761812807703,241.47488403320312,189554600,0.0,0 -2020-04-02,240.53311112410248,247.88084404724194,239.94450093064017,247.0469970703125,177660400,0.0,0 -2020-04-03,245.9973309340812,248.50872164244254,240.5625582764185,243.4761505126953,135561200,0.0,0 -2020-04-06,252.94283996469383,261.92886763025683,243.45650412057958,259.8294982910156,188061200,0.0,0 -2020-04-07,269.00195282495207,269.80638584497115,259.85898925908833,260.09442138671875,201427200,0.0,0 -2020-04-08,262.8706580471995,270.7579636312611,260.21213714924636,268.82537841796875,153774500,0.0,0 -2020-04-09,272.3079491548729,275.85922015486074,270.2380384566057,272.91619873046875,189999200,0.0,0 -2020-04-13,271.87630917582237,272.239276995542,266.25512798163066,270.4244079589844,114839100,0.0,0 -2020-04-14,275.6433665325658,279.48889733140317,270.27725687188524,278.3999938964844,134143400,0.0,0 -2020-04-15,272.29813385349087,278.5471439673248,270.2281934117733,272.4845275878906,121775000,0.0,0 -2020-04-16,273.8481411657386,274.7114322516927,270.52254248003186,273.7991027832031,131798300,0.0,0 -2020-04-17,279.9597834120126,281.843300093228,277.036371764303,281.19586181640625,146684800,0.0,0 -2020-04-20,277.24240725420157,281.3430397603875,276.00635864330656,276.2417907714844,100109300,0.0,0 -2020-04-21,271.4740746763567,272.7591914361182,266.8535102056844,267.8541564941406,126385700,0.0,0 -2020-04-22,273.06334741739624,275.66301038898814,271.65069472000994,273.7991027832031,92951600,0.0,0 -2020-04-23,275.16264240058206,278.5471285079392,273.45569980971896,273.7794189453125,104709700,0.0,0 -2020-04-24,275.3981347247383,278.31172701734033,273.2104781080029,277.5955810546875,85063200,0.0,0 -2020-04-27,279.7047166159488,282.79488273985044,279.21421310618797,281.5980529785156,77896600,0.0,0 -2020-04-28,285.49263639704327,285.86542383410415,279.97938203955994,280.3031311035156,105270000,0.0,0 -2020-04-29,285.9929373390306,289.2793164069132,284.8942144127073,287.6410217285156,118745600,0.0,0 -2020-04-30,286.16957122640184,287.7490082166862,283.1088338808608,284.96295166015625,122901700,0.0,0 -2020-05-01,279.8911334633807,285.1395274389247,276.17310816966057,277.41900634765625,125180000,0.0,0 -2020-05-04,275.407876681854,278.5078621814683,273.8284698905645,278.18414306640625,80873200,0.0,0 -2020-05-05,281.1958902201207,283.756304387327,278.3215166570548,280.7544250488281,79569900,0.0,0 -2020-05-06,282.56928247117037,282.9812886718801,278.39018280764185,278.85125732421875,73632600,0.0,0 -2020-05-07,282.28480317286073,284.27624645993274,281.67658353906955,282.21612548828125,75250400,0.0,0 -2020-05-08,285.5613461298709,287.3860348587758,284.3546966488538,286.8857116699219,76452400,0.0,0 -2020-05-11,284.8256011249927,288.41609074519226,284.37434623638785,286.944580078125,79514200,0.0,0 -2020-05-12,288.2100768611455,288.6515120819882,281.07813626772264,281.2253112792969,95870800,0.0,0 -2020-05-13,280.6268699054825,281.7354126445861,273.66171397265657,276.2515869140625,144721100,0.0,0 -2020-05-14,273.651934870671,279.69491209234354,267.8051111627781,279.5575866699219,121977900,0.0,0 -2020-05-15,277.00697103496844,280.8917507823361,275.9965349158463,280.84271240234375,111146300,0.0,0 -2020-05-18,287.4840885890301,291.11382645429194,287.1407600903347,289.3970642089844,119703800,0.0,0 -2020-05-19,288.75942308585996,290.58408177765034,286.4050122192446,286.42462158203125,95189300,0.0,0 -2020-05-20,290.2015018588968,292.21255426097423,289.9562501055723,291.2904052734375,85861700,0.0,0 -2020-05-21,291.15308493074764,292.0163759075603,288.1119571489608,289.27935791015625,78293900,0.0,0 -2020-05-22,288.9752639783807,290.01512909259765,287.6508984271165,289.8287353515625,63958200,0.0,0 -2020-05-26,296.1954426476647,296.4505140532561,289.84832602465684,293.3995666503906,88951400,0.0,0 -2020-05-27,296.3818521180462,297.80432434914434,291.23156498456405,297.76507568359375,104817400,0.0,0 -2020-05-28,298.8637828517603,301.01219062200846,296.49955233945195,297.2156982421875,90405200,0.0,0 -2020-05-29,296.7154112491019,299.16792909965847,293.78220947998415,298.54010009765625,119090800,0.0,0 -2020-06-01,297.85336760469585,300.3941723729286,297.3040060298326,299.7467041015625,55758300,0.0,0 -2020-06-02,300.7276981117738,302.27770601041016,299.30525585720767,302.2286376953125,74267200,0.0,0 -2020-06-03,304.34760693418633,307.2710186182873,304.0533168046949,306.2507629394531,92567600,0.0,0 -2020-06-04,305.2011288206996,307.0552467576052,303.2096854465953,305.4463806152344,75794400,0.0,0 -2020-06-05,311.20483950164913,315.1680859475974,311.1361618313691,313.2747497558594,150524700,0.0,0 -2020-06-08,314.13808111126843,317.2674960193427,313.5592905399703,317.0614929199219,73641200,0.0,0 -2020-06-09,314.2165518040222,317.1399636433862,313.29440276536235,314.697265625,77479200,0.0,0 -2020-06-10,315.3153346806508,316.26691282379403,312.17609979206446,312.9412841796875,95000800,0.0,0 -2020-06-11,305.54444509473706,306.22134234914375,294.31193242014064,294.9005126953125,209243600,0.0,0 -2020-06-12,302.3856168116949,303.2096591773836,292.9287240014907,298.4321594238281,194678900,0.0,0 -2020-06-15,292.3596786596257,302.4248194847437,291.1039909688823,301.2181701660156,135782700,0.0,0 -2020-06-16,309.4881176414586,309.6450823633982,301.8264549084168,307.0159606933594,137627500,0.0,0 -2020-06-17,308.104922964097,308.41885243738983,304.9558684554398,305.7406921386719,83398900,0.0,0 -2020-06-18,304.1219962697921,306.3684807933604,303.63149275911553,305.8583679199219,80828700,0.0,0 -2020-06-19,309.55925309344263,309.7661627131124,302.0313633295862,304.11041259765625,135549600,1.366,0 -2020-06-22,303.46996362396993,306.485053001028,302.24817134743034,306.0613708496094,74649400,0.0,0 -2020-06-23,308.8892058553746,309.88439270066056,307.0367919559004,307.4703369140625,68471200,0.0,0 -2020-06-24,305.29277965721644,305.95295995730146,297.6663816417867,299.6271667480469,132813500,0.0,0 -2020-06-25,299.0162933096727,303.1251078653027,296.8584312154481,302.83935546875,89468000,0.0,0 -2020-06-26,301.66679912725874,301.89343447211024,295.02572499663245,295.6464538574219,127961000,0.0,0 -2020-06-29,296.9865036331724,300.1395223189962,294.54288934415524,299.9917297363281,79773300,0.0,0 -2020-06-30,299.5286720629897,305.6475565483558,299.3611838002569,303.83453369140625,113394800,0.0,0 -2020-07-01,305.02678973396564,307.3127488692916,304.53412768254395,305.96282958984375,72396500,0.0,0 -2020-07-02,309.62818575313406,311.0667803579012,306.9382706374605,307.647705078125,69344200,0.0,0 -2020-07-06,311.72698998261825,313.0177621864543,310.9288798434435,312.3970031738281,61713800,0.0,0 -2020-07-07,310.7514816575032,312.8600593386887,308.7709708351823,309.1749572753906,82910000,0.0,0 -2020-07-08,309.9927983620726,311.6579984842647,308.1108558064095,311.5397644042969,54638600,0.0,0 -2020-07-09,312.1900630181532,312.44625688904705,306.12046336629885,309.76617431640625,83354200,0.0,0 -2020-07-10,309.6972153049008,313.2148295568068,308.1699749785745,312.9290771484375,57550400,0.0,0 -2020-07-13,315.43176301876105,317.97388559596953,309.51981927951243,310.2193908691406,102997500,0.0,0 -2020-07-14,308.7020165029715,315.0672315917454,307.4211072506421,314.23956298828125,93657000,0.0,0 -2020-07-15,317.67830489673975,318.29906380922773,314.58437319630787,317.12652587890625,87196500,0.0,0 -2020-07-16,315.096768291685,316.56489141965255,314.4070294683744,316.08209228515625,54622500,0.0,0 -2020-07-17,317.15609905442665,317.8359750309854,315.0474912269076,316.9984436035156,62774900,0.0,0 -2020-07-20,316.7127017997887,320.3584128083537,315.91459172580994,319.560302734375,56308800,0.0,0 -2020-07-21,321.65907300463266,322.1320093405656,319.18589982572803,320.2402038574219,57292200,0.0,0 -2020-07-22,319.8558721430427,322.39802490266555,319.7376380741676,322.06298828125,57792900,0.0,0 -2020-07-23,321.67874238384,322.42759828083376,316.76198501838684,318.2202453613281,75738000,0.0,0 -2020-07-24,316.23978678955643,317.2645022191382,314.5647237658965,316.1708068847656,73766600,0.0,0 -2020-07-27,316.9097787996052,318.66365440463795,316.0623844809289,318.4764404296875,48293000,0.0,0 -2020-07-28,317.69803084239607,318.89029461041184,316.1412320514382,316.45654296875,57495000,0.0,0 -2020-07-29,317.39256919341244,320.9496045495029,317.35314781301093,320.3485412597656,48454200,0.0,0 -2020-07-30,317.17583162040575,319.6490048104725,314.94901953269056,319.2055969238281,61861700,0.0,0 -2020-07-31,321.1170702688856,321.83636757966815,316.6141325869457,321.72796630859375,84986800,0.0,0 -2020-08-03,323.50161814643826,324.78252745812915,322.9202805314582,323.9647216796875,53077900,0.0,0 -2020-08-04,323.04833944248503,325.216064453125,323.04833944248503,325.216064453125,41917900,0.0,0 -2020-08-05,326.60533968777963,327.5118509768131,326.319587315083,327.2359313964844,42697700,0.0,0 -2020-08-06,326.61522092611705,329.5514672966303,326.2703515010042,329.4233703613281,43679400,0.0,0 -2020-08-07,328.38883552082956,329.9653601940298,327.42320701693893,329.659912109375,57308300,0.0,0 -2020-08-10,330.1426777428928,330.8422494072574,328.07349119916097,330.64520263671875,44282100,0.0,0 -2020-08-11,331.90641297246236,332.58628897400536,327.1374481685387,327.91583251953125,69601100,0.0,0 -2020-08-12,330.5170839397103,333.31540048266055,330.4875254225901,332.48773193359375,53774400,0.0,0 -2020-08-13,331.66991573752233,333.2858616212651,330.9013641773701,331.8866882324219,41816100,0.0,0 -2020-08-14,331.47286329047023,332.46805019133103,330.69444888079585,331.89654541015625,47260400,0.0,0 -2020-08-17,332.9804252191162,333.37454884098463,331.9064255699003,332.95086669921875,35481000,0.0,0 -2020-08-18,333.3745507452565,334.12340667987735,331.6699292355502,333.670166015625,38733900,0.0,0 -2020-08-19,334.0740895777421,334.6258686111842,331.67975947873055,332.28082275390625,68054200,0.0,0 -2020-08-20,330.4382701336667,333.8277873857093,330.30034039825466,333.3154296875,42207800,0.0,0 -2020-08-21,332.9607300755044,334.7343014114976,332.5961349032668,334.4978332519531,55106600,0.0,0 -2020-08-24,337.0990822832813,337.96617231787366,334.4682837284718,337.8873596191406,48588700,0.0,0 -2020-08-25,338.4883645760912,339.15837770323236,337.2468466695566,339.0697021484375,38463400,0.0,0 -2020-08-26,339.70032753122456,342.75480801635297,339.11898995277426,342.4690856933594,50790200,0.0,0 -2020-08-27,343.3953072140425,344.7648920588511,341.4443546965106,343.2179260253906,58034100,0.0,0 -2020-08-28,344.31165218027223,345.5728658118236,343.0405756855221,345.4349060058594,48588900,0.0,0 -2020-08-31,345.20827812020474,346.1443179019907,343.93720170863116,344.18353271484375,66099200,0.0,0 -2020-09-01,345.07032356256684,347.5336336620934,344.11455804116235,347.4252624511719,54908700,0.0,0 -2020-09-02,349.4649086595517,353.48501772101184,348.243086339054,352.450439453125,69540000,0.0,0 -2020-09-03,350.647283299371,351.1498082135989,337.5621804301448,340.32110595703125,148011100,0.0,0 -2020-09-04,341.0502009861614,342.7252337446103,329.95544312206727,337.5424499511719,139156300,0.0,0 -2020-09-08,331.7684276179883,337.6114216500247,327.9946500196835,328.3197937011719,114465300,0.0,0 -2020-09-09,332.59613726440455,337.43408219412936,331.66993020667365,334.80328369140625,91462300,0.0,0 -2020-09-10,336.8034808223932,337.5030524925724,327.9651227117506,328.9898681640625,90569500,0.0,0 -2020-09-11,330.891528577298,332.0246452170547,326.1422594555071,329.1573486328125,84680200,0.0,0 -2020-09-14,332.5370322391455,335.384633418818,329.31503314724387,333.4927978515625,65605700,0.0,0 -2020-09-15,336.11374368694413,337.00052934059323,333.50264090187017,335.1777038574219,52920900,0.0,0 -2020-09-16,336.49799701680286,338.02523713515967,333.55185791311624,333.84747314453125,82096000,0.0,0 -2020-09-17,328.6646867406648,332.7439427290615,328.10304481709653,330.9112243652344,91523300,0.0,0 -2020-09-18,331.77087920380495,331.88958656018474,324.45030038970583,327.1015319824219,105877900,1.339,0 -2020-09-21,322.20466928971535,323.61931558974044,318.2772732735822,323.4610290527344,99450800,0.0,0 -2020-09-22,325.04389520738874,327.34887708141235,322.36295635583804,326.75531005859375,63612100,0.0,0 -2020-09-23,327.3488872174343,327.64568583307016,318.64333791344586,319.17755126953125,93112200,0.0,0 -2020-09-24,317.7727593155621,323.2928630552864,316.3679850689975,320.0282897949219,76681300,0.0,0 -2020-09-25,319.1181248146024,326.043002261089,318.1882404752242,325.2021484375,71069400,0.0,0 -2020-09-28,329.64398519845355,331.36530238519066,328.58546086841676,330.60357666015625,64584600,0.0,0 -2020-09-29,330.38593590657734,331.1773384718609,328.0611493597069,328.8031005859375,51304000,0.0,0 -2020-09-30,329.5153501027799,334.6595569591169,329.3076122283333,331.2960510253906,104081100,0.0,0 -2020-10-01,334.065991946112,335.10471151451344,331.41476032785135,333.4229736328125,88698700,0.0,0 -2020-10-02,328.1403057647572,333.3933179997991,327.63576927311925,330.25732421875,89431100,0.0,0 -2020-10-05,332.4534651561859,336.3116050549942,332.4040138227672,336.11376953125,45713100,0.0,0 -2020-10-06,336.2621994433775,338.4979555151759,330.79154682235594,331.33563232421875,90128900,0.0,0 -2020-10-07,334.49140547642617,337.9637470022001,334.4617286339013,337.10308837890625,56999600,0.0,0 -2020-10-08,339.1706249816913,340.15989323003214,338.19122888658546,340.09063720703125,45242500,0.0,0 -2020-10-09,341.8515210995184,343.62231965312964,341.1887283035763,343.127685546875,59528600,0.0,0 -2020-10-12,345.8383087394127,350.2207601561271,345.3139977368495,348.6478271484375,80388500,0.0,0 -2020-10-13,348.4994182602636,348.6873816431623,345.3436501243173,346.37249755859375,73255500,0.0,0 -2020-10-14,346.9858497566436,348.1531790832653,343.414605751452,344.19610595703125,57727900,0.0,0 -2020-10-15,340.02135608034916,344.2850995007018,339.4475938223034,343.77069091796875,60357700,0.0,0 -2020-10-16,345.2150493969607,346.98584806554027,343.3750248934642,343.56298828125,89501900,0.0,0 -2020-10-19,344.90835534181844,345.58105047893014,337.38003875575237,338.3396301269531,68425600,0.0,0 -2020-10-20,339.7740744677079,343.1573852636833,338.9628974238229,339.6949462890625,60051900,0.0,0 -2020-10-21,339.6454777556973,341.96039218313126,338.7254654671449,339.05194091796875,63575000,0.0,0 -2020-10-22,339.27942609351965,341.5349564661785,336.99421888135487,340.9117126464844,55399300,0.0,0 -2020-10-23,342.2175675530305,342.27692123439897,339.4476284555958,342.0691833496094,49143900,0.0,0 -2020-10-26,338.45838837625956,339.29927251912926,332.0182417006067,335.747802734375,91473000,0.0,0 -2020-10-27,336.1138136956006,336.46993579917506,334.36278944947304,334.59033203125,65994100,0.0,0 -2020-10-28,328.53597940123024,334.61997286994244,322.63004696514685,323.15435791015625,127094300,0.0,0 -2020-10-29,323.40169041765375,329.8220317626079,321.60121493929176,326.4387512207031,90597700,0.0,0 -2020-10-30,324.75699220823026,326.1518641163939,319.13795558092835,323.0356750488281,120287300,0.0,0 -2020-11-02,326.6564025878906,328.79319553457486,323.7281467022949,326.6564025878906,86068300,0.0,0 -2020-11-03,330.1089136034495,334.6199742558167,326.74540770511527,332.4237976074219,93294200,0.0,0 -2020-11-04,337.2019789726345,344.20601545815225,335.9456190973562,339.8532409667969,126959700,0.0,0 -2020-11-05,345.49205493216294,348.4104085227646,345.1161281439419,346.4813232421875,82039700,0.0,0 -2020-11-06,346.1746180874066,347.73767876838974,343.9190877722497,346.40216064453125,74973000,0.0,0 -2020-11-09,360.0639615846726,360.469565184827,350.2603097289417,350.75494384765625,172304200,0.0,0 -2020-11-10,349.6964543696119,351.36832027236045,346.7484540505035,350.2405700683594,85552000,0.0,0 -2020-11-11,352.5752244224408,353.7227793003958,351.24960849189097,352.84234619140625,58649000,0.0,0 -2020-11-12,351.7639945390848,352.8917748465008,347.49037860116624,349.41943359375,67546200,0.0,0 -2020-11-13,351.4573422963045,355.04839109887246,350.9033544568526,354.2569885253906,62892200,0.0,0 -2020-11-16,357.1060527494804,358.886723468582,355.7309554331188,358.6789855957031,74541100,0.0,0 -2020-11-17,356.10689012825446,358.0359752753589,354.4943780640718,356.7499084472656,66111000,0.0,0 -2020-11-18,357.03679864495183,357.62046327479106,352.416902751381,352.45648193359375,70591300,0.0,0 -2020-11-19,351.7838015946351,354.3361004384954,350.34935053222546,353.9403991699219,59940900,0.0,0 -2020-11-20,353.66341861817114,353.881058852616,351.43756493456027,351.5166931152344,70417300,0.0,0 -2020-11-23,353.4457487802955,354.9692302938078,351.0616087449511,353.6238098144531,63230600,0.0,0 -2020-11-24,356.3443143370891,359.9056861396439,355.43420443703553,359.322021484375,62415900,0.0,0 -2020-11-25,359.2330237422052,359.2627005853481,357.6007369891963,358.76806640625,45330900,0.0,0 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"numpy>=2.2.6", - "pandas>=2.3.2", - "tqdm>=4.67.1", -] -readme = { file = "README.md", content-type = "text/markdown" } -license-files = ["LICENSE"] -keywords = [ - "library", - "technical analysis", - "ta", - "indicators", - "pandas", - "dataframe extension", - "finance", - "trading", - "machine learning", - "ai", -] -classifiers = [ - "Development Status :: 4 - Beta", - "Programming Language :: Python :: 3.12", - "Operating System :: OS Independent", - "Natural Language :: English", - "Intended Audience :: Developers", - "Intended Audience :: Financial and Insurance Industry", - "Intended Audience :: Science/Research", - "Topic :: Office/Business :: Financial", - "Topic :: Office/Business :: Financial :: Investment", - "Topic :: Scientific/Engineering", - "Topic :: Scientific/Engineering :: Information Analysis", -] - -[project.urls] -Homepage = "https://www.pandas-ta.dev" -Documentation = "https://www.pandas-ta.dev/api" -Repository = "https://github.com/twopirllc/pandas-ta" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[dependency-groups] -dev = [ - "bokeh>=3.7.3", - "cryptography>=45.0.6", - "ipython>=9.4.0", - "jupyterlab>=4.4.6", - "matplotlib>=3.10.5", - "pytest>=8.4.1", - "requests>=2.32.5", - "ta-lib==0.6.4", - "yfinance>=0.2.65", -] -docs = [ - "mkdocs>=1.6.1", - "mkdocs-include-markdown-plugin>=7.1.6", - "mkdocs-macros-plugin>=1.3.9", - "mkdocs-material>=9.6.18", - "mkdocs-minify-plugin>=0.8.0", - "mkdocs-plotly-plugin>=0.1.3", - "mkdocs-redirects>=1.2.2", - "mkdocstrings-python>=1.17.0", - "ruff>=0.12.10", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/requirements.dev.txt b/temp_pandas_ta/pandas_ta-0.4.71b0/requirements.dev.txt deleted file mode 100644 index daf9f4e7..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/requirements.dev.txt +++ /dev/null @@ -1,383 +0,0 @@ -# This file was autogenerated by uv via the following command: -# uv export --dev --no-hashes -vn -o requirements.dev.txt --e . -anyio==4.10.0 - # via - # httpx - # jupyter-server -appnope==0.1.4 ; sys_platform == 'darwin' - # via ipykernel -argon2-cffi==25.1.0 - # via jupyter-server -argon2-cffi-bindings==21.2.0 ; python_full_version >= '3.14' - # via argon2-cffi -argon2-cffi-bindings==25.1.0 ; python_full_version < '3.14' - # via argon2-cffi -arrow==1.3.0 - # via isoduration -asttokens==3.0.0 - # via stack-data -async-lru==2.0.5 - # via jupyterlab -attrs==25.3.0 - # via - # jsonschema - # referencing -babel==2.17.0 - # via jupyterlab-server -beautifulsoup4==4.13.5 - # via - # nbconvert - # yfinance -bleach==6.2.0 - # via nbconvert -bokeh==3.7.3 -certifi==2025.8.3 - # via - # curl-cffi - # httpcore - # httpx - # requests -cffi==1.17.1 - # via - # argon2-cffi-bindings - # cryptography - # curl-cffi - # pyzmq -charset-normalizer==3.4.3 - # via requests -colorama==0.4.6 ; sys_platform == 'win32' - # via - # ipython - # pytest - # tqdm -comm==0.2.3 - # via ipykernel -contourpy==1.3.3 - # via - # bokeh - # matplotlib -cryptography==45.0.6 -curl-cffi==0.13.0 - # via yfinance -cycler==0.12.1 - # via matplotlib -debugpy==1.8.16 - # via ipykernel -decorator==5.2.1 - # via ipython -defusedxml==0.7.1 - # via nbconvert -executing==2.2.0 - # via stack-data -fastjsonschema==2.21.2 - # via nbformat -fonttools==4.59.1 - # via matplotlib -fqdn==1.5.1 - # via jsonschema -frozendict==2.4.6 - # via yfinance -h11==0.16.0 - # via httpcore -httpcore==1.0.9 - # via httpx -httpx==0.28.1 - # via jupyterlab -idna==3.10 - # via - # anyio - # httpx - # jsonschema - # requests -iniconfig==2.1.0 - # via pytest -ipykernel==6.30.1 - # via jupyterlab -ipython==9.4.0 - # via ipykernel -ipython-pygments-lexers==1.1.1 - # via ipython -isoduration==20.11.0 - # via jsonschema -jedi==0.19.2 - # via ipython -jinja2==3.1.6 - # via - # bokeh - # jupyter-server - # jupyterlab - # jupyterlab-server - # nbconvert -json5==0.12.1 - # via jupyterlab-server -jsonpointer==3.0.0 - # via jsonschema -jsonschema==4.25.1 - # via - # jupyter-events - # jupyterlab-server - # nbformat -jsonschema-specifications==2025.4.1 - # via jsonschema -jupyter-client==8.6.3 - # via - # ipykernel - # jupyter-server - # nbclient -jupyter-core==5.8.1 - # via - # ipykernel - # jupyter-client - # jupyter-server - # jupyterlab - # nbclient - # nbconvert - # nbformat -jupyter-events==0.12.0 - # via jupyter-server -jupyter-lsp==2.2.6 - # via jupyterlab -jupyter-server==2.17.0 - # via - # jupyter-lsp - # jupyterlab - # jupyterlab-server - # notebook-shim -jupyter-server-terminals==0.5.3 - # via jupyter-server -jupyterlab==4.4.6 -jupyterlab-pygments==0.3.0 - # via nbconvert -jupyterlab-server==2.27.3 - # via jupyterlab -kiwisolver==1.4.9 - # via matplotlib -lark==1.2.2 - # via rfc3987-syntax -llvmlite==0.44.0 - # via numba -markupsafe==3.0.2 - # via - # jinja2 - # nbconvert -matplotlib==3.10.5 -matplotlib-inline==0.1.7 - # via - # ipykernel - # ipython -mistune==3.1.3 - # via nbconvert -multitasking==0.0.12 - # via yfinance -narwhals==2.2.0 - # via bokeh -nbclient==0.10.2 - # via nbconvert -nbconvert==7.16.6 - # via jupyter-server -nbformat==5.10.4 - # via - # jupyter-server - # nbclient - # nbconvert -nest-asyncio==1.6.0 - # via ipykernel -notebook-shim==0.2.4 - # via jupyterlab -numba==0.61.2 - # via pandas-ta -numpy==2.2.6 - # via - # bokeh - # contourpy - # matplotlib - # numba - # pandas - # pandas-ta - # ta-lib - # yfinance -packaging==25.0 - # via - # bokeh - # ipykernel - # jupyter-events - # jupyter-server - # jupyterlab - # jupyterlab-server - # matplotlib - # nbconvert - # pytest -pandas==2.3.2 - # via - # bokeh - # pandas-ta - # yfinance -pandocfilters==1.5.1 - # via nbconvert -parso==0.8.5 - # via jedi -peewee==3.18.2 - # via yfinance -pexpect==4.9.0 ; sys_platform != 'emscripten' and sys_platform != 'win32' - # via ipython -pillow==11.3.0 - # via - # bokeh - # matplotlib -platformdirs==4.3.8 - # via - # jupyter-core - # yfinance -pluggy==1.6.0 - # via pytest -prometheus-client==0.22.1 - # via jupyter-server -prompt-toolkit==3.0.51 - # via ipython -protobuf==6.32.0 - # via yfinance -psutil==7.0.0 - # via ipykernel -ptyprocess==0.7.0 ; os_name != 'nt' or (sys_platform != 'emscripten' and sys_platform != 'win32') - # via - # pexpect - # terminado -pure-eval==0.2.3 - # via stack-data -pycparser==2.22 - # via cffi -pygments==2.19.2 - # via - # ipython - # ipython-pygments-lexers - # nbconvert - # pytest -pyparsing==3.2.3 - # via matplotlib -pytest==8.4.1 -python-dateutil==2.9.0.post0 - # via - # arrow - # jupyter-client - # matplotlib - # pandas -python-json-logger==3.3.0 - # via jupyter-events -pytz==2025.2 - # via - # pandas - # yfinance -pywin32==311 ; platform_python_implementation != 'PyPy' and sys_platform == 'win32' - # via jupyter-core -pywinpty==3.0.0 ; os_name == 'nt' - # via - # jupyter-server - # jupyter-server-terminals - # terminado -pyyaml==6.0.2 - # via - # bokeh - # jupyter-events -pyzmq==27.0.2 - # via - # ipykernel - # jupyter-client - # jupyter-server -referencing==0.36.2 - # via - # jsonschema - # jsonschema-specifications - # jupyter-events -requests==2.32.5 - # via - # jupyterlab-server - # yfinance -rfc3339-validator==0.1.4 - # via - # jsonschema - # jupyter-events -rfc3986-validator==0.1.1 - # via - # jsonschema - # jupyter-events -rfc3987-syntax==1.1.0 - # via jsonschema -rpds-py==0.27.0 - # via - # jsonschema - # referencing -send2trash==1.8.3 - # via jupyter-server -setuptools==80.9.0 - # via - # jupyterlab - # ta-lib -six==1.17.0 - # via - # python-dateutil - # rfc3339-validator -sniffio==1.3.1 - # via anyio -soupsieve==2.7 - # via beautifulsoup4 -stack-data==0.6.3 - # via ipython -ta-lib==0.6.4 -terminado==0.18.1 - # via - # jupyter-server - # jupyter-server-terminals -tinycss2==1.4.0 - # via bleach -tornado==6.5.2 - # via - # bokeh - # ipykernel - # jupyter-client - # jupyter-server - # jupyterlab - # terminado -tqdm==4.67.1 - # via pandas-ta -traitlets==5.14.3 - # via - # ipykernel - # ipython - # jupyter-client - # jupyter-core - # jupyter-events - # jupyter-server - # jupyterlab - # matplotlib-inline - # nbclient - # nbconvert - # nbformat -types-python-dateutil==2.9.0.20250822 - # via arrow -typing-extensions==4.15.0 - # via - # anyio - # beautifulsoup4 - # referencing -tzdata==2025.2 - # via pandas -uri-template==1.3.0 - # via jsonschema -urllib3==2.5.0 - # via requests -wcwidth==0.2.13 - # via prompt-toolkit -webcolors==24.11.1 - # via jsonschema -webencodings==0.5.1 - # via - # bleach - # tinycss2 -websocket-client==1.8.0 - # via jupyter-server -websockets==15.0.1 - # via yfinance -xyzservices==2025.4.0 - # via bokeh -yfinance==0.2.65 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/requirements.txt b/temp_pandas_ta/pandas_ta-0.4.71b0/requirements.txt deleted file mode 100644 index 49b828ca..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/requirements.txt +++ /dev/null @@ -1,383 +0,0 @@ -# This file was autogenerated by uv via the following command: -# uv export --no-hashes -vn -o requirements.txt --e . -anyio==4.10.0 - # via - # httpx - # jupyter-server -appnope==0.1.4 ; sys_platform == 'darwin' - # via ipykernel -argon2-cffi==25.1.0 - # via jupyter-server -argon2-cffi-bindings==21.2.0 ; python_full_version >= '3.14' - # via argon2-cffi -argon2-cffi-bindings==25.1.0 ; python_full_version < '3.14' - # via argon2-cffi -arrow==1.3.0 - # via isoduration -asttokens==3.0.0 - # via stack-data -async-lru==2.0.5 - # via jupyterlab -attrs==25.3.0 - # via - # jsonschema - # referencing -babel==2.17.0 - # via jupyterlab-server -beautifulsoup4==4.13.5 - # via - # nbconvert - # yfinance -bleach==6.2.0 - # via nbconvert -bokeh==3.7.3 -certifi==2025.8.3 - # via - # curl-cffi - # httpcore - # httpx - # requests -cffi==1.17.1 - # via - # argon2-cffi-bindings - # cryptography - # curl-cffi - # pyzmq -charset-normalizer==3.4.3 - # via requests -colorama==0.4.6 ; sys_platform == 'win32' - # via - # ipython - # pytest - # tqdm -comm==0.2.3 - # via ipykernel -contourpy==1.3.3 - # via - # bokeh - # matplotlib -cryptography==45.0.6 -curl-cffi==0.13.0 - # via yfinance -cycler==0.12.1 - # via matplotlib -debugpy==1.8.16 - # via ipykernel -decorator==5.2.1 - # via ipython -defusedxml==0.7.1 - # via nbconvert -executing==2.2.0 - # via stack-data -fastjsonschema==2.21.2 - # via nbformat -fonttools==4.59.1 - # via matplotlib -fqdn==1.5.1 - # via jsonschema -frozendict==2.4.6 - # via yfinance -h11==0.16.0 - # via httpcore -httpcore==1.0.9 - # via httpx -httpx==0.28.1 - # via jupyterlab -idna==3.10 - # via - # anyio - # httpx - # jsonschema - # requests -iniconfig==2.1.0 - # via pytest -ipykernel==6.30.1 - # via jupyterlab -ipython==9.4.0 - # via ipykernel -ipython-pygments-lexers==1.1.1 - # via ipython -isoduration==20.11.0 - # via jsonschema -jedi==0.19.2 - # via ipython -jinja2==3.1.6 - # via - # bokeh - # jupyter-server - # jupyterlab - # jupyterlab-server - # nbconvert -json5==0.12.1 - # via jupyterlab-server -jsonpointer==3.0.0 - # via jsonschema -jsonschema==4.25.1 - # via - # jupyter-events - # jupyterlab-server - # nbformat -jsonschema-specifications==2025.4.1 - # via jsonschema -jupyter-client==8.6.3 - # via - # ipykernel - # jupyter-server - # nbclient -jupyter-core==5.8.1 - # via - # ipykernel - # jupyter-client - # jupyter-server - # jupyterlab - # nbclient - # nbconvert - # nbformat -jupyter-events==0.12.0 - # via jupyter-server -jupyter-lsp==2.2.6 - # via jupyterlab -jupyter-server==2.17.0 - # via - # jupyter-lsp - # jupyterlab - # jupyterlab-server - # notebook-shim -jupyter-server-terminals==0.5.3 - # via jupyter-server -jupyterlab==4.4.6 -jupyterlab-pygments==0.3.0 - # via nbconvert -jupyterlab-server==2.27.3 - # via jupyterlab -kiwisolver==1.4.9 - # via matplotlib -lark==1.2.2 - # via rfc3987-syntax -llvmlite==0.44.0 - # via numba -markupsafe==3.0.2 - # via - # jinja2 - # nbconvert -matplotlib==3.10.5 -matplotlib-inline==0.1.7 - # via - # ipykernel - # ipython -mistune==3.1.3 - # via nbconvert -multitasking==0.0.12 - # via yfinance -narwhals==2.2.0 - # via bokeh -nbclient==0.10.2 - # via nbconvert -nbconvert==7.16.6 - # via jupyter-server -nbformat==5.10.4 - # via - # jupyter-server - # nbclient - # nbconvert -nest-asyncio==1.6.0 - # via ipykernel -notebook-shim==0.2.4 - # via jupyterlab -numba==0.61.2 - # via pandas-ta -numpy==2.2.6 - # via - # bokeh - # contourpy - # matplotlib - # numba - # pandas - # pandas-ta - # ta-lib - # yfinance -packaging==25.0 - # via - # bokeh - # ipykernel - # jupyter-events - # jupyter-server - # jupyterlab - # jupyterlab-server - # matplotlib - # nbconvert - # pytest -pandas==2.3.2 - # via - # bokeh - # pandas-ta - # yfinance -pandocfilters==1.5.1 - # via nbconvert -parso==0.8.5 - # via jedi -peewee==3.18.2 - # via yfinance -pexpect==4.9.0 ; sys_platform != 'emscripten' and sys_platform != 'win32' - # via ipython -pillow==11.3.0 - # via - # bokeh - # matplotlib -platformdirs==4.3.8 - # via - # jupyter-core - # yfinance -pluggy==1.6.0 - # via pytest -prometheus-client==0.22.1 - # via jupyter-server -prompt-toolkit==3.0.51 - # via ipython -protobuf==6.32.0 - # via yfinance -psutil==7.0.0 - # via ipykernel -ptyprocess==0.7.0 ; os_name != 'nt' or (sys_platform != 'emscripten' and sys_platform != 'win32') - # via - # pexpect - # terminado -pure-eval==0.2.3 - # via stack-data -pycparser==2.22 - # via cffi -pygments==2.19.2 - # via - # ipython - # ipython-pygments-lexers - # nbconvert - # pytest -pyparsing==3.2.3 - # via matplotlib -pytest==8.4.1 -python-dateutil==2.9.0.post0 - # via - # arrow - # jupyter-client - # matplotlib - # pandas -python-json-logger==3.3.0 - # via jupyter-events -pytz==2025.2 - # via - # pandas - # yfinance -pywin32==311 ; platform_python_implementation != 'PyPy' and sys_platform == 'win32' - # via jupyter-core -pywinpty==3.0.0 ; os_name == 'nt' - # via - # jupyter-server - # jupyter-server-terminals - # terminado -pyyaml==6.0.2 - # via - # bokeh - # jupyter-events -pyzmq==27.0.2 - # via - # ipykernel - # jupyter-client - # jupyter-server -referencing==0.36.2 - # via - # jsonschema - # jsonschema-specifications - # jupyter-events -requests==2.32.5 - # via - # jupyterlab-server - # yfinance -rfc3339-validator==0.1.4 - # via - # jsonschema - # jupyter-events -rfc3986-validator==0.1.1 - # via - # jsonschema - # jupyter-events -rfc3987-syntax==1.1.0 - # via jsonschema -rpds-py==0.27.0 - # via - # jsonschema - # referencing -send2trash==1.8.3 - # via jupyter-server -setuptools==80.9.0 - # via - # jupyterlab - # ta-lib -six==1.17.0 - # via - # python-dateutil - # rfc3339-validator -sniffio==1.3.1 - # via anyio -soupsieve==2.7 - # via beautifulsoup4 -stack-data==0.6.3 - # via ipython -ta-lib==0.6.4 -terminado==0.18.1 - # via - # jupyter-server - # jupyter-server-terminals -tinycss2==1.4.0 - # via bleach -tornado==6.5.2 - # via - # bokeh - # ipykernel - # jupyter-client - # jupyter-server - # jupyterlab - # terminado -tqdm==4.67.1 - # via pandas-ta -traitlets==5.14.3 - # via - # ipykernel - # ipython - # jupyter-client - # jupyter-core - # jupyter-events - # jupyter-server - # jupyterlab - # matplotlib-inline - # nbclient - # nbconvert - # nbformat -types-python-dateutil==2.9.0.20250822 - # via arrow -typing-extensions==4.15.0 - # via - # anyio - # beautifulsoup4 - # referencing -tzdata==2025.2 - # via pandas -uri-template==1.3.0 - # via jsonschema -urllib3==2.5.0 - # via requests -wcwidth==0.2.13 - # via prompt-toolkit -webcolors==24.11.1 - # via jsonschema -webencodings==0.5.1 - # via - # bleach - # tinycss2 -websocket-client==1.8.0 - # via jupyter-server -websockets==15.0.1 - # via yfinance -xyzservices==2025.4.0 - # via bokeh -yfinance==0.2.65 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/__init__.py deleted file mode 100644 index 703f7496..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/__init__.py +++ /dev/null @@ -1,65 +0,0 @@ -# -*- coding: utf-8 -*- -from importlib.metadata import version -version = version("pandas-ta") - -from pandas_ta.maps import EXCHANGE_TZ, RATE, Category, Imports -from pandas_ta.utils import * -from pandas_ta.utils import __all__ as utils_all - -# Flat Structure. Supports ta.ema() or ta.overlap.ema() -from pandas_ta.candle import * -from pandas_ta.cycle import * -from pandas_ta.momentum import * -from pandas_ta.overlap import * -from pandas_ta.performance import * -from pandas_ta.statistics import * -from pandas_ta.trend import * -from pandas_ta.volatility import * -from pandas_ta.volume import * -from pandas_ta.candle import __all__ as candle_all -from pandas_ta.cycle import __all__ as cycle_all -from pandas_ta.momentum import __all__ as momentum_all -from pandas_ta.overlap import __all__ as overlap_all -from pandas_ta.performance import __all__ as performance_all -from pandas_ta.statistics import __all__ as statistics_all -from pandas_ta.trend import __all__ as trend_all -from pandas_ta.volatility import __all__ as volatility_all -from pandas_ta.volume import __all__ as volume_all - -# Common Averages useful for Indicators -# with a mamode argument, like ta.adx() -from pandas_ta.ma import ma - -# Custom External Directory Commands. See help(import_dir) -from pandas_ta.custom import create_dir, import_dir - -# Enable "ta" DataFrame Extension -from pandas_ta.core import AnalysisIndicators - -__all__ = [ - # "name", - "EXCHANGE_TZ", - "RATE", - "Category", - "Imports", - "version", - "ma", - "create_dir", - "import_dir", - "AnalysisIndicators", - "AllStudy", - "CommonStudy", -] - -__all__ += [ - utils_all - + candle_all - + cycle_all - + momentum_all - + overlap_all - + performance_all - + statistics_all - + trend_all - + volatility_all - + volume_all -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/__main__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/__main__.py deleted file mode 100644 index c842f73f..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/__main__.py +++ /dev/null @@ -1,7 +0,0 @@ -#-*- coding: utf-8 -*- -from pandas_ta import version - -SUPPORT="http://www.pandas-ta.dev/support" - -if __name__ == "__main__": - print(f"Pandas TA: {version}\nSupport: {SUPPORT}") diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/_typing.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/_typing.py deleted file mode 100644 index ffc1165d..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/_typing.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- -from pathlib import Path -from typing import ( - Any, - Dict, - Iterable, - List, - Optional, - Sequence, - TextIO, - Tuple, - TypeVar, - Union -) - -from numpy import ndarray, recarray, void -from numpy import bool_ as np_bool_ -from numpy import floating as np_floating -from numpy import generic as np_generic -from numpy import integer as np_integer -from numpy import number as np_number -from pandas import DataFrame, Series - - - -# Generic types -T = TypeVar("T") - -# Scalars -Scalar = Union[str, float, int, complex, bool, object, np_generic] -Number = Union[int, float, complex, np_number, np_bool_] -Int = Union[int, np_integer] -Float = Union[float, np_floating] -IntFloat = Union[Int, Float] - -# Basic sequences -MaybeTuple = Union[T, Tuple[T, ...]] -MaybeList = Union[T | List[T]] -TupleList = Union[List[T], Tuple[T, ...]] -MaybeTupleList = Union[T, List[T], Tuple[T, ...]] -MaybeIterable = Union[T, Iterable[T]] -MaybeSequence = Union[T, Sequence[T]] -ListStr = List[str] - -DictLike = Union[None, dict] -DictLikeSequence = MaybeSequence[DictLike] -Args = Tuple[Any, ...] -ArgsLike = Union[None, Args] -Kwargs = Dict[str, Any] -KwargsLike = Union[None, Kwargs] -KwargsLikeSequence = MaybeSequence[KwargsLike] -FileName = Union[str, Path] - -DTypeLike = Any -PandasDTypeLike = Any -Shape = Tuple[int, ...] -RelaxedShape = Union[int, Shape] -Array = ndarray -Array1d = ndarray -Array2d = ndarray -Array3d = ndarray -Record = void -RecordArray = ndarray -RecArray = recarray -MaybeArray = Union[T, Array] -SeriesFrame = Union[Series, DataFrame] -MaybeSeries = Union[T, Series] -MaybeSeriesFrame = Union[T, Series, DataFrame] -AnyArray = Union[Array, Series, DataFrame] -AnyArray1d = Union[Array1d, Series] -AnyArray2d = Union[Array2d, DataFrame] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/__init__.py deleted file mode 100644 index 9da8e2ae..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# -*- coding: utf-8 -*- -from .cdl_doji import cdl_doji -from .cdl_inside import cdl_inside -from .cdl_pattern import cdl_pattern, cdl, ALL_PATTERNS as CDL_PATTERN_NAMES -from .cdl_z import cdl_z -from .ha import ha - -__all__ = [ - "cdl_doji", - "cdl_inside", - "cdl_pattern", - "cdl", - "CDL_PATTERN_NAMES", - "cdl_z", - "ha", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_doji.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_doji.py deleted file mode 100644 index bd279d88..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_doji.py +++ /dev/null @@ -1,90 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta.overlap import sma -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import high_low_range, v_percent -from pandas_ta.utils import real_body, v_offset, v_pos_default -from pandas_ta.utils import v_bool, v_scalar, v_series - - - -def cdl_doji( - open_: Series, high: Series, low: Series, close: Series, - length: Int = None, factor: IntFloat = None, - scalar: IntFloat = None, asint: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Doji - - Attempts to identify a "Doji" candle which is shorter than 10% of - the average of the 10 previous bars High-Low range. - - Sources: - * [TA Lib](https://github.com/TA-Lib/ta-lib/blob/main/src/ta_func/ta_CDLDOJI.c) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - factor (float): Doji value. Default: ```100``` - scalar (float): Scalar. Default: ```100``` - asint (bool): Returns as ```Int```. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - naive (bool): Prefills potential Doji; bodies that are less - than a percentage, ```factor```, of it's High-Low range. - Default: ```False``` - fillna (value): Replaces ```na```'s with ```value```. - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9434563530497265)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 10) - open_ = v_series(open_, length) - high = v_series(high, length) - low = v_series(low, length) - close = v_series(close, length) - - if open_ is None or high is None or low is None or close is None: - return - - factor = v_scalar(factor, 10) if v_percent(factor) else 10 - scalar = v_scalar(scalar, 100) - asint = v_bool(asint, True) - offset = v_offset(offset) - naive = kwargs.pop("naive", False) - - # Calculate - body = real_body(open_, close).abs() - hl_range = high_low_range(high, low).abs() - hl_range_avg = sma(hl_range, length) - doji = body < 0.01 * factor * hl_range_avg - - if naive: - doji.iat[:length] = body < 0.01 * factor * hl_range - if asint: - doji = scalar * doji.astype(int) - - # Offset - if offset != 0: - doji = doji.shift(offset) - - # Fill - if "fillna" in kwargs: - doji.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - doji.name = f"CDL_DOJI_{length}_{0.01 * factor}" - doji.category = "candle" - - return doji diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_inside.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_inside.py deleted file mode 100644 index 99b1a19d..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_inside.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import roll, where -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import ( - v_bool, - v_offset, - v_offset, - v_scalar, - v_series -) - - - -@njit(cache=True) -def np_cdl_inside(high, low): - hdiff = where(high - roll(high, 1) < 0, 1, 0) - ldiff = where(low - roll(low, 1) > 0, 1, 0) - return hdiff & ldiff - - -def cdl_inside( - open_: Series, high: Series, low: Series, close: Series, - asbool: bool = None, scalar: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Inside Bar - - Attempts to identify an "Inside" candle which is smaller than it's - previous candle. - - Sources: - * [TA Lib](https://github.com/TA-Lib/ta-lib/blob/main/src/ta_func/ta_CDL3INSIDE.c) - * [tradingview](https://www.tradingview.com/script/IyIGN1WO-Inside-Bar/) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - asbool (bool): Return booleans. Default: ```False``` - scalar (float): Scalar. Default: ```100``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): Replaces ```na```'s with ```value```. - - Returns: - (Series): 1 column - """ - # Validate - open_ = v_series(open_) - high = v_series(high) - low = v_series(low) - close = v_series(close) - - if open_ is None or high is None or low is None or close is None: - return - - asbool = v_bool(asbool, False) - scalar = v_scalar(scalar, 100) - offset = v_offset(offset) - - # Calculate - np_high, np_low = high.to_numpy(), low.to_numpy() - np_inside = np_cdl_inside(np_high, np_low) - inside = Series(np_inside, index=close.index, dtype=bool) - - if not asbool: - inside = scalar * inside.astype(int) - - # Offset - if offset != 0: - inside = inside.shift(offset) - - # Fill - if "fillna" in kwargs: - inside.fillna(kwargs["fillna"], inplace=True) - # Name and Category - inside.name = f"CDL_INSIDE" - inside.category = "candle" - - return inside diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_pattern.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_pattern.py deleted file mode 100644 index 2f9c40e3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_pattern.py +++ /dev/null @@ -1,126 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series, DataFrame -from pandas_ta._typing import DictLike, Int, IntFloat, List -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_scalar, v_series -from pandas_ta.candle import cdl_doji, cdl_inside - - - -ALL_PATTERNS = [ - "2crows", "3blackcrows", "3inside", "3linestrike", "3outside", - "3starsinsouth", "3whitesoldiers", "abandonedbaby", "advanceblock", - "belthold", "breakaway", "closingmarubozu", "concealbabyswall", - "counterattack", "darkcloudcover", "doji", "dojistar", "dragonflydoji", - "engulfing", "eveningdojistar", "eveningstar", "gapsidesidewhite", - "gravestonedoji", "hammer", "hangingman", "harami", "haramicross", - "highwave", "hikkake", "hikkakemod", "homingpigeon", "identical3crows", - "inneck", "inside", "invertedhammer", "kicking", "kickingbylength", - "ladderbottom", "longleggeddoji", "longline", "marubozu", "matchinglow", - "mathold", "morningdojistar", "morningstar", "onneck", "piercing", - "rickshawman", "risefall3methods", "separatinglines", "shootingstar", - "shortline", "spinningtop", "stalledpattern", "sticksandwich", "takuri", - "tasukigap", "thrusting", "tristar", "unique3river", "upsidegap2crows", - "xsidegap3methods" -] - - -def cdl_pattern( - open_: Series, high: Series, low: Series, close: Series, - name: str | List[str] = "all", - scalar: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Candle Pattern - - This function wraps TA Lib candle patterns. - - Sources: - * [TA Lib](https://ta-lib.org) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - name (str | List[str]): Pattern name or a list of pattern names. - Default: ```"all"``` - scalar (float): Scalar. Default: ```100``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): Replaces ```na```'s with ```value```. - - Returns: - (DataFrame): Pattern Column(s) - - Warning: TA Lib - TA Lib must be installed - """ - # Validate Arguments - open_ = v_series(open_, 1) - high = v_series(high, 1) - low = v_series(low, 1) - close = v_series(close, 1) - - if open_ is None or high is None or low is None or close is None: - return - - offset = v_offset(offset) - scalar = v_scalar(scalar, 100) - - pta_patterns = {"doji": cdl_doji, "inside": cdl_inside} - - if name == "all": - name = ALL_PATTERNS - - if isinstance(name, str): - name = [name] - - if Imports["talib"]: - import talib.abstract as tala - - result = {} - for n in name: - if n not in ALL_PATTERNS: - print(f"[X] There is no candle pattern named {n} available!") - continue - - if n in pta_patterns: - pattern_result = pta_patterns[n]( - open_, high, low, close, offset=offset, scalar=scalar, **kwargs - ) - if not isinstance(pattern_result, Series): - continue - result[pattern_result.name] = pattern_result - - else: - if not Imports["talib"]: - print(f"[i] Requires TA-Lib to use {n}. (pip install TA-Lib)") - continue - - pf = tala.Function(f"CDL{n.upper()}") - pattern_result = Series( - 0.01 * scalar * pf(open_, high, low, close, **kwargs) - ) - pattern_result.index = close.index - - # Offset - if offset != 0: - pattern_result = pattern_result.shift(offset) - - # Fill - if "fillna" in kwargs: - pattern_result.fillna(kwargs["fillna"], inplace=True) - result[f"CDL_{n.upper()}"] = pattern_result - - if len(result) == 0: - return - - # Name and Category - df = DataFrame(result) - df.name = "CDL_PATTERN" - df.category = "candle" - return df - -cdl = cdl_pattern # Alias diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_z.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_z.py deleted file mode 100644 index 24a5d9e5..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/cdl_z.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.statistics import zscore -from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series - - - -def cdl_z( - open_: Series, high: Series, low: Series, close: Series, - length: Int = None, full: bool = None, ddof: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Z Candles - - Creates candlesticks using a rolling Z Score. - - Sources: - * Kevin Johnson - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - full (bool): Apply ```length``` to whole DataFrame. - Default: ```False``` - ddof (int): By default, uses Pandas ```ddof=1```. - For Numpy calculation, use ```0```. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - naive (bool): If ```True```, prefills potential Doji less - than the length if it less than a percentage of it's - High-Low range. Default: ```False``` - fillna (value): Replaces ```na```'s with ```value```. - - Returns: - (DataFrame): 4 columns - - Note: - * Numpy ```std()``` [ddof](https://numpy.org/doc/stable/reference/generated/numpy.std.html) explanation. - """ - # Validate - length = v_pos_default(length, 30) - open_ = v_series(open_, length) - high = v_series(high, length) - low = v_series(low, length) - close = v_series(close, length) - - if open_ is None or high is None or low is None or close is None: - return - - full = v_bool(full, False) - ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1 - offset = v_offset(offset) - - # Calculate - if full: - length = close.size - - z_open = zscore(open_, length=length, ddof=ddof) - z_high = zscore(high, length=length, ddof=ddof) - z_low = zscore(low, length=length, ddof=ddof) - z_close = zscore(close, length=length, ddof=ddof) - - _full = "a" if full else "" - _props = _full if full else f"_{length}_{ddof}" - data = { - f"open_Z{_props}": z_open, - f"high_Z{_props}": z_high, - f"low_Z{_props}": z_low, - f"close_Z{_props}": z_close, - } - df = DataFrame(data, index=close.index) - - if full: - df.fillna(method="backfill", axis=0, inplace=True) - - # Offset - if offset != 0: - df = df.shift(offset) - - # Fill - if "fillna" in kwargs: - df.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - df.name = f"CDL_Z{_props}" - df.category = "candle" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/ha.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/ha.py deleted file mode 100644 index 59a02971..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/candle/ha.py +++ /dev/null @@ -1,86 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import empty_like, maximum, minimum -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_series - - - -@njit(cache=True) -def np_ha(np_open, np_high, np_low, np_close): - ha_close = 0.25 * (np_open + np_high + np_low + np_close) - ha_open = empty_like(ha_close) - ha_open[0] = 0.5 * (np_open[0] + np_close[0]) - - m = np_close.size - for i in range(1, m): - ha_open[i] = 0.5 * (ha_open[i - 1] + ha_close[i - 1]) - - ha_high = maximum(maximum(ha_open, ha_close), np_high) - ha_low = minimum(minimum(ha_open, ha_close), np_low) - - return ha_open, ha_high, ha_low, ha_close - - -def ha( - open_: Series, high: Series, low: Series, close: Series, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Heikin Ashi Candles - - Creates Japanese _ohlc_ candlesticks that attempts to filter out market - noise. Developed by Munehisa Homma in the 1700s, Heikin Ashi Candles share - some characteristics with standard candlestick charts but creates a - smoother candlestick appearance. - - Sources: - * [Investopedia](https://www.investopedia.com/terms/h/heikinashi.asp) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): Replaces ```na```'s with ```value```. - - Returns: - (DataFrame): 4 columns - """ - # Validate - open_ = v_series(open_, 1) - high = v_series(high, 1) - low = v_series(low, 1) - close = v_series(close, 1) - offset = v_offset(offset) - - if open_ is None or high is None or low is None or close is None: - return - - # Calculate - np_open, np_high = open_.to_numpy(), high.to_numpy() - np_low, np_close = low.to_numpy(), close.to_numpy() - ha_open, ha_high, ha_low, ha_close = np_ha(np_open, np_high, np_low, np_close) - df = DataFrame({ - "HA_open": ha_open, - "HA_high": ha_high, - "HA_low": ha_low, - "HA_close": ha_close, - }, index=close.index) - - # Offset - if offset != 0: - df = df.shift(offset) - - # Fill - if "fillna" in kwargs: - df.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - df.name = "Heikin-Ashi" - df.category = "candle" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/core.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/core.py deleted file mode 100644 index 832a3257..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/core.py +++ /dev/null @@ -1,1834 +0,0 @@ -# -*- coding: utf-8 -*- -from dataclasses import dataclass -from multiprocessing import cpu_count, Pool -from pathlib import Path -from time import perf_counter -from warnings import simplefilter - -from numpy import log, log10, ndarray -from pandas.api.extensions import register_dataframe_accessor -from pandas.errors import PerformanceWarning -from pandas import DataFrame, Series -from pandas import options as pd_options -from tqdm import tqdm - -from pandas_ta._typing import * -from pandas_ta import * - -# Recommended moving forward to Pandas 3 -pd_options.mode.copy_on_write = True - - - -@register_dataframe_accessor("ta") -class AnalysisIndicators(object): - """Pandas DataFrame Extension: "ta" - - The "ta" extension simplifies the processing of concatenating - Technical Analysis indicators onto the existing Pandas DataFrame. - To do so, this extension assumes that the DataFrame includes a DateTime - oriented index and columns named: "open", "high", "low", "close", "volume". - - Returns: - (Series | DataFrame | None): See Notes - - Features: - * Attributes and methods for _ohlcv_ data. - * Indicator Wrappers for each. Simplifies - ```sma = ta.sma(df["Close"]); df["sma"] = sma``` to ```df.ta.sma(append=True)``` - * A ```study``` method, that simplifies processing multiple indicators - with or without multiprocessing. See: ```help(ta.study)``` - - See Also: - * Pandas TA [DataFrame Extension](http://127.0.0.1:8000/docs/api/dataframe/) Documention - * [Pandas DataFrame Accessor](https://pandas.pydata.org/docs/reference/api/pandas.api.extensions.register_dataframe_accessor.html#pandas.api.extensions.register_dataframe_accessor) - - Notes: - Most Indicators will return a Pandas Series. Others like MACD, - BBANDS, KC, et al will return a Pandas DataFrame. Ichimoku on the - other hand will return two DataFrames, the Ichimoku DataFrame for - the known period and a Span DataFrame for the future of the Span values. - - Documentation is formatted for [mkdocs](https://www.mkdocs.org/) and [mkdocs-docstrings](https://mkdocstrings.github.io/). - - Tip: - Remember to adjust the ```cores``` for maximum speed! - """ - # DataFrame Extension Properties/Attributes - _adjusted = None - _cores = cpu_count() - _custom = None - _df = DataFrame() - _ds = "yf" if Imports["yfinance"] else None - _exchange = "NYSE" - _last_run = get_time(_exchange, to_string=True) - _time_range = "years" - - - def __init__(self, obj: SeriesFrame): - v_dataframe(obj) - self._df = obj - self._last_run = get_time(self._exchange, to_string=True) - - - # DataFrame Behavioral Methods - def __call__( - self, kind: str = None, timed: bool = False, - version: bool = False, **kwargs: DictLike - ): - if version: - print(f"Pandas TA - Technical Analysis Indicators - v{version}") - try: - if isinstance(kind, str): - # Get the indicator named "kind" as fn - kind = kind.lower() - - # if kind == "ta": - # self.help() - - fn = getattr(self, kind) - - if timed: - stime = perf_counter() - - # Run the indicator - # Equivalent: fn(**kwargs) = getattr(self, kind)(**kwargs) - result = fn(**kwargs) - - if timed: - result.timed = final_time(stime) - print(f"[+] {kind}: {result.timed}") - - self._last_run = get_time(self.exchange, to_string=True) - return result - else: - self.help() - - except BaseException: - pass - - - @property - def adjusted(self) -> str: - """Get/Set the **adjusted_close** column. - - Parameters: - name (str): The column ```name```, to set as the *adjusted* - column. Default: ```None``` - - Returns: - (str): The *adjusted* column. - """ - return self._adjusted - - - @adjusted.setter - def adjusted(self, name: str) -> None: - if name is not None and isinstance(name, str): - self._adjusted = name - else: - self._adjusted = None - - - @property - def cores(self) -> Int: - """Manage the number of ```cpus``` to utilize for multiprocessing. - - Parameters: - cpus (Int): Number of ```cores``` Default: ```cpu_count()``` - - Returns: - (Int): The number of ```cores``` set for multiprocessing - - !!! tip "Disable multiprocessing" - Set: ```df.ta.cores = 0``` - - !!! warning "Multiprocessing caveats" - * Multiprocessing **will not** work when using the following keys: ```"col_names"```. - * Multiprocessing **may not** work or complete as expected when indicator _chaining_. - * Chained indicators use non-default, _ohlcv_, sources as input Series. - """ - return self._cores - - @cores.setter - def cores(self, cpus: Int) -> None: - _cpus = cpu_count() - if cpus is not None and isinstance(cpus, int): - self._cores = int(cpus) if 0 <= cpus <= _cpus else _cpus - else: - self._cores = _cpus - - - @property - def exchange(self) -> str: - """A label property of the major _exchange_ the _ohlcv_ data is - associated with. - - Parameters: - value (str): The ```exchange```. Must be one of the following - ```ta.EXCHANGE_TZ.keys()```. Default: ```"NYSE"``` - - Returns: - (str): Exchange label - """ - return self._exchange - - @exchange.setter - def exchange(self, value: str) -> None: - if value is not None and isinstance(value, str) and value in EXCHANGE_TZ.keys(): - self._exchange = value - - - @property - def time_range(self) -> IntFloat: - """Useful for annualization. - - Parameters: - value (str): ```"months", "weeks", "days", "hours", "minutes", - "seconds"```. Default: ```"years"``` - - Returns: - (float): Time elapsed between the first and last DateTimeIndex - """ - return total_time(self._df, self._time_range) - - - @time_range.setter - def time_range(self, value: str) -> None: - if value is not None and isinstance(value, str): - self._time_range = value - else: - self._time_range = "years" - - - # Private DataFrame Methods - def _add_prefix_suffix(self, - result: MaybeSeriesFrame = None, **kwargs: DictLike - ) -> MaybeSeriesFrame: - """Add prefix and/or suffix to the result columns""" - if result is None: - return - else: - prefix = suffix = "" - delimiter = kwargs.setdefault("delimiter", "_") - - if "prefix" in kwargs: - prefix = f"{kwargs['prefix']}{delimiter}" - if "suffix" in kwargs: - suffix = f"{delimiter}{kwargs['suffix']}" - - if isinstance(result, Series): - result.name = prefix + result.name + suffix - else: - result.columns = [prefix + column + suffix for column in result.columns] - - - def _append(self, - result: MaybeSeriesFrame = None, **kwargs: DictLike - ) -> MaybeSeriesFrame: - """Appends a Pandas Series or DataFrame columns to self._df.""" - if result is None: return - - if "col_names" in kwargs and not isinstance(kwargs["col_names"], tuple): - # Note: tuple(kwargs["col_names"]) doesn't work - kwargs["col_names"] = (kwargs["col_names"],) - - df = self._df - if isinstance(result, DataFrame): - simplefilter(action="ignore", category=PerformanceWarning) - pd_options.mode.chained_assignment = None - - # Rename the columns if kwargs["col_names"] - if "col_names" in kwargs and isinstance(kwargs["col_names"], tuple): - if len(kwargs["col_names"]) >= len(result.columns): - for col, ind_name in zip(result.columns, kwargs["col_names"]): - df[ind_name] = result.loc[:, col] - else: - print(f"[!] Not enough col_names were specified : got {len(kwargs['col_names'])}, expected {len(result.columns)}.") - return - else: - for i, column in enumerate(result.columns): - df.loc[:, (column)] = result.iloc[:, i] - else: - ind_name = ( - kwargs["col_names"][0] - if "col_names" in kwargs and isinstance(kwargs["col_names"], tuple) - else result.name - ) - df.loc[:, (ind_name)] = result - - - def _check_na_columns(self): - """Returns the columns in which all it's values are na.""" - return [x for x in self._df.columns if all(self._df[x].isna())] - - - def _get_column(self, series: Series | str | None): - """Attempts to get the correct series or 'column' and return it.""" - df = self._df - if df is None: return - - # Explicitly passing a Series to override default. - if isinstance(series, Series): - return series - # Apply default if no series nor a default. - elif series is None: - return df[self.adjusted] if self.adjusted is not None else None - # Ok. So it's a str. - elif isinstance(series, str): - # Return the df column since it's in there. - if series in df.columns: - return df[series] - else: - # Attempt to match the 'series' because it was likely - # misspelled. - matches = df.columns.str.match(series, case=False) - match = [i for i, x in enumerate(matches) if x] - # If found, awesome. Return it or return the 'series'. - NOT_FOUND = f"[X] The '{series}' column was not found in" - cols = ", ".join(list(df.columns)) - - if len(df.columns): NOT_FOUND += f": {cols}" - else: NOT_FOUND += " the DataFrame" - - if len(match): - return df.iloc[:, match[0]] - else: - print(NOT_FOUND) - - - def _indicators_by_category(self, name: str) -> List: - """Returns indicators by Categorical name.""" - return Category[name] if name in self.categories() else None - - - def _mp_worker(self, arguments: Tuple): - """Multiprocessing Worker to handle different Methods.""" - method, args, kwargs = arguments - - if method != "ichimoku": - return getattr(self, method)(*args, **kwargs) - else: - return getattr(self, method)(*args, **kwargs)[0] - - - def _post_process(self, - result: Series | DataFrame, **kwargs: DictLike - ) -> Series | DataFrame: - """Applies any additional modifications to the DataFrame - - * Applies prefixes and/or suffixes - * Appends the result to main DataFrame - """ - verbose = kwargs.pop("verbose", False) - if not isinstance(result, (Series, DataFrame)): - if verbose: - print(f"[X] The result is not a Series or DataFrame.") - return self._df - else: - # Append only specific columns to the dataframe (via - # 'col_numbers':(0,1,3) for example) - result = ( - result.iloc[:, [int(n) for n in kwargs["col_numbers"]]] - if isinstance(result, DataFrame) and - "col_numbers" in kwargs and - kwargs["col_numbers"] is not None else result - ) - # Add prefix/suffix and append to the dataframe - self._add_prefix_suffix(result=result, **kwargs) - - if "append" in kwargs and isinstance(kwargs["append"], bool): - if not kwargs["append"]: - # Issue 388 - No appending, just print to stdout - # No DatetimeIndex could break execution. - print(result) - else: - # Default: Appends result to DataFrame - self._append(result=result, **kwargs) - return result - - - def _study_mode(self, *args: Args) -> Tuple: - """Returns tuple: (name:str, mode:dict)""" - name = "All" - mode = {"all": False, "category": False, "custom": False} - - if len(args) == 0: - mode["all"] = True - else: - _categories = self.categories() - if isinstance(args[0], str): - if args[0].lower() == "all": - name, mode["all"] = name, True - if args[0].lower() in _categories: - name, mode["category"] = args[0], True - - if isinstance(args[0], Study): - study_ = args[0] - if study_.ta is None or study_.name.lower() == "all": - name, mode["all"] = name, True - elif study_.name.lower() in _categories: - name, mode["category"] = study_.name, True - else: - name, mode["custom"] = study_.name, True - - return name, mode - - - # Public DataFrame Methods - def baseline(self, - zero: bool = False, index: int = 0, - k: IntFloat = 1, to_log: bool = False, save: bool = False - ) -> DataFrame: - """baseline - - This method updates the DataFrame _ohlc_ values with a baseline - of ```k=1```. Useful for comparisons. - - Parameters: - zero (bool): Zero the _ohlc_ data. - index (bool): Index to baseline at. - k (IntFloat): Scaler. - to_log (bool): Pre apply ```np.log```. - save (bool): Preserve _ohlc_ when using ```to_log```. - """ - open_ = self._get_column("open") - high = self._get_column("high") - low = self._get_column("low") - close = self._get_column("close") - - zero = v_bool(zero, False) - index = v_pos_default(index, 0) - k = v_scalar(k, 1) - to_log = v_bool(to_log, False) - save = v_bool(save, False) - - if index >= self._df.shape[0]: - index = self._df.shape[0] - 1 - - if to_log: - if save: - self._df["_open"] = open_ - self._df["_high"] = high - self._df["_low"] = low - self._df["_close"] = close - - open_ = log(open_) - high = log(high) - low = log(low) - close = log(close) - - self._df.loc[:, (open_.name)] = k * open_ / open_.iloc[index] - self._df.loc[:, (high.name)] = k * high / high.iloc[index] - self._df.loc[:, (low.name)] = k * low / low.iloc[index] - self._df.loc[:, (close.name)] = k * close / close.iloc[index] - - if zero: - self._df.loc[:, (open_.name)] -= k - self._df.loc[:, (high.name)] -= k - self._df.loc[:, (low.name)] -= k - self._df.loc[:, (close.name)] -= k - - - def categories(self) -> ListStr: - """categories - - List of categories. - - Returns: - (ListStr): List of the indicator categories. - """ - return list(Category.keys()) - - - def constants(self, append: bool, values: Array | List) -> PandasDTypeLike | None: - """constants - - Concatenate / Drop constant(s) to the DataFrame. - - Parameters: - append (bool): Concatenate if ```True```. Drop if ```False```. - Default: ```None``` - values (Array): List/Numpy array of ```values``` to append/drop from - the DataFrame. - - Returns: - (Series, DataFrame, None): Depends upon parameters. - - See Also: - * [TA DataFrame Constants](../support/how-to.md) - """ - if isinstance(values, ndarray) or isinstance(values, list): - if append: - for x in values: - self._df[f"{x}"] = x - return self._df[self._df.columns[-len(values):]] - else: - for x in values: - del self._df[f"{x}"] - - - def datetime_ordered(self) -> bool: - """datetime_ordered - - DataFrame DateTime ordered? - - Returns: - (bool): ```True``` if the DataFrame is DateTime ordered, - otherwise ```False```. - """ - return v_datetime_ordered(self._df) - - - def help(self, s: str ="") -> None | TextIO: - """help - - Help! - - Parameters: - s (str): String to search for. Default: ```""``` - - Returns: - (None | TextIO): Opens web browser to relevant Pandas TA website - page or prints all search keywords. - """ - return help(s) - - - def indicators(self, as_list: bool = None, exclude: ListStr = None) -> TextIO | ListStr: - """indicators - - List indicators. - - Parameters: - as_list (bool): Return as a list. Default: ```False``` - exclude (ListStr): The passed in list will be excluded - from the indicators list. Default: ```None``` - - Returns: - (TextIO | ListStr): Prints list or returns a ```ListStr```. - """ - as_list = bool(as_list) if isinstance(as_list, bool) else False - user_excluded = [] - if isinstance(exclude, list) and len(exclude): - user_excluded = exclude - - # Public DataFrame Extension methods - df_ext_methods = [ - "baseline", - "categories", - "constants", - "datetime_ordered", - "help", - "indicators", - "last_run", - "reverse", - "study", - "ticker", - "to_utc", - ] - # Public df.ta.properties - ta_properties = [ - "adjusted", - "cores", - # "custom", - # "ds", - "exchange", - "time_range" - ] - - # Public non-indicator methods - ta_indicators = list((x for x in dir(DataFrame().ta) if not x.startswith("_") and not x.endswith("_"))) - - # Add Pandas TA methods and properties to be removed - removed = df_ext_methods + ta_properties - - # Add user excluded methods to be removed - if isinstance(user_excluded, list) and len(user_excluded) > 0: - removed += user_excluded - - # Remove the unwanted indicators - [ta_indicators.remove(x) for x in removed] - - if as_list: - return ta_indicators - - indicator_count = len(ta_indicators) - header = f"Pandas TA - Technical Analysis Indicators - v{version}" - - s, _count = f"{header}\n", 0 - if indicator_count > 0: - from pandas_ta.candle.cdl_pattern import ALL_PATTERNS - s += f"\nIndicators and Utilities [{indicator_count}]:\n {', '.join(ta_indicators)}\n" - _count += indicator_count - if Imports["talib"]: - s += f"\nCandle Patterns [{len(ALL_PATTERNS)}]:\n {', '.join(ALL_PATTERNS)}\n" - _count += len(ALL_PATTERNS) - s += f"\nTotal Candles, Indicators and Utilities: {_count}" - print(s) - - - def last_run(self) -> str: - """last_run - - Detailed string of last run time. - - Returns: - (str): Detailed date and time of the lastest run. - """ - return self._last_run - - - def reverse(self) -> None: - """reverse - - Reverse the DataFrame inplace. - - Returns: - (None): DataFrame reversed inplace. - """ - self._df.index = self._df.iloc[::-1].index - - - def study(self, *args: Args, **kwargs: DictLike) -> dataclass: - """study - - Applies the ```ta``` listed in a [```Study```](studies.md). - - Other Parameters: - chunksize (int): Multiprocessing Pool chunksize. - Default: ```df.ta.cores``` - cores (int): Number of Multiprocessing cores. - Default: ```df.ta.cores``` - exclude (ListStr): List of indicator names. Default: ```[]``` - ordered (bool): Run ```ta``` in order. Default: ```True``` - returns (bool): Return the DataFrame. Default: ```False``` - timed (bool): Print the process time. Default: ```False``` - verbose (bool): More verbose output. Default: ```False``` - - Note: Multiprocessing - Multiprocessing is **not** viable or efficient for some cases. - Customize for production. See [Multiprocessing](https://docs.python.org/3.12/library/multiprocessing.html) - for more information. - """ - all_ordered = kwargs.pop("ordered", True) - # Append indicators to the DataFrame by default - kwargs.setdefault("append", True) - # If True, it returns the resultant DataFrame. Default: False - returns = kwargs.pop("returns", False) - - mp_chunksize = kwargs.pop("chunksize", self.cores) - cores = kwargs.pop("cores", self.cores) - self.cores = cores - - # Initialize - initial_column_count = self._df.shape[1] - excluded = ["long_run", "short_run", "tsignals", "xsignals"] - - # Get the Study Name and mode - name, mode = self._study_mode(*args) - - # If All or a Category, exclude user list if any - user_excluded = kwargs.pop("exclude", []) - if isinstance(user_excluded, str) and len(user_excluded) > 1: - user_excluded = [user_excluded] - if mode["all"] or mode["category"]: - excluded += user_excluded - - # Collect the indicators, remove excluded or include kwarg["append"] - if mode["category"]: - ta = self._indicators_by_category(name.lower()) - [ta.remove(x) for x in excluded if x in ta] - elif mode["custom"]: - if hasattr(args[0], "cores") and isinstance(args[0].cores, int): - self.cores = min(args[0].cores, self.cores) - ta = args[0].ta - for kwds in ta: - kwds["append"] = True - elif mode["all"]: - ta = self.indicators(as_list=True, exclude=excluded) - else: - print(f"[X] Study not available.") - return None - - verbose = kwargs.pop("verbose", False) - if verbose: - print(f"[+] Study: {name}\n[i] Indicator arguments: {kwargs}") - if mode["all"] or mode["category"]: - excluded_str = ", ".join(excluded) - print(f"[i] Excluded[{len(excluded)}]: {excluded_str}") - - timed = kwargs.pop("timed", False) - results = [] - use_multiprocessing = True if self.cores > 0 else False - has_col_names = False - - if timed: - stime = perf_counter() - - if use_multiprocessing and mode["custom"]: - # Determine if the Custom Study has "col_names" key - has_col_names = (True if len([ - True for x in ta - if "col_names" in x and isinstance(x["col_names"], tuple) - ]) else False) - - if has_col_names: - use_multiprocessing = False - print(f"[i] Multiprocessing is disabled (cores=0) when using custom \"col_names\".") - - if use_multiprocessing: - _total_ta = len(ta) - with Pool(self.cores) as pool: - # Some magic to optimize chunksize for speed - # based on total ta indicators - if mp_chunksize > _total_ta: - _chunksize = mp_chunksize - 1 - elif mp_chunksize > 0: - _chunksize = mp_chunksize - else: - _chunksize = int(log10(_total_ta)) + 1 - if verbose: - print(f"[i] Multiprocessing {_total_ta} indicators with chunksize {_chunksize} and {self.cores}/{cpu_count()} cpus.") - - results = None - if mode["custom"]: - # Create a list of all the custom indicators into a list - custom_ta = [( - ind["kind"], - ind["params"] if "params" in ind and isinstance(ind["params"], tuple) else (), - {**ind, **kwargs}, - ) for ind in ta] - # Custom multiprocessing pool. Must be ordered for Chained Strategies - # May fix this to cpus if Chaining/Composition if it remains - if verbose: - results = tqdm(pool.map(self._mp_worker, custom_ta, _chunksize), total=len(custom_ta) // _chunksize) - else: - results = pool.map(self._mp_worker, custom_ta, _chunksize) - else: - default_ta = [(ind, tuple(), kwargs) for ind in ta] - tqdm_total = len(default_ta) // _chunksize - # All and Categorical multiprocessing pool. - if all_ordered: - if verbose: - results = tqdm(pool.imap(self._mp_worker, default_ta, _chunksize), total=tqdm_total) # Order over Speed - else: - results = pool.imap(self._mp_worker, default_ta, _chunksize) # Order over Speed - else: - if verbose: - results = tqdm(pool.imap_unordered(self._mp_worker, default_ta, _chunksize), total=tqdm_total) # Speed over Order - else: - results = pool.imap_unordered(self._mp_worker, default_ta, _chunksize) # Speed over Order - if results is None: - print(f"[X] ta.study('{name}') has no results.") - return - - pool.close() - pool.join() - self._last_run = get_time(self.exchange, to_string=True) - - else: - # Without multiprocessing: - if verbose: - _col_msg = f"[i] No multiprocessing. (cores = 0)" - if has_col_names: - _col_msg = f"[i] No multiprocessing support with the 'col_names' keyword." - print(_col_msg) - - if mode["custom"]: - if verbose: - pbar = tqdm(ta, f"[i] Progress") - for ind in pbar: - params = ind["params"] if "params" in ind and isinstance(ind["params"], tuple) else tuple() - getattr(self, ind["kind"])(*params, **{**ind, **kwargs}) - else: - for ind in ta: - params = ind["params"] if "params" in ind and isinstance(ind["params"], tuple) else tuple() - getattr(self, ind["kind"])(*params, **{**ind, **kwargs}) - else: - if verbose: - pbar = tqdm(ta, f"[i] Progress") - for ind in pbar: - getattr(self, ind)(*tuple(), **kwargs) - else: - for ind in ta: - getattr(self, ind)(*tuple(), **kwargs) - self._last_run = get_time(self.exchange, to_string=True) - - # Apply prefixes/suffixes and appends indicator results to the DataFrame - [self._post_process(r, **kwargs) for r in results] - - final_column_count = self._df.shape[1] - _added_columns = final_column_count - initial_column_count - - if verbose: - print(f"[i] Total indicators: {len(ta)}") - print(f"[i] Columns added: {_added_columns}") - print(f"[i] Last Run: {self._last_run}") - if timed: - ft = final_time(stime) - if _added_columns > 0: - avgtd = (perf_counter() - stime) / _added_columns - else: - avgtd = perf_counter() - stime - print(f"[i] Pandas TA Time: {ft} for {_added_columns} columns (avg {avgtd * 1000:2.4f} ms / col)") - - if returns: - return self._df - - - def ticker(self, - ticker: str = None, period: str = None, interval: str = None, - study: Study = None, proxy: dict = None, - timed: bool = False, **kwargs: DictLike - ): - """ticker - - Download Historical _ohlcv_ data as a Pandas DataFrame if _yfinance_ - package is installed. It also can run a ```ta.Study``` afterwards. - - Parameters: - ticker (str): Any string for a ticker you would use - with ```yfinance```. Default: ```"SPY"``` - period (str): See the yfinance ```history()``` method for - more options. Default: ```"max"``` - interval (str): Default: ```"1d"``` - study (ta.Study | str): After downloading, apply ```Study``` - Default: ```None``` - proxy (dict): Proxy dictionary. Default: ```{}``` - timed (bool): Print download time to stdout. Default: ```False``` - - Returns: - (DataFrame | None): _ohlcv_ ```df``` or ```None``` - - Tip: YFinance ```history``` parameters - * [_yfinance_](https://ranaroussi.github.io/yfinance/index.html) - * _yfinance_ [```history()```](https://github.com/ranaroussi/yfinance/blob/main/yfinance/scrapers/history.py) - - Example: - ```py - import panadas as pd - import pandas_ta as ta - - # Simple - df = pd.DataFrame().ta.ticker("SPY", period="2y", timed=True) - - # Built In Study - df = pd.DataFrame().ta.ticker("SPY", period="2y", study=ta.AllStudy, timed=True) - ``` - """ - if self._ds is None: - print(f"[X] Please install yfinance to use this method. (pip install yfinance)") - return - - ticker = v_str(ticker, "SPY") - period = v_str(period, "max") - interval = v_str(interval, "1d") - proxy = proxy if isinstance(proxy, dict) else {} - timed = v_bool(timed, False) - - df, stime = None, None - if self._ds == "yf" and ticker is not None: - import yfinance as yf - yft = yf.Ticker(ticker) - - if timed: stime = perf_counter() - df = yft.history( - period=period, interval=interval, - proxy=proxy, **kwargs - ) - df.name = ticker - else: - return None - - if timed: - df.timed = final_time(stime) - print(f"[+] yf | {ticker}{df.shape}: {df.timed}") - - self._df = df - - if study is not None and isinstance(study, Study): - self.study(study, timed=timed, **kwargs) - - return self._df - - - def to_utc(self) -> None: - """to_utc - - Set the DataFrame index to UTC. - - Returns: - (None): Performs the operation. - """ - self._df = to_utc(self._df) - - - # def version(self) -> str: - # return version - - - # Public DataFrame Methods: Indicators and Utilities - # Candles - def cdl_pattern(self, name: str = "all", offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = cdl_pattern(open_=open_, high=high, low=low, close=close, name=name, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def cdl_z(self, full=None, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = cdl_z(open_=open_, high=high, low=low, close=close, full=full, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ha(self, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = ha(open_=open_, high=high, low=low, close=close, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - # Cycles - def ebsw(self, close=None, length=None, bars=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = ebsw(close=close, length=length, bars=bars, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def reflex(self, close=None, length=None, smooth=None, alpha=None, pi=None, sqrt2=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = reflex(close=close, length=length, smooth=smooth, alpha=alpha, pi=pi, sqrt2=sqrt2, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - # Momentum - def ao(self, fast=None, slow=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = ao(high=high, low=low, fast=fast, slow=slow, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def apo(self, fast=None, slow=None, mamode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = apo(close=close, fast=fast, slow=slow, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def bias(self, length=None, mamode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = bias(close=close, length=length, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def bop(self, percentage=False, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = bop(open_=open_, high=high, low=low, close=close, percentage=percentage, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def brar(self, length=None, scalar=None, drift=None, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = brar(open_=open_, high=high, low=low, close=close, length=length, scalar=scalar, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def cci(self, length=None, c=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = cci(high=high, low=low, close=close, length=length, c=c, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def cfo(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = cfo(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def cg(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = cg(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def cmo(self, length=None, scalar=None, drift=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = cmo(close=close, length=length, scalar=scalar, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def coppock(self, length=None, fast=None, slow=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = coppock(close=close, length=length, fast=fast, slow=slow, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def crsi(self, rsi_length=None, streak_length=None, rank_length=None, drift=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = crsi(close=close, rsi_length=rsi_length, streak_length=streak_length, rank_length=rank_length, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def cti(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = cti(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def dm(self, drift=None, offset=None, mamode=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = dm(high=high, low=low, drift=drift, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def er(self, length=None, drift=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = er(close=close, length=length, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def eri(self, length=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = eri(high=high, low=low, close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def exhc(self, length=None, cap=None, asint=None, show_all=None, nozeros=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = exhc(close=close, length=length, cap=cap, asint=asint, show_all=show_all, nozeros=nozeros, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def fisher(self, length=None, signal=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = fisher(high=high, low=low, length=length, signal=signal, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def inertia(self, length=None, rvi_length=None, scalar=None, refined=None, thirds=None, mamode=None, drift=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - if refined is not None or thirds is not None: - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = inertia(close=close, high=high, low=low, length=length, rvi_length=rvi_length, scalar=scalar, refined=refined, thirds=thirds, mamode=mamode, drift=drift, offset=offset, **kwargs) - else: - result = inertia(close=close, length=length, rvi_length=rvi_length, scalar=scalar, refined=refined, thirds=thirds, mamode=mamode, drift=drift, offset=offset, **kwargs) - - return self._post_process(result, **kwargs) - - def kdj(self, length=None, signal=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = kdj(high=high, low=low, close=close, length=length, signal=signal, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def kst(self, roc1=None, roc2=None, roc3=None, roc4=None, sma1=None, sma2=None, sma3=None, sma4=None, signal=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = kst(close=close, roc1=roc1, roc2=roc2, roc3=roc3, roc4=roc4, sma1=sma1, sma2=sma2, sma3=sma3, sma4=sma4, signal=signal, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def macd(self, fast=None, slow=None, signal=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = macd(close=close, fast=fast, slow=slow, signal=signal, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def mom(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = mom(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def pgo(self, length=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = pgo(high=high, low=low, close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ppo(self, fast=None, slow=None, scalar=None, mamode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = ppo(close=close, fast=fast, slow=slow, scalar=scalar, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def psl(self, open_=None, length=None, scalar=None, drift=None, offset=None, **kwargs): - if open_ is not None: - open_ = self._get_column(kwargs.pop("open", "open")) - - close = self._get_column(kwargs.pop("close", "close")) - result = psl(close=close, open_=open_, length=length, scalar=scalar, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def qqe(self, length=None, smooth=None, factor=None, mamode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = qqe(close=close, length=length, smooth=smooth, factor=factor, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def roc(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = roc(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def rsi(self, length=None, scalar=None, drift=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = rsi(close=close, length=length, scalar=scalar, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def rsx(self, length=None, drift=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = rsx(close=close, length=length, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def rvgi(self, length=None, swma_length=None, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = rvgi(open_=open_, high=high, low=low, close=close, length=length, swma_length=swma_length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def slope(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = slope(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def smc(self, abr_length=None, close_length=None, vol_length=None, percent=None, vol_ratio=None, asint=None, mamode=None, talib=None, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = smc(open_=open_, high=high, low=low, close=close, abr_length=abr_length, close_length=close_length, vol_length=vol_length, percent=percent, vol_ratio=vol_ratio, asint=asint, mamode=mamode, talib=talib, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def smi(self, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = smi(close=close, fast=fast, slow=slow, signal=signal, scalar=scalar, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def squeeze(self, bb_length=None, bb_std=None, kc_length=None, kc_scalar=None, mom_length=None, mom_smooth=None, use_tr=None, mamode=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = squeeze(high=high, low=low, close=close, bb_length=bb_length, bb_std=bb_std, kc_length=kc_length, kc_scalar=kc_scalar, mom_length=mom_length, mom_smooth=mom_smooth, use_tr=use_tr, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def squeeze_pro(self, bb_length=None, bb_std=None, kc_length=None, kc_scalar_wide=None, kc_scalar_normal=None, kc_scalar_narrow=None, mom_length=None, mom_smooth=None, use_tr=None, mamode=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = squeeze_pro(high=high, low=low, close=close, bb_length=bb_length, bb_std=bb_std, kc_length=kc_length, kc_scalar_wide=kc_scalar_wide, kc_scalar_normal=kc_scalar_normal, kc_scalar_narrow=kc_scalar_narrow, mom_length=mom_length, mom_smooth=mom_smooth, use_tr=use_tr, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def stc(self, tclength=None, ma1=None, ma2=None, osc=None, fast=None, slow=None, factor=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = stc(close=close, tclength=tclength, ma1=ma1, ma2=ma2, osc=osc, fast=fast, slow=slow, factor=factor, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def stoch(self, k=None, d=None, smooth_k=None, mamode=None, talib=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = stoch(high=high, low=low, close=close, k=k, d=d, smooth_k=smooth_k, mamode=mamode, talib=talib, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def stochf(self, k=None, d=None, mamode=None, talib=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = stochf(high=high, low=low, close=close, k=k, d=d, mamode=mamode, talib=talib, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def stochrsi(self, length=None, rsi_length=None, k=None, d=None, mamode=None, talib=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = stochrsi(high=high, low=low, close=close, length=length, rsi_length=rsi_length, k=k, d=d, mamode=mamode, talib=talib, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def tmo(self, tmo_length=None, calc_length=None, smooth_length=None, mamode=None, compute_momentum=False, normalize_signal=False, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - close = self._get_column(kwargs.pop("close", "close")) - result = tmo(open_=open_, close=close, tmo_length=tmo_length, calc_length=calc_length, smooth_length=smooth_length, mamode=mamode, compute_momentum=compute_momentum, normalize_signal=normalize_signal, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def trix(self, length=None, signal=None, scalar=None, drift=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = trix(close=close, length=length, signal=signal, scalar=scalar, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def tsi(self, fast=None, slow=None, drift=None, mamode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = tsi(close=close, fast=fast, slow=slow, drift=drift, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def uo(self, fast=None, medium=None, slow=None, fast_w=None, medium_w=None, slow_w=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = uo(high=high, low=low, close=close, fast=fast, medium=medium, slow=slow, fast_w=fast_w, medium_w=medium_w, slow_w=slow_w, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def willr(self, length=None, percentage=True, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = willr(high=high, low=low, close=close, length=length, percentage=percentage, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - # Overlap - def alligator(self, jaw=None, teeth=None, lips=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = alligator(close=close, jaw=jaw, teeth=teeth, lips=lips, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def alma(self, length=None, sigma=None, distribution_offset=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = alma(close=close, length=length, sigma=sigma, distribution_offset=distribution_offset, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def dema(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = dema(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ema(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = ema(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def fwma(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = fwma(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def hilo(self, high_length=None, low_length=None, mamode=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = hilo(high=high, low=low, close=close, high_length=high_length, low_length=low_length, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def hl2(self, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = hl2(high=high, low=low, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def hlc3(self, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = hlc3(high=high, low=low, close=close, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def hma(self, length=None, mamode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = hma(close=close, length=length, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def hwma(self, na=None, nb=None, nc=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = hwma(close=close, na=na, nb=nb, nc=nc, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def jma(self, length=None, phase=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = jma(close=close, length=length, phase=phase, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def kama(self, length=None, fast=None, slow=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = kama(close=close, length=length, fast=fast, slow=slow, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ichimoku(self, tenkan=None, kijun=None, senkou=None, include_chikou=True, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result, span = ichimoku(high=high, low=low, close=close, tenkan=tenkan, kijun=kijun, senkou=senkou, include_chikou=include_chikou, offset=offset, **kwargs) - self._add_prefix_suffix(result, **kwargs) - self._add_prefix_suffix(span, **kwargs) - self._append(result, **kwargs) - # return self._post_process(result, **kwargs), span - return result, span - - def linreg(self, length=None, offset=None, adjust=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = linreg(close=close, length=length, offset=offset, adjust=adjust, **kwargs) - return self._post_process(result, **kwargs) - - def mama(self, fastlimit=None, slowlimit=None, prenan=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = mama(close=close, fastlimit=fastlimit, slowlimit=slowlimit, prenan=prenan, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def mcgd(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = mcgd(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def midpoint(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = midpoint(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def midprice(self, length=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = midprice(high=high, low=low, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ohlc4(self, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = ohlc4(open_=open_, high=high, low=low, close=close, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def pivots(self, method=None, anchor=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = pivots(open_=open_, high=high, low=low, close=close, method=method, anchor=anchor, **kwargs) - return self._post_process(result, **kwargs) - - def pwma(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = pwma(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def rma(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = rma(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def rwi(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = rwi(high=high, low=low, close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def sinwma(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = sinwma(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def sma(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = sma(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def smma(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = smma(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ssf(self, length=None, everget=None, pi=None, sqrt2=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = ssf(close=close, length=length, everget=everget, pi=pi, sqrt2=sqrt2, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ssf3(self, length=None, pi=None, sqrt3=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = ssf3(close=close, length=length, pi=pi, sqrt3=sqrt3, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def supertrend(self, length=None, multiplier=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = supertrend(high=high, low=low, close=close, length=length, multiplier=multiplier, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def swma(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = swma(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def t3(self, length=None, a=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = t3(close=close, length=length, a=a, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def tema(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = tema(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def trima(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = trima(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def vidya(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = vidya(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def wcp(self, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = wcp(high=high, low=low, close=close, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def wma(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = wma(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def zlma(self, length=None, mamode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = zlma(close=close, length=length, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - # Performance - def log_return(self, length=None, cumulative=False, percent=False, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = log_return(close=close, length=length, cumulative=cumulative, percent=percent, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def percent_return(self, length=None, cumulative=False, percent=False, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = percent_return(close=close, length=length, cumulative=cumulative, percent=percent, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - # Statistics - def entropy(self, length=None, base=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = entropy(close=close, length=length, base=base, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def kurtosis(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = kurtosis(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def mad(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = mad(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def median(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = median(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def quantile(self, length=None, q=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = quantile(close=close, length=length, q=q, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def skew(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = skew(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def stdev(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = stdev(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def tos_stdevall(self, length=None, stds=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = tos_stdevall(close=close, length=length, stds=stds, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def variance(self, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = variance(close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def zscore(self, length=None, std=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = zscore(close=close, length=length, std=std, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - # Trend - def adx(self, length=None, lensig=None, mamode=None, scalar=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = adx(high=high, low=low, close=close, length=length, lensig=lensig, mamode=mamode, scalar=scalar, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def alphatrend(self, volume=None, src=None, length=None, multiplier=None, threshold=None, lag=None, mamode=None, talib=None, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - if volume is not None: - volume = self._get_column(kwargs.pop("volume", "volume")) - result = alphatrend(open_=open_, high=high, low=low, close=close, volume=volume, src=src, length=length, multiplier=multiplier, threshold=threshold, lag=lag, mamode=mamode, talib=talib, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def amat(self, fast=None, slow=None, mamode=None, lookback=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = amat(close=close, fast=fast, slow=slow, mamode=mamode, lookback=lookback, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def aroon(self, length=None, scalar=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = aroon(high=high, low=low, length=length, scalar=scalar, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def chop(self, length=None, atr_length=None, ln=None, scalar=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = chop(high=high, low=low, close=close, length=length, atr_length=atr_length, ln=ln, scalar=scalar, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def cksp(self, p=None, x=None, q=None, mamode=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = cksp(high=high, low=low, close=close, p=p, x=x, q=q, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def decay(self, length=None, mode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = decay(close=close, length=length, mode=mode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def decreasing(self, length=None, strict=None, asint=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = decreasing(close=close, length=length, strict=strict, asint=asint, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def dpo(self, length=None, centered=True, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = dpo(close=close, length=length, centered=centered, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ht_trendline(self, talib=None, prenan=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = ht_trendline(close=close, talib=talib, prenan=prenan, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def increasing(self, length=None, strict=None, asint=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = increasing(close=close, length=length, strict=strict, asint=asint, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def long_run(self, fast=None, slow=None, length=None, offset=None, **kwargs): - if fast is None and slow is None: - return self._df - else: - result = long_run(fast=fast, slow=slow, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def psar(self, af0=None, af=None, max_af=None, tv=False, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", None)) - result = psar(high=high, low=low, close=close, af0=af0, af=af, max_af=max_af, tv=tv, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def qstick(self, length=None, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - close = self._get_column(kwargs.pop("close", "close")) - result = qstick(open_=open_, close=close, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def rwi(self, length=None, lensig=None, mamode=None, scalar=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = rwi(high=high, low=low, close=close, length=length, lensig=lensig, mamode=mamode, scalar=scalar, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def short_run(self, fast=None, slow=None, length=None, offset=None, **kwargs): - if fast is None and slow is None: - return self._df - else: - result = short_run(fast=fast, slow=slow, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def supertrend(self, period=None, multiplier=None, mamode=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = supertrend(high=high, low=low, close=close, period=period, multiplier=multiplier, mamode=mamode, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def trendflex(self, close=None, length=None, smooth=None, alpha=None, pi=None, sqrt2=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = trendflex(close=close, length=length, smooth=smooth, alpha=alpha, pi=pi, sqrt2=sqrt2, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def tsignals(self, trend=None, asbool=None, trend_reset=None, trend_offset=None, offset=None, **kwargs): - if trend is None: - return self._df - else: - result = tsignals(trend, asbool=asbool, trend_offset=trend_offset, trend_reset=trend_reset, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def vhf(self, length=None, drift=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = vhf(close=close, length=length, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def vortex(self, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = vortex(high=high, low=low, close=close, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def xsignals(self, signal=None, xa=None, xb=None, above=None, long=None, asbool=None, trend_reset=None, trend_offset=None, offset=None, **kwargs): - if signal is None: - return self._df - else: - result = xsignals(signal=signal, xa=xa, xb=xb, above=above, long=long, asbool=asbool, trend_offset=trend_offset, trend_reset=trend_reset, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def zigzag(self, close=None, legs=None, deviation=None, retrace=None, last_extreme=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - if close is not None: - close = self._get_column(kwargs.pop("close", "close")) - result = zigzag(high=high, low=low, close=close, legs=legs, deviation=deviation, retrace=retrace, last_extreme=last_extreme, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - # Volatility - def aberration(self, length=None, atr_length=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = aberration(high=high, low=low, close=close, length=length, atr_length=atr_length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def accbands(self, length=None, c=None, mamode=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = accbands(high=high, low=low, close=close, length=length, c=c, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def atr(self, length=None, mamode=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = atr(high=high, low=low, close=close, length=length, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def atrts(self, length=None, ma_length=None, multiplier=None, mamode=None, talib=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = atrts(high=high, low=low, close=close, length=length, ma_length=ma_length, multiplier=multiplier, mamode=mamode, talib=talib, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def bbands(self, length=None, lower_std=None, upper_std=None, mamode=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = bbands(close=close, length=length, lower_std=lower_std, upper_std=upper_std, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def chandelier_exit(self, high_length=None, low_length=None, atr_length=None, multiplier=None, mamode=None, talib=None, use_close=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = chandelier_exit(high=high, low=low, close=close, high_length=high_length, low_length=low_length, atr_length=atr_length, multiplier=multiplier, mamode=mamode, talib=talib, use_close=use_close, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def donchian(self, lower_length=None, upper_length=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = donchian(high=high, low=low, lower_length=lower_length, upper_length=upper_length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def hwc(self, na=None, nb=None, nc=None, nd=None, scalar=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = hwc(close=close, na=na, nb=nb, nc=nc, nd=nd, scalar=scalar, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def kc(self, length=None, scalar=None, mamode=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = kc(high=high, low=low, close=close, length=length, scalar=scalar, mamode=mamode, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def massi(self, fast=None, slow=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = massi(high=high, low=low, fast=fast, slow=slow, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def natr(self, length=None, mamode=None, scalar=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = natr(high=high, low=low, close=close, length=length, mamode=mamode, scalar=scalar, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def pdist(self, drift=None, offset=None, **kwargs): - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = pdist(open_=open_, high=high, low=low, close=close, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def rvi(self, length=None, scalar=None, refined=None, thirds=None, mamode=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = rvi(high=high, low=low, close=close, length=length, scalar=scalar, refined=refined, thirds=thirds, mamode=mamode, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def thermo(self, long=None, short= None, length=None, mamode=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - result = thermo(high=high, low=low, long=long, short=short, length=length, mamode=mamode, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def true_range(self, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - result = true_range(high=high, low=low, close=close, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def ui(self, length=None, scalar=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - result = ui(close=close, length=length, scalar=scalar, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - # Volume - def ad(self, open_=None, signed=True, offset=None, **kwargs): - if open_ is not None: - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = ad(high=high, low=low, close=close, volume=volume, open_=open_, signed=signed, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def adosc(self, open_=None, fast=None, slow=None, signed=True, offset=None, **kwargs): - if open_ is not None: - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = adosc(high=high, low=low, close=close, volume=volume, open_=open_, fast=fast, slow=slow, signed=signed, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def aobv(self, fast=None, slow=None, mamode=None, max_lookback=None, min_lookback=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = aobv(close=close, volume=volume, fast=fast, slow=slow, mamode=mamode, max_lookback=max_lookback, min_lookback=min_lookback, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def cmf(self, open_=None, length=None, offset=None, **kwargs): - if open_ is not None: - open_ = self._get_column(kwargs.pop("open", "open")) - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = cmf(high=high, low=low, close=close, volume=volume, open_=open_, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def efi(self, length=None, mamode=None, offset=None, drift=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = efi(close=close, volume=volume, length=length, offset=offset, mamode=mamode, drift=drift, **kwargs) - return self._post_process(result, **kwargs) - - def eom(self, length=None, divisor=None, offset=None, drift=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = eom(high=high, low=low, close=close, volume=volume, length=length, divisor=divisor, offset=offset, drift=drift, **kwargs) - return self._post_process(result, **kwargs) - - def kvo(self, fast=None, slow=None, length_sig=None, mamode=None, offset=None, drift=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = kvo(high=high, low=low, close=close, volume=volume, fast=fast, slow=slow, length_sig=length_sig, mamode=mamode, offset=offset, drift=drift, **kwargs) - return self._post_process(result, **kwargs) - - def mfi(self, length=None, drift=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = mfi(high=high, low=low, close=close, volume=volume, length=length, drift=drift, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def nvi(self, length=None, initial=None, signed=True, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = nvi(close=close, volume=volume, length=length, initial=initial, signed=signed, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def obv(self, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = obv(close=close, volume=volume, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def pvi(self, length=None, initial=None, mamode=None, overlay=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = pvi(close=close, volume=volume, length=length, initial=initial, mamode=mamode, overlay=overlay, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def pvo(self, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs): - volume = self._get_column(kwargs.pop("volume", "volume")) - result = pvo(volume=volume, fast=fast, slow=slow, signal=signal, scalar=scalar, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def pvol(self, volume=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = pvol(close=close, volume=volume, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def pvr(self, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = pvr(close=close, volume=volume) - return self._post_process(result, **kwargs) - - def pvt(self, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = pvt(close=close, volume=volume, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def tsv(self, length=None, signal=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = tsv(close=close, volume=volume, signal=signal, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def vhm(self, length=None, std_length=None, offset=None, **kwargs): - volume = self._get_column(kwargs.pop("volume", "volume")) - result = vhm(volume=volume, length=length, std_length=std_length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def vwap(self, anchor=None, offset=None, **kwargs): - high = self._get_column(kwargs.pop("high", "high")) - low = self._get_column(kwargs.pop("low", "low")) - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - - if not self.datetime_ordered(): - volume.index = self._df.index - - result = vwap(high=high, low=low, close=close, volume=volume, anchor=anchor, offset=offset, **kwargs) - return self._post_process(result, **kwargs) - - def vwma(self, volume=None, length=None, offset=None, **kwargs): - close = self._get_column(kwargs.pop("close", "close")) - volume = self._get_column(kwargs.pop("volume", "volume")) - result = vwma(close=close, volume=volume, length=length, offset=offset, **kwargs) - return self._post_process(result, **kwargs) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/custom.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/custom.py deleted file mode 100644 index 2af2044c..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/custom.py +++ /dev/null @@ -1,177 +0,0 @@ -# -*- coding: utf-8 -*- -import importlib -import os -import sys -import types -from glob import glob -from os.path import abspath, basename, exists, join, splitext - -import pandas_ta -from pandas_ta._typing import DictLike - - - -def bind(name: str, fn: types.FunctionType, method: types.MethodType = None): - """Bind - - Helper function to bind the function and class method defined in a custom - indicator module to the active pandas_ta instance. - - Parameters: - name (str): The name of the indicator within pandas_ta - fn (types.FunctionType): The indicator function - method (types.MethodType): The class method corresponding to the passed function - """ - setattr(pandas_ta, name, fn) - setattr(pandas_ta.AnalysisIndicators, name, method) - - -def create_dir(path: str, categories: bool = True, verbose: bool = True): - """Create Dir - - Sets up a suitable folder structure for working with custom indicators. - Use it **once** to setup and initialize the custom folder. - - Parameters: - path (str): Indicator directory full path - categories (bool): Create category sub-folders - verbose (bool): Verbose output - """ - - # ensure that the passed directory exists / is readable - if not exists(path): - os.makedirs(path) - if verbose: - print(f"[i] Created main directory '{path}'.") - - # list the contents of the directory - # dirs = glob(abspath(join(path, '*'))) - - # optionally add any missing category subdirectories - if categories: - for _ in [*pandas_ta.Category]: - d = abspath(join(path, _)) - if not exists(d): - os.makedirs(d) - if verbose: - dirname = basename(d) - print(f"[i] Created an empty sub-directory '{dirname}'.") - - -def get_module_functions(module: types.ModuleType) -> DictLike: - """Get Module Functions - - Returns a dictionary with the mapping: "name" to a _function_. - - Parameters: - module (types.ModuleType): python module - - Returns: - (DictLike): Returns a dictionary with the mapping: "name" to a _function_ - - Example: - Example return - ```py - { - "func1_name": func1, - "func2_name": func2, # ... - } - ``` - """ - module_functions = {} - - for name, item in vars(module).items(): - if isinstance(item, types.FunctionType): - module_functions[name] = item - - return module_functions - - -def import_dir(path: str, verbose: bool = True): - """Import Dir - - Import a directory of custom (proprietary) indicators into Pandas TA. - - Parameters: - path (str): Full path to indicator directory. - verbose (bool): Output process to STDOUT. - """ - # ensure that the passed directory exists / is readable - if not exists(path): - print(f"[X] Unable to read the directory '{path}'.") - return - - # list the contents of the directory - dirs = glob(abspath(join(path, "*"))) - - # traverse full directory, importing all modules found there - for d in dirs: - dirname = basename(d) - - # only look in directories which are valid pandas_ta categories - if dirname not in [*pandas_ta.Category]: - if verbose and dirname not in ["__pycache__", "__init__.py"]: - print( - f"[i] Skipping the sub-directory '{dirname}' since it's not a valid pandas_ta category." - ) - continue - - # for each module found in that category (directory)... - for module in glob(abspath(join(path, dirname, "*.py"))): - module_name = splitext(basename(module))[0] - if module_name not in ["__init__"]: - # ensure that the supplied path is included in our python path - if d not in sys.path: - sys.path.append(d) - - # (re)load the indicator module - module_functions = load_indicator_module(module_name) - - # figure out which of the modules functions to bind to pandas_ta - _callable = module_functions.get(module_name, None) - _method_callable = module_functions.get(f"{module_name}_method", None) - - if _callable == None: - print( - f"[X] Unable to find a function named '{module_name}' in the module '{module_name}.py'." - ) - continue - if _method_callable == None: - missing_method = f"{module_name}_method" - print( - f"[X] Unable to find a method function named '{missing_method}' in the module '{module_name}.py'." - ) - continue - - # add it to the correct category if it's not there yet - if module_name not in pandas_ta.Category[dirname]: - pandas_ta.Category[dirname].append(module_name) - - bind(module_name, _callable, _method_callable) - if verbose: - print( - f"[i] Successfully imported the custom indicator '{module}' into category '{dirname}'." - ) - - -def load_indicator_module(name: str) -> dict: - """ - Helper function to (re)load an indicator module. - - Returns: - dict: module functions mapping - ```{ - "func1_name": func1, - "func2_name": func2, # ... - }``` - - """ - try: - module = importlib.import_module(name) - except Exception as ex: - print(f"[X] An error occurred when attempting to load module {name}: {ex}") - sys.exit(1) - - # reload to refresh previously loaded module - module = importlib.reload(module) - return get_module_functions(module) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/__init__.py deleted file mode 100644 index ae54cda9..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# -*- coding: utf-8 -*- -from .ebsw import ebsw -from .reflex import reflex - -__all__ = [ - "ebsw", - "reflex", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/ebsw.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/ebsw.py deleted file mode 100644 index a1824569..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/ebsw.py +++ /dev/null @@ -1,142 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import cos, exp, mean, nan, pi, roll, sin, sqrt, zeros -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series - - - -def ebsw( - close: Series, length: Int = None, bars: Int = None, - initial_version: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Even Better SineWave - - This indicator attempts to quantify market cycles using a low pass filter. - - Sources: - * [rengel8](https://github.com/rengel8) - * J.F.Ehlers 'Cycle Analytics for Traders', 2014 - * [Pandas TA Issue #350](https://github.com/twopirllc/pandas-ta/issues/350) - * [Proreal Code](https://www.prorealcode.com/prorealtime-indicators/even-better-sinewave/) - - Parameters: - close (Series): ```close``` Series - length (int): Max cycle/trend period. Values between ```40-48``` work - as expected with minimum value: ```39```. Default: ```40``` - bars (int): Period of low pass filtering. Default: ```10``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): Replaces ```na```'s with ```value```. - - Returns: - (Series): 1 column - - Note: - The _default_ is more cycle oriented and seems to be less - whipsaw-prune. The older version might offer earlier signals at medium - and stronger reversals. Compared to TradingView, returns very close - results but appears to be one bar earlier. - """ - # Validate - length = v_pos_default(length, 40) - close = v_series(close, length) - - if close is None: - return - - initial_version = v_bool(initial_version, False) - bars = v_pos_default(bars, 10) - offset = v_offset(offset) - - # Calculate - # allow initial version to be used (more responsive/caution!) - m = close.size - if isinstance(initial_version, bool) and initial_version: - # not the default version that is active - alpha1 = hp = 0 # alpha and HighPass - a1 = b1 = c1 = c2 = c3 = 0 - filter_ = power_ = wave = 0 - lastClose = lastHP = 0 - filtHist = [0, 0] # Filter history - - result = [nan for _ in range(0, length - 1)] + [0] - for i in range(length, m): - # HighPass filter cyclic components whose periods are shorter than - # Duration input - alpha1 = (1 - sin(360 / length)) / cos(360 / length) - hp = 0.5 * (1 + alpha1) * (close.iloc[i] - lastClose) + alpha1 * lastHP - - # Smooth with a Super Smoother Filter from equation 3-3 - a1 = exp(-sqrt(2) * pi / bars) - b1 = 2 * a1 * cos(sqrt(2) * 180 / bars) - c2 = b1 - c3 = -1 * a1 * a1 - c1 = 1 - c2 - c3 - filter_ = 0.5 * c1 * (hp + lastHP) + c2 * \ - filtHist[1] + c3 * filtHist[0] - # filter_ = float("{:.8f}".format(float(filter_))) # to fix for - # small scientific notations, the big ones fail - - # 3 Bar average of wave amplitude and power - wave = (filter_ + filtHist[1] + filtHist[0]) / 3 - power_ = (filter_ * filter_ + filtHist[1] * filtHist[1] \ - + filtHist[0] * filtHist[0]) / 3 - # Normalize the Average Wave to Square Root of the Average Power - wave = wave / sqrt(power_) - - # update storage, result - filtHist.append(filter_) # append new filter_ value - # remove first element of list (left) -> updating/trim - filtHist.pop(0) - lastHP = hp - lastClose = close.iloc[i] - result.append(wave) - - else: # Default - lastHP = lastClose = 0 - filtHist = zeros(3) - result = [nan] * (length - 1) + [0] - - angle = 2 * pi / length - alpha1 = (1 - sin(angle)) / cos(angle) - ang = 2 ** .5 * pi / bars - a1 = exp(-ang) - c2 = 2 * a1 * cos(ang) - c3 = -a1 ** 2 - c1 = 1 - c2 - c3 - - for i in range(length, m): - hp = 0.5 * (1 + alpha1) * (close.iloc[i] - lastClose) + alpha1 * lastHP - - # Rotate filters to overwrite oldest value - filtHist = roll(filtHist, -1) - filtHist[-1] = 0.5 * c1 * \ - (hp + lastHP) + c2 * filtHist[1] + c3 * filtHist[0] - - # Wave calculation - wave = mean(filtHist) - rms = sqrt(mean(filtHist ** 2)) - wave = wave / rms - - # Update past values - lastHP = hp - lastClose = close.iloc[i] - result.append(wave) - - ebsw = Series(result, index=close.index) - - # Offset - if offset != 0: - ebsw = ebsw.shift(offset) - - # Fill - if "fillna" in kwargs: - ebsw.fillna(kwargs["fillna"], inplace=True) - # Name and Category - ebsw.name = f"EBSW_{length}_{bars}" - ebsw.category = "cycle" - - return ebsw diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/reflex.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/reflex.py deleted file mode 100644 index c5d24111..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/cycle/reflex.py +++ /dev/null @@ -1,115 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import cos, exp, nan, sqrt, zeros_like -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -@njit(cache=True) -def np_reflex(x, n, k, alpha, pi, sqrt2): - m, ratio = x.size, 2 * sqrt2 / k - a = exp(-pi * ratio) - b = 2 * a * cos(180 * ratio) - c = a * a - b + 1 - - _f = zeros_like(x) - _ms = zeros_like(x) - result = zeros_like(x) - - for i in range(2, m): - _f[i] = 0.5 * c * (x[i] + x[i - 1]) + b * _f[i - 1] - a * a * _f[i - 2] - - for i in range(n, m): - slope = (_f[i - n] - _f[i]) / n - - _sum = 0 - for j in range(1, n): - _sum += _f[i] - _f[i - j] + j * slope - _sum /= n - - _ms[i] = alpha * _sum * _sum + (1 - alpha) * _ms[i - 1] - if _ms[i] != 0.0: - result[i] = _sum / sqrt(_ms[i]) - - return result - - -def reflex( - close: Series, length: Int = None, - smooth: Int = None, alpha: IntFloat = None, - pi: IntFloat = None, sqrt2: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Reflex - - This cycle indicator, by John F. Ehlers, attempts to reduce lag. - - Sources: - * [rengel8](https://github.com/rengel8) (2021-08-11) based on the - implementation from "ProRealCode" - * [traders.com](http://traders.com/Documentation/FEEDbk_docs/2020/02/TradersTips.html) - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/reflex-and-trendflex-indicators-john-f-ehlers/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - smooth (int): SuperSmoother period. Default: ```20``` - alpha (float): Alpha weight of Difference Sums. Default: ```0.04``` - pi (float): Ehlers's truncated value: ```3.14159```. - Default: ```3.14159``` - sqrt2 (float): Ehlers's truncated value: ```1.414```. - Default: ```1.414``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): Replaces ```na```'s with ```value```. - - Returns: - (Series): 1 column - - Tip: - This implementation has a separate control parameter for the - internal applied SuperSmoother. - - Note: - John F. Ehlers introduced two indicators within the article - "Reflex: A New Zero-Lag Indicator” in February 2020, TASC magazine. - One of which is Reflex, a lag reduced cycle indicator. Both indicators - (Reflex/Trendflex) are oscillators that complement each other with the - focus for cycle and trend. - """ - # Validate - length = v_pos_default(length, 20) - smooth = v_pos_default(smooth, 20) - _length = max(length, smooth) + 1 - close = v_series(close, _length) - - if close is None: - return - - alpha = v_pos_default(alpha, 0.04) - pi = v_pos_default(pi, 3.14159) - sqrt2 = v_pos_default(sqrt2, 1.414) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - result = np_reflex(np_close, length, smooth, alpha, pi, sqrt2) - result[:length] = nan - result = Series(result, index=close.index) - - # Offset - if offset != 0: - result = result.shift(offset) - - # Fill - if "fillna" in kwargs: - result.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - result.name = f"REFLEX_{length}_{smooth}_{alpha}" - result.category = "cycle" - - return result diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/ma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/ma.py deleted file mode 100644 index 92a0814f..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/ma.py +++ /dev/null @@ -1,75 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike -from pandas_ta.overlap.dema import dema -from pandas_ta.overlap.ema import ema -from pandas_ta.overlap.fwma import fwma -from pandas_ta.overlap.hma import hma -from pandas_ta.overlap.linreg import linreg -from pandas_ta.overlap.midpoint import midpoint -from pandas_ta.overlap.pwma import pwma -from pandas_ta.overlap.rma import rma -from pandas_ta.overlap.sinwma import sinwma -from pandas_ta.overlap.sma import sma -from pandas_ta.overlap.ssf import ssf -from pandas_ta.overlap.swma import swma -from pandas_ta.overlap.t3 import t3 -from pandas_ta.overlap.tema import tema -from pandas_ta.overlap.trima import trima -from pandas_ta.overlap.vidya import vidya -from pandas_ta.overlap.wma import wma - - - - -def ma(name: str = None, source: Series = None, **kwargs: DictLike) -> Series: - """MA Selection Utility - - Available MAs: dema, ema, fwma, hma, linreg, midpoint, pwma, rma, - sinwma, sma, ssf, swma, t3, tema, trima, vidya, wma. - - Parameters: - name (str): One of the Available MAs. Default: "ema" - source (Series): Input Series ```source```. - - Other Parameters: - kwargs (**kwargs): Additional args for the MA. - - Returns: - (Series): Selected MA - - Esourceample: - ```py linenums="0" - ema8 = ta.ma("ema", df.close, length=8) - sma50 = ta.ma("sma", df.close, length=50) - pwma10 = ta.ma("pwma", df.close, length=10, asc=False) - ``` - """ - _mas = [ - "dema", "ema", "fwma", "hma", "linreg", "midpoint", "pwma", "rma", - "sinwma", "sma", "ssf", "swma", "t3", "tema", "trima", "vidya", "wma" - ] - if name is None and source is None: - return _mas - elif isinstance(name, str) and name.lower() in _mas: - name = name.lower() - else: # "ema" - name = _mas[1] - - if name == "dema": return dema(source, **kwargs) - elif name == "fwma": return fwma(source, **kwargs) - elif name == "hma": return hma(source, **kwargs) - elif name == "linreg": return linreg(source, **kwargs) - elif name == "midpoint": return midpoint(source, **kwargs) - elif name == "pwma": return pwma(source, **kwargs) - elif name == "rma": return rma(source, **kwargs) - elif name == "sinwma": return sinwma(source, **kwargs) - elif name == "sma": return sma(source, **kwargs) - elif name == "ssf": return ssf(source, **kwargs) - elif name == "swma": return swma(source, **kwargs) - elif name == "t3": return t3(source, **kwargs) - elif name == "tema": return tema(source, **kwargs) - elif name == "trima": return trima(source, **kwargs) - elif name == "vidya": return vidya(source, **kwargs) - elif name == "wma": return wma(source, **kwargs) - else: return ema(source, **kwargs) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/maps.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/maps.py deleted file mode 100644 index 81fef8c3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/maps.py +++ /dev/null @@ -1,90 +0,0 @@ -# -*- coding: utf-8 -*- -from importlib.util import find_spec -from pandas_ta._typing import Dict, IntFloat, ListStr - - -Imports: Dict[str, bool] = { - "talib": find_spec("talib") is not None, - "vectorbt": find_spec("vectorbt") is not None, - "yfinance": find_spec("yfinance") is not None, -} - - -# Not ideal and not dynamic but it works. -# TODO: find a dynamic solution later. -Category: Dict[str, ListStr] = { - "candle": [ - "cdl_pattern", "cdl_z", "ha" - ], - "cycle": ["ebsw", "reflex"], - "momentum": [ - "ao", "apo", "bias", "bop", "brar", "cci", "cfo", "cg", "cmo", - "coppock", "crsi", "cti", "er", "eri", "exhc", "fisher", - "inertia", "kdj", "kst", "macd", "mom", "pgo", "ppo", "psl", "qqe", - "roc", "rsi", "rsx", "rvgi", "slope", "smc", "smi", "squeeze", - "squeeze_pro", "stc", "stoch", "stochf", "stochrsi", "tmo", "trix", - "tsi", "uo", "willr" - ], - "overlap": [ - "alligator", "alma", "dema", "ema", "fwma", "hilo", "hl2", "hlc3", - "hma", "hwma", "ichimoku", "jma", "kama", "linreg", "mama", - "mcgd", "midpoint", "midprice", "ohlc4", "pivots", "pwma", "rma", - "sinwma", "sma", "smma", "ssf", "ssf3", "supertrend", "swma", "t3", - "tema", "trima", "vidya", "wcp", "wma", "zlma" - ], - "performance": ["log_return", "percent_return"], - "statistics": [ - "entropy", "kurtosis", "mad", "median", "quantile", "skew", "stdev", - "tos_stdevall", "variance", "zscore" - ], - "trend": [ - "adx", "alphatrend", "amat", "aroon", "chop", "cksp", "decay", - "decreasing", "dpo", "ht_trendline", "increasing", - "long_run", "psar", "qstick", "rwi", "short_run", "trendflex", - "vhf", "vortex", "zigzag" - ], - "volatility": [ - "aberration", "accbands", "atr", "atrts", "bbands", "chandelier_exit", - "donchian", "hwc", "kc", "massi", "natr", "pdist", "rvi", "thermo", - "true_range", "ui" - ], - # Note: "vp" or "Volume Profile" is excluded since it does not - # return a Time Series - "volume": [ - "ad", "adosc", "aobv", "cmf", "efi", "eom", "kvo", "mfi", "nvi", - "obv", "pvi", "pvo", "pvol", "pvr", "pvt", "tsv", "vhm", "vwap", - "vwma" - ], -} - - -CANDLE_AGG: Dict[str, str] = { - "open": "first", - "high": "max", - "low": "min", - "close": "last", - "volume": "sum" -} - - -# https://www.worldtimezone.com/markets24.php -EXCHANGE_TZ: Dict[str, IntFloat] = { - "NZSX": 12, "ASX": 11, - "TSE": 9, "HKE": 8, "SSE": 8, "SGX": 8, - "NSE": 5.5, "DIFX": 4, "RTS": 3, - "JSE": 2, "FWB": 1, "LSE": 1, - "BMF": -2, "NYSE": -4, "TSX": -4, - "GENR": 0 # Generated Data -} - - -RATE: Dict[str, IntFloat] = { - "DAYS_PER_MONTH": 21, - "MINUTES_PER_HOUR": 60, - "MONTHS_PER_YEAR": 12, - "QUARTERS_PER_YEAR": 4, - "TRADING_DAYS_PER_YEAR": 252, # Keep even - "TRADING_HOURS_PER_DAY": 6.5, - "WEEKS_PER_YEAR": 52, - "YEARLY": 1, -} diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/__init__.py deleted file mode 100644 index f773d51f..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/__init__.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -from .ao import ao -from .apo import apo -from .bias import bias -from .bop import bop -from .brar import brar -from .cci import cci -from .cfo import cfo -from .cg import cg -from .cmo import cmo -from .coppock import coppock -from .crsi import crsi -from .cti import cti -from .dm import dm -from .er import er -from .eri import eri -from .exhc import exhc -from .fisher import fisher -from .inertia import inertia -from .kdj import kdj -from .kst import kst -from .macd import macd -from .mom import mom -from .pgo import pgo -from .ppo import ppo -from .psl import psl -from .qqe import qqe -from .roc import roc -from .rsi import rsi -from .rsx import rsx -from .rvgi import rvgi -from .slope import slope -from .smc import smc -from .smi import smi -from .squeeze import squeeze -from .squeeze_pro import squeeze_pro -from .stc import stc -from .stoch import stoch -from .stochf import stochf -from .stochrsi import stochrsi -from .tmo import tmo -from .trix import trix -from .tsi import tsi -from .uo import uo -from .willr import willr - - -__all__ = [ - "ao", - "apo", - "bias", - "bop", - "brar", - "cci", - "cfo", - "cg", - "cmo", - "coppock", - "crsi", - "cti", - "dm", - "er", - "eri", - "exhc", - "fisher", - "inertia", - "kdj", - "kst", - "macd", - "mom", - "pgo", - "ppo", - "psl", - "qqe", - "roc", - "rsi", - "rsx", - "rvgi", - "slope", - "smc", - "smi", - "squeeze", - "squeeze_pro", - "stc", - "stoch", - "stochf", - "stochrsi", - "tmo", - "trix", - "tsi", - "uo", - "willr", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/ao.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/ao.py deleted file mode 100644 index 71399528..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/ao.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import sma -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def ao( - high: Series, low: Series, fast: Int = None, slow: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Awesome Oscillator - - This indicator attempts to identify momentum with the intention to - affirm trends or anticipate possible reversals. - - Sources: - * [ifcm](https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator) - * [tradingview](https://www.tradingview.com/wiki/Awesome_Oscillator_(AO)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - fast (int): Fast period. Default: ```5``` - slow (int): Slow period. Default: ```34``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - fast = v_pos_default(fast, 5) - slow = v_pos_default(slow, 34) - if slow < fast: - fast, slow = slow, fast - _length = max(fast, slow) - high = v_series(high, _length) - low = v_series(low, _length) - - if high is None or low is None: - return - - offset = v_offset(offset) - - # Calculate - median_price = 0.5 * (high + low) - fast_sma = sma(median_price, fast) - slow_sma = sma(median_price, slow) - ao = fast_sma - slow_sma - - # Offset - if offset != 0: - ao = ao.shift(offset) - - # Fill - if "fillna" in kwargs: - ao.fillna(kwargs["fillna"], inplace=True) - # Name and Category - ao.name = f"AO_{fast}_{slow}" - ao.category = "momentum" - - return ao diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/apo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/apo.py deleted file mode 100644 index 16775968..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/apo.py +++ /dev/null @@ -1,75 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.utils import tal_ma, v_mamode, v_offset -from pandas_ta.utils import v_pos_default, v_series, v_talib - - - -def apo( - close: Series, fast: Int = None, slow: Int = None, - mamode: str = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Absolute Price Oscillator - - This indicator attempts to quantify momentum. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/absolute-price-oscillator-apo/) - - Parameters: - close (Series): ```close``` Series - fast (int): Fast period. Default: ```12``` - slow (int): Slow period. Default: ```26``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * Simply the difference of two different EMAs. - * APO and MACD lines are equivalent. - """ - # Validate - fast = v_pos_default(fast, 12) - slow = v_pos_default(slow, 26) - if slow < fast: - fast, slow = slow, fast - close = v_series(close, max(fast, slow)) - - if close is None: - return - - mamode = v_mamode(mamode, "sma") - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import APO - apo = APO(close, fast, slow, tal_ma(mamode)) - else: - fastma = ma(mamode, close, length=fast, talib=mode_tal) - slowma = ma(mamode, close, length=slow, talib=mode_tal) - apo = fastma - slowma - - # Offset - if offset != 0: - apo = apo.shift(offset) - - # Fill - if "fillna" in kwargs: - apo.fillna(kwargs["fillna"], inplace=True) - # Name and Category - apo.name = f"APO_{fast}_{slow}" - apo.category = "momentum" - - return apo diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/bias.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/bias.py deleted file mode 100644 index ad7e030b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/bias.py +++ /dev/null @@ -1,59 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series - - - -def bias( - close: Series, length: Int = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Bias - - This indicator computes the Rate of Change between the source and a - moving average. - - Sources: - * Few internet resources on definitive definition. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```26``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 26) - close = v_series(close, length) - - if close is None: - return - - mamode = v_mamode(mamode, "sma") - offset = v_offset(offset) - - # Calculate - bma = ma(mamode, close, length=length, **kwargs) - bias = (close / bma) - 1 - - # Offset - if offset != 0: - bias = bias.shift(offset) - - # Fill - if "fillna" in kwargs: - bias.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - bias.name = f"BIAS_{bma.name}" - bias.category = "momentum" - - return bias diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/bop.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/bop.py deleted file mode 100644 index 23740edd..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/bop.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - non_zero_range, - v_offset, - v_scalar, - v_series, - v_talib -) - - - -def bop( - open_: Series, high: Series, low: Series, close: Series, - scalar: IntFloat = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Balance of Power - - This indicator attempts to quantify the market strength of buyers - versus sellers. - - Sources: - * [worden](http://www.worden.com/TeleChartHelp/Content/Indicators/Balance_of_Power.htm) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - scalar (float): Scalar. Default: ```1``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - open_ = v_series(open_) - high = v_series(high) - low = v_series(low) - close = v_series(close) - scalar = v_scalar(scalar, 1) - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal and close.size: - from talib import BOP - bop = BOP(open_, high, low, close) - else: - high_low_range = non_zero_range(high, low) - close_open_range = non_zero_range(close, open_) - bop = scalar * close_open_range / high_low_range - - # Offset - if offset != 0: - bop = bop.shift(offset) - - # Fill - if "fillna" in kwargs: - bop.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - bop.name = f"BOP" - bop.category = "momentum" - - return bop diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/brar.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/brar.py deleted file mode 100644 index c54b93e6..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/brar.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import ( - non_zero_range, - v_drift, - v_offset, - v_pos_default, - v_scalar, - v_series -) - - - -def brar( - open_: Series, high: Series, low: Series, close: Series, - length: Int = None, scalar: IntFloat = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """BRAR - - BR and AR - - Sources: - * No internet resources on definitive definition. - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```26``` - scalar (float): Scalar. Default: ```100``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - length = v_pos_default(length, 26) - open_ = v_series(open_, length) - high = v_series(high, length) - low = v_series(low, length) - close = v_series(close, length) - - if open_ is None or high is None or low is None or close is None: - return - - scalar = v_scalar(scalar, 100) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - high_open_range = non_zero_range(high, open_) - open_low_range = non_zero_range(open_, low) - hcy = non_zero_range(high, close.shift(drift)) - cyl = non_zero_range(close.shift(drift), low) - - hcy[hcy < 0] = 0 # Zero negative values - cyl[cyl < 0] = 0 # "" - - ar = scalar * high_open_range.rolling(length).sum() \ - / open_low_range.rolling(length).sum() - - br = scalar * hcy.rolling(length).sum() \ - / cyl.rolling(length).sum() - - # Offset - if offset != 0: - ar = ar.shift(offset) - br = ar.shift(offset) - - # Fill - if "fillna" in kwargs: - ar.fillna(kwargs["fillna"], inplace=True) - br.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}" - ar.name = f"AR{_props}" - br.name = f"BR{_props}" - ar.category = br.category = "momentum" - - data = {ar.name: ar, br.name: br} - df = DataFrame(data, index=close.index) - df.name = f"BRAR{_props}" - df.category = "momentum" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cci.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cci.py deleted file mode 100644 index 1d7071aa..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cci.py +++ /dev/null @@ -1,75 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.overlap import hlc3, sma -from pandas_ta.statistics import mad -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib - - - -def cci( - high: Series, low: Series, close: Series, length: Int = None, - c: IntFloat = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Commodity Channel Index - - This indicator attempts to identify "overbought" and "oversold" levels - relative to a mean. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Commodity_Channel_Index_(CCI)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - c (float): Scaling Constant. Default: ```0.015``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - high = v_series(high, length) - low = v_series(low, length) - close = v_series(close, length) - - if high is None or low is None or close is None: - return - - c = v_pos_default(c, 0.015) - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import CCI - cci = CCI(high, low, close, length) - else: - typical_price = hlc3(high=high, low=low, close=close, talib=mode_tal) - mean_typical_price = sma(typical_price, length=length, talib=mode_tal) - mad_typical_price = mad(typical_price, length=length) - - cci = typical_price - mean_typical_price / (c * mad_typical_price) - - # Offset - if offset != 0: - cci = cci.shift(offset) - - # Fill - if "fillna" in kwargs: - cci.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - cci.name = f"CCI_{length}_{c}" - cci.category = "momentum" - - return cci diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cfo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cfo.py deleted file mode 100644 index df40aa8d..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cfo.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap import linreg -from pandas_ta.utils import ( - v_drift, - v_offset, - v_pos_default, - v_scalar, - v_series -) - - - -def cfo( - close: Series, length: Int = None, - scalar: IntFloat = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Chande Forcast Oscillator - - This indicator attempts to calculate the percentage difference between - the actual price and the Time Series Forecast (the endpoint of a - linear regression line). - - Sources: - * [fmlabs](https://www.fmlabs.com/reference/default.htm?url=ForecastOscillator.htm) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```9``` - scalar (float): Scalar. Default: ```100``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 9) - close = v_series(close, length) - - if close is None: - return - - scalar = v_scalar(scalar, 100) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - # Finding linear regression of Series - cfo = scalar * (close - linreg(close, length=length, tsf=True)) / close - - # Offset - if offset != 0: - cfo = cfo.shift(offset) - - # Fill - if "fillna" in kwargs: - cfo.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - cfo.name = f"CFO_{length}" - cfo.category = "momentum" - - return cfo diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cg.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cg.py deleted file mode 100644 index 581d48ce..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cg.py +++ /dev/null @@ -1,57 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series, weights - - - -def cg( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Center of Gravity - - This indicator, by John Ehlers, attempts to identify turning points with - minimal to zero lag and smoothing. - - Sources: - * [MESA Software](http://www.mesasoftware.com/papers/TheCGOscillator.pdf) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - coefficients = range(1, length + 1) - numerator = close.rolling(length).apply(weights(coefficients), raw=True) - cg = -numerator / close.rolling(length).sum() - - # Offset - if offset != 0: - cg = cg.shift(offset) - - # Fill - if "fillna" in kwargs: - cg.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - cg.name = f"CG_{length}" - cg.category = "momentum" - - return cg diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cmo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cmo.py deleted file mode 100644 index 137ed7be..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cmo.py +++ /dev/null @@ -1,90 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.overlap import rma -from pandas_ta.utils import ( - v_drift, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib -) - - - -def cmo( - close: Series, length: Int = None, scalar: IntFloat = None, - talib: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Chande Momentum Oscillator - - This indicator attempts to capture momentum. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/chande-momentum-oscillator-cmo/) - * [tradingview](https://www.tradingview.com/script/hdrf0fXV-Variable-Index-Dynamic-Average-VIDYA/) - - Parameters: - close (Series): ```close``` Series - scalar (float): Scalar. Default: ```100``` - talib (bool): If installed, use TA Lib. Uses EMA if ```False```. - Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * Overbought around 50 - * Oversold around -50. - """ - # Validate - length = v_pos_default(length, 14) - close = v_series(close, length + 1) - - if close is None: - return - - scalar = v_scalar(scalar, 100) - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import CMO - cmo = CMO(close, length) - else: - mom = close.diff(drift) - positive = mom.copy().clip(lower=0) - negative = mom.copy().clip(upper=0).abs() - - if mode_tal: - pos_ = rma(positive, length) - neg_ = rma(negative, length) - else: - pos_ = positive.rolling(length).sum() - neg_ = negative.rolling(length).sum() - - cmo = scalar * (pos_ - neg_) / (pos_ + neg_) - - # Offset - if offset != 0: - cmo = cmo.shift(offset) - - # Fill - if "fillna" in kwargs: - cmo.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - cmo.name = f"CMO_{length}" - cmo.category = "momentum" - - return cmo diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/coppock.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/coppock.py deleted file mode 100644 index 8070e2ce..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/coppock.py +++ /dev/null @@ -1,70 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import wma -from pandas_ta.utils import v_offset, v_pos_default, v_series -from .roc import roc - - - -def coppock( - close: Series, length: Int = None, - fast: Int = None, slow: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Coppock Curve - - This indicator, by Edwin Coppock 1962, was originally called the - "Trendex Model", attempts to identify major upturns and downturns. - - Sources: - * [wikipedia](https://en.wikipedia.org/wiki/Coppock_curve) - - Parameters: - close (Series): ```close``` Series - length (int): WMA period. Default: ```10``` - fast (int): Fast ROC period. Default: ```11``` - slow (int): Slow ROC period. Default: ```14``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - Although designed for monthly use, a daily calculation over the same - period length can be made, converting the periods to 294-day and - 231-day rate of changes, and a 210-day WMA. - - """ - # Validate - length = v_pos_default(length, 10) - fast = v_pos_default(fast, 11) - slow = v_pos_default(slow, 14) - _length = length + fast + slow - close = v_series(close, _length) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - total_roc = roc(close, fast) + roc(close, slow) - coppock = wma(total_roc, length) - - # Offset - if offset != 0: - coppock = coppock.shift(offset) - - # Fill - if "fillna" in kwargs: - coppock.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - coppock.name = f"COPC_{fast}_{slow}_{length}" - coppock.category = "momentum" - - return coppock diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/crsi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/crsi.py deleted file mode 100644 index 275c9b1b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/crsi.py +++ /dev/null @@ -1,106 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.momentum.rsi import rsi -from pandas_ta.utils import ( - consecutive_streak, - percent_rank, - v_drift, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib, -) - - - -def crsi( - close: Series, rsi_length: Int = None, streak_length: Int = None, - rank_length: Int = None, scalar: IntFloat = None, - talib: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike, -) -> Series: - """Connors Relative Strength Index - - This indicator attempts to identify momentum and potential reversals at - "overbought" or "oversold" conditions. - - Sources: - * [alvarezquanttrading](https://alvarezquanttrading.com/blog/connorsrsi-analysis/) - * [tradingview](https://www.tradingview.com/support/solutions/43000502017-connors-rsi-crsi/) - * An Introduction to ConnorsRSI. Connors Research Trading Strategy Series. - Connors, L., Alvarez, C., & Radtke, M. (2012). ISBN 978-0-9853072-9-5. - - Parameters: - close (Series): ```close``` Series - rsi_length (int): The RSI period. Default: ```3``` - streak_length (int): Streak RSI period. Default: ```2``` - rank_length (int): Percent Rank length. Default: ```100``` - scalar (float): Scalar. Default: ```100``` - talib (bool): If installed, use TA Lib. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - rsi_length = v_pos_default(rsi_length, 3) - streak_length = v_pos_default(streak_length, 2) - rank_length = v_pos_default(rank_length, 100) - _length = max(rsi_length, streak_length, rank_length) - close = v_series(close, _length) - - if "length" in kwargs: - kwargs.pop("length") - - if close is None: - return None - - scalar = v_scalar(scalar, 100) - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - streak = Series(consecutive_streak(np_close), index=close.index) - - if Imports["talib"] and mode_tal: - from talib import RSI - _rsi = RSI(close, rsi_length) - _streak_rsi = RSI(streak, streak_length) - else: - # Both TA-lib and Pandas-TA use the Wilder's RSI - # and its smoothing function - _rsi = rsi( - close, length=rsi_length, scalar=scalar, talib=talib, - drift=drift, offset=offset, **kwargs - ) - - _streak_rsi = rsi( - streak, length=streak_length, scalar=scalar, talib=talib, - drift=drift, offset=offset, **kwargs - ) - - _crsi = (_rsi + _streak_rsi + percent_rank(close, rank_length)) / 3.0 - crsi = Series(_crsi, index=close.index) - - # Offset - if offset != 0: - crsi = crsi.shift(offset) - - # Fill - if "fillna" in kwargs: - crsi.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - crsi.name = f"CRSI_{rsi_length}_{streak_length}_{rank_length}" - crsi.category = "momentum" - - return crsi diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cti.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cti.py deleted file mode 100644 index f310500f..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/cti.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import linreg -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def cti( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Correlation Trend Indicator - - This oscillator, by John Ehlers' in 2020, attempts to identify the - magnitude and direction of a trend using linear regession. - - Note: - This is a wrapper for ```ta.linreg(close, r=True)```. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```12``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 12) - close = v_series(close, length) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - cti = linreg(close, length=length, r=True) - - # Offset - if offset != 0: - cti = cti.shift(offset) - - # Fill - if "fillna" in kwargs: - cti.fillna(method=kwargs["fillna"], inplace=True) - - # Name and Category - cti.name = f"CTI_{length}" - cti.category = "momentum" - - return cti diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/dm.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/dm.py deleted file mode 100644 index faaa8fdd..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/dm.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib, - zero -) - - - -def dm( - high: Series, low: Series, length: Int = None, - mamode: str = None, talib: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Directional Movement - - This indicator, by J. Welles Wilder in 1978, attempts to - determine direction. - - Sources: - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=24&Name=Directional_Movement_Index) - * [tradingview](https://www.tradingview.com/pine-script-reference/#fun_dmi) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - mamode (str): See ```help(ta.ma)```. Default: ```"rma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - length = v_pos_default(length, 14) - high = v_series(high, length) - low = v_series(low, length) - - if high is None or low is None: - return - - mamode = v_mamode(mamode, "rma") - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - if Imports["talib"] and mode_tal and high.size and low.size: - from talib import MINUS_DM, PLUS_DM - pos = PLUS_DM(high, low, length) - neg = MINUS_DM(high, low, length) - else: - up = high - high.shift(drift) - dn = low.shift(drift) - low - - pos_ = ((up > dn) & (up > 0)) * up - neg_ = ((dn > up) & (dn > 0)) * dn - - pos_ = pos_.apply(zero) - neg_ = neg_.apply(zero) - - # Not the same values as TA Lib's -+DM (Good First Issue) - pos = ma(mamode, pos_, length=length, talib=mode_tal) - neg = ma(mamode, neg_, length=length, talib=mode_tal) - - # Offset - if offset != 0: - pos = pos.shift(offset) - neg = neg.shift(offset) - - # Fill - if "fillna" in kwargs: - pos.fillna(kwargs["fillna"], inplace=True) - neg.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}" - data = {f"DMP{_props}": pos, f"DMN{_props}": neg} - df = DataFrame(data, index=high.index) - df.name = f"DM{_props}" - df.category = "momentum" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/er.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/er.py deleted file mode 100644 index 2a838eba..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/er.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series, concat -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - signals, - v_drift, - v_offset, - v_pos_default, - v_series -) - - - -def er( - close: Series, length: Int = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Efficiency Ratio - - This indicator, by Perry J. Kaufman, attempts to identify market noise - or volatility. - - Sources: - * "New Trading Systems and Methods", Perry J. Kaufman - * [tc2000](https://help.tc2000.com/m/69404/l/749623-kaufman-efficiency-ratio) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - It is calculated by dividing the net change in price movement over - ```n``` periods by the sum of the absolute net changes over the - same ```n``` periods. - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length + 1) - - if close is None: - return - - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - abs_diff = close.diff(length).abs() - abs_volatility = close.diff(drift).abs() - abs_volatility_rsum = abs_volatility.rolling(window=length).sum() - - er = abs_diff / abs_volatility_rsum - - # Offset - if offset != 0: - er = er.shift(offset) - - # Fill - if "fillna" in kwargs: - er.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - er.name = f"ER_{length}" - er.category = "momentum" - - signal_indicators = kwargs.pop("signal_indicators", False) - if not signal_indicators: - return er - else: - signalsdf = concat( - [ - DataFrame({er.name: er}), - signals( - indicator=er, - xa=kwargs.pop("xa", 80), - xb=kwargs.pop("xb", 20), - xseries=kwargs.pop("xseries", None), - xseries_a=kwargs.pop("xseries_a", None), - xseries_b=kwargs.pop("xseries_b", None), - cross_values=kwargs.pop("cross_values", False), - cross_series=kwargs.pop("cross_series", True), - offset=offset, - ), - ], - axis=1, - ) - return signalsdf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/eri.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/eri.py deleted file mode 100644 index a1c32842..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/eri.py +++ /dev/null @@ -1,75 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import ema -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def eri( - high: Series, low: Series, close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Elder Ray Index - - This indicator, by Dr Alexander Elder, attempts to identify market - strength. - - Sources: - * [admiralmarkets](https://admiralmarkets.com/education/articles/forex-indicators/bears-and-bulls-power-indicator) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Note: - * Possible entry signals when used in combination with a trend, - * Bear Power attempts to quantify lower value appeal. - * Bull Power attempts the to quantify higher value appeal. - """ - # Validate - length = v_pos_default(length, 13) - high = v_series(high, length) - low = v_series(low, length) - close = v_series(close, length) - - if high is None or low is None or close is None: - return - - offset = v_offset(offset) - - # Calculate - ema_ = ema(close, length) - bull = high - ema_ - bear = low - ema_ - - # Offset - if offset != 0: - bull = bull.shift(offset) - bear = bear.shift(offset) - - # Fill - if "fillna" in kwargs: - bull.fillna(kwargs["fillna"], inplace=True) - bear.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - bull.name = f"BULLP_{length}" - bear.name = f"BEARP_{length}" - bull.category = bear.category = "momentum" - - data = {bull.name: bull, bear.name: bear} - df = DataFrame(data, index=close.index) - df.name = f"ERI_{length}" - df.category = bull.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/exhc.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/exhc.py deleted file mode 100644 index 8e3acb33..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/exhc.py +++ /dev/null @@ -1,110 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import clip, cumsum, int64, nan, where -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - nb_ffill, - nb_idiff, - v_bool, - v_int, - v_offset, - v_pos_default, - v_series -) - - - -@njit(cache=True) -def nb_exhc(x, n, cap, lb, ub, show_all): - x_diff = nb_idiff(x, n) - neg_diff, pos_diff = x_diff < 0, x_diff > 0 - - dn_csum = cumsum(neg_diff) - up_csum = cumsum(pos_diff) - - dn = dn_csum - nb_ffill(where(~neg_diff, dn_csum, nan)) - up = up_csum - nb_ffill(where(~pos_diff, up_csum, nan)) - - if cap > 0: - dn = clip(dn, 0, cap) - up = clip(up, 0, cap) - - if show_all: - dn = where(dn == 0, 0, dn) - up = where(up == 0, 0, up) - else: - between_lu = (dn >= lb) & (dn <= ub) - dn = where(between_lu, dn, 0) - up = where(between_lu, up, 0) - - return dn, up - - -def exhc( - close: Series, length: Int = None, cap: Int = None, - asint: bool = None, show_all: bool = None, nozeros: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Exhaustion Count - - This indicator attempts to identify rising/falling exhaustion. - - Sources: - * [demark](https://demark.com) - * [practicaltechnicalanalysis](http://practicaltechnicalanalysis.blogspot.com/2013/01/tom-demark-sequential.html) - - Parameters: - close (Series): Series of close's - length (int): The period. Default: ```4``` - cap (int): Count cap. For no cap, set to ```0```. Default: ```13``` - show_all (bool): Counts 1 - 13. For 6 - 9, set to ```False```. - Default: ```True``` - asint (bool): Returns as ```Int```. Default: ```False``` - nozeros (bool): Replace zeros with ```np.nan```. Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Returns: - (DataFrame): 2 columns - - Note: - Similar to TD Sequential - """ - # Validate - length = v_pos_default(length, 4) - close = v_series(close, length + 1) - - if close is None: - return - - cap = v_int(cap, 13, -1) - show_all = v_bool(show_all, True) - asint = v_bool(asint, False) - nozeros = v_bool(nozeros, False) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - dn, up = nb_exhc(np_close, length, cap, 6, 9, show_all) - - if asint: - dn = dn.astype(int64) - up = up.astype(int64) - - # Name and Category - data = { - "EXHC_DNa" if show_all else "EXHC_DN": dn, - "EXHC_UPa" if show_all else "EXHC_UP": up - } - df = DataFrame(data, index=close.index) - df.name = "EXHCa" if show_all else "EXHC" - df.category = "momentum" - - if nozeros: - df.replace({0: nan}, inplace=True) - - # Offset - if offset != 0: - df = df.shift(offset) - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/fisher.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/fisher.py deleted file mode 100644 index 12297a60..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/fisher.py +++ /dev/null @@ -1,95 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan, log, nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import hl2 -from pandas_ta.utils import high_low_range, v_offset, v_pos_default, v_series - - - -def fisher( - high: Series, low: Series, length: Int = None, signal: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Fisher Transform - - This indicator attempts to identify significant reversals through - normalization. - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - length (int): The period. Default: ```9``` - signal (int): Signal period. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Tip: Reversal Signal - When the two lines cross. - """ - # Validate - length = v_pos_default(length, 9) - signal = v_pos_default(signal, 1) - _length = max(length, signal) - high = v_series(high, _length) - low = v_series(low, _length) - - if high is None or low is None: - return - - offset = v_offset(offset) - - # Calculate - hl2_ = hl2(high, low) - highest_hl2 = hl2_.rolling(length).max() - lowest_hl2 = hl2_.rolling(length).min() - - hlr = high_low_range(highest_hl2, lowest_hl2) - hlr[hlr < 0.001] = 0.001 - - position = ((hl2_ - lowest_hl2) / hlr) - 0.5 - - v = 0 - m = high.size - result = [nan for _ in range(0, length - 1)] + [0] - for i in range(length, m): - v = 0.66 * position.iat[i] + 0.67 * v - if v < -0.99: - v = -0.999 - if v > 0.99: - v = 0.999 - result.append(0.5 * (log((1 + v) / (1 - v)) + result[i - 1])) - - fisher = Series(result, index=high.index) - if all(isnan(fisher)): - return # Emergency Break - - signalma = fisher.shift(signal) - - # Offset - if offset != 0: - fisher = fisher.shift(offset) - signalma = signalma.shift(offset) - - # Fill - if "fillna" in kwargs: - fisher.fillna(kwargs["fillna"], inplace=True) - signalma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}_{signal}" - fisher.name = f"FISHERT{_props}" - signalma.name = f"FISHERTs{_props}" - fisher.category = signalma.category = "momentum" - - data = {fisher.name: fisher, signalma.name: signalma} - df = DataFrame(data, index=high.index) - df.name = f"FISHERT{_props}" - df.category = fisher.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/inertia.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/inertia.py deleted file mode 100644 index d902da35..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/inertia.py +++ /dev/null @@ -1,118 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap import linreg -from pandas_ta.utils import ( - v_bool, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series -) -from pandas_ta.volatility import rvi - - - -def inertia( - close: Series, high: Series = None, low: Series = None, - length: Int = None, rvi_length: Int = None, scalar: IntFloat = None, - refined: bool = None, thirds: bool = None, - drift: Int = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Inertia - - This indicator, by Donald Dorsey, is the _rvi_ smoothed by the Least Squares - MA. - - Sources: - * Donald Dorsey, some article in September, 1995. - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=285&Name=Inertia) - * [tradingview](https://www.tradingview.com/script/mLZJqxKn-Relative-Volatility-Index/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - rvi_length (int): RVI period. Default: ```14``` - refined (bool): Use 'refined' calculation. Default: ```False``` - thirds (bool): Use 'thirds' calculation. Default: ```False``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * Negative Inertia when less than 50. - * Positive Inertia when greater than 50. - """ - # Validate - length = v_pos_default(length, 20) - rvi_length = v_pos_default(rvi_length, 14) - _length = 2 * max(length, rvi_length) - min(length, rvi_length) // 2 - 1 - close = v_series(close, _length) - - if close is None: - return - - refined = v_bool(refined, False) - thirds = v_bool(thirds, False) - - if refined or thirds: - high = v_series(high, _length) - low = v_series(low, _length) - if high is None or low is None: - return - - scalar = v_scalar(scalar, 100) - mamode = v_mamode(mamode, "ema") - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if refined: - _mode = "r" - rvi_ = rvi( - close, high=high, low=low, length=rvi_length, - scalar=scalar, refined=refined, mamode=mamode - ) - elif thirds: - _mode = "t" - rvi_ = rvi( - close, high=high, low=low, length=rvi_length, - scalar=scalar, thirds=thirds, mamode=mamode - ) - else: - _mode = "" - rvi_ = rvi(close, length=rvi_length, scalar=scalar, mamode=mamode) - - if all(isnan(rvi_)): - return # Emergency Break - - inertia = linreg(rvi_, length=length) - if all(isnan(inertia)): - return # Emergency Break - - # Offset - if offset != 0: - inertia = inertia.shift(offset) - - # Fill - if "fillna" in kwargs: - inertia.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}_{rvi_length}" - inertia.name = f"INERTIA{_mode}{_props}" - inertia.category = "momentum" - - return inertia diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/kdj.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/kdj.py deleted file mode 100644 index 32c8a9a5..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/kdj.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - non_zero_range, - pd_rma, - v_offset, - v_pos_default, - v_series -) - - - -def kdj( - high: Series, low: Series, close: Series, - length: Int = None, signal: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """KDJ - - This indicator, derived from the Slow Stochastic, includes an - extra signal named the J line. The J line represents the divergence - of the %D value from the %K. - - Sources: - * [anychart](https://docs.anychart.com/Stock_Charts/Technical_Indicators/Mathematical_Description#kdj) - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/kdj/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```9``` - signal (int): Signal period. Default: ```3``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - - Note: - The J can go beyond ```[0, 100]``` for %K and %D lines when charted. - """ - # Validate - length = v_pos_default(length, 9) - signal = v_pos_default(signal, 3) - _length = length + signal + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - offset = v_offset(offset) - - # Calculate - highest_high = high.rolling(length).max() - lowest_low = low.rolling(length).min() - - fastk = 100 * (close - lowest_low) / \ - non_zero_range(highest_high, lowest_low) - - k = pd_rma(fastk, n=signal) - d = pd_rma(k, n=signal) - j = 3 * k - 2 * d - - # Offset - if offset != 0: - k = k.shift(offset) - d = d.shift(offset) - j = j.shift(offset) - - # Fill - if "fillna" in kwargs: - k.fillna(kwargs["fillna"], inplace=True) - d.fillna(kwargs["fillna"], inplace=True) - j.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}_{signal}" - k.name = f"K{_props}" - d.name = f"D{_props}" - j.name = f"J{_props}" - k.category = d.category = j.category = "momentum" - - data = {k.name: k, d.name: d, j.name: j} - df = DataFrame(data, index=close.index) - df.name = f"KDJ{_props}" - df.category = "momentum" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/kst.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/kst.py deleted file mode 100644 index 00bbd517..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/kst.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series -from .roc import roc - - - -def kst( - close: Series, signal: Int = None, - roc1: Int = None, roc2: Int = None, roc3: Int = None, roc4: Int = None, - sma1: Int = None, sma2: Int = None, sma3: Int = None, sma4: Int = None, - drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """'Know Sure Thing' - - This indicator, by Martin Pring, attempts to capture trends using a - smoothed indicator of four different smoothed ROCs. - - Sources: - * [incrediblecharts](https://www.incrediblecharts.com/indicators/kst.php) - * [tradingview](https://www.tradingview.com/wiki/Know_Sure_Thing_(KST)) - - Parameters: - close (Series): ```close``` Series - roc1 (int): ROC 1 period. Default: ```10``` - roc2 (int): ROC 2 period. Default: ```15``` - roc3 (int): ROC 3 period. Default: ```20``` - roc4 (int): ROC 4 period. Default: ```30``` - sma1 (int): SMA 1 period. Default: ```10``` - sma2 (int): SMA 2 period. Default: ```10``` - sma3 (int): SMA 3 period. Default: ```10``` - sma4 (int): SMA 4 period. Default: ```15``` - signal (int): Signal period. Default: ```9``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - roc1 = int(roc1) if roc1 and roc1 > 0 else 10 - roc2 = int(roc2) if roc2 and roc2 > 0 else 15 - roc3 = int(roc3) if roc3 and roc3 > 0 else 20 - roc4 = int(roc4) if roc4 and roc4 > 0 else 30 - - sma1 = int(sma1) if sma1 and sma1 > 0 else 10 - sma2 = int(sma2) if sma2 and sma2 > 0 else 10 - sma3 = int(sma3) if sma3 and sma3 > 0 else 10 - sma4 = int(sma4) if sma4 and sma4 > 0 else 15 - - signal = v_pos_default(signal, 9) - _rmax = max(roc1, roc2, roc3, roc4) - _smax = max(sma1, sma2, sma3, sma4) - _length = _rmax + _smax - close = v_series(close, _length) - - if close is None: - return - - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - rocma1 = roc(close, roc1).rolling(sma1).mean() - rocma2 = roc(close, roc2).rolling(sma2).mean() - rocma3 = roc(close, roc3).rolling(sma3).mean() - rocma4 = roc(close, roc4).rolling(sma4).mean() - - kst = 100 * (rocma1 + 2 * rocma2 + 3 * rocma3 + 4 * rocma4) - kst_signal = kst.rolling(signal).mean() - - # Offset - if offset != 0: - kst = kst.shift(offset) - kst_signal = kst_signal.shift(offset) - - # Fill - if "fillna" in kwargs: - kst.fillna(kwargs["fillna"], inplace=True) - kst_signal.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - kst.name = f"KST_{roc1}_{roc2}_{roc3}_{roc4}_{sma1}_{sma2}_{sma3}_{sma4}" - kst_signal.name = f"KSTs_{signal}" - kst.category = kst_signal.category = "momentum" - - data = {kst.name: kst, kst_signal.name: kst_signal} - df = DataFrame(data, index=close.index) - df.name = f"KST_{roc1}_{roc2}_{roc3}_{roc4}_{sma1}_{sma2}_{sma3}_{sma4}_{signal}" - df.category = "momentum" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/macd.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/macd.py deleted file mode 100644 index 791364c3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/macd.py +++ /dev/null @@ -1,141 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import concat, DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.overlap import ema -from pandas_ta.utils import ( - signals, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def macd( - close: Series, fast: Int = None, slow: Int = None, - signal: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Moving Average Convergence Divergence - - This indicator attempts to identify trends. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/MACD_(Moving_Average_Convergence/Divergence)) - * [tradingview (AS Mode)](https://tr.tradingview.com/script/YFlKXHnP/) - - Parameters: - close (Series): ```close``` Series - fast (int): Fast MA period. Default: ```12``` - slow (int): Slow MA period. Default: ```26``` - signal (int): Signal period. Default: ```9``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - asmode (value): Enable AS version of MACD. Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - fast = v_pos_default(fast, 12) - slow = v_pos_default(slow, 26) - signal = v_pos_default(signal, 9) - if slow < fast: - fast, slow = slow, fast - _length = slow + signal - 1 - close = v_series(close, _length) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - as_mode = kwargs.setdefault("asmode", False) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import MACD - macd, signalma, histogram = MACD(close, fast, slow, signal) - else: - fastma = ema(close, length=fast, talib=mode_tal) - slowma = ema(close, length=slow, talib=mode_tal) - - macd = fastma - slowma - macd_fvi = macd.loc[macd.first_valid_index():, ] - signalma = ema(close=macd_fvi, length=signal, talib=mode_tal) - histogram = macd - signalma - - if as_mode: - macd = macd - signalma - macd_fvi = macd.loc[macd.first_valid_index():, ] - signalma = ema(close=macd_fvi, length=signal, talib=mode_tal) - histogram = macd - signalma - - # Offset - if offset != 0: - macd = macd.shift(offset) - histogram = histogram.shift(offset) - signalma = signalma.shift(offset) - - # Fill - if "fillna" in kwargs: - macd.fillna(kwargs["fillna"], inplace=True) - histogram.fillna(kwargs["fillna"], inplace=True) - signalma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _asmode = "AS" if as_mode else "" - _props = f"_{fast}_{slow}_{signal}" - macd.name = f"MACD{_asmode}{_props}" - histogram.name = f"MACD{_asmode}h{_props}" - signalma.name = f"MACD{_asmode}s{_props}" - macd.category = histogram.category = signalma.category = "momentum" - - data = { - macd.name: macd, - histogram.name: histogram, - signalma.name: signalma - } - df = DataFrame(data, index=close.index) - df.name = f"MACD{_asmode}{_props}" - df.category = macd.category - - signal_indicators = kwargs.pop("signal_indicators", False) - if not signal_indicators: - return df - else: - signalsdf = concat( - [ - df, - signals( - indicator=histogram, - xa=kwargs.pop("xa", 0), - xb=kwargs.pop("xb", None), - xseries=kwargs.pop("xseries", None), - xseries_a=kwargs.pop("xseries_a", None), - xseries_b=kwargs.pop("xseries_b", None), - cross_values=kwargs.pop("cross_values", True), - cross_series=kwargs.pop("cross_series", True), - offset=offset, - ), - signals( - indicator=macd, - xa=kwargs.pop("xa", 0), - xb=kwargs.pop("xb", None), - xseries=kwargs.pop("xseries", None), - xseries_a=kwargs.pop("xseries_a", None), - xseries_b=kwargs.pop("xseries_b", None), - cross_values=kwargs.pop("cross_values", False), - cross_series=kwargs.pop("cross_series", True), - offset=offset, - ), - ], - axis=1, - ) - - return signalsdf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/mom.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/mom.py deleted file mode 100644 index 6f6ec352..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/mom.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - nb_idiff, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -@njit(cache=True) -def nb_mom(x, n): - return nb_idiff(x, n) - - -def mom( - close: Series, length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Momentum - - This indicator attempts to quantify speed by using the differences over - a bar length. - - Sources: - * [onlinetradingconcepts](http://www.onlinetradingconcepts.com/TechnicalAnalysis/Momentum.html) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```1``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length + 1) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import MOM - mom = MOM(close, length) - else: - np_close = close.to_numpy() - _mom = nb_mom(np_close, length) - mom = Series(_mom, index=close.index) - - # Offset - if offset != 0: - mom = mom.shift(offset) - - # Fill - if "fillna" in kwargs: - mom.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - mom.name = f"MOM_{length}" - mom.category = "momentum" - - return mom diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/pgo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/pgo.py deleted file mode 100644 index 5fc121d8..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/pgo.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import ema, sma -from pandas_ta.utils import v_offset, v_pos_default, v_series -from pandas_ta.volatility import atr - - - -def pgo( - high: Series, low: Series, close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Pretty Good Oscillator - - This indicator, by Mark Johnson, attempts to identify breakouts for longer - time periods based on the distance of the current bar to its N-day - SMA, expressed in terms of an ATR over a similar length. - - Sources: - * [tradingtechnologies](https://library.tradingtechnologies.com/trade/chrt-ti-pretty-good-oscillator.html) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: Entry - * Long when greater than 3. - * Short when less than -3. - """ - # Validate - length = v_pos_default(length, 14) - _length = 2 * length - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - offset = v_offset(offset) - - # Calculate - pgo = (close - sma(close, length)) \ - / ema(atr(high, low, close, length), length) - - # Offset - if offset != 0: - pgo = pgo.shift(offset) - - # Fill - if "fillna" in kwargs: - pgo.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - pgo.name = f"PGO_{length}" - pgo.category = "momentum" - - return pgo diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/ppo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/ppo.py deleted file mode 100644 index 71d09079..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/ppo.py +++ /dev/null @@ -1,107 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - tal_ma, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib -) - - - -def ppo( - close: Series, fast: Int = None, slow: Int = None, signal: Int = None, - scalar: IntFloat = None, mamode: str = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Percentage Price Oscillator - - Similar to MACD. - - Sources: - * [investopedia](https://www.investopedia.com/terms/p/ppo.asp) - - Parameters: - close (Series): ```close``` Series - fast (int): Fast MA period. Default: ```12``` - slow (int): Slow MA period. Default: ```26``` - signal (int): Signal period. Default: ```9``` - scalar (float): Scalar. Default: ```100``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - fast = v_pos_default(fast, 12) - slow = v_pos_default(slow, 26) - signal = v_pos_default(signal, 9) - if slow < fast: - fast, slow = slow, fast - _length = max(fast, slow, signal) - close = v_series(close, _length) - - if close is None: - return - - scalar = v_scalar(scalar, 100) - mamode = v_mamode(mamode, "sma") - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import PPO - ppo = PPO(close, fast, slow, tal_ma(mamode)) - else: - fastma = ma(mamode, close, length=fast, talib=mode_tal) - slowma = ma(mamode, close, length=slow, talib=mode_tal) - ppo = scalar * (fastma - slowma) / slowma - - if all(isnan(ppo)): - return # Emergency Break - - signalma = ma("ema", ppo, length=signal, talib=mode_tal) - histogram = ppo - signalma - - # Offset - if offset != 0: - ppo = ppo.shift(offset) - histogram = histogram.shift(offset) - signalma = signalma.shift(offset) - - # Fill - if "fillna" in kwargs: - ppo.fillna(kwargs["fillna"], inplace=True) - histogram.fillna(kwargs["fillna"], inplace=True) - signalma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{fast}_{slow}_{signal}" - ppo.name = f"PPO{_props}" - histogram.name = f"PPOh{_props}" - signalma.name = f"PPOs{_props}" - ppo.category = histogram.category = signalma.category = "momentum" - - data = { - ppo.name: ppo, - histogram.name: histogram, - signalma.name: signalma - } - df = DataFrame(data, index=close.index) - df.name = f"PPO{_props}" - df.category = ppo.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/psl.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/psl.py deleted file mode 100644 index 7e1774e9..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/psl.py +++ /dev/null @@ -1,79 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import sign -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import ( - v_drift, - v_offset, - v_pos_default, - v_scalar, - v_series -) - - - -def psl( - close: Series, open_: Series = None, - length: Int = None, scalar: IntFloat = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Psychological Line - - This indicator compares the number of the rising bars to the total number - of bars. In other words, it is the percentage of bars that are above the - previous bar over a given length. - - Sources: - * [quantshare](https://www.quantshare.com/item-851-psychological-line) - - Parameters: - close (Series): ```close``` Series - open_ (Series): ```open``` Series - length (int): The period. Default: ```12``` - scalar (float): Scalar. Default: ```100``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 12) - close = v_series(close, length) - - if close is None: - return - - scalar = v_scalar(scalar, 100) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if open_ is not None: - open_ = v_series(open_) - diff = sign(close - open_) - else: - diff = sign(close.diff(drift)) - - diff.fillna(0, inplace=True) - diff[diff <= 0] = 0 # Set negative values to zero - - psl = scalar * diff.rolling(length).sum() / length - - # Offset - if offset != 0: - psl = psl.shift(offset) - - # Fill - if "fillna" in kwargs: - psl.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}" - psl.name = f"PSL{_props}" - psl.category = "momentum" - - return psl diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/qqe.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/qqe.py deleted file mode 100644 index 10aa0769..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/qqe.py +++ /dev/null @@ -1,174 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan, maximum, minimum, nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.utils import ( - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series -) -from .rsi import rsi - - - -def qqe( - close: Series, length: Int = None, - smooth: Int = None, factor: IntFloat = None, - mamode: str = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Quantitative Qualitative Estimation - - This indicator is similar to SuperTrend but uses a Smoothed ```rsi``` - with upper and lower bands. - - Sources: - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/qqe-quantitative-qualitative-estimation/) - * [tradingpedia](https://www.tradingpedia.com/forex-trading-indicators/quantitative-qualitative-estimation) - * [tradingview](https://www.tradingview.com/script/IYfA9R2k-QQE-MT4/) - - Parameters: - close (Series): ```close``` Series - length (int): RSI period. Default: ```14``` - smooth (int): RSI smoothing period. Default: ```5``` - factor (float): QQE Factor. Default: ```4.236``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 4 columns - - Tip: Trend - * Long: When the Smoothed RSI crosses the previous upperband. - * Short: When the Smoothed RSI crosses the previous lowerband. - - Note: See also - * QQE.mq5 by EarnForex Copyright © 2010 - * Tim Hyder (2008) version - * Roman Ignatov (2006) version - """ - # Validate - length = v_pos_default(length, 14) - smooth = v_pos_default(smooth, 5) - wilders_length = 2 * length - 1 - _length = wilders_length + smooth - close = v_series(close, _length) - - if close is None: - return - - factor = v_scalar(factor, 4.236) - mamode = v_mamode(mamode, "ema") - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - rsi_ = rsi(close, length) - _mode = mamode.lower()[0] if mamode != "ema" else "" - rsi_ma = ma(mamode, rsi_, length=smooth) - - # RSI MA True Range - rsi_ma_tr = rsi_ma.diff(drift).abs() - if all(isnan(rsi_ma_tr)): - return - - # Double Smooth the RSI MA True Range using Wilder's Length with a default - # width of 4.236. - smoothed_rsi_tr_ma = ma("ema", rsi_ma_tr, length=wilders_length) - if all(isnan(smoothed_rsi_tr_ma)): - return # Emergency Break - dar = factor * ma("ema", smoothed_rsi_tr_ma, length=wilders_length) - if all(isnan(dar)): - return # Emergency Break - - # Create the Upper and Lower Bands around RSI MA. - upperband = rsi_ma + dar - lowerband = rsi_ma - dar - - m = close.size - long = Series(0, index=close.index) - short = Series(0, index=close.index) - trend = Series(1, index=close.index) - qqe = Series(rsi_ma.iat[0], index=close.index) - qqe_long = Series(nan, index=close.index) - qqe_short = Series(nan, index=close.index) - - for i in range(1, m): - c_rsi, p_rsi = rsi_ma.iat[i], rsi_ma.iat[i - 1] - c_long, p_long = long.iat[i - 1], long.iat[i - 2] - c_short, p_short = short.iat[i - 1], short.iat[i - 2] - - # Long Line - if p_rsi > c_long and c_rsi > c_long: - long.iat[i] = maximum(c_long, lowerband.iat[i]) - else: - long.iat[i] = lowerband.iat[i] - - # Short Line - if p_rsi < c_short and c_rsi < c_short: - short.iat[i] = minimum(c_short, upperband.iat[i]) - else: - short.iat[i] = upperband.iat[i] - - # Trend & QQE Calculation - # Long: Current RSI_MA value Crosses the Prior Short Line Value - # Short: Current RSI_MA Crosses the Prior Long Line Value - if (c_rsi > c_short and p_rsi < p_short) or \ - (c_rsi <= c_short and p_rsi >= p_short): - trend.iat[i] = 1 - qqe.iat[i] = qqe_long.iat[i] = long.iat[i] - elif (c_rsi > c_long and p_rsi < p_long) or \ - (c_rsi <= c_long and p_rsi >= p_long): - trend.iat[i] = -1 - qqe.iat[i] = qqe_short.iat[i] = short.iat[i] - else: - trend.iat[i] = trend.iat[i - 1] - if trend.iat[i] == 1: - qqe.iat[i] = qqe_long.iat[i] = long.iat[i] - else: - qqe.iat[i] = qqe_short.iat[i] = short.iat[i] - - # Offset - if offset != 0: - rsi_ma = rsi_ma.shift(offset) - qqe = qqe.shift(offset) - long = long.shift(offset) - short = short.shift(offset) - - # Fill - if "fillna" in kwargs: - rsi_ma.fillna(kwargs["fillna"], inplace=True) - qqe.fillna(kwargs["fillna"], inplace=True) - qqe_long.fillna(kwargs["fillna"], inplace=True) - qqe_short.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"{_mode}_{length}_{smooth}_{factor}" - qqe.name = f"QQE{_props}" - rsi_ma.name = f"QQE{_props}_RSI{_mode.upper()}MA" - qqe_long.name = f"QQEl{_props}" - qqe_short.name = f"QQEs{_props}" - qqe.category = rsi_ma.category = "momentum" - qqe_long.category = qqe_short.category = qqe.category - - data = { - qqe.name: qqe, - rsi_ma.name: rsi_ma, - # long.name: long, - # short.name: short - qqe_long.name: qqe_long, - qqe_short.name: qqe_short - } - df = DataFrame(data, index=close.index) - df.name = f"QQE{_props}" - df.category = qqe.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/roc.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/roc.py deleted file mode 100644 index c7d3bcf5..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/roc.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - nb_idiff, - nb_shift, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib -) -# from .mom import mom - - - -@njit(cache=True) -def nb_roc(x, n, k): - return k * nb_idiff(x, n) / nb_shift(x, n) - - -def roc( - close: Series, length: Int = None, - scalar: IntFloat = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rate of Change - - This indicator, also (confusingly) known as Momentum, is a pure - oscillator that quantifies the percent change. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Rate_of_Change_(ROC)) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - scalar (float): Scalar. Default: ```100``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length + 1) - - if close is None: - return - - scalar = v_scalar(scalar, 100) - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import ROC - roc = ROC(close, length) - else: - # roc = scalar * mom(close=close, length=length, talib=mode_tal) \ - # / close.shift(length) - np_close = close.to_numpy() - _roc = nb_roc(np_close, length, scalar) - roc = Series(_roc, index=close.index) - - # Offset - if offset != 0: - roc = roc.shift(offset) - - # Fill - if "fillna" in kwargs: - roc.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - roc.name = f"ROC_{length}" - roc.category = "momentum" - - return roc diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rsi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rsi.py deleted file mode 100644 index b4d51d5e..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rsi.py +++ /dev/null @@ -1,115 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series, concat -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.ma import ma -from pandas_ta.utils import ( - signals, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib -) - - - -def rsi( - close: Series, length: Int = None, scalar: IntFloat = None, - mamode: str = None, talib: bool = None, - drift: Int = None, offset: Int = None, - **kwargs: DictLike -) -> Series: - """Relative Strength Index - - This oscillator used to attempts to quantify "velocity" and "magnitude". - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Relative_Strength_Index_(RSI)) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - scalar (float): Scalar. Default: ```100``` - mamode (str): See ```help(ta.ma)```. Default: ```"rma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9289853267851295)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 14) - close = v_series(close, length + 1) - - if close is None: - return - - scalar = v_scalar(scalar, 100) - mamode = v_mamode(mamode, "rma") - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import RSI - rsi = RSI(close, length) - else: - negative = close.diff(drift) - positive = negative.copy() - - positive[positive < 0] = 0 # Make negatives 0 for the positive series - negative[negative > 0] = 0 # Make positives 0 for the negative series - - positive_avg = ma(mamode, positive, length=length, talib=mode_tal) - negative_avg = ma(mamode, negative, length=length, talib=mode_tal) - - rsi = scalar * positive_avg / (positive_avg + negative_avg.abs()) - - # Offset - if offset != 0: - rsi = rsi.shift(offset) - - # Fill - if "fillna" in kwargs: - rsi.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - rsi.name = f"RSI_{length}" - rsi.category = "momentum" - - signal_indicators = kwargs.pop("signal_indicators", False) - if not signal_indicators: - return rsi - else: - signalsdf = concat( - [ - DataFrame({rsi.name: rsi}), - signals( - indicator=rsi, - xa=kwargs.pop("xa", 80), - xb=kwargs.pop("xb", 20), - xseries=kwargs.pop("xseries", None), - xseries_a=kwargs.pop("xseries_a", None), - xseries_b=kwargs.pop("xseries_b", None), - cross_values=kwargs.pop("cross_values", False), - cross_series=kwargs.pop("cross_series", True), - offset=offset, - ), - ], - axis=1, - ) - return signalsdf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rsx.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rsx.py deleted file mode 100644 index c028a2e8..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rsx.py +++ /dev/null @@ -1,148 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import DataFrame, Series, concat -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - signals, - v_drift, - v_offset, - v_pos_default, - v_series -) - - - -def rsx( - close: Series, length: Int = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Relative Strength Xtra - - This indicator, by Jurik Research, is an enhanced version of the RSI which - attemps to reduce noise and provide a clearer, though slightly - delayed, signal. - - Sources: - * [jurikres](http://www.jurikres.com/catalog1/ms_rsx.htm) - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/jurik-rsx/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - close = v_series(close, length) - - if close is None: - return - - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - m = close.size - vC, v1C = 0, 0 - v4, v8, v10, v14, v18, v20 = 0, 0, 0, 0, 0, 0 - - f0, f8, f10, f18, f20, f28, f30, f38 = 0, 0, 0, 0, 0, 0, 0, 0 - f40, f48, f50, f58, f60, f68, f70, f78 = 0, 0, 0, 0, 0, 0, 0, 0 - f80, f88, f90 = 0, 0, 0 - - result = [nan for _ in range(0, length - 1)] + [50] - for i in range(length, m): - if f90 == 0: - f90 = 1.0 - f0 = 0.0 - if length - 1.0 >= 5: - f88 = length - 1.0 - else: - f88 = 5.0 - f8 = 100.0 * close.iat[i] - f18 = 3.0 / (length + 2.0) - f20 = 1.0 - f18 - else: - if f88 <= f90: - f90 = f88 + 1 - else: - f90 = f90 + 1 - f10 = f8 - f8 = 100 * close.iat[i] - v8 = f8 - f10 - f28 = f20 * f28 + f18 * v8 - f30 = f18 * f28 + f20 * f30 - vC = 1.5 * f28 - 0.5 * f30 - f38 = f20 * f38 + f18 * vC - f40 = f18 * f38 + f20 * f40 - v10 = 1.5 * f38 - 0.5 * f40 - f48 = f20 * f48 + f18 * v10 - f50 = f18 * f48 + f20 * f50 - v14 = 1.5 * f48 - 0.5 * f50 - f58 = f20 * f58 + f18 * abs(v8) - f60 = f18 * f58 + f20 * f60 - v18 = 1.5 * f58 - 0.5 * f60 - f68 = f20 * f68 + f18 * v18 - f70 = f18 * f68 + f20 * f70 - v1C = 1.5 * f68 - 0.5 * f70 - f78 = f20 * f78 + f18 * v1C - f80 = f18 * f78 + f20 * f80 - v20 = 1.5 * f78 - 0.5 * f80 - - if f88 >= f90 and f8 != f10: - f0 = 1.0 - if f88 == f90 and f0 == 0.0: - f90 = 0.0 - - if f88 < f90 and v20 > 0.0000000001: - v4 = (v14 / v20 + 1.0) * 50.0 - if v4 > 100.0: - v4 = 100.0 - if v4 < 0.0: - v4 = 0.0 - else: - v4 = 50.0 - result.append(v4) - rsx = Series(result, index=close.index) - - # Offset - if offset != 0: - rsx = rsx.shift(offset) - - # Fill - if "fillna" in kwargs: - rsx.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - rsx.name = f"RSX_{length}" - rsx.category = "momentum" - - signal_indicators = kwargs.pop("signal_indicators", False) - if not signal_indicators: - return rsx - else: - signalsdf = concat( - [ - DataFrame({rsx.name: rsx}), - signals( - indicator=rsx, - xa=kwargs.pop("xa", 80), - xb=kwargs.pop("xb", 20), - xseries=kwargs.pop("xseries", None), - xseries_a=kwargs.pop("xseries_a", None), - xseries_b=kwargs.pop("xseries_b", None), - cross_values=kwargs.pop("cross_values", False), - cross_series=kwargs.pop("cross_series", True), - offset=offset, - ), - ], - axis=1 - ) - return signalsdf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rvgi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rvgi.py deleted file mode 100644 index da302012..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/rvgi.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import swma -from pandas_ta.utils import non_zero_range, v_offset, v_pos_default, v_series - - - -def rvgi( - open_: Series, high: Series, low: Series, close: Series, - length: Int = None, swma_length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Relative Vigor Index - - This indicator attempts to quantify the strength of a trend relative to - its trading range. - - Sources: - * [investopedia](https://www.investopedia.com/terms/r/relative_vigor_index.asp) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - swma_length (int): SWMA period. Default: ```4``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - swma_length = v_pos_default(swma_length, 4) - _length = length + swma_length - 1 - open_ = v_series(open_, _length) - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if open_ is None or high is None or low is None or close is None: - return - - offset = v_offset(offset) - - # Calculate - high_low_range = non_zero_range(high, low) - close_open_range = non_zero_range(close, open_) - - numerator = swma(close_open_range, length=swma_length) \ - .rolling(length).sum() - denominator = swma(high_low_range, length=swma_length) \ - .rolling(length).sum() - - rvgi = numerator / denominator - signal = swma(rvgi, length=swma_length) - - if all(isnan(signal.to_numpy())): - return # Emergency Break - - # Offset - if offset != 0: - rvgi = rvgi.shift(offset) - signal = signal.shift(offset) - - # Fill - if "fillna" in kwargs: - rvgi.fillna(kwargs["fillna"], inplace=True) - signal.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - rvgi.name = f"RVGI_{length}_{swma_length}" - signal.name = f"RVGIs_{length}_{swma_length}" - rvgi.category = signal.category = "momentum" - - data = {rvgi.name: rvgi, signal.name: signal} - df = DataFrame(data, index=close.index) - df.name = f"RVGI_{length}_{swma_length}" - df.category = rvgi.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/slope.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/slope.py deleted file mode 100644 index ebe36e68..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/slope.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import arctan, rad2deg -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - nb_idiff, - v_bool, - v_offset, - v_pos_default, - v_series -) - - - -def slope( - close: Series, length: Int = None, - as_angle: bool = None, to_degrees: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Slope - - Calculates a rolling slope. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```1``` - as_angle (bool): Converts slope to an angle in radians - per ```np.arctan()```. Default: ```False``` - to_degrees (value): If ```as_angle=True```, converts radians to - degrees. Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 1) - close = v_series(close, length + 1) - - if close is None: - return - - as_angle = v_bool(as_angle, False) - to_degrees = v_bool(to_degrees, False) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - _slope = nb_idiff(np_close, length) / length - if as_angle: - _slope = arctan(_slope) - if to_degrees: - _slope = rad2deg(_slope) - slope = Series(_slope, index=close.index) - - # Offset - if offset != 0: - slope = slope.shift(offset) - - # Fill - if "fillna" in kwargs: - slope.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - slope.name = f"SLOPE_{length}" if not as_angle else f"ANGLE{'d' if to_degrees else 'r'}_{length}" - slope.category = "momentum" - - return slope diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/smc.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/smc.py deleted file mode 100644 index b47da384..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/smc.py +++ /dev/null @@ -1,118 +0,0 @@ -# -*- coding: utf-8 -*- -from sys import float_info as sflt -from numpy import maximum, minimum -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.utils import ( - v_bool, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib -) - - -def smc( - open_: Series, high: Series, low: Series, close: Series, - abr_length: Int = None, close_length: Int = None, vol_length: Int = None, - percent: Int = None, vol_ratio: IntFloat = None, asint: bool = None, - mamode: str = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DictLike: - """Smart Money Concept - - This indicator combines several techniques in an attempt to identify - significant movements that might indicate "smart money" actions. - It uses candlestick patterns, moving averages, and imbalance calculations. - - Sources: - * [tradingview](https://www.tradingview.com/script/CnB3fSph-Smart-Money-Concepts-LuxAlgo/) - - Parameters: - abr_length (int): ABR length. Default: ```14``` - close_length (int): The ```close``` MA period. Default: ```50``` - vol_length (int): Volatility period. Default: ```20``` - percent (int): Percent of wick that exceeds the body. Default: ```5``` - vol_ratio (float): Volatility ratio (high) limit. Default: ```1.5``` - asint (bool): Returns as ```Int```. Default: ```True``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Returns: - (DataFrame): 7 columns - """ - # Validate - abr_length = v_pos_default(abr_length, 14) - close_length = v_pos_default(close_length, 50) - vol_length = v_pos_default(vol_length, 20) - if close_length < abr_length: - abr_length, close_length = close_length, abr_length - _length = max(abr_length, close_length, vol_length) + 1 - - open_ = v_series(open_, _length) - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if open_ is None or high is None or low is None or close is None: - return - - percent = v_pos_default(percent, 5) - body_percent = 0.01 * percent - vol_ratio = v_scalar(vol_ratio, 1.5) - asint = v_bool(asint) - mamode = v_mamode(mamode, "sma") - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - body_high, body_low = maximum(open_, close), minimum(open_, close) - body = body_high - body_low + sflt.epsilon - close_ma = ma(mamode, body, length=close_length, talib=mode_tal) - - # Calculate imbalance sizes and percentages based on Average Bar Range (abr) - abr = high.rolling(window=abr_length).max() - low.rolling(window=abr_length).min() - top_imbalance = low.shift(2) - high - btm_imbalance = low - high.shift(2) - top_imbalance_pct = 100 * top_imbalance / abr - btm_imbalance_pct = 100 * btm_imbalance / abr - hld = high - low + sflt.epsilon - high_volatility = hld > vol_ratio * ma(mamode, hld, length=vol_length, talib=mode_tal) - - btm_imbalance_flag = (btm_imbalance > 0) & (btm_imbalance_pct > 1) - top_imbalance_flag = (top_imbalance > 0) & (top_imbalance_pct > 1) - - if asint: - high_volatility = high_volatility.astype(int) - btm_imbalance_flag = btm_imbalance_flag.astype(int) - top_imbalance_flag = top_imbalance_flag.astype(int) - - _props = f"_{abr_length}_{close_length}_{vol_length}_{percent}" - data = { - f"SMChv{_props}": high_volatility, - f"SMCbf{_props}": btm_imbalance_flag, - f"SMCbi{_props}": btm_imbalance, - f"SMCbp{_props}": btm_imbalance_pct, - f"SMCtf{_props}": top_imbalance_flag, - f"SMCti{_props}": top_imbalance, - f"SMCtp{_props}": top_imbalance_pct, - } - df = DataFrame(data, index=close.index) - - # Offset - if offset != 0: - df = df.shift(offset) - - # Fill - df.ffill(inplace=True) - df.bfill(inplace=True) - - # Name and Category - df.name = f"SMC{_props}" - df.category = "momentum" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/smi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/smi.py deleted file mode 100644 index 5c3fa566..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/smi.py +++ /dev/null @@ -1,92 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series -from .tsi import tsi - - - -def smi( - close: Series, fast: Int = None, slow: Int = None, - signal: Int = None, scalar: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """SMI Ergodic Indicator - - This indicator, by William Blau, is the same as the TSI except the SMI - includes a signal line. A trend is considered bullish when crossing above - zero and bearish when crossing below zero. This implementation includes - both the SMI Ergodic Indicator and SMI Ergodic Oscillator. - - Sources: - * [motivewave](https://www.motivewave.com/studies/smi_ergodic_indicator.htm) - * [tradingview A](https://www.tradingview.com/script/Xh5Q0une-SMI-Ergodic-Oscillator/) - * [tradingview B](https://www.tradingview.com/script/cwrgy4fw-SMIIO/) - - Parameters: - close (Series): ```close``` Series - fast (int): The short period. Default: ```5``` - slow (int): The long period. Default: ```20``` - signal (int): Signal period. Default: ```5``` - scalar (float): Scalar. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - fast = v_pos_default(fast, 5) - slow = v_pos_default(slow, 20) - signal = v_pos_default(signal, 5) - if slow < fast: - fast, slow = slow, fast - _length = slow + signal + 1 - close = v_series(close, _length) - - if close is None: - return - - scalar = v_scalar(scalar, 1) - offset = v_offset(offset) - - # Calculate - tsi_df = tsi(close, fast=fast, slow=slow, signal=signal, scalar=scalar) - if tsi_df is None: - return # Emergency Break - - smi = tsi_df.iloc[:, 0] - signalma = tsi_df.iloc[:, 1] - if all(isnan(signalma)): - return # Emergency Break - osc = smi - signalma - - # Offset - if offset != 0: - smi = smi.shift(offset) - signalma = signalma.shift(offset) - osc = osc.shift(offset) - - # Fill - if "fillna" in kwargs: - smi.fillna(kwargs["fillna"], inplace=True) - signalma.fillna(kwargs["fillna"], inplace=True) - osc.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - # _scalar = f"_{scalar}" if scalar != 1 else "" - _props = f"_{fast}_{slow}_{signal}_{scalar}" - smi.name = f"SMI{_props}" - signalma.name = f"SMIs{_props}" - osc.name = f"SMIo{_props}" - smi.category = signalma.category = osc.category = "momentum" - - data = {smi.name: smi, signalma.name: signalma, osc.name: osc} - df = DataFrame(data, index=close.index) - df.name = f"SMI{_props}" - df.category = smi.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/squeeze.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/squeeze.py deleted file mode 100644 index b4330676..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/squeeze.py +++ /dev/null @@ -1,205 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap import ema, linreg, sma -from pandas_ta.trend import decreasing, increasing -from pandas_ta.utils import ( - simplify_columns, - unsigned_differences, - v_bool, - v_mamode, - v_offset, - v_pos_default, - v_series -) -from pandas_ta.volatility import bbands, kc -from .mom import mom - - - -def squeeze( - high: Series, low: Series, close: Series, - bb_length: Int = None, bb_std: IntFloat = None, - kc_length: Int = None, kc_scalar: IntFloat = None, - mom_length: Int = None, mom_smooth: Int = None, - use_tr: bool = None, mamode: str = None, - prenan: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Squeeze - - This indicator, based on John Carter's "TTM Squeeze" indicator, attempts - identify momentum using volatility. - - Sources: - * "Mastering the Trade" (chapter 11), John Carter - * [thinkorswim](https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/T-U/TTM-Squeeze) - * [tradestation](https://tradestation.tradingappstore.com/products/TTMSqueeze) - * [tradingview](https://www.tradingview.com/scripts/lazybear/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - bb_length (int): BB period. Default: ```20``` - bb_std (float): BB Std. Dev. Default: ```2``` - kc_length (int): KC period. Default: ```20``` - kc_scalar (float): KC scalar. Default: ```1.5``` - mom_length (int): Momentum Period. Default: ```12``` - mom_smooth (int): Momentum Smoothing period. Default: ```6``` - mamode (str): One of: "ema" or "sma". Default: ```"sma"``` - prenan (bool): Apply prenans. Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - tr (value): Use True Range for Keltner Channels. Default: ```True``` - asint (bool): Returns as ```Int```. Default: ```True``` - lazybear (value): LazyBear's TradingView. Default: ```False``` - detailed (value): Extra detailed. Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): - * Default: 4 columns - * Detailed: 10 columns - - Note: Volatility - * Increasing: ```kc``` and ```bbands``` difference increases - * Decreasing: ```kc``` and ```bbands``` difference decreases - """ - # Validate - bb_length = v_pos_default(bb_length, 20) - kc_length = v_pos_default(kc_length, 20) - mom_length = v_pos_default(mom_length, 12) - mom_smooth = v_pos_default(mom_smooth, 6) - _length = max(bb_length, kc_length, mom_length, mom_smooth) + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - bb_std = v_pos_default(bb_std, 2.0) - kc_scalar = v_pos_default(kc_scalar, 1.5) - mamode = v_mamode(mamode, "sma") - prenan = v_bool(prenan, False) - offset = v_offset(offset) - - use_tr = kwargs.pop("tr", True) - asint = kwargs.pop("asint", True) - detailed = kwargs.pop("detailed", False) - lazybear = kwargs.pop("lazybear", False) - - # Calculate - bbd = bbands(close, length=bb_length, std=bb_std, mamode=mamode) - kch = kc( - high, low, close, length=kc_length, scalar=kc_scalar, - mamode=mamode, tr=use_tr - ) - - # Simplify KC and BBAND column names for dynamic access - bbd.columns = simplify_columns(bbd) - kch.columns = simplify_columns(kch) - - if lazybear: - highest_high = high.rolling(kc_length).max() - lowest_low = low.rolling(kc_length).min() - avg_ = 0.5 * (0.5 * (highest_high + lowest_low) + kch.b) - - squeeze = linreg(close - avg_, length=kc_length) - - else: - momo = mom(close, length=mom_length) - if mamode.lower() == "ema": - squeeze = ema(momo, length=mom_smooth) - else: # "sma" - squeeze = sma(momo, length=mom_smooth) - - # Classify Squeezes - squeeze_on = (bbd.l > kch.l) & (bbd.u < kch.u) - squeeze_off = (bbd.l < kch.l) & (bbd.u > kch.u) - no_squeeze = ~squeeze_on & ~squeeze_off - - # Offset - if offset != 0: - squeeze = squeeze.shift(offset) - squeeze_on = squeeze_on.shift(offset) - squeeze_off = squeeze_off.shift(offset) - no_squeeze = no_squeeze.shift(offset) - - # Fill - if "fillna" in kwargs: - squeeze.fillna(kwargs["fillna"], inplace=True) - squeeze_on.fillna(kwargs["fillna"], inplace=True) - squeeze_off.fillna(kwargs["fillna"], inplace=True) - no_squeeze.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = "" if use_tr else "hlr" - _props += f"_{bb_length}_{bb_std}_{kc_length}_{kc_scalar}" - _props += "_LB" if lazybear else "" - squeeze.name = f"SQZ{_props}" - - if asint: - squeeze_on = squeeze_on.astype(int) - squeeze_off = squeeze_off.astype(int) - no_squeeze = no_squeeze.astype(int) - - if prenan: - nanlength = max(bb_length, kc_length) - 2 - squeeze_on[:nanlength] = nan - squeeze_off[:nanlength] = nan - no_squeeze[:nanlength] = nan - - data = { - squeeze.name: squeeze, - f"SQZ_ON": squeeze_on, - f"SQZ_OFF": squeeze_off, - f"SQZ_NO": no_squeeze - } - df = DataFrame(data, index=close.index) - df.name = squeeze.name - df.category = squeeze.category = "momentum" - - # More Detail - if detailed: - pos_squeeze = squeeze[squeeze >= 0] - neg_squeeze = squeeze[squeeze < 0] - - pos_inc, pos_dec = unsigned_differences(pos_squeeze, asint=True) - neg_inc, neg_dec = unsigned_differences(neg_squeeze, asint=True) - - pos_inc *= squeeze - pos_dec *= squeeze - neg_dec *= squeeze - neg_inc *= squeeze - - pos_inc.replace(0, nan, inplace=True) - pos_dec.replace(0, nan, inplace=True) - neg_dec.replace(0, nan, inplace=True) - neg_inc.replace(0, nan, inplace=True) - - sqz_inc = squeeze * increasing(squeeze) - sqz_dec = squeeze * decreasing(squeeze) - sqz_inc.replace(0, nan, inplace=True) - sqz_dec.replace(0, nan, inplace=True) - - # Handle fills - if "fillna" in kwargs: - sqz_inc.fillna(kwargs["fillna"], inplace=True) - sqz_dec.fillna(kwargs["fillna"], inplace=True) - pos_inc.fillna(kwargs["fillna"], inplace=True) - pos_dec.fillna(kwargs["fillna"], inplace=True) - neg_dec.fillna(kwargs["fillna"], inplace=True) - neg_inc.fillna(kwargs["fillna"], inplace=True) - - df[f"SQZ_INC"] = sqz_inc - df[f"SQZ_DEC"] = sqz_dec - df[f"SQZ_PINC"] = pos_inc - df[f"SQZ_PDEC"] = pos_dec - df[f"SQZ_NDEC"] = neg_dec - df[f"SQZ_NINC"] = neg_inc - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/squeeze_pro.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/squeeze_pro.py deleted file mode 100644 index 524c66a8..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/squeeze_pro.py +++ /dev/null @@ -1,222 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.momentum import mom -from pandas_ta.trend import decreasing, increasing -from pandas_ta.utils import ( - simplify_columns, - unsigned_differences, - v_bool, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series -) -from pandas_ta.volatility import bbands, kc - - - -def squeeze_pro( - high: Series, low: Series, close: Series, - bb_length: Int = None, bb_std: IntFloat = None, - kc_length: Int = None, kc_scalar_narrow: IntFloat = None, - kc_scalar_normal: IntFloat = None, kc_scalar_wide: IntFloat = None, - mom_length: Int = None, mom_smooth: Int = None, - use_tr: bool = None, mamode: str = None, - prenan: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Squeeze Pro - - This indicator, based on John Carter's "TTM Squeeze" indicator, attempts - identify momentum using volatility with additional details. - - Sources: - * [usethinkscript](https://usethinkscript.com/threads/john-carters-squeeze-pro-indicator-for-thinkorswim-free.4021/) - * [tradingview](https://www.tradingview.com/script/TAAt6eRX-Squeeze-PRO-Indicator-Makit0/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - bb_length (int): BB period. Default: ```20``` - bb_std (float): BB Std. Dev. Default: ```2``` - kc_length (int): KC period. Default: ```20``` - kc_scalar_normal (float): Keltner Channel scalar for normal channel. - Default: ```1.5``` - kc_scalar_narrow (float): Narrow channel KC scalar. Default: ```1``` - kc_scalar_wide (float): Wide channel KC scalar. Default: ```2``` - mom_length (int): Momentum Period. Default: ```12``` - mom_smooth (int): Momentum Smoothing period. Default: ```6``` - mamode (str): One of: "ema" or "sma". Default: ```"sma"``` - prenan (bool): Apply prenans. Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - tr (value): Use True Range for Keltner Channels. - Default: ```True``` - asint (bool): Returns as ```Int```. Default: ```True``` - mamode (value): Which MA to use. Default: ```"sma"``` - detailed (value): Extra detailed. Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 6 columns (_default_) or 12 columns if ```detailed=True``` - - Warning: - May be depreciated in the future and combined with ```squeeze```. - """ - # Validate - bb_length = v_pos_default(bb_length, 20) - kc_length = v_pos_default(kc_length, 20) - mom_length = v_pos_default(mom_length, 12) - mom_smooth = v_pos_default(mom_smooth, 6) - _length = max(bb_length, kc_length, mom_length, mom_smooth) + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - kc_scalar_narrow = v_scalar(kc_scalar_narrow, 1) - kc_scalar_normal = v_scalar(kc_scalar_normal, 1.5) - kc_scalar_wide = v_scalar(kc_scalar_wide, 2) - prenan = v_bool(prenan, False) - valid_kc_scaler = kc_scalar_wide > kc_scalar_normal \ - and kc_scalar_normal > kc_scalar_narrow - - if not valid_kc_scaler: - return - - bb_std = v_pos_default(bb_std, 2.0) - mamode = v_mamode(mamode, "sma") - offset = v_offset(offset) - use_tr = kwargs.pop("tr", True) - asint = kwargs.pop("asint", True) - detailed = kwargs.pop("detailed", False) - - # Calculate - bbd = bbands(close, length=bb_length, std=bb_std, mamode=mamode) - kch_wide = kc( - high, low, close, length=kc_length, scalar=kc_scalar_wide, - mamode=mamode, tr=use_tr - ) - kch_normal = kc( - high, low, close, length=kc_length, scalar=kc_scalar_normal, - mamode=mamode, tr=use_tr - ) - kch_narrow = kc( - high, low, close, length=kc_length, scalar=kc_scalar_narrow, - mamode=mamode, tr=use_tr - ) - - # Simplify KC and BBAND column names for dynamic access - bbd.columns = simplify_columns(bbd) - kch_wide.columns = simplify_columns(kch_wide) - kch_normal.columns = simplify_columns(kch_normal) - kch_narrow.columns = simplify_columns(kch_narrow) - - momo = mom(close, length=mom_length) - squeeze = ma(mamode, momo, length=mom_smooth) - - # Classify Squeezes - squeeze_on_wide = (bbd.l > kch_wide.l) & (bbd.u < kch_wide.u) - squeeze_on_normal = (bbd.l > kch_normal.l) & (bbd.u < kch_normal.u) - squeeze_on_narrow = (bbd.l > kch_narrow.l) & (bbd.u < kch_narrow.u) - squeeze_off_wide = (bbd.l < kch_wide.l) & (bbd.u > kch_wide.u) - no_squeeze = ~squeeze_on_wide & ~squeeze_off_wide - - # Offset - if offset != 0: - squeeze = squeeze.shift(offset) - squeeze_on_wide = squeeze_on_wide.shift(offset) - squeeze_on_normal = squeeze_on_normal.shift(offset) - squeeze_on_narrow = squeeze_on_narrow.shift(offset) - squeeze_off_wide = squeeze_off_wide.shift(offset) - no_squeeze = no_squeeze.shift(offset) - - # Fill - if "fillna" in kwargs: - squeeze.fillna(kwargs["fillna"], inplace=True) - squeeze_on_wide.fillna(kwargs["fillna"], inplace=True) - squeeze_on_normal.fillna(kwargs["fillna"], inplace=True) - squeeze_on_narrow.fillna(kwargs["fillna"], inplace=True) - squeeze_off_wide.fillna(kwargs["fillna"], inplace=True) - no_squeeze.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = "" if use_tr else "hlr" - _props += f"_{bb_length}_{bb_std}_{kc_length}_{kc_scalar_wide}_{kc_scalar_normal}_{kc_scalar_narrow}" - squeeze.name = f"SQZPRO{_props}" - - if asint: - squeeze_on_wide = squeeze_on_wide.astype(int) - squeeze_on_narrow = squeeze_on_narrow.astype(int) - squeeze_on_normal = squeeze_on_normal.astype(int) - squeeze_off_wide = squeeze_off_wide.astype(int) - no_squeeze = no_squeeze.astype(int) - - if prenan: - nanlength = max(bb_length, kc_length) - 2 - squeeze_on_wide[:nanlength] = nan - squeeze_on_narrow[:nanlength] = nan - squeeze_on_normal[:nanlength] = nan - squeeze_off_wide[:nanlength] = nan - no_squeeze[:nanlength] = nan - - data = { - squeeze.name: squeeze, - f"SQZPRO_ON_WIDE": squeeze_on_wide, - f"SQZPRO_ON_NORMAL": squeeze_on_normal, - f"SQZPRO_ON_NARROW": squeeze_on_narrow, - f"SQZPRO_OFF": squeeze_off_wide, - f"SQZPRO_NO": no_squeeze - } - df = DataFrame(data, index=close.index) - df.name = squeeze.name - df.category = squeeze.category = "momentum" - - # More Detail - if detailed: - pos_squeeze = squeeze[squeeze >= 0] - neg_squeeze = squeeze[squeeze < 0] - - pos_inc, pos_dec = unsigned_differences(pos_squeeze, asint=True) - neg_inc, neg_dec = unsigned_differences(neg_squeeze, asint=True) - - pos_inc *= squeeze - pos_dec *= squeeze - neg_dec *= squeeze - neg_inc *= squeeze - - pos_inc.replace(0, nan, inplace=True) - pos_dec.replace(0, nan, inplace=True) - neg_dec.replace(0, nan, inplace=True) - neg_inc.replace(0, nan, inplace=True) - - sqz_inc = squeeze * increasing(squeeze) - sqz_dec = squeeze * decreasing(squeeze) - sqz_inc.replace(0, nan, inplace=True) - sqz_dec.replace(0, nan, inplace=True) - - # Fill - if "fillna" in kwargs: - sqz_inc.fillna(kwargs["fillna"], inplace=True) - sqz_dec.fillna(kwargs["fillna"], inplace=True) - pos_inc.fillna(kwargs["fillna"], inplace=True) - pos_dec.fillna(kwargs["fillna"], inplace=True) - neg_dec.fillna(kwargs["fillna"], inplace=True) - neg_inc.fillna(kwargs["fillna"], inplace=True) - - df[f"SQZPRO_INC"] = sqz_inc - df[f"SQZPRO_DEC"] = sqz_dec - df[f"SQZPRO_PINC"] = pos_inc - df[f"SQZPRO_PDEC"] = pos_dec - df[f"SQZPRO_NDEC"] = neg_dec - df[f"SQZPRO_NINC"] = neg_inc - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stc.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stc.py deleted file mode 100644 index 518b3d25..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stc.py +++ /dev/null @@ -1,175 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap import ema -from pandas_ta.utils import ( - non_zero_range, - v_offset, - v_pos_default, - v_series -) - - - -def schaff_tc(close: Series, seed: Series, tc_length: int, factor: IntFloat): - lowest_xmacd = seed.rolling(tc_length).min() - xmacd_range = non_zero_range(seed.rolling(tc_length).max(), lowest_xmacd) - m = len(seed) - - # Initialize lists - stoch1, pf = [0] * m, [0] * m - stoch2, pff = [0] * m, [0] * m - - for i in range(1, m): - # %Fast K of MACD - if lowest_xmacd.iloc[i] > 0: - stoch1[i] = 100 * ((seed.iloc[i] - lowest_xmacd.iloc[i]) / xmacd_range.iloc[i]) - else: - stoch1[i] = stoch1[i - 1] - # Smoothed Calculation for % Fast D of MACD - pf[i] = round(pf[i - 1] + (factor * (stoch1[i] - pf[i - 1])), 8) - - # find min and max so far - if i < tc_length: - # If there are not enough elements for a full tclength window, - # use what is available - lowest_pf = min(pf[:i+1]) - highest_pf = max(pf[:i+1]) - else: - lowest_pf = min(pf[i - tc_length + 1:i + 1]) - highest_pf = max(pf[i - tc_length + 1:i + 1]) - - # Ensure non-zero range - pf_range = highest_pf - lowest_pf if highest_pf - lowest_pf > 0 else 1 - - # % of Fast K of PF - if pf_range > 0: - stoch2[i] = 100 * ((pf[i] - lowest_pf) / pf_range) - else: - stoch2[i] = stoch2[i - 1] - pff[i] = round(pff[i - 1] + (factor * (stoch2[i] - pff[i - 1])), 8) - - pf_series = Series(pf, index=close.index) - pff_series = Series(pff, index=close.index) - - return pff_series, pf_series - - -def stc( - close: Series, tc_length: Int = None, - fast: Int = None, slow: Int = None, factor: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Schaff Trend Cycle - - This indicator is an evolved MACD with additional smoothing. - - Sources: - * [rengel8](https://github.com/rengel8) - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/schaff-trend-cycle2/) - - Parameters: - close (Series): ```close``` Series - tc_length (int): TC period. (Adjust to the half of cycle) - Default: ```10``` - fast (int): Fast MA period. Default: ```12``` - slow (int): Slow MA period. Default: ```26``` - factor (float): Smoothing factor for last stoch. calculation. - Default: ```0.5``` - offset (int): How many bars to shift the results. Default: ```0`` - - Other Parameters: - ma1 (Series): User chosen MA. Default: ```False``` - ma2 (Series): User chosen MA. Default: ```False``` - osc (Series): User chosen oscillator. Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - - Note: - Can also seed STC with two MAs, ```ma1``` and ```ma2```, or an oscillator ```osc```. - - * ```ma1``` and ```ma2``` are **both** required if this option is used. - """ - # Validate - fast = v_pos_default(fast, 12) - slow = v_pos_default(slow, 26) - tc_length = v_pos_default(tc_length, 10) - if slow < fast: - fast, slow = slow, fast - _length = max(tc_length, fast, slow) - close = v_series(close, _length) - - if close is None: - return - - factor = v_pos_default(factor, 0.5) - offset = v_offset(offset) - - # Calculate - # kwargs allows for three more series (ma1, ma2 and osc) which can be passed - # here ma1 and ma2 input negate internal ema calculations, osc substitutes - # both ma's. - ma1 = kwargs.pop("ma1", False) - ma2 = kwargs.pop("ma2", False) - osc = kwargs.pop("osc", False) - - if isinstance(ma1, Series) and isinstance(ma2, Series) and not osc: - ma1 = v_series(ma1, _length) - ma2 = v_series(ma2, _length) - - if ma1 is None or ma2 is None: - return - seed = ma1 - ma2 - - elif isinstance(osc, Series): - osc = v_series(osc, _length) - if osc is None: - return - seed = osc - - else: - fastma = ema(close, length=fast) - slowma = ema(close, length=slow) - seed = fastma - slowma - - pff, pf = schaff_tc(close, seed, tc_length, factor) - pf[:_length - 1] = nan - - stc = Series(pff, index=close.index) - macd = Series(seed, index=close.index) - stoch = Series(pf, index=close.index) - - stc.iloc[:_length - 1] = nan - - # Offset - if offset != 0: - stc = stc.shift(offset) - macd = macd.shift(offset) - stoch = stoch.shift(offset) - - # Fill - if "fillna" in kwargs: - stc.fillna(kwargs["fillna"], inplace=True) - macd.fillna(kwargs["fillna"], inplace=True) - stoch.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{tc_length}_{fast}_{slow}_{factor}" - stc.name = f"STC{_props}" - macd.name = f"STCmacd{_props}" - stoch.name = f"STCstoch{_props}" - stc.category = macd.category = stoch.category = "momentum" - - data = { - stc.name: stc, - macd.name: macd, - stoch.name: stoch - } - df = DataFrame(data, index=close.index) - df.name = f"STC{_props}" - df.category = stc.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stoch.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stoch.py deleted file mode 100644 index 0d10ca64..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stoch.py +++ /dev/null @@ -1,121 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - non_zero_range, - tal_ma, - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def stoch( - high: Series, low: Series, close: Series, - k: Int = None, d: Int = None, smooth_k: Int = None, - mamode: str = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Stochastic - - This indicator, by George Lane in the 1950's, attempts to identify and - quantify momentum; it assumes that momentum precedes value change. - - Sources: - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=332&Name=KD_-_Slow) - * [tradingview](https://www.tradingview.com/wiki/Stochastic_(STOCH)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - k (int): The Fast %K period. Default: ```14``` - d (int): The Slow %D period. Default: ```3``` - smooth_k (int): The Slow %K period. Default: ```3``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - k = v_pos_default(k, 14) - d = v_pos_default(d, 3) - smooth_k = v_pos_default(smooth_k, 3) - _length = k + d + smooth_k - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mode_tal = v_talib(talib) - mamode = v_mamode(mamode, "sma") - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal and smooth_k > 2: - from talib import STOCH - stoch_ = STOCH( - high, low, close, k, d, tal_ma(mamode), d, tal_ma(mamode) - ) - stoch_k, stoch_d = stoch_[0], stoch_[1] - else: - ll = low.rolling(k).min() - hh = high.rolling(k).max() - - stoch = 100 * (close - ll) / non_zero_range(hh, ll) - - if stoch is None: return - - stoch_fvi = stoch.loc[stoch.first_valid_index():, ] - if smooth_k == 1: - stoch_k = stoch - else: - stoch_k = ma(mamode, stoch_fvi, length=smooth_k) - - stochk_fvi = stoch_k.loc[stoch_k.first_valid_index():, ] - stoch_d = ma(mamode, stochk_fvi, length=d) - - stoch_h = stoch_k - stoch_d # Histogram - - # Offset - if offset != 0: - stoch_k = stoch_k.shift(offset) - stoch_d = stoch_d.shift(offset) - stoch_h = stoch_h.shift(offset) - - # Fill - if "fillna" in kwargs: - stoch_k.fillna(kwargs["fillna"], inplace=True) - stoch_d.fillna(kwargs["fillna"], inplace=True) - stoch_h.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _name = "STOCH" - _props = f"_{k}_{d}_{smooth_k}" - stoch_k.name = f"{_name}k{_props}" - stoch_d.name = f"{_name}d{_props}" - stoch_h.name = f"{_name}h{_props}" - stoch_k.category = stoch_d.category = stoch_h.category = "momentum" - - data = { - stoch_k.name: stoch_k, - stoch_d.name: stoch_d, - stoch_h.name: stoch_h - } - df = DataFrame(data, index=close.index) - df.name = f"{_name}{_props}" - df.category = stoch_k.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stochf.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stochf.py deleted file mode 100644 index 8b214321..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stochf.py +++ /dev/null @@ -1,100 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - non_zero_range, - tal_ma, - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def stochf( - high: Series, low: Series, close: Series, - k: Int = None, d: Int = None, - mamode: str = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Fast Stochastic - - This indicator, by George Lane in the 1950's, attempts to identify and - quantify momentum like STOCH, but is more volatile. - - Sources: - * [corporatefinanceinstitute](https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/fast-stochastic-indicator/) - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=333&Name=KD_-_Fast) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - k (int): The Fast %K period. Default: ```14``` - d (int): The Slow %D period. Default: ```3``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - k = v_pos_default(k, 14) - d = v_pos_default(d, 3) - _length = k + d - 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mamode = v_mamode(mamode, "sma") - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import STOCHF - stochf_ = STOCHF(high, low, close, k, d, tal_ma(mamode)) - stochf_k, stochf_d = stochf_[0], stochf_[1] - else: - lowest_low = low.rolling(k).min() - highest_high = high.rolling(k).max() - - stochf_k = 100 * (close - lowest_low) \ - / non_zero_range(highest_high, lowest_low) - stochfk_fvi = stochf_k.loc[stochf_k.first_valid_index():, ] - stochf_d = ma(mamode, stochfk_fvi, length=d, talib=mode_tal) - - # Offset - if offset != 0: - stochf_k = stochf_k.shift(offset) - stochf_d = stochf_d.shift(offset) - - # Fill - if "fillna" in kwargs: - stochf_k.fillna(kwargs["fillna"], inplace=True) - stochf_d.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _name = "STOCHF" - _props = f"_{k}_{d}" - stochf_k.name = f"{_name}k{_props}" - stochf_d.name = f"{_name}d{_props}" - stochf_k.category = stochf_d.category = "momentum" - - data = {stochf_k.name: stochf_k, stochf_d.name: stochf_d} - df = DataFrame(data, index=close.index) - df.name = f"{_name}{_props}" - df.category = stochf_k.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stochrsi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stochrsi.py deleted file mode 100644 index 4fe88410..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/stochrsi.py +++ /dev/null @@ -1,104 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.momentum import rsi -from pandas_ta.utils import ( - non_zero_range, - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def stochrsi( - close: Series, length: Int = None, rsi_length: Int = None, - k: Int = None, d: Int = None, mamode: str = None, - talib: bool = None, offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Stochastic RSI - - This indicator attempts to quantify RSI relative to its High-Low range. - - Sources: - * "Stochastic RSI and Dynamic Momentum Index", Tushar Chande and - Stanley Kroll, Stock & Commodities V.11:5 (189-199) - * [tradingview](https://www.tradingview.com/wiki/Stochastic_(STOCH)) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - rsi_length (int): RSI period. Default: ```14``` - k (int): The Fast %K period. Default: ```3``` - d (int): The Slow %K period. Default: ```3``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Note: - May be more sensitive to RSI and thus identify potential "overbought" - or "oversold" signals. - """ - # Validate - length = v_pos_default(length, 14) - rsi_length = v_pos_default(rsi_length, 14) - k = v_pos_default(k, 3) - d = v_pos_default(d, 3) - _length = length + rsi_length + 2 - close = v_series(close, _length) - - if close is None: - return - - mamode = v_mamode(mamode, "sma") - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - # if Imports["talib"] and mode_tal: - # from talib import RSI - # rsi_ = RSI(close, length) - # else: - - rsi_ = rsi(close, length=rsi_length) - lowest_rsi = rsi_.rolling(length).min() - highest_rsi = rsi_.rolling(length).max() - - stoch = 100 * (rsi_ - lowest_rsi) / non_zero_range(highest_rsi, lowest_rsi) - - stochrsi_k = ma(mamode, stoch, length=k) - stochrsi_d = ma(mamode, stochrsi_k, length=d) - - # Offset - if offset != 0: - stochrsi_k = stochrsi_k.shift(offset) - stochrsi_d = stochrsi_d.shift(offset) - - # Fill - if "fillna" in kwargs: - stochrsi_k.fillna(kwargs["fillna"], inplace=True) - stochrsi_d.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _name = "STOCHRSI" - _props = f"_{length}_{rsi_length}_{k}_{d}" - stochrsi_k.name = f"{_name}k{_props}" - stochrsi_d.name = f"{_name}d{_props}" - stochrsi_k.category = stochrsi_d.category = "momentum" - - data = {stochrsi_k.name: stochrsi_k, stochrsi_d.name: stochrsi_d} - df = DataFrame(data, index=close.index) - df.name = f"{_name}{_props}" - df.category = stochrsi_k.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/tmo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/tmo.py deleted file mode 100644 index b046602a..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/tmo.py +++ /dev/null @@ -1,129 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan, zeros -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - sum_signed_rolling_deltas, - v_bool, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - -def tmo( - open_: Series, close: Series, tmo_length: Int = None, - calc_length: Int = None, smooth_length: Int = None, - momentum: bool = None, normalize: bool = None, exclusive: bool = None, - mamode: str = None, offset: Int = None, **kwargs: DictLike, -) -> DataFrame: - """True Momentum Oscillator - - This indicator attempts to quantify momentum. - - Sources: - * [tradingview A](https://www.tradingview.com/script/VRwDppqd-True-Momentum-Oscillator/) - * [tradingview B](https://www.tradingview.com/script/65vpO7T5-True-Momentum-Oscillator-Universal-Edition/) - - Parameters: - open_ (Series): ```open``` Series - close (Series): ```close``` Series - tmo_length (int): TMO period. Default: ```14``` - calc_length (int): Initial MA period. Default: ```5``` - smooth_length (int): Main and smooth signal MA period. Default: ```3``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - momentum (bool): Compute main and smooth momentum. Default: ```False``` - normalize (bool): Normalize. Default: ```False``` - exclusive (bool): Exclusive period over ```n``` bars, or inclusively - over ```n-1``` bars. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): DataFrame.fillna(value) - - Returns: - (DataFrame): 4 columns - """ - # Validate - tmo_length = v_pos_default(tmo_length, 14) - calc_length = v_pos_default(calc_length, 5) - smooth_length = v_pos_default(smooth_length, 3) - _length = max(tmo_length, calc_length, smooth_length) - - open_ = v_series(open_, _length) - close = v_series(close, _length) - offset = v_offset(offset) - - if "length" in kwargs: - kwargs.pop("length") - - if open_ is None or close is None: - return None - - mamode = v_mamode(mamode, "ema") - compute_momentum = v_bool(momentum, False) - normalize_signal = v_bool(normalize, False) - exclusive = v_bool(exclusive, True) - - signed_diff_sum = sum_signed_rolling_deltas( - open_, close, tmo_length, exclusive=exclusive - ) - if all(isnan(signed_diff_sum)): - return None # Emergency Break - - initial_ma = ma(mamode, signed_diff_sum, length=calc_length) - if all(isnan(initial_ma)): - return None # Emergency Break - - main = ma(mamode, initial_ma, length=smooth_length) - if all(isnan(main)): - return None # Emergency Break - - smooth = ma(mamode, main, length=smooth_length) - if all(isnan(smooth)): - return None # Emergency Break - - if compute_momentum: - mom_main = main - main.shift(tmo_length) - mom_smooth = smooth - smooth.shift(tmo_length) - else: - zero_array = zeros(main.size) - mom_main = Series(zero_array, index=main.index) - mom_smooth = Series(zero_array, index=smooth.index) - - # Offset - if offset != 0: - main = main.shift(offset) - smooth = smooth.shift(offset) - mom_main = mom_main.shift(offset) - mom_smooth = mom_smooth.shift(offset) - - # Fill - if "fillna" in kwargs: - main.fillna(kwargs["fillna"], inplace=True) - smooth.fillna(kwargs["fillna"], inplace=True) - mom_main.fillna(kwargs["fillna"], inplace=True) - mom_smooth.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{tmo_length}_{calc_length}_{smooth_length}" - main.name = f"TMO{_props}" - smooth.name = f"TMOs{_props}" - mom_main.name = f"TMOM{_props}" - mom_smooth.name = f"TMOMs{_props}" - main.category = smooth.category = "momentum" - mom_main.category = mom_smooth.category = main.category - - data = { - main.name: main, - smooth.name: smooth, - mom_main.name: mom_main, - mom_smooth.name: mom_smooth, - } - df = DataFrame(data, index=close.index) - df.name = f"TMO{_props}" - df.category = main.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/trix.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/trix.py deleted file mode 100644 index 123bf7de..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/trix.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap.ema import ema -from pandas_ta.utils import ( - v_drift, - v_offset, - v_pos_default, - v_scalar, - v_series -) - - - -def trix( - close: Series, length: Int = None, signal: Int = None, - scalar: IntFloat = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Trix - - This indicator attempts to identify divergences as an oscillator. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/TRIX) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```18``` - signal (int): Signal period. Default: ```9``` - scalar (float): Scalar. Default: ```100``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 30) - signal = v_pos_default(signal, 9) - if length < signal: - length, signal = signal, length - _length = 3 * length - 1 - close = v_series(close, _length) - - if close is None: - return - - scalar = v_scalar(scalar, 100) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - ema1 = ema(close=close, length=length, **kwargs) - if all(isnan(ema1)): - return # Emergency Break - - ema2 = ema(close=ema1, length=length, **kwargs) - if all(isnan(ema2)): - return # Emergency Break - - ema3 = ema(close=ema2, length=length, **kwargs) - if all(isnan(ema3)): - return # Emergency Break - - trix = scalar * ema3.pct_change(drift) - trix_signal = trix.rolling(signal).mean() - - # Offset - if offset != 0: - trix = trix.shift(offset) - trix_signal = trix_signal.shift(offset) - - # Fill - if "fillna" in kwargs: - trix.fillna(kwargs["fillna"], inplace=True) - trix_signal.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - trix.name = f"TRIX_{length}_{signal}" - trix_signal.name = f"TRIXs_{length}_{signal}" - trix.category = trix_signal.category = "momentum" - - data = {trix.name: trix, trix_signal.name: trix_signal} - df = DataFrame(data, index=close.index) - df.name = f"TRIX_{length}_{signal}" - df.category = "momentum" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/tsi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/tsi.py deleted file mode 100644 index 025a0f47..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/tsi.py +++ /dev/null @@ -1,106 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.overlap import ema -from pandas_ta.utils import ( - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series -) - - - -def tsi( - close: Series, fast: Int = None, slow: Int = None, - signal: Int = None, scalar: IntFloat = None, - mamode: str = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """True Strength Index - - This indicator attempts to identify short-term swings in trend direction - as well as identifying possible "overbought" and "oversold" signals. - - Sources: - * [investopedia](https://www.investopedia.com/terms/t/tsi.asp) - - Parameters: - close (Series): ```close``` Series - fast (int): Fast MA period. Default: ```13``` - slow (int): Slow MA period. Default: ```25``` - signal (int): Signal period. Default: ```13``` - scalar (float): Scalar. Default: ```100``` - mamode (str): Signal MA. See ```help(ta.ma)```. Default: ```"ema"``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - fast = v_pos_default(fast, 13) - slow = v_pos_default(slow, 25) - signal = v_pos_default(signal, 13) - if slow < fast: - fast, slow = slow, fast - _length = slow + signal + 1 - close = v_series(close, _length) - - if "length" in kwargs: - kwargs.pop("length") - - if close is None: - return - - scalar = v_scalar(scalar, 100) - mamode = v_mamode(mamode, "ema") - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - diff = close.diff(drift) - slow_ema = ema(close=diff, length=slow, **kwargs) - if all(isnan(slow_ema)): - return # Emergency Break - fast_slow_ema = ema(close=slow_ema, length=fast, **kwargs) - - abs_diff = diff.abs() - abs_slow_ema = ema(close=abs_diff, length=slow, **kwargs) - if all(isnan(abs_slow_ema)): - return # Emergency Break - abs_fast_slow_ema = ema(close=abs_slow_ema, length=fast, **kwargs) - - tsi = scalar * fast_slow_ema / abs_fast_slow_ema - if all(isnan(tsi)): - return # Emergency Break - tsi_signal = ma(mamode, tsi, length=signal) - - # Offset - if offset != 0: - tsi = tsi.shift(offset) - tsi_signal = tsi_signal.shift(offset) - - # Fill - if "fillna" in kwargs: - tsi.fillna(kwargs["fillna"], inplace=True) - tsi_signal.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - tsi.name = f"TSI_{fast}_{slow}_{signal}" - tsi_signal.name = f"TSIs_{fast}_{slow}_{signal}" - tsi.category = tsi_signal.category = "momentum" - - data = {tsi.name: tsi, tsi_signal.name: tsi_signal} - df = DataFrame(data, index=close.index) - df.name = f"TSI_{fast}_{slow}_{signal}" - df.category = "momentum" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/uo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/uo.py deleted file mode 100644 index ab407b3a..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/uo.py +++ /dev/null @@ -1,105 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_drift, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def uo( - high: Series, low: Series, close: Series, - fast: Int = None, medium: Int = None, slow: Int = None, - fast_w: IntFloat = None, medium_w: IntFloat = None, slow_w: IntFloat = None, - talib: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Ultimate Oscillator - - This indicator, by Larry Williams, attempts to identify momentum. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Ultimate_Oscillator_(UO)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - fast (int): The Fast %K period. Default: ```7``` - medium (int): The Slow %K period. Default: ```14``` - slow (int): The Slow %D period. Default: ```28``` - fast_w (float): The Fast %K period. Default: ```4.0``` - medium_w (float): The Slow %K period. Default: ```2.0``` - slow_w (float): The Slow %D period. Default: ```1.0``` - talib (bool): If installed, use TA Lib. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - fast = v_pos_default(fast, 7) - medium = v_pos_default(medium, 14) - slow = v_pos_default(slow, 28) - _length = max(fast, medium, slow) + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - fast_w = v_pos_default(fast_w, 4.0) - medium_w = v_pos_default(medium_w, 2.0) - slow_w = v_pos_default(slow_w, 1.0) - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import ULTOSC - uo = ULTOSC(high, low, close, fast, medium, slow) - else: - close_drift = close.shift(drift) - tdf = DataFrame({ - "high": high, "low": low, f"close_{drift}": close_drift - }) - max_h_or_pc = tdf.loc[:, ["high", f"close_{drift}"]].max(axis=1) - min_l_or_pc = tdf.loc[:, ["low", f"close_{drift}"]].min(axis=1) - del tdf - - bp = close - min_l_or_pc - tr = max_h_or_pc - min_l_or_pc - - fast_avg = bp.rolling(fast).sum() / tr.rolling(fast).sum() - medium_avg = bp.rolling(medium).sum() / tr.rolling(medium).sum() - slow_avg = bp.rolling(slow).sum() / tr.rolling(slow).sum() - - total_weight = fast_w + medium_w + slow_w - weights = (fast_w * fast_avg) + (medium_w * medium_avg) \ - + (slow_w * slow_avg) - uo = 100 * weights / total_weight - - # Offset - if offset != 0: - uo = uo.shift(offset) - - # Fill - if "fillna" in kwargs: - uo.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - uo.name = f"UO_{fast}_{medium}_{slow}" - uo.category = "momentum" - - return uo diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/willr.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/willr.py deleted file mode 100644 index c83c1272..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/momentum/willr.py +++ /dev/null @@ -1,75 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib - - - -def willr( - high: Series, low: Series, close: Series, - length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """William's Percent R - - This indicator attempts to identify "overbought" and "oversold" - conditions similar to the RSI. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Williams_%25R_(%25R)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - _length = max(length, min_periods) - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import WILLR - willr = WILLR(high, low, close, length) - else: - lowest_low = low.rolling(length, min_periods=min_periods).min() - highest_high = high.rolling(length, min_periods=min_periods).max() - - willr = 100 * ((close - lowest_low) / (highest_high - lowest_low) - 1) - - # Offset - if offset != 0: - willr = willr.shift(offset) - - # Fill - if "fillna" in kwargs: - willr.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - willr.name = f"WILLR_{length}" - willr.category = "momentum" - - return willr diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/__init__.py deleted file mode 100644 index d68ac52d..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/__init__.py +++ /dev/null @@ -1,77 +0,0 @@ -# -*- coding: utf-8 -*- -from .alligator import alligator -from .alma import alma -from .dema import dema -from .ema import ema -from .fwma import fwma -from .hilo import hilo -from .hl2 import hl2 -from .hlc3 import hlc3 -from .hma import hma -from .hwma import hwma -from .ichimoku import ichimoku -from .jma import jma -from .kama import kama -from .linreg import linreg -from .mama import mama -from .mcgd import mcgd -from .midpoint import midpoint -from .midprice import midprice -from .ohlc4 import ohlc4 -from .pivots import pivots -from .pwma import pwma -from .rma import rma -from .sinwma import sinwma -from .sma import sma -from .smma import smma -from .ssf import ssf -from .ssf3 import ssf3 -from .supertrend import supertrend -from .swma import swma -from .t3 import t3 -from .tema import tema -from .trima import trima -from .vidya import vidya -from .wcp import wcp -from .wma import wma -from .zlma import zlma - - -__all__ = [ - "alligator", - "alma", - "dema", - "ema", - "fwma", - "hilo", - "hl2", - "hlc3", - "hma", - "hwma", - "ichimoku", - "jma", - "kama", - "linreg", - "mama", - "mcgd", - "midpoint", - "midprice", - "ohlc4", - "pivots", - "pwma", - "rma", - "sinwma", - "sma", - "smma", - "ssf", - "ssf3", - "supertrend", - "swma", - "t3", - "tema", - "trima", - "vidya", - "wcp", - "wma", - "zlma", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/alligator.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/alligator.py deleted file mode 100644 index 67dd95f0..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/alligator.py +++ /dev/null @@ -1,86 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib -from .smma import smma - - - -def alligator( - close: Series, jaw: Int = None, teeth: Int = None, lips: Int = None, - talib: bool = None, offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Bill Williams Alligator - - This indicator, by Bill Williams, attempts to identify trends. - - Sources: - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=175&Name=Bill_Williams_Alligator) - * [tradingview](https://www.tradingview.com/scripts/alligator/) - - Parameters: - close (Series): ```close``` Series - jaw (int): Jaw period. Default: ```13``` - teeth (int): Teeth period. Default: ```8``` - lips (int): Lips period. Default: ```5``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - - Tip: - To avoid data leaks, offsets are to be done manually. - - Note: - Williams believed the fx market trends between 15% and 30% of the - time. Otherwise it is range bound. Inspired by fractal geometry, - where the outputs are meant to resemble an alligator opening and - closing its mouth. It It consists of 3 lines: Jaw, Teeth, and - Lips which each have differing lengths. - """ - # Validate - jaw = v_pos_default(jaw, 13) - teeth = v_pos_default(teeth, 8) - lips = v_pos_default(lips, 5) - close = v_series(close, max(jaw, teeth, lips)) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - gator_jaw = smma(close, length=jaw, talib=mode_tal) - gator_teeth = smma(close, length=teeth, talib=mode_tal) - gator_lips = smma(close, length=lips, talib=mode_tal) - - # Offset - if offset != 0: - gator_jaw = gator_jaw.shift(offset) - gator_teeth = gator_teeth.shift(offset) - gator_lips = gator_lips.shift(offset) - - # Fill - if "fillna" in kwargs: - gator_jaw.fillna(kwargs["fillna"], inplace=True) - gator_teeth.fillna(kwargs["fillna"], inplace=True) - gator_lips.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{jaw}_{teeth}_{lips}" - data = { - f"AGj{_props}": gator_jaw, - f"AGt{_props}": gator_teeth, - f"AGl{_props}": gator_lips - } - df = DataFrame(data, index=close.index) - - df.name = f"AG{_props}" - df.category = "overlap" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/alma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/alma.py deleted file mode 100644 index 1ca3f3b6..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/alma.py +++ /dev/null @@ -1,81 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import append, arange, array, exp, floor, nan, tensordot -from numpy.version import version as np_version -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas import Series -from pandas_ta.utils import strided_window, v_offset, v_pos_default, v_series - - - -def alma( - close: Series, length: Int = None, - sigma: IntFloat = None, dist_offset: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Arnaud Legoux Moving Average - - This indicator attempts to reduce lag with Gaussian smoothing. - - Sources: - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/) - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=475&Name=Moving_Average_-_Arnaud_Legoux) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```9``` - sigma (float): Smoothing value. Default ```6.0``` - dist_offset (float): Distribution offset, range ```[0, 1]```. - Default ```0.85``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 9) - close = v_series(close, length) - - if close is None: - return - - sigma = v_pos_default(sigma, 6.0) - - if isinstance(dist_offset, float) and 0 <= dist_offset <= 1: - offset_ = float(dist_offset) - else: - offset_ = 0.85 - - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - x = arange(length) - k = floor(offset_ * (length - 1)) - weights = exp(-0.5 * ((sigma / length) * (x - k)) ** 2) - weights /= weights.sum() - - if np_version >= "1.20.0": - from numpy.lib.stride_tricks import sliding_window_view - window = sliding_window_view(np_close, length) - else: - window = strided_window(np_close, length) - result = append(array([nan] * (length - 1)), - tensordot(window, weights, axes=1)) - alma = Series(result, index=close.index) - - # Offset - if offset != 0: - alma = alma.shift(offset) - - # Fill - if "fillna" in kwargs: - alma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - alma.name = f"ALMA_{length}_{sigma}_{offset_}" - alma.category = "overlap" - - return alma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/dema.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/dema.py deleted file mode 100644 index e58ab701..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/dema.py +++ /dev/null @@ -1,75 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib -from .ema import ema - - - -def dema( - close: Series, length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Double Exponential Moving Average - - This indicator attempts to create a smoother average with less lag than - the EMA. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/double-exponential-moving-average-dema/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9999894518202522)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import DEMA - dema = DEMA(close, length) - else: - ema1 = ema(close=close, length=length, talib=mode_tal) - ema2 = ema(close=ema1, length=length, talib=mode_tal) - dema = 2 * ema1 - ema2 - - if all(isnan(dema.to_numpy())): - return # Emergency Break - - # Offset - if offset != 0: - dema = dema.shift(offset) - - # Fill - if "fillna" in kwargs: - dema.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - dema.name = f"DEMA_{length}" - dema.category = "overlap" - - return dema diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ema.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ema.py deleted file mode 100644 index b41e5521..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ema.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_bool, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def ema( - close: Series, length: Int = None, - talib: bool = None, presma: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Exponential Moving Average - - This Moving Average is more responsive than the Simple Moving - Average (SMA). - - Sources: - * [investopedia](https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp) - * [stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - talib (bool): If installed, use TA Lib. Default: ```True``` - presma (bool): Initialize with SMA like TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - adjust (bool): Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - mode_tal = v_talib(talib) - presma = v_bool(presma, True) - offset = v_offset(offset) - adjust = kwargs.setdefault("adjust", False) - - # Calculate - if Imports["talib"] and mode_tal and length > 1: - from talib import EMA - ema = EMA(close, length) - else: - if presma: # TA Lib implementation - close = close.copy() - sma_nth = close.iloc[0:length].mean() - close.iloc[:length - 1] = nan - close.iloc[length - 1] = sma_nth - ema = close.ewm(span=length, adjust=adjust).mean() - - # Offset - if offset != 0: - ema = ema.shift(offset) - - # Fill - if "fillna" in kwargs: - ema.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - ema.name = f"EMA_{length}" - ema.category = "overlap" - - return ema diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/fwma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/fwma.py deleted file mode 100644 index eee67a80..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/fwma.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - fibonacci, - v_ascending, - v_offset, - v_pos_default, - v_series, - weights -) - - - -def fwma( - close: Series, length: Int = None, asc: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Fibonacci's Weighted Moving Average - - This indicator, by Kevin Johnson, is similar to a Weighted Moving Average - (WMA) where the weights are based on the Fibonacci Sequence. - - Sources: - * Kevin Johnson - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - asc (bool): Recent values weigh more. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - asc = v_ascending(asc) - offset = v_offset(offset) - - # Calculate - fibs = fibonacci(n=length, weighted=True) - fwma = close.rolling(length, min_periods=length) \ - .apply(weights(fibs), raw=True) - - # Offset - if offset != 0: - fwma = fwma.shift(offset) - - # Fill - if "fillna" in kwargs: - fwma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - fwma.name = f"FWMA_{length}" - fwma.category = "overlap" - - return fwma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hilo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hilo.py deleted file mode 100644 index d359be13..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hilo.py +++ /dev/null @@ -1,100 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series - - - -def hilo( - high: Series, low: Series, close: Series, - high_length: Int = None, low_length: Int = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Gann HiLo Activator - - This indicator, by Robert Krausz, uses two different Moving Averages to - identify trends. - - Sources: - * Gann HiLo Activator, , Stocks & Commodities Magazine, 1998 - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=447&Name=Gann_HiLo_Activator) - * [tradingview](https://www.tradingview.com/script/XNQSLIYb-Gann-High-Low/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - high_length (int): High period. Default: ```13``` - low_length (int): Low period. Default: ```21``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - - Note: - Increasing ```high_length``` and decreasing ```low_length``` is - better for short trades and vice versa for long trades. - """ - # Validate - high_length = v_pos_default(high_length, 13) - low_length = v_pos_default(low_length, 21) - _length = max(high_length, low_length) + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mamode = v_mamode(mamode, "sma") - offset = v_offset(offset) - - # Calculate - m = close.size - hilo = Series(nan, index=close.index) - long = Series(nan, index=close.index) - short = Series(nan, index=close.index) - - high_ma = ma(mamode, high, length=high_length) - low_ma = ma(mamode, low, length=low_length) - - for i in range(1, m): - if close.iat[i] > high_ma.iat[i - 1]: - hilo.iat[i] = long.iat[i] = low_ma.iat[i] - elif close.iat[i] < low_ma.iat[i - 1]: - hilo.iat[i] = short.iat[i] = high_ma.iat[i] - else: - hilo.iat[i] = hilo.iat[i - 1] - long.iat[i] = short.iat[i] = hilo.iat[i - 1] - - # Offset - if offset != 0: - hilo = hilo.shift(offset) - long = long.shift(offset) - short = short.shift(offset) - - # Fill - if "fillna" in kwargs: - hilo.fillna(kwargs["fillna"], inplace=True) - long.fillna(kwargs["fillna"], inplace=True) - short.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{high_length}_{low_length}" - data = { - f"HILO{_props}": hilo, - f"HILOl{_props}": long, - f"HILOs{_props}": short - } - df = DataFrame(data, index=close.index) - - df.name = f"HILO{_props}" - df.category = "overlap" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hl2.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hl2.py deleted file mode 100644 index 281bc72b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hl2.py +++ /dev/null @@ -1,52 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_series - - - -def hl2( - high: Series, low: Series, - offset: Int = None, **kwargs: DictLike -) -> Series: - """HL2 - - HL2 is the midpoint/average of high and low. - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)```. - Only works when offset. - - Returns: - (Series): 1 column - """ - # Validate - high = v_series(high) - low = v_series(low) - offset = v_offset(offset) - - if high is None or low is None: - return - - # Calculate - avg = 0.5 * (high.to_numpy() + low.to_numpy()) - hl2 = Series(avg, index=high.index) - - # Offset - if offset != 0: - hl2 = hl2.shift(offset) - - # Fill - if "fillna" in kwargs: - hl2.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - hl2.name = "HL2" - hl2.category = "overlap" - - return hl2 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hlc3.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hlc3.py deleted file mode 100644 index 7287d3ab..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hlc3.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_series, v_talib - - - -def hlc3( - high: Series, low: Series, close: Series, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """HLC3 - - HLC3 is the average of high, low and close. - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)```. - Only works when offset. - - Returns: - (Series): 1 column - """ - # Validate - high = v_series(high) - low = v_series(low) - close = v_series(close) - mode_tal = v_talib(talib) - offset = v_offset(offset) - - if high is None or low is None or close is None: - return - - # Calculate - if Imports["talib"] and mode_tal and close.size: - from talib import TYPPRICE - hlc3 = TYPPRICE(high, low, close) - else: - avg = (high.to_numpy() + low.to_numpy() + close.to_numpy()) / 3.0 - hlc3 = Series(avg, index=close.index) - - # Offset - if offset != 0: - hlc3 = hlc3.shift(offset) - - # Fill - if "fillna" in kwargs: - hlc3.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - hlc3.name = "HLC3" - hlc3.category = "overlap" - - return hlc3 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hma.py deleted file mode 100644 index db39e9eb..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hma.py +++ /dev/null @@ -1,72 +0,0 @@ -# -*- coding: utf-8 -*- -from sys import modules as module_ -from numpy import sqrt -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series -from .ema import ema -from .sma import sma -from .wma import wma - - - -def hma( - close: Series, length: Int = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Hull Moving Average - - This indicator, by Alan Hull, attempts to reduce lag compared to - classical moving averages. - - Sources: - * [Alan Hull](https://alanhull.com/hull-moving-average) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - mamode (str): One of: 'ema', 'sma', or 'wma'. Default: ```"wma"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length + 2) - - if close is None: - return - - mamode = v_mamode(mamode, "wma") - offset = v_offset(offset) - - if mamode not in ["ema", "sma", "wma"]: - return - - _ma = getattr(module_[__name__], mamode) - - # Calculate - half_length = int(length / 2) - sqrt_length = int(sqrt(length)) - - maf = _ma(close, length=half_length) - mas = _ma(close, length=length) - hma = _ma(close=2 * maf - mas, length=sqrt_length) - - # Offset - if offset != 0: - hma = hma.shift(offset) - - # Fill - if "fillna" in kwargs: - hma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - hma.name = f"HMA{"" if mamode == "wma" else mamode[0]}_{length}" - hma.category = "overlap" - - return hma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hwma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hwma.py deleted file mode 100644 index 8a43c301..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/hwma.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_series - - - -def hwma( - close: Series, - na: IntFloat = None, nb: IntFloat = None, nc: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Holt-Winter Moving Average - - This indicator uses a three parameter Holt-Winter Moving Average for - smoothing. - - Sources: - * [rengel8](https://github.com/rengel8) based on a publication for - MetaTrader 5. - * [mql5](https://www.mql5.com/en/code/20856) - - Parameters: - close (Series): ```close``` Series - na (float): Smoothed series parameter (from 0 to 1). Default: 0.2 - nb (float): Trend parameter (from 0 to 1). Default: 0.1 - nc (float): Seasonality parameter (from 0 to 1). Default: 0.1 - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - Series: 1 column - """ - # Validate - close = v_series(close, 1) - na = float(na) if isinstance(na, float) and 0 < na < 1 else 0.2 - nb = float(nb) if isinstance(nb, float) and 0 < nb < 1 else 0.1 - nc = float(nc) if isinstance(nc, float) and 0 < nc < 1 else 0.1 - offset = v_offset(offset) - - if close is None: - return - - # Calculate - last_a = last_v = 0 - last_f = close.iloc[0] - - result = [] - m = close.size - for i in range(m): - F = (1.0 - na) * (last_f + last_v + 0.5 * last_a) + na * close.iloc[i] - V = (1.0 - nb) * (last_v + last_a) + nb * (F - last_f) - A = (1.0 - nc) * last_a + nc * (V - last_v) - result.append((F + V + 0.5 * A)) - last_a, last_f, last_v = A, F, V # update values - - hwma = Series(result, index=close.index) - - # Offset - if offset != 0: - hwma = hwma.shift(offset) - - # Fill - if "fillna" in kwargs: - hwma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - hwma.name = f"HWMA_{na}_{nb}_{nc}" - hwma.category = "overlap" - - return hwma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ichimoku.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ichimoku.py deleted file mode 100644 index cf4df242..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ichimoku.py +++ /dev/null @@ -1,131 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, RangeIndex, Timedelta, Series, date_range -from pandas_ta._typing import DictLike, Int, Tuple -from pandas_ta.utils import v_offset, v_pos_default, v_series -from .midprice import midprice - - - -def ichimoku( - high: Series, low: Series, close: Series, - tenkan: Int = None, kijun: Int = None, senkou: Int = None, - include_chikou: bool = True, - offset: Int = None, **kwargs: DictLike -) -> Tuple[DataFrame, DataFrame]: - """Ichimoku Kinkō Hyō - - A forecasting model used in Japaese financial markets Pre WWII. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/ichimoku-ich/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - tenkan (int): Tenkan period. Default: ```9``` - kijun (int): Kijun period. Default: ```26``` - senkou (int): Senkou period. Default: ```52``` - include_chikou (bool): Whether to include chikou component. - Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - lookahead (value): To avoid data leakage set to ```False```. - - Returns: - (Tuple[pd.DataFrame, pd.DataFrame]): - * Historical DataFrame, 5 columns - * Forward Looking DataFrame, 2 columns - - Danger: Possible Data Leak - Set ```lookahead=False``` to avoid data leakage. Issue [#60](https://github.com/twopirllc/pandas-ta/issues/60#). - """ - # Validate - tenkan = v_pos_default(tenkan, 9) - kijun = v_pos_default(kijun, 26) - senkou = v_pos_default(senkou, 52) - _length = max(tenkan, kijun, senkou) - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return None, None - - offset = v_offset(offset) - if not kwargs.get("lookahead", True): - include_chikou = False - - # Calculate - tenkan_sen = midprice(high=high, low=low, length=tenkan) - kijun_sen = midprice(high=high, low=low, length=kijun) - span_a = 0.5 * (tenkan_sen + kijun_sen) - span_b = midprice(high=high, low=low, length=senkou) - - # Copy Span A and B values before their shift - _span_a = span_a[-kijun:].shift(-1).copy() - _span_b = span_b[-kijun:].shift(-1).copy() - - span_a = span_a.shift(kijun - 1) - span_b = span_b.shift(kijun - 1) - chikou_span = close.shift(-kijun + 1) - - # Offset - if offset != 0: - tenkan_sen = tenkan_sen.shift(offset) - kijun_sen = kijun_sen.shift(offset) - span_a = span_a.shift(offset) - span_b = span_b.shift(offset) - chikou_span = chikou_span.shift(offset) - - # Fill - if "fillna" in kwargs: - span_a.fillna(kwargs["fillna"], inplace=True) - span_b.fillna(kwargs["fillna"], inplace=True) - chikou_span.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - span_a.name = f"ISA_{tenkan}" - span_b.name = f"ISB_{kijun}" - tenkan_sen.name = f"ITS_{tenkan}" - kijun_sen.name = f"IKS_{kijun}" - chikou_span.name = f"ICS_{kijun}" - - chikou_span.category = kijun_sen.category = tenkan_sen.category = "overlap" - span_b.category = span_a.category = chikou_span - - # Prepare Ichimoku DataFrame - data = { - span_a.name: span_a, - span_b.name: span_b, - tenkan_sen.name: tenkan_sen, - kijun_sen.name: kijun_sen, - } - if include_chikou: - data[chikou_span.name] = chikou_span - - ichimokudf = DataFrame(data, index=close.index) - ichimokudf.name = f"ICHIMOKU_{tenkan}_{kijun}_{senkou}" - ichimokudf.category = "overlap" - - # Prepare Span DataFrame - last = close.index[-1] - if close.index.dtype == "int64": - ext_index = RangeIndex(start=last + 1, stop=last + kijun + 1) - spandf = DataFrame(index=ext_index, columns=[span_a.name, span_b.name]) - _span_a.index = _span_b.index = ext_index - else: - df_freq = close.index.value_counts().mode()[0] - tdelta = Timedelta(df_freq, unit="d") - new_dt = date_range(start=last + tdelta, periods=kijun, freq="B") - spandf = DataFrame(index=new_dt, columns=[span_a.name, span_b.name]) - _span_a.index = _span_b.index = new_dt - - spandf[span_a.name] = _span_a - spandf[span_b.name] = _span_b - spandf.name = f"ICHISPAN_{tenkan}_{kijun}" - spandf.category = "overlap" - - return ichimokudf, spandf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/jma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/jma.py deleted file mode 100644 index d47d7799..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/jma.py +++ /dev/null @@ -1,117 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import average, log, nan, sqrt, zeros_like -from numpy import power as np_power -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_float, v_offset, v_pos_default, v_series - - - -def jma( - close: Series, length: IntFloat = None, phase: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Jurik Moving Average Average - - This indicator, by Mark Jurik, attempts to eliminate noise. It claims - to have extremely low lag, is very smooth and is responsive to gaps. - - Sources: - * [mql5](https://c.mql5.com/forextsd/forum/164/jurik_1.pdf) - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/jurik-volatility-bands/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```7``` - phase (float): Phase value between [-100, 100]. Default: ```0``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - _length = v_pos_default(length, 7) - close = v_series(close, _length) - - if close is None: - return - - phase = v_float(phase, 0.0) - offset = v_offset(offset) - - # Calculate - jma = zeros_like(close) - volty = zeros_like(close) - v_sum = zeros_like(close) - - kv = det0 = det1 = ma2 = 0.0 - jma[0] = ma1 = uBand = lBand = close.iloc[0] - - # Static variables - sum_length = 10 - length = 0.5 * (_length - 1) - pr = 0.5 if phase < -100 else 2.5 if phase > 100 else 1.5 + phase * 0.01 - length1 = max((log(sqrt(length)) / log(2.0)) + 2.0, 0) - pow1 = max(length1 - 2.0, 0.5) - length2 = length1 * sqrt(length) - bet = length2 / (length2 + 1) - beta = 0.45 * (_length - 1) / (0.45 * (_length - 1) + 2.0) - - m = close.shape[0] - for i in range(1, m): - price = close.iloc[i] - - # Price volatility - del1 = price - uBand - del2 = price - lBand - volty[i] = max(abs(del1), abs(del2)) if abs(del1) != abs(del2) else 0 - - # Relative price volatility factor - v_sum[i] = v_sum[i - 1] + \ - (volty[i] - volty[max(i - sum_length, 0)]) / sum_length - avg_volty = average(v_sum[max(i - 65, 0):i + 1]) - d_volty = 0 if avg_volty == 0 else volty[i] / avg_volty - r_volty = max(1.0, min(np_power(length1, 1 / pow1), d_volty)) - # r_volty = max(1.0, min(length1 **(1 / pow1), d_volty)) - - # Jurik volatility bands - pow2 = np_power(r_volty, pow1) - kv = np_power(bet, sqrt(pow2)) - uBand = price if (del1 > 0) else price - (kv * del1) - lBand = price if (del2 < 0) else price - (kv * del2) - - # Jurik Dynamic Factor - power = np_power(r_volty, pow1) - alpha = np_power(beta, power) - - # 1st stage - preliminary smoothing by adaptive EMA - ma1 = (1 - alpha) * price + alpha * ma1 - - # 2nd stage - one more preliminary smoothing by Kalman filter - det0 = (1 - beta) * (price - ma1) + beta * det0 - ma2 = ma1 + pr * det0 - - # 3rd stage - final smoothing by unique Jurik adaptive filter - det1 = ((ma2 - jma[i - 1]) * (1 - alpha) * \ - (1 - alpha)) + (alpha * alpha * det1) - jma[i] = jma[i - 1] + det1 - - jma = Series(jma, index=close.index) - jma.iloc[0:_length - 1] = nan - - # Offset - if offset != 0: - jma = jma.shift(offset) - - # Fill - if "fillna" in kwargs: - jma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - jma.name = f"JMA_{_length}_{phase}" - jma.category = "overlap" - - return jma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/kama.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/kama.py deleted file mode 100644 index c98f28eb..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/kama.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - non_zero_range, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - - -def kama( - close: Series, length: Int = None, fast: Int = None, slow: Int = None, - mamode: str = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Kaufman's Adaptive Moving Average - - This indicator, by Perry Kaufman, attempts to find the overall trend by - adapting to volatility. - - Sources: - * [stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average) - * [tradingview](https://www.tradingview.com/script/wZGOIz9r-REPOST-Indicators-3-Different-Adaptive-Moving-Averages/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - fast (int): Fast MA period. Default: ```2``` - slow (int): Slow MA period. Default: ```30``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - fast = v_pos_default(fast, 2) - slow = v_pos_default(slow, 30) - close = v_series(close, max(fast, slow, length)) - - if close is None: - return - - mamode = v_mamode(mamode, "sma") - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - def weight(length: int) -> float: - return 2 / (length + 1) - - fr = weight(fast) - sr = weight(slow) - - abs_diff = non_zero_range(close, close.shift(length)).abs() - peer_diff = non_zero_range(close, close.shift(drift)).abs() - peer_diff_sum = peer_diff.rolling(length).sum() - er = abs_diff / peer_diff_sum - x = er * (fr - sr) + sr - sc = x * x - - m = close.size - ma0 = ma(mamode, close.iloc[:length], length=length, **kwargs).iloc[-1] - result = [nan for _ in range(0, length - 1)] + [ma0] - for i in range(length, m): - result.append(sc.iat[i] * close.iat[i] \ - + (1 - sc.iat[i]) * result[i - 1]) - - kama = Series(result, index=close.index) - - # Offset - if offset != 0: - kama = kama.shift(offset) - - # Fill - if "fillna" in kwargs: - kama.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - kama.name = f"KAMA_{length}_{fast}_{slow}" - kama.category = "overlap" - - return kama diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/linreg.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/linreg.py deleted file mode 100644 index 670863dd..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/linreg.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -from sys import float_info as sflt -from numpy import arctan, nan, pi, zeros_like -from numpy.version import version as np_version -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - strided_window, - v_offset, - v_pos_default, - v_series, - v_talib, - zero -) - - - -def linreg( - close: Series, length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Linear Regression Moving Average - - This indicator is a simplified version of Standard Linear Regression. It is - one variable rolling regression whereas a Standard Linear Regression is - between two or more variables. - - Sources: - * [TA Lib](https://ta-lib.org) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - angle (bool): Returns the slope angle in radians. - Default: ```False``` - degrees (bool): Return the slope angle in degrees. - Default: ```False``` - intercept (bool): Return the intercept. Default: ```False``` - r (bool): Return the 'r' correlation. Default: ```False``` - slope (bool): Return the slope. Default: ```False``` - tsf (bool): Return the Time Series Forecast value. - Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9985638477660118)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 14) - close = v_series(close, length) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - angle = kwargs.pop("angle", False) - intercept = kwargs.pop("intercept", False) - degrees = kwargs.pop("degrees", False) - r = kwargs.pop("r", False) - slope = kwargs.pop("slope", False) - tsf = kwargs.pop("tsf", False) - - # Calculate - np_close = close.to_numpy() - - if Imports["talib"] and mode_tal and not r: - from talib import LINEARREG, LINEARREG_ANGLE, LINEARREG_INTERCEPT, LINEARREG_SLOPE, TSF - if tsf: - linreg = TSF(close, timeperiod=length) - elif slope: - linreg = LINEARREG_SLOPE(close, timeperiod=length) - elif intercept: - linreg = LINEARREG_INTERCEPT(close, timeperiod=length) - elif angle: - linreg = LINEARREG_ANGLE(close, timeperiod=length) - else: - linreg = LINEARREG(close, timeperiod=length) - else: - linreg_ = zeros_like(np_close) - # [1, 2, ..., n] from 1 to n keeps Sum(xy) low - x = range(1, length + 1) - x_sum = 0.5 * length * (length + 1) - x2_sum = x_sum * (2 * length + 1) / 3 - divisor = length * x2_sum - x_sum * x_sum - - # Needs to be reworked outside the method - def linear_regression(series): - y_sum = series.sum() - xy_sum = (x * series).sum() - - m = (length * xy_sum - x_sum * y_sum) / divisor - if slope: - return m - b = (y_sum * x2_sum - x_sum * xy_sum) / divisor - if intercept: - return b - - if angle: - theta = arctan(m) - if degrees: - theta *= 180 / pi - return theta - - if r: - y2_sum = (series * series).sum() - rn = length * xy_sum - x_sum * y_sum - rd = (divisor * (length * y2_sum - y_sum * y_sum)) ** 0.5 - if zero(rd) == 0: - rd = sflt.epsilon - return rn / rd - - return m * length + b if not tsf else m * (length - 1) + b - - if np_version >= "1.20.0": - from numpy.lib.stride_tricks import sliding_window_view - linreg_ = [ - linear_regression(_) for _ in sliding_window_view( - np_close, length) - ] - - else: - linreg_ = [ - linear_regression(_) for _ in strided_window( - np_close, length) - ] - - linreg = Series([nan] * (length - 1) + linreg_, index=close.index) - - # Offset - if offset != 0: - linreg = linreg.shift(offset) - - # Fill - if "fillna" in kwargs: - linreg.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - linreg.name = f"LINREG" - if slope: - linreg.name += "m" - if intercept: - linreg.name += "b" - if angle: - linreg.name += "a" - if r: - linreg.name += "r" - - linreg.name += f"_{length}" - linreg.category = "overlap" - - return linreg diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/mama.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/mama.py deleted file mode 100644 index a7a51a3c..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/mama.py +++ /dev/null @@ -1,167 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import arctan, isnan, nan, zeros_like -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib - - - -# Ehler's Mother of Adaptive Moving Averages -# http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html -@njit(cache=True) -def nb_mama(x, fastlimit, slowlimit, prenan): - a, b, m = 0.0962, 0.5769, x.size - p_w, smp_w, smp_w_c = 0.2, 0.33, 0.67 - - wma4 = zeros_like(x) - dt, smp = zeros_like(x), zeros_like(x) - i1, i2 = zeros_like(x), zeros_like(x) - ji, jq = zeros_like(x), zeros_like(x) - q1, q2 = zeros_like(x), zeros_like(x) - re, im, alpha = zeros_like(x), zeros_like(x), zeros_like(x) - period, phase = zeros_like(x), zeros_like(x) - mama, fama = zeros_like(x), zeros_like(x) - - # Ehler's starts from 6, TV-LB from 3, TALib from 32 - for i in range(3, m): - adj_prev_period = 0.075 * period[i - 1] + 0.54 - - # WMA(x,4) & Detrended WMA(x,4) - wma4[i] = 0.4 * x[i] + 0.3 * x[i - 1] + 0.2 * x[i - 2] + 0.1 * x[i - 3] - dt[i] = adj_prev_period * (a * wma4[i] + b * wma4[i - 2] - b * wma4[i - 4] - a * wma4[i - 6]) - - # Quadrature(Detrender) and In Phase Component - q1[i] = adj_prev_period * (a * dt[i] + b * dt[i - 2] - b * dt[i - 4] - a * dt[i - 6]) - i1[i] = dt[i - 3] - - # Phase Q1 and I1 by 90 degrees - ji[i] = adj_prev_period * (a * i1[i] + b * i1[i - 2] - b * i1[i - 4] - a * i1[i - 6]) - jq[i] = adj_prev_period * (a * q1[i] + b * q1[i - 2] - b * q1[i - 4] - a * q1[i - 6]) - - # Phasor Addition for 3 Bar Averaging - i2[i] = i1[i] - jq[i] - q2[i] = q1[i] + ji[i] - - # Smooth I2 & Q2 - i2[i] = p_w * i2[i] + (1 - p_w) * i2[i - 1] - q2[i] = p_w * q2[i] + (1 - p_w) * q2[i - 1] - - # Homodyne Discriminator - re[i] = i2[i] * i2[i - 1] + q2[i] * q2[i - 1] - im[i] = i2[i] * q2[i - 1] + q2[i] * i2[i - 1] - - # Smooth Re & Im - re[i] = p_w * re[i] + (1 - p_w) * re[i - 1] - im[i] = p_w * im[i] + (1 - p_w) * im[i - 1] - - if im[i] != 0.0 and re[i] != 0.0: - period[i] = 360 / arctan(im[i] / re[i]) - else: - period[i] = 0 - - if period[i] > 1.5 * period[i - 1]: - period[i] = 1.5 * period[i - 1] - if period[i] < 0.67 * period[i - 1]: - period[i] = 0.67 * period[i - 1] - if period[i] < 6: - period[i] = 6 - if period[i] > 50: - period[i] = 50 - - period[i] = p_w * period[i] + (1 - p_w) * period[i - 1] - smp[i] = smp_w * period[i] + smp_w_c * smp[i - 1] - - if i1[i] != 0.0: - phase[i] = arctan(q1[i] / i1[i]) - - dphase = phase[i - 1] - phase[i] - if dphase < 1: - dphase = 1 - - alpha[i] = fastlimit / dphase - if alpha[i] > fastlimit: - alpha[i] = fastlimit - if alpha[i] < slowlimit: - alpha[i] = slowlimit - - mama[i] = alpha[i] * x[i] + (1 - alpha[i]) * mama[i - 1] - fama[i] = 0.5 * alpha[i] * mama[i] + (1 - 0.5 * alpha[i]) * fama[i - 1] - - mama[:prenan], fama[:prenan] = nan, nan - return mama, fama - - -def mama( - close: Series, fastlimit: IntFloat = None, slowlimit: IntFloat = None, - prenan: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """MESA Adaptive Moving Average - - This indicator, aka the Mother of All Moving Averages by John Ehlers, - attempts to adapt to volatility by using a Hilbert Transform Discriminator - - Sources: - * [Ehlers's Mother of Adaptive Moving Averages](http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html) - * [tradingview](https://www.tradingview.com/script/foQxLbU3-Ehlers-MESA-Adaptive-Moving-Average-LazyBear/) - - Parameters: - close (Series): ```close``` Series - fastlimit (float): Fast limit. Default: ```0.5``` - slowlimit (float): Slow limit. Default: ```0.05``` - prenan (int): Prenans to apply. TV-LB ```3```, Ehler's ```6```, - TA Lib ```32```. Default: ```3``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Tip: - **FAMA** also included - """ - # Validate - close = v_series(close, 1) - - if close is None: - return - - fastlimit = v_pos_default(fastlimit, 0.5) - slowlimit = v_pos_default(slowlimit, 0.05) - prenan = v_pos_default(prenan, 3) - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - if Imports["talib"] and mode_tal: - from talib import MAMA - mama, fama = MAMA(np_close, fastlimit, slowlimit) - else: - mama, fama = nb_mama(np_close, fastlimit, slowlimit, prenan) - - if all(isnan(mama)) or all(isnan(fama)): - return # Emergency Break - - # Name and Category - _props = f"_{fastlimit}_{slowlimit}" - data = {f"MAMA{_props}": mama, f"FAMA{_props}": fama} - df = DataFrame(data, index=close.index) - - df.name = f"MAMA{_props}" - df.category = "overlap" - - # Offset - if offset != 0: - df = df.shift(offset) - - # Fill - if "fillna" in kwargs: - df.fillna(kwargs["fillna"], inplace=True) - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/mcgd.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/mcgd.py deleted file mode 100644 index 5c60429b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/mcgd.py +++ /dev/null @@ -1,72 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def _mcgd(x, n, k): - d = (k * n * (x[1] / x[0]) ** 4) - x[1] = (x[0] + ((x[1] - x[0]) / d)) - return x[1] - - -def mcgd( - close: Series, length: Int = None, c: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """McGinley Dynamic Indicator - - This indicator, by John R. McGinley, is not a moving average but a - differential smoothing technique. - - Sources: - * John R. McGinley, a Certified Market Technician (CMT) and former - editor of the Market Technicians Association's Journal of - Technical Analysis. - * [investopedia](https://www.investopedia.com/articles/forex/09/mcginley-dynamic-indicator.asp) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - c (float): Denominator multiplier. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - Sometimes ```c``` is set to ```0.6```. - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - c = float(c) if isinstance(c, float) and 0 < c <= 1 else 1 - offset = v_offset(offset) - - # Calculate - close = close.copy() - - mcg_ds = close[0:].rolling(2, min_periods=2) \ - .apply(_mcgd, kwargs={"n": length, "k": c}, raw=True) - - # Offset - if offset != 0: - mcg_ds = mcg_ds.shift(offset) - - # Fill - if "fillna" in kwargs: - mcg_ds.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - mcg_ds.name = f"MCGD_{length}" - mcg_ds.category = "overlap" - - return mcg_ds diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/midpoint.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/midpoint.py deleted file mode 100644 index 20ec369e..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/midpoint.py +++ /dev/null @@ -1,64 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib - - - -def midpoint( - close: Series, length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Midpoint - - The Midpoint is the average of the rolling high and low of period length. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```2``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 2) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import MIDPOINT - midpoint = MIDPOINT(close, length) - else: - lowest = close.rolling(length, min_periods=min_periods).min() - highest = close.rolling(length, min_periods=min_periods).max() - midpoint = 0.5 * (lowest + highest) - - # Offset - if offset != 0: - midpoint = midpoint.shift(offset) - - # Fill - if "fillna" in kwargs: - midpoint.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - midpoint.name = f"MIDPOINT_{length}" - midpoint.category = "overlap" - - return midpoint diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/midprice.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/midprice.py deleted file mode 100644 index 6593cd74..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/midprice.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib - - - -def midprice( - high: Series, low: Series, length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Midprice - - The Midprice is the average of the rolling high and low of period length. - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - length (int): The period. Default: ```2``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 2) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - _length = max(length, min_periods) - high = v_series(high, _length) - low = v_series(low, _length) - - if high is None or low is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import MIDPRICE - midprice = MIDPRICE(high, low, length) - else: - lowest_low = low.rolling(length, min_periods=min_periods).min() - highest_high = high.rolling(length, min_periods=min_periods).max() - midprice = 0.5 * (lowest_low + highest_high) - - # Offset - if offset != 0: - midprice = midprice.shift(offset) - - # Fill - if "fillna" in kwargs: - midprice.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - midprice.name = f"MIDPRICE_{length}" - midprice.category = "overlap" - - return midprice diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ohlc4.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ohlc4.py deleted file mode 100644 index 30ad87dc..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ohlc4.py +++ /dev/null @@ -1,53 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_series - - - -def ohlc4( - open_: Series, high: Series, low: Series, close: Series, - offset: Int = None, **kwargs: DictLike -) -> Series: - """OHLC4 - - OHLC4 is the average of open, high, low and close. - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)```. - Only works when offset. - - Returns: - (Series): 1 column - """ - # Validate - open_ = v_series(open_) - high = v_series(high) - low = v_series(low) - close = v_series(close) - offset = v_offset(offset) - - # Calculate - avg = 0.25 * (open_.to_numpy() + high.to_numpy() + low.to_numpy() + close.to_numpy()) - ohlc4 = Series(avg, index=close.index) - - # Offset - if offset != 0: - ohlc4 = ohlc4.shift(offset) - - # Fill - if "fillna" in kwargs: - ohlc4.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - ohlc4.name = "OHLC4" - ohlc4.category = "overlap" - - return ohlc4 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/pivots.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/pivots.py deleted file mode 100644 index 157c3da6..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/pivots.py +++ /dev/null @@ -1,264 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import greater, nan, zeros_like -from pandas import DataFrame, DateOffset, Series, infer_freq -from pandas_ta._typing import DictLike -from pandas_ta.utils import ( - nb_nonzero_range, - v_datetime_ordered, - v_series, - v_str -) - -# Support for Pandas v1.4.x and v2.2.x -td_mapping = { - 'Y': 'years', - 'YE': 'years', - 'M': 'months', - 'ME': 'months', - 'D': 'days', -} - - - -@njit(cache=True) -def pivot_camarilla(high, low, close): - tp = (high + low + close) / 3 - hl_range = nb_nonzero_range(high, low) - - s1 = close - 11 / 120 * hl_range - s2 = close - 11 / 60 * hl_range - s3 = close - 0.275 * hl_range - s4 = close - 0.55 * hl_range - - r1 = close + 11 / 120 * hl_range - r2 = close + 11 / 60 * hl_range - r3 = close + 0.275 * hl_range - r4 = close + 0.55 * hl_range - - return tp, s1, s2, s3, s4, r1, r2, r3, r4 - - -@njit(cache=True) -def pivot_classic(high, low, close): - tp = (high + low + close) / 3 - hl_range = nb_nonzero_range(high, low) - - s1 = 2 * tp - high - s2 = tp - hl_range - s3 = tp - 2 * hl_range - s4 = tp - 3 * hl_range - - r1 = 2 * tp - low - r2 = tp + hl_range - r3 = tp + 2 * hl_range - r4 = tp + 3 * hl_range - - return tp, s1, s2, s3, s4, r1, r2, r3, r4 - - -@njit(cache=True) -def pivot_demark(open_, high, low, close): - if (open_ == close).all(): - tp = 0.25 * (high + low + 2 * close) - elif greater(close, open_).all(): - tp = 0.25 * (2 * high + low + close) - else: - tp = 0.25 * (high + 2 * low + close) - - s1 = 2 * tp - high - r1 = 2 * tp - low - - return tp, s1, r1 - - -@njit(cache=True) -def pivot_fibonacci(high, low, close): - tp = (high + low + close) / 3 - hl_range = nb_nonzero_range(high, low) - - s1 = tp - 0.382 * hl_range - s2 = tp - 0.618 * hl_range - s3 = tp - hl_range - - r1 = tp + 0.382 * hl_range - r2 = tp + 0.618 * hl_range - r3 = tp + hl_range - - return tp, s1, s2, s3, r1, r2, r3 - - -@njit(cache=True) -def pivot_traditional(high, low, close): - tp = (high + low + close) / 3 - hl_range = nb_nonzero_range(high, low) - - s1 = 2 * tp - high - s2 = tp - hl_range - s3 = tp - 2 * hl_range - s4 = tp - 2 * hl_range - - r1 = 2 * tp - low - r2 = tp + hl_range - r3 = tp + 2 * hl_range - r4 = tp + 2 * hl_range - - return tp, s1, s2, s3, s4, r1, r2, r3, r4 - - -@njit(cache=True) -def pivot_woodie(open_, high, low): - tp = (2 * open_ + high + low) / 4 - hl_range = nb_nonzero_range(high, low) - - s1 = 2 * tp - high - s2 = tp - hl_range - s3 = low - 2 * (high - tp) - s4 = s3 - hl_range - - r1 = 2 * tp - low - r2 = tp + hl_range - r3 = high + 2 * (tp - low) - r4 = r3 + hl_range - - return tp, s1, s2, s3, s4, r1, r2, r3, r4 - - -def pivots( - open_: Series, high: Series, - low: Series, close: Series, - method: str = None, anchor: str = None, - **kwargs: DictLike -) -> DataFrame: - """Pivot Points - - Pivot Points attempt to identify support and resistance levels. - There are many different methods of calculating Pivot Points. The most - common (and default) method is: Traditional. Other methods include: - Camarilla, Classic, Demark, Fibonacci, and Woodie. - - Sources: - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/PivotPoints.html) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - method (str): Pivot methode. Default: ```'traditional'``` - anchor (str): Anchor frequency. Default: ```'D'``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3, 7 or 9 columns - - Note: - [Pandas Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases) - """ - # Validate - open_ = v_series(open_) - high = v_series(high) - low = v_series(low) - close = v_series(close) - - if open_ is None or high is None or low is None or close is None: - return None - - methods = [ - "traditional", "fibonacci", "woodie", "classic", "demark", "camarilla" - ] - method = v_str(method, methods[0]) - - if close.index.size < 3: - return # Emergency Break - - if not v_datetime_ordered(close): - print("[!] Pivots requires an ordered DatetimeIndex.") - return - - dt_index = close.index - freq = infer_freq(dt_index) - - if anchor and isinstance(anchor, str) and len(anchor) >= 1: - anchor = anchor.upper() - else: - anchor = "D" - - # Resample if freq does not match the anchor - if freq is not anchor: - df = DataFrame( - data={ - "open": open_.resample(anchor).first(), - "high": high.resample(anchor).max(), - "low": low.resample(anchor).min(), - "close": close.resample(anchor).last() - } - ) - df.dropna(inplace=True) - else: - df = DataFrame( - data={"open": open_, "high": high, "low": low, "close": close}, - index=dt_index - ) - - np_open = df.open.to_numpy() - np_high = df.high.to_numpy() - np_low = df.low.to_numpy() - np_close = df.close.to_numpy() - - # Create nan arrays for "demark" and "fibonacci" pivots - _nan_array = zeros_like(np_close) - _nan_array[:] = nan - tp = s1 = s2 = s3 = s4 = r1 = r2 = r3 = r4 = _nan_array - - # Calculate - if method == "camarilla": - tp, s1, s2, s3, s4, r1, r2, r3, r4 = \ - pivot_camarilla(np_high, np_low, np_close) - - elif method == "classic": - tp, s1, s2, s3, s4, r1, r2, r3, r4 = \ - pivot_classic(np_high, np_low, np_close) - - elif method == "demark": - tp, s1, r1 = pivot_demark(np_open, np_high, np_low, np_close) - - elif method == "fibonacci": - tp, s1, s2, s3, r1, r2, r3 = pivot_fibonacci(np_high, np_low, np_close) - - elif method == "woodie": - tp, s1, s2, s3, s4, r1, r2, r3, r4 = \ - pivot_woodie(np_open, np_high, np_low) - - else: # Traditional - tp, s1, s2, s3, s4, r1, r2, r3, r4 = \ - pivot_traditional(np_high, np_low, np_close) - - # Name and Category - _props = f"PIVOTS_{method[:4].upper()}_{anchor}" - df[f"{_props}_P"] = tp - df[f"{_props}_S1"], df[f"{_props}_S2"] = s1, s2 - df[f"{_props}_S3"], df[f"{_props}_S4"] = s3, s4 - df[f"{_props}_R1"], df[f"{_props}_R2"] = r1, r2 - df[f"{_props}_R3"], df[f"{_props}_R4"] = r3, r4 - - time_unit = td_mapping.get(anchor.upper(), None) - if time_unit: - time_delta = DateOffset(**{time_unit: 1}) - df.index = df.index + time_delta - else: - print(f"[!] Unsupported time anchor {anchor}.") - - if freq is not anchor: - df = df.reindex(dt_index, method="ffill") - df = df.iloc[:,4:] - - if method in ["demark", "fibonacci"]: - df.drop(columns=[x for x in df.columns if all(df[x].isna())], inplace=True) - - df.name = _props - df.category = "overlap" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/pwma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/pwma.py deleted file mode 100644 index 3c3ccdfd..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/pwma.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - pascals_triangle, - v_offset, - v_ascending, - v_pos_default, - v_series, - weights -) - - - -def pwma( - close: Series, length: Int = None, asc: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Pascal's Weighted Moving Average - - This indicator, by Kevin Johnson, creates a weighted moving average using - Pascal's Triangle. - - Sources: - * Kevin Johnson - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - asc (bool): Ascending. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - asc = v_ascending(asc) - offset = v_offset(offset) - - # Calculate - triangle = pascals_triangle(n=length - 1, weighted=True) - pwma = close.rolling(length, min_periods=length) \ - .apply(weights(triangle), raw=True) - - # Offset - if offset != 0: - pwma = pwma.shift(offset) - - # Fill - if "fillna" in kwargs: - pwma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - pwma.name = f"PWMA_{length}" - pwma.category = "overlap" - - return pwma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/rma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/rma.py deleted file mode 100644 index 8162933e..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/rma.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def rma( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """wildeR's Moving Average - - This indicator, by Wilder, is simply an EMA where _alpha_ is - the recipical of its _length_. - - Sources: - * [incrediblecharts](https://www.incrediblecharts.com/indicators/wilder_moving_average.php) - * [thinkorswim](https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - alpha = (1.0 / length) if length > 0 else 0.5 - offset = v_offset(offset) - - rma = close.ewm(alpha=alpha, adjust=False).mean() - - # Offset - if offset != 0: - rma = rma.shift(offset) - - # Fill - if "fillna" in kwargs: - rma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - rma.name = f"RMA_{length}" - rma.category = "overlap" - - return rma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/sinwma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/sinwma.py deleted file mode 100644 index cdeb3cff..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/sinwma.py +++ /dev/null @@ -1,63 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import pi, sin -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series, weights - - - -def sinwma( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Sine Weighted Moving Average - - This indicator is a weighted average using sine cycles where the central - values have greater weight. - - Source: - * [Everget](https://www.tradingview.com/u/everget/) - * [tradingview](https://www.tradingview.com/script/6MWFvnPO-Sine-Weighted-Moving-Average/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - close = v_series(close, length) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - sines = Series( - [sin((i + 1) * pi / (length + 1)) for i in range(0, length)] - ) - w = sines / sines.sum() - - sinwma = close.rolling(length, min_periods=length) \ - .apply(weights(w), raw=True) - - # Offset - if offset != 0: - sinwma = sinwma.shift(offset) - - # Fill - if "fillna" in kwargs: - sinwma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - sinwma.name = f"SINWMA_{length}" - sinwma.category = "overlap" - - return sinwma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/sma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/sma.py deleted file mode 100644 index efe058e3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/sma.py +++ /dev/null @@ -1,85 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import convolve, ones -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - nb_prepend, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -# Fast SMA Options: https://github.com/numba/numba/issues/4119 -@njit(cache=True) -def nb_sma(x, n): - result = convolve(ones(n) / n, x)[n - 1:1 - n] - return nb_prepend(result, n - 1) - - -def sma( - close: Series, length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Simple Moving Average - - This indicator is the the textbook moving average, a rolling sum of - values divided by the window period (or length). - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - adjust (bool): Adjust the values. Default: ```True``` - presma (bool): If True, uses SMA for initial value. - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal and length > 1: - from talib import SMA - sma = SMA(close, length) - else: - np_close = close.to_numpy() - sma = nb_sma(np_close, length) - sma = Series(sma, index=close.index) - - # Offset - if offset != 0: - sma = sma.shift(offset) - - # Fill - if "fillna" in kwargs: - sma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - sma.name = f"SMA_{length}" - sma.category = "overlap" - - return sma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/smma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/smma.py deleted file mode 100644 index 99af8e43..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/smma.py +++ /dev/null @@ -1,82 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def smma( - close: Series, length: Int = None, - mamode: str = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """SMoothed Moving Average - - This indicator attempts to confirm trends and identify support and - resistance areas. It tries to reduce noise in contrast to reducing lag. - - Sources: - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=173&Name=Moving_Average_-_Smoothed) - * [tradingview](https://www.tradingview.com/scripts/smma/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - A core component of Bill Williams Alligator indicator. - """ - # Validate - length = v_pos_default(length, 7) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - mamode = v_mamode(mamode, "sma") - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - m = close.size - smma = close.copy() - smma[:length - 1] = nan - smma.iloc[length - 1] = ma(mamode, close[0:length], length=length, talib=mode_tal).iloc[-1] - - for i in range(length, m): - smma.iat[i] = ((length - 1) * smma.iat[i - 1] + smma.iat[i]) / length - - # Offset - if offset != 0: - smma = smma.shift(offset) - - # Fill - if "fillna" in kwargs: - smma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - smma.name = f"SMMA_{length}" - smma.category = "overlap" - - return smma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ssf.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ssf.py deleted file mode 100644 index 3f50a18f..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ssf.py +++ /dev/null @@ -1,115 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import copy, cos, exp -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series - - - -# Ehlers's Super Smoother Filter -# http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html -@njit(cache=True) -def nb_ssf(x, n, pi, sqrt2): - m, ratio, result = x.size, sqrt2 / n, copy(x) - a = exp(-pi * ratio) - b = 2 * a * cos(180 * ratio) - c = a * a - b + 1 - - # result[:2] = x[:2] - for i in range(2, m): - result[i] = 0.5 * c * (x[i] + x[i - 1]) + b * result[i - 1] \ - - a * a * result[i - 2] - - return result - - -# John F. Ehlers's Super Smoother Filter by Everget (2 poles), Tradingview -# https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/ -@njit(cache=True) -def nb_ssf_everget(x, n, pi, sqrt2): - m, arg, result = x.size, pi * sqrt2 / n, copy(x) - a = exp(-arg) - b = 2 * a * cos(arg) - - # result[:2] = x[:2] - for i in range(2, m): - result[i] = 0.5 * (a * a - b + 1) * (x[i] + x[i - 1]) \ - + b * result[i - 1] - a * a * result[i - 2] - - return result - - -def ssf( - close: Series, length: Int = None, - everget: bool = None, pi: IntFloat = None, sqrt2: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Ehlers's Super Smoother Filter - - This indicator, by John F. Ehlers's © 2013, is a (Recursive) Digital - Filter that attempts to reduce lag and remove aliases. This version - has two poles. - - Sources: - * [mql5](https://www.mql5.com/en/code/588) - * [traders.com](http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html) - * [tradingview](https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - everget (bool): Everget's implementation of ssf that uses pi - instead of 180 for the b factor of ssf. Default: ```False``` - pi (float): The default is Ehlers's truncated value: ```3.14159```. - Default: ```3.14159``` - sqrt2 (float): The default is Ehlers's truncated value: ```1.414```. - Default: ```1.414``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * Everget's calculation on TradingView: - ```pi=np.pi```, ```sqrt2=np.sqrt(2)``` - - Danger: - Possible Data Leak - """ - # Validate - length = v_pos_default(length, 20) - close = v_series(close, length) - - if close is None: - return - - pi = v_pos_default(pi, 3.14159) - sqrt2 = v_pos_default(sqrt2, 1.414) - everget = v_bool(everget, False) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - if everget: - ssf = nb_ssf_everget(np_close, length, pi, sqrt2) - else: - ssf = nb_ssf(np_close, length, pi, sqrt2) - ssf = Series(ssf, index=close.index) - - # Offset - if offset != 0: - ssf = ssf.shift(offset) - - # Fill - if "fillna" in kwargs: - ssf.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - ssf.name = f"SSF{'e' if everget else ''}_{length}" - ssf.category = "overlap" - - return ssf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ssf3.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ssf3.py deleted file mode 100644 index 497abbb4..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/ssf3.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import copy, cos, exp -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -# John F. Ehler's Super Smoother Filter by Everget (3 poles), Tradingview -# https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/ -@njit(cache=True) -def nb_ssf3(x, n, pi, sqrt3): - m, result = x.size, copy(x) - a = exp(-pi / n) - b = 2 * a * cos(-pi * sqrt3 / n) - c = a * a - - d4 = c * c - d3 = -c * (1 + b) - d2 = b + c - d1 = 1 - d2 - d3 - d4 - - # result[:3] = x[:3] - for i in range(3, m): - result[i] = d1 * x[i] + d2 * result[i - 1] \ - + d3 * result[i - 2] + d4 * result[i - 3] - - return result - - -def ssf3( - close: Series, length: Int = None, - pi: IntFloat = None, sqrt3: IntFloat = None, - offset: Int = None, **kwargs: DictLike -): - """Ehlers's 3 Pole Super Smoother Filter - - This indicator, by John F. Ehlers's © 2013, is a (Recursive) Digital - Filter that attempts to reduce lag and remove aliases. This version - has two poles. - - Sources: - * [mql5](https://www.mql5.com/en/code/589) - * [tradingview](https://www.tradingview.com/script/VdJy0yBJ-Ehlers-Super-Smoother-Filter/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - pi (float): The value of ```PI```. The default is Ehler's truncated - value: ```3.14159```. Default: ```3.14159``` - sqrt3 (float): The value of ```sqrt(3)``` to use. The default is - Ehler's truncated value: ```1.732```. Default: ```1.732``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * Everget's calculation on TradingView: - ```pi=np.pi```, ```sqrt2=np.sqrt(2)``` - """ - # Validate - length = v_pos_default(length, 20) - close = v_series(close, length) - - if close is None: - return - - pi = v_pos_default(pi, 3.14159) - sqrt3 = v_pos_default(sqrt3, 1.732) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - ssf = nb_ssf3(np_close, length, pi, sqrt3) - ssf = Series(ssf, index=close.index) - - # Offset - if offset != 0: - ssf = ssf.shift(offset) - - # Fill - if "fillna" in kwargs: - ssf.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - ssf.name = f"SSF3_{length}" - ssf.category = "overlap" - - return ssf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/supertrend.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/supertrend.py deleted file mode 100644 index 8adb08bf..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/supertrend.py +++ /dev/null @@ -1,108 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap import hl2 -from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series -from pandas_ta.volatility import atr - - - -def supertrend( - high: Series, low: Series, close: Series, - length: Int = None, atr_length: Int = None, - multiplier: IntFloat = None, - atr_mamode : str = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Supertrend - - This indicator attempts to identify trend direction as well as support and - resistance levels. - - Sources: - * [freebsensetips](http://www.freebsensetips.com/blog/detail/7/What-is-supertrend-indicator-its-calculation) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```7``` - atr_length (int): ATR period. Default: ```length``` - multiplier (float): Coefficient for upper and lower band distance to - midrange. Default: ```3.0``` - atr_mamode (str) : MA type to be used for ATR calculation. - See ```help(ta.ma)```. Default: ```"rma"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 4 columns - """ - # Validate - length = v_pos_default(length, 7) - atr_length = v_pos_default(atr_length, length) - high = v_series(high, length + 1) - low = v_series(low, length + 1) - close = v_series(close, length + 1) - - if high is None or low is None or close is None: - return - - multiplier = v_pos_default(multiplier, 3.0) - atr_mamode = v_mamode(atr_mamode, "rma") - offset = v_offset(offset) - - # Calculate - m = close.size - dir_, trend = [1] * m, [0] * m - long, short = [nan] * m, [nan] * m - - hl2_ = hl2(high, low) - matr = multiplier * atr(high, low, close, atr_length, mamode=atr_mamode) - lb = hl2_ - matr - ub = hl2_ + matr - - for i in range(1, m): - if close.iat[i] > ub.iat[i - 1]: - dir_[i] = 1 - elif close.iat[i] < lb.iat[i - 1]: - dir_[i] = -1 - else: - dir_[i] = dir_[i - 1] - if dir_[i] > 0 and lb.iat[i] < lb.iat[i - 1]: - lb.iat[i] = lb.iat[i - 1] - if dir_[i] < 0 and ub.iat[i] > ub.iat[i - 1]: - ub.iat[i] = ub.iat[i - 1] - - if dir_[i] > 0: - trend[i] = long[i] = lb.iat[i] - else: - trend[i] = short[i] = ub.iat[i] - - trend[0] = nan - dir_[:length] = [nan] * length - - _props = f"_{length}_{multiplier}" - data = { - f"SUPERT{_props}": trend, - f"SUPERTd{_props}": dir_, - f"SUPERTl{_props}": long, - f"SUPERTs{_props}": short - } - df = DataFrame(data, index=close.index) - - df.name = f"SUPERT{_props}" - df.category = "overlap" - - # Offset - if offset != 0: - df = df.shift(offset) - - # Fill - if "fillna" in kwargs: - df.fillna(kwargs["fillna"], inplace=True) - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/swma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/swma.py deleted file mode 100644 index bbab9005..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/swma.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - symmetric_triangle, - v_offset, - v_pos_default, - v_series, - weights -) - - - -def swma( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Symmetric Weighted Moving Average - - This indicator is based on a Symmetric Weighted Moving Average where - weights are based on a symmetric triangle. - - Source: - * [tradingview](https://www.tradingview.com/study-script-reference/#fun_swma) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * ```n=3``` -> ```[1, 2, 1]``` - * ```n=4``` -> ```[1, 2, 2, 1]``` - * etc... - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - triangle = symmetric_triangle(length, weighted=True) - swma = close.rolling(length, min_periods=length) \ - .apply(weights(triangle), raw=True) - - # Offset - if offset != 0: - swma = swma.shift(offset) - - # Fill - if "fillna" in kwargs: - swma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - swma.name = f"SWMA_{length}" - swma.category = "overlap" - - return swma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/t3.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/t3.py deleted file mode 100644 index 8aee19e3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/t3.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib -from .ema import ema - - - -def t3( - close: Series, length: Int = None, a: IntFloat = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """T3 - - This indicator, by Tim Tillson, attempts to be smoother and more - responsive relative to other moving averages. - - Sources: - * [binarytribune](http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - a (float): The a factor, 0 < a < 1. Default: ```0.7``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - adjust (bool): Default: True - presma (bool): If True, uses SMA for initial value. - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9999994265973177)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, 5 * (length + 1)) - - if close is None: - return - - a = float(a) if isinstance(a, float) and 0 < a < 1 else 0.7 - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import T3 - t3 = T3(close, length, a) - else: - c1 = -a * a**2 - c2 = 3 * a**2 + 3 * a**3 - c3 = -6 * a**2 - 3 * a - 3 * a**3 - c4 = a**3 + 3 * a**2 + 3 * a + 1 - - e1 = ema(close=close, length=length, talib=mode_tal, **kwargs) - e2 = ema(close=e1, length=length, talib=mode_tal, **kwargs) - e3 = ema(close=e2, length=length, talib=mode_tal, **kwargs) - e4 = ema(close=e3, length=length, talib=mode_tal, **kwargs) - e5 = ema(close=e4, length=length, talib=mode_tal, **kwargs) - e6 = ema(close=e5, length=length, talib=mode_tal, **kwargs) - t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 - - # Offset - if offset != 0: - t3 = t3.shift(offset) - - # Fill - if "fillna" in kwargs: - t3.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - t3.name = f"T3_{length}_{a}" - t3.category = "overlap" - - return t3 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/tema.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/tema.py deleted file mode 100644 index b5555595..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/tema.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib -from .ema import ema - - - -def tema( - close: Series, length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Triple Exponential Moving Average - - This indicator attempts to be less laggy than the EMA. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - adjust (bool): Default: ```True``` - presma (bool): If True, uses SMA for initial value. - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9999355450605516)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, 3 * length) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import TEMA - tema = TEMA(close, length) - else: - ema1 = ema(close=close, length=length, talib=mode_tal, **kwargs) - ema2 = ema(close=ema1, length=length, talib=mode_tal, **kwargs) - ema3 = ema(close=ema2, length=length, talib=mode_tal, **kwargs) - tema = 3 * (ema1 - ema2) + ema3 - - # Offset - if offset != 0: - tema = tema.shift(offset) - - # Fill - if "fillna" in kwargs: - tema.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - tema.name = f"TEMA_{length}" - tema.category = "overlap" - - return tema diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/trima.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/trima.py deleted file mode 100644 index 41f887f5..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/trima.py +++ /dev/null @@ -1,77 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib -from .sma import sma - - - -def trima( - close: Series, length: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Triangular Moving Average - - This indicator is a weighted moving average where the shape of the - weights are triangular with the greatest weight is in the middle - of the period. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triangular-moving-average-trima/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - adjust (bool): Default: True - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - tma = sma(sma(src, ceil(length / 2)), floor(length / 2) + 1) # Tradingview - trima = sma(sma(x, n), n) # Tradingview - - Warning: - TA-Lib Correlation: ```np.float64(0.9991752493891967)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import TRIMA - trima = TRIMA(close, length) - else: - half_length = round(0.5 * (length + 1)) - sma1 = sma(close, length=half_length, talib=mode_tal) - trima = sma(sma1, length=half_length, talib=mode_tal) - - # Offset - if offset != 0: - trima = trima.shift(offset) - - # Fill - if "fillna" in kwargs: - trima.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - trima.name = f"TRIMA_{length}" - trima.category = "overlap" - - return trima diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/vidya.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/vidya.py deleted file mode 100644 index c1e44ab1..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/vidya.py +++ /dev/null @@ -1,113 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_drift, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def vidya( - close: Series, length: Int = None, - talib: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Variable Index Dynamic Average - - This indicator, by Tushar Chande, is similar to an EMA but it has a - dynamically adjusted lookback period dependent based on CMO. - - Sources: - * [perfecttrendsystem](https://www.perfecttrendsystem.com/blog_mt4_2/en/vidya-indicator-for-mt4) - * [tradingview](https://www.tradingview.com/script/hdrf0fXV-Variable-Index-Dynamic-Average-VIDYA/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - talib (bool): If installed, use TA Lib. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - Sometimes used as a moving average or a trend identifier. - """ - # Validate - length = v_pos_default(length, 14) - close = v_series(close, length + 1) - - if close is None: - return - - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - m = close.size - alpha = 2 / (length + 1) - - if Imports["talib"] and mode_tal: - from talib import CMO - cmo_ = 0.01 * CMO(close, length) - else: - cmo_ = _cmo(close, length, drift) - abs_cmo = cmo_.abs().astype(float) - - vidya = Series(0.0, index=close.index) - for i in range(length, m): - vidya.iloc[i] = alpha * abs_cmo.iloc[i] * close.iloc[i] + \ - vidya.iloc[i - 1] * (1 - alpha * abs_cmo.iloc[i]) - vidya.replace({0: nan}, inplace=True) - - # Offset - if offset != 0: - vidya = vidya.shift(offset) - - # Fill - if "fillna" in kwargs: - vidya.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - vidya.name = f"VIDYA_{length}" - vidya.category = "overlap" - - return vidya - - -def _cmo(x: Series, length: Int, drift: Int): - """Chande Momentum Oscillator Patch - - Unguarded CMO Patch - - Parameters: - x (Series): ```x``` Series - length (int): The period. - drift (int): Difference amount. - - Returns: - (Series): 1 column - - Info: Weird Circular TypeError!? - For some reason: from pandas_ta.momentum import cmo causes - pandas_ta.momentum.coppock to not be able to import it's _wma_ like - from pandas_ta.overlap import wma? - """ - mom = x.diff(drift) - positive = mom.copy().clip(lower=0) - negative = mom.copy().clip(upper=0).abs() - pos_sum = positive.rolling(length).sum() - neg_sum = negative.rolling(length).sum() - - return (pos_sum - neg_sum) / (pos_sum + neg_sum) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/wcp.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/wcp.py deleted file mode 100644 index 14ac9d79..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/wcp.py +++ /dev/null @@ -1,65 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_series, v_talib - - - -def wcp( - high: Series, low: Series, close: Series, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Weighted Closing Price - - This indicator is a weighted value of: high, low and twice the close. - - Sources: - * [fmlabs](https://www.fmlabs.com/reference/default.htm?url=WeightedCloses.htm) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - _length = 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import WCLPRICE - wcp = WCLPRICE(high, low, close) - else: - weight = high.to_numpy() + low.to_numpy() + 2 * close.to_numpy() - wcp = Series(weight, index=close.index) - - # Offset - if offset != 0: - wcp = wcp.shift(offset) - - # Fill - if "fillna" in kwargs: - wcp.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - wcp.name = "WCP" - wcp.category = "overlap" - - return wcp diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/wma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/wma.py deleted file mode 100644 index 4b05de16..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/wma.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import arange, float64, nan, zeros_like -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_ascending, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -@njit(cache=True) -def nb_wma(x, n, asc, prenan): - m = x.size - w = arange(1, n + 1, dtype=float64) - result = zeros_like(x, dtype=float64) - - if not asc: - w = w[::-1] - - for i in range(n - 1, m): - result[i] = (w * x[i - n + 1:i + 1]).sum() - result *= 2 / (n * n + n) - - if prenan: - result[:n - 1] = nan - - return result - - -def wma( - close: Series, length: Int = None, - asc: bool = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Weighted Moving Average - - This indicator is a Moving Average where the weights are linearly - increasing and the most recent data has the heaviest weight. - - Sources: - * [wikipedia](https://en.wikipedia.org/wiki/Moving_average#Weighted_moving_average) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - asc (bool): Recent values weigh more. Default: ```True``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - asc = v_ascending(asc) - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import WMA - wma = WMA(close, length) - else: - np_close = close.to_numpy() - wma_ = nb_wma(np_close, length, asc, True) - wma = Series(wma_, index=close.index) - - # Offset - if offset != 0: - wma = wma.shift(offset) - - # Fill - if "fillna" in kwargs: - wma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - wma.name = f"WMA_{length}" - wma.category = "overlap" - - return wma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/zlma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/zlma.py deleted file mode 100644 index 2e57d347..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/overlap/zlma.py +++ /dev/null @@ -1,99 +0,0 @@ -# -*- coding: utf-8 -*- -from sys import modules as sys_modules -from numpy import isnan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series - -# Available MAs for zlma -from .dema import dema -from .ema import ema -from .fwma import fwma -from .hma import hma -from .linreg import linreg -from .midpoint import midpoint -from .pwma import pwma -from .rma import rma -from .sinwma import sinwma -from .sma import sma -from .ssf import ssf -from .swma import swma -from .t3 import t3 -from .tema import tema -from .trima import trima -from .vidya import vidya -from .wma import wma - - - -def zlma( - close: Series, length: Int = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Zero Lag Moving Average - - This indicator, by John Ehlers and Ric Way, attempts to eliminate the lag - often introduced in other moving averages. - - Sources: - * [wikipedia](https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - mamode (str): One of: "dema", "ema", "fwma", "hma", "linreg", - "midpoint", "pwma", "rma", "sinwma", "ssf", "swma", "t3", - "tema", "trima", "vidya", or "wma". Default: ```"ema"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - - if close is None: - return - - mamode = v_mamode(mamode, "ema") - supported_mas = [ - "dema", "ema", "fwma", "hma", "linreg", - "midpoint", "pwma", "rma", "sinwma", "sma", - "ssf", "swma", "t3", "tema", "trima", "vidya", "wma" - ] - - if mamode not in supported_mas: - return - - offset = v_offset(offset) - - # Calculate - lag = int(0.5 * (length - 1)) - close_ = 2 * close - close.shift(lag) - - kwargs.update({"close": close_}) - kwargs.update({"length": length}) - - fn = getattr(sys_modules[__name__], mamode) - zlma = fn(**kwargs) - - if zlma is None or all(isnan(zlma)): - return # Emergency Break - - # Offset - if offset != 0: - zlma = zlma.shift(offset) - - # Fill - if "fillna" in kwargs: - zlma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - zlma.name = f"ZL_{zlma.name}" - zlma.category = "overlap" - - return zlma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/__init__.py deleted file mode 100644 index c30db9ec..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# -*- coding: utf-8 -*- -from .drawdown import drawdown -from .log_return import log_return -from .percent_return import percent_return - -__all__ = [ - "drawdown", - "log_return", - "percent_return", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/drawdown.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/drawdown.py deleted file mode 100644 index 1976c5e2..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/drawdown.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import log, seterr -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_series - - - -def drawdown( - close: Series, offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Drawdown - - This indicator traces the peak-to-trough decline over a specific period. - Commonly quoted as the percentage between the peak and the subsequent - trough. - - Sources: - * [investopedia](https://www.investopedia.com/terms/d/drawdown.asp) - - Parameters: - close (Series): ```close``` Series. - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - close = v_series(close) - offset = v_offset(offset) - - # Calculate - max_close = close.cummax() - dd = max_close - close - dd_pct = 1 - (close / max_close) - - _np_err = seterr() - seterr(divide="ignore", invalid="ignore") - dd_log = log(max_close) - log(close) - seterr(divide=_np_err["divide"], invalid=_np_err["invalid"]) - - # Offset - if offset != 0: - dd = dd.shift(offset) - dd_pct = dd_pct.shift(offset) - dd_log = dd_log.shift(offset) - - # Fill - if "fillna" in kwargs: - dd.fillna(kwargs["fillna"], inplace=True) - dd_pct.fillna(kwargs["fillna"], inplace=True) - dd_log.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - dd.name = "DD" - dd_pct.name = f"{dd.name}_PCT" - dd_log.name = f"{dd.name}_LOG" - dd.category = dd_pct.category = dd_log.category = "performance" - - data = {dd.name: dd, dd_pct.name: dd_pct, dd_log.name: dd_log} - df = DataFrame(data, index=close.index) - df.name = dd.name - df.category = dd.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/log_return.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/log_return.py deleted file mode 100644 index b9c1c2ab..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/log_return.py +++ /dev/null @@ -1,64 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import log, nan, roll -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series - - - -def log_return( - close: Series, length: Int = None, cumulative: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Log Return - - Calculates the logarithmic return. - - Sources: - * [stackoverflow](https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - cumulative (bool): If True, returns the cumulative returns. - Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 1) - close = v_series(close, length + 1) - - if close is None: - return - - cumulative = v_bool(cumulative, False) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - if cumulative: - r = np_close / np_close[0] - else: - r = np_close / roll(np_close, length) - r[:length] = nan - log_return = Series(log(r), index=close.index) - - # Offset - if offset != 0: - log_return = log_return.shift(offset) - - # Fill - if "fillna" in kwargs: - log_return.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - log_return.name = f"{'CUM' if cumulative else ''}LOGRET_{length}" - log_return.category = "performance" - - return log_return diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/percent_return.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/percent_return.py deleted file mode 100644 index 221fffc7..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/performance/percent_return.py +++ /dev/null @@ -1,64 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan, roll -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series - - - -def percent_return( - close: Series, length: Int = None, cumulative: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Percent Return - - Calculates the percent return. - - Sources: - * [stackoverflow](https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```1``` - cumulative (bool): If True, returns the cumulative returns. - Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 1) - close = v_series(close, length + 1) - - if close is None: - return - - cumulative = v_bool(cumulative, False) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - if cumulative: - pr = (np_close / np_close[0]) - 1 - else: - pr = (np_close / roll(np_close, length)) - 1 - pr[:length] = nan - pct_return = Series(pr, index=close.index) - - # Offset - if offset != 0: - pct_return = pct_return.shift(offset) - - # Fill - if "fillna" in kwargs: - pct_return.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - pct_return.name = f"{'CUM' if cumulative else ''}PCTRET_{length}" - pct_return.category = "performance" - - return pct_return diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/py.typed b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/py.typed deleted file mode 100644 index e69de29b..00000000 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/__init__.py deleted file mode 100644 index 594392bc..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# -*- coding: utf-8 -*- -from .entropy import entropy -from .kurtosis import kurtosis -from .mad import mad -from .median import median -from .quantile import quantile -from .skew import skew -from .stdev import stdev -from .tos_stdevall import tos_stdevall -from .variance import variance -from .zscore import zscore - -__all__ = [ - "entropy", - "kurtosis", - "mad", - "median", - "quantile", - "skew", - "stdev", - "tos_stdevall", - "variance", - "zscore", -] \ No newline at end of file diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/entropy.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/entropy.py deleted file mode 100644 index 198123cf..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/entropy.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import log -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def entropy( - close: Series, length: Int = None, base: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Entropy - - This indicator attempts to quantify the unpredictability of the data, - or equivalently, its average information. It is a rolling entropy - calculation. - - Sources: - * [wikipedia](https://en.wikipedia.org/wiki/Entropy_(information_theory)) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - base (float): Logarithmic Base. Default: ```2``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, 2 * length - 1) - - if close is None: - return - - base = v_pos_default(base, 2.0) - offset = v_offset(offset) - - # Calculate - p = close / close.rolling(length).sum() - entropy = (-p * log(p) / log(base)).rolling(length).sum() - - # Offset - if offset != 0: - entropy = entropy.shift(offset) - - # Fill - if "fillna" in kwargs: - entropy.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - entropy.name = f"ENTP_{length}" - entropy.category = "statistics" - - return entropy diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/kurtosis.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/kurtosis.py deleted file mode 100644 index de5b79f0..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/kurtosis.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def kurtosis( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rolling Kurtosis - - Calculates a rolling Kurtosis. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```30``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Danger: - Possible Data Leak - """ - # Validate - length = v_pos_default(length, 30) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - kurtosis = close.rolling(length, min_periods=min_periods).kurt() - - # Offset - if offset != 0: - kurtosis = kurtosis.shift(offset) - - # Fill - if "fillna" in kwargs: - kurtosis.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - kurtosis.name = f"KURT_{length}" - kurtosis.category = "statistics" - - return kurtosis diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/mad.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/mad.py deleted file mode 100644 index 50541c47..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/mad.py +++ /dev/null @@ -1,61 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import fabs -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def _mad(series: Series): - """Mean Absolute Deviation""" - return fabs(series - series.mean()).mean() - - -def mad( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rolling Mean Absolute Deviation - - Calculates a rolling Mean Absolute Deviation (MAD). - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```30``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 30) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - mad = close.rolling(length, min_periods=min_periods).apply(_mad, raw=True) - - # Offset - if offset != 0: - mad = mad.shift(offset) - - # Fill - if "fillna" in kwargs: - mad.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - mad.name = f"MAD_{length}" - mad.category = "statistics" - - return mad diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/median.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/median.py deleted file mode 100644 index 03e57005..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/median.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def median( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rolling Median - - Calculates a rolling Median. - - Sources: - * [incrediblecharts](https://www.incrediblecharts.com/indicators/median_price.php) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```30``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 30) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - median = close.rolling(length, min_periods=min_periods).median() - - # Offset - if offset != 0: - median = median.shift(offset) - - # Fill - if "fillna" in kwargs: - median.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - median.name = f"MEDIAN_{length}" - median.category = "statistics" - - return median diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/quantile.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/quantile.py deleted file mode 100644 index 35439167..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/quantile.py +++ /dev/null @@ -1,57 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def quantile( - close: Series, length: Int = None, q: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rolling Quantile - - Calculates a rolling Quantile. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```30``` - q (float): The quantile. Default: ```0.5``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 30) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - q = float(q) if isinstance(q, float) and 0 < q < 1 else 0.5 - offset = v_offset(offset) - - # Calculate - quantile = close.rolling(length, min_periods=min_periods).quantile(q) - - # Offset - if offset != 0: - quantile = quantile.shift(offset) - - # Fill - if "fillna" in kwargs: - quantile.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - quantile.name = f"QTL_{length}_{q}" - quantile.category = "statistics" - - return quantile diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/skew.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/skew.py deleted file mode 100644 index e1068d04..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/skew.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def skew( - close: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rolling Skew - - Calculates a rolling Skew. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```30``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Danger: - Possible Data Leak - """ - # Validate - length = v_pos_default(length, 30) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - skew = close.rolling(length, min_periods=min_periods).skew() - - # Offset - if offset != 0: - skew = skew.shift(offset) - - # Fill - if "fillna" in kwargs: - skew.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - skew.name = f"SKEW_{length}" - skew.category = "statistics" - - return skew diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/stdev.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/stdev.py deleted file mode 100644 index 90f7b2d4..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/stdev.py +++ /dev/null @@ -1,70 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import sqrt -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib -from .variance import variance - - - -def stdev( - close: Series, length: Int = None, - ddof: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rolling Standard Deviation - - Calculates a rolling Standard Deviation. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```30``` - ddof (int): Delta Degrees of Freedom. Default: ```1``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * TA Lib does not have a ```ddof``` parameter. - * The divisor used in calculations is: ```N - ddof```, where ```N``` - is the number of elements. To use ```ddof```, set ```talib=False```. - """ - # Validate - length = v_pos_default(length, 30) - close = v_series(close, length) - - if close is None: - return - - ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1 - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import STDDEV - stdev = STDDEV(close, length) - else: - stdev = variance( - close=close, length=length, ddof=ddof, talib=mode_tal - ).apply(sqrt) - - # Offset - if offset != 0: - stdev = stdev.shift(offset) - - # Fill - if "fillna" in kwargs: - stdev.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - stdev.name = f"STDEV_{length}" - stdev.category = "statistics" - - return stdev diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/tos_stdevall.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/tos_stdevall.py deleted file mode 100644 index b3876a7d..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/tos_stdevall.py +++ /dev/null @@ -1,96 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import arange, array, polyfit, std -from pandas import DataFrame, DatetimeIndex, Series -from pandas_ta._typing import DictLike, Int, List -from pandas_ta.utils import v_list, v_lowerbound, v_offset, v_series - - - -def tos_stdevall( - close: Series, length: Int = None, - stds: List = None, ddof: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """TD Ameritrade's Think or Swim Standard Deviation All - - This indicator returns the standard deviation(s) over all the bars or the - last ```n``` (length) bars. - - Sources: - * [thinkorswim](https://tlc.thinkorswim.com/center/reference/thinkScript/Functions/Statistical/StDevAll) - - Parameters: - close (Series): ```close``` Series - length (int): Bars since current/last bar, Series[-1]. Default: ```None``` - stds (list): List of standard deviations in increasing order from the - central Linear Regression line. Default: ```[1,2,3]``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 7+ columns - - Note: - * TA Lib does not have a ```ddof``` parameter. - * The divisor used in calculations is: ```N - ddof```, where ```N``` - is the number of elements. To use ```ddof```, set ```talib=False```. - - Danger: - Possible Data Leak - """ - # Validate - _props = f"TOS_STDEVALL" - if length is None: - length = close.size - else: - length = v_lowerbound(length, 2, 30) - close = close.iloc[-length:] - _props = f"{_props}_{length}" - - close = v_series(close, 2) - - if close is None: - return - - stds = v_list(stds, [1, 2, 3]) - if min(stds) <= 0: - return - - if not all(i < j for i, j in zip(stds, stds[1:])): - stds = stds[::-1] - - ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1 - offset = v_offset(offset) - - # Calculate - X = src_index = close.index - if isinstance(close.index, DatetimeIndex): - X = arange(length) - close = array(close) - - m, b = polyfit(X, close, 1) - lr = Series(m * X + b, index=src_index) - stdev = std(close, ddof=ddof) - - # Name and Category - df = DataFrame({f"{_props}_LR": lr}, index=src_index) - for i in stds: - df[f"{_props}_L_{i}"] = lr - i * stdev - df[f"{_props}_U_{i}"] = lr + i * stdev - df[f"{_props}_L_{i}"].name = df[f"{_props}_U_{i}"].name = f"{_props}" - df[f"{_props}_L_{i}"].category = df[f"{_props}_U_{i}"].category = "statistics" - - # Offset - if offset != 0: - df = df.shift(offset) - - # Fill - if "fillna" in kwargs: - df.fillna(kwargs["fillna"], inplace=True) - - df.name = f"{_props}" - df.category = "statistics" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/variance.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/variance.py deleted file mode 100644 index 85e9f850..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/variance.py +++ /dev/null @@ -1,70 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_lowerbound, v_offset, v_series, v_talib - - - -def variance( - close: Series, length: Int = None, - ddof: Int = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rolling Variance - - Calculates a rolling Variance. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```30``` - ddof (int): Delta Degrees of Freedom. Default: ```1``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * TA Lib does not have a ```ddof``` parameter. - * The divisor used in calculations is: ```N - ddof```, where ```N``` - is the number of elements. To use ```ddof```, set ```talib=False```. - """ - # Validate - length = v_lowerbound(length, 1, 30) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - close = v_series(close, max(length, min_periods)) - - if close is None: - return - - ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1 - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import VAR - variance = VAR(close, length) - else: - variance = close.rolling(length, min_periods=min_periods).var(ddof) - - # Offset - if offset != 0: - variance = variance.shift(offset) - - # Fill - if "fillna" in kwargs: - variance.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - variance.name = f"VAR_{length}" - variance.category = "statistics" - - return variance diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/zscore.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/zscore.py deleted file mode 100644 index a1ff7459..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/statistics/zscore.py +++ /dev/null @@ -1,57 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap import sma -from pandas_ta.statistics import stdev -from pandas_ta.utils import v_lowerbound, v_offset, v_series - - - -def zscore( - close: Series, length: Int = None, std: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Rolling Z Score - - Calculates a rolling Z Score. - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```30``` - std (float): Number of deviation standards. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_lowerbound(length, 1, 30) - close = v_series(close, length) - - if close is None: - return - - std = v_lowerbound(std, 1, 1.0) - offset = v_offset(offset) - - # Calculate - std *= stdev(close=close, length=length, **kwargs) - mean = sma(close=close, length=length, **kwargs) - zscore = (close - mean) / std - - # Offset - if offset != 0: - zscore = zscore.shift(offset) - - # Fill - if "fillna" in kwargs: - zscore.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - zscore.name = f"ZS_{length}" - zscore.category = "statistics" - - return zscore diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/__init__.py deleted file mode 100644 index afb75b32..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/__init__.py +++ /dev/null @@ -1,46 +0,0 @@ -# -*- coding: utf-8 -*- -from .adx import adx -from .alphatrend import alphatrend -from .amat import amat -from .aroon import aroon -from .chop import chop -from .cksp import cksp -from .decay import decay -from .decreasing import decreasing -from .dpo import dpo -from .ht_trendline import ht_trendline -from .increasing import increasing -from .long_run import long_run -from .psar import psar -from .qstick import qstick -from .rwi import rwi -from .short_run import short_run -from .trendflex import trendflex -from .ttm_trend import ttm_trend -from .vhf import vhf -from .vortex import vortex -from .zigzag import zigzag - -__all__ = [ - "adx", - "alphatrend", - "amat", - "aroon", - "chop", - "cksp", - "decay", - "decreasing", - "dpo", - "ht_trendline", - "increasing", - "long_run", - "psar", - "qstick", - "rwi", - "short_run", - "trendflex", - "ttm_trend", - "vhf", - "vortex", - "zigzag", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/adx.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/adx.py deleted file mode 100644 index 12c6fb22..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/adx.py +++ /dev/null @@ -1,168 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan, nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_bool, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib, - zero -) -from pandas_ta.volatility import atr - - - -def adx( - high: Series, low: Series, close: Series, length: Int = None, - signal_length: Int = None, adxr_length: Int = None, scalar: IntFloat = None, - talib: bool = None, tvmode: bool = None, mamode: str = None, - drift: Int = None, offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Average Directional Movement - - This indicator attempts to quantify trend strength by measuring the - amount of movement in a single direction. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/average-directional-movement-adx/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - signal_length (int): Signal period. Default: ```length``` - adxr_length (int): ADXR period. Default: ```2``` - scalar (float): Scalar. Default: ```100``` - talib (bool): If installed, use TA Lib. Default: ```True``` - tvmode (bool): Trading View. Default: ```False``` - mamode (str): See ```help(ta.ma)```. Default: ```"rma"``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 4 columns - - Note: - ```signal_length``` is like TradingView's default ADX. - """ - # Validate - length = v_pos_default(length, 14) - signal_length = v_pos_default(signal_length, length) - adxr_length = v_pos_default(adxr_length, 2) - _length = max(length, signal_length, adxr_length) - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - scalar = v_scalar(scalar, 100) - mamode = v_mamode(mamode, "rma") - mode_tal = v_talib(talib) - mode_tv = v_bool(tvmode, False) - - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - atr_ = atr( - high=high, low=low, close=close, - length=length, prenan=kwargs.pop("prenan", True) - ) - if atr_ is None or all(isnan(atr_)): - return - - k = scalar / atr_ - - up = high - high.shift(drift) # high.diff(drift) - dn = low.shift(drift) - low # low.diff(-drift).shift(drift) - - pos = ((up > dn) & (up > 0)) * up - neg = ((dn > up) & (dn > 0)) * dn - - # Issue #671 Solution - # not_close = ~isclose(up, dn) - # pos = ((up > dn) & (up > 0) * up & not_close) * up - # neg = ((dn > up) & (dn > 0) * dn & not_close) * dn - - pos = pos.apply(zero) - neg = neg.apply(zero) - - if not mode_tv and Imports["talib"] and mode_tal and length > 1: - from talib import ADX, MINUS_DM, PLUS_DM - adx = ADX(high, low, close, length) - dmp = PLUS_DM(high, low, length) - dmn = MINUS_DM(high, low, length) - - elif mode_tv: - # How to treat the initial value of RMA varies from one another. - # It follows the way TradingView does, setting it to the average of - # previous values. Since 'pandas' does not provide API to control - # the initial value, work around it by modifying input value to get - # desired output. - pos.iloc[length - 1] = pos[:length].sum() - pos[:length - 1] = 0 - neg.iloc[length - 1] = neg[:length].sum() - neg[:length - 1] = 0 - - alpha = 1 / length - dmp = k * pos.ewm(alpha=alpha, adjust=False, min_periods=length).mean() - dmn = k * neg.ewm(alpha=alpha, adjust=False, min_periods=length).mean() - - # The same goes with dx. - dx = scalar * (dmp - dmn).abs() / (dmp + dmn) - dx = dx.shift(-length) - dx.iloc[length - 1] = dx[:length].sum() - dx[:length - 1] = 0 - - adx = ma(mamode, dx, length=signal_length) - # Rollback shifted rows. - adx[:length - 1] = nan - adx = adx.shift(length) - else: - dmp = k * ma(mamode, pos, length=length) - dmn = k * ma(mamode, neg, length=length) - dx = scalar * (dmp - dmn).abs() / (dmp + dmn) - adx = ma(mamode, dx, length=signal_length) - - adxr = 0.5 * (adx + adx.shift(adxr_length)) - - # Offset - if offset != 0: - adx = adx.shift(offset) - adxr = adxr.shift(offset) - dmn = dmn.shift(offset) - dmp = dmp.shift(offset) - - # Fill - if "fillna" in kwargs: - adx.fillna(kwargs["fillna"], inplace=True) - adxr.fillna(kwargs["fillna"], inplace=True) - dmp.fillna(kwargs["fillna"], inplace=True) - dmn.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - adx.name = f"ADX_{signal_length}" - adxr.name = f"ADXR_{signal_length}_{adxr_length}" - dmp.name = f"DMP_{length}" - dmn.name = f"DMN_{length}" - adx.category = dmp.category = dmn.category = "trend" - - data = {adx.name: adx, adxr.name: adxr, dmp.name: dmp, dmn.name: dmn} - df = DataFrame(data, index=close.index) - df.name = f"ADX_{signal_length}" - df.category = "trend" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/alphatrend.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/alphatrend.py deleted file mode 100644 index 5c1070ff..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/alphatrend.py +++ /dev/null @@ -1,161 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import isnan, nan, zeros_like -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.momentum import rsi -from pandas_ta.volatility import atr -from pandas_ta.volume.mfi import mfi -from pandas_ta.utils import ( - v_mamode, - v_offset, - v_pos_default, - v_series, - v_str, - v_talib -) - - - -@njit(cache=True) -def nb_alpha(low_atr, high_atr, momo_threshold): - m = momo_threshold.size - result = zeros_like(low_atr) - - for i in range(1, m): - if momo_threshold[i]: - if low_atr[i] < result[i - 1]: - result[i] = result[i - 1] - else: - result[i] = low_atr[i] - else: - if high_atr[i] > result[i - 1]: - result[i] = result[i - 1] - else: - result[i] = high_atr[i] - result[0] = nan - - return result - - -def alphatrend( - open_: Series, high: Series, low: Series, close: Series, - volume: Series = None, src: str = None, - length: int = None, multiplier: IntFloat = None, - threshold: IntFloat = None, lag: Int = None, - mamode: str = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -): - """Alpha Trend - - This indicator attempts to filter sideways movement for accurate signals. - - Sources: - * [OnlyFibonacci](https://github.com/OnlyFibonacci/AlgoSeyri/blob/main/alphaTrendIndicator.py) - * [tradingview](https://www.tradingview.com/script/o50NYLAZ-AlphaTrend/) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - volume (Series): ```volume``` Series. Default: ```None``` - src (str): One of: "open", "high", "low" or "close". - Default: ```"close"``` - length (int): ATR, MFI, or RSI period. Default: ```14``` - multiplier (float): Trailing ATR multiple. Default: ```1``` - threshold (float): Momentum threshold. Default: ```50``` - lag (int): Lag period of main trend. Default: ```2``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - length = v_pos_default(length, 14) - open_ = v_series(open_, length) - high = v_series(high, length) - low = v_series(low, length) - close = v_series(close, length) - - if open_ is None or high is None or low is None or close is None: - return - - _src = {"open": open_, "high": high, "low": low, "close": close} - src = v_str(src, "close") - src = src if src in _src.keys() else "close" - - multiplier = v_pos_default(multiplier, 1) - threshold = v_pos_default(threshold, 50) - lag = v_pos_default(lag, 2) - - mamode = v_mamode(mamode, "sma") - mode_tal = v_talib(talib) - offset = v_offset(offset) - - if volume is not None: - volume = v_series(volume) - if volume is None: - return - - # Calculate - atr_ = atr( - high=high, low=low, close=close, length=length, - mamode=mamode, talib=mode_tal - ) - - if atr_ is None or all(isnan(atr_)): - return - - lower_atr = low - atr_ * multiplier - upper_atr = high + atr_ * multiplier - - momo = None - if volume is None: - momo = rsi(close=_src[src], length=length, mamode=mamode, talib=mode_tal) - else: - momo = mfi( - high=high, low=low, close=close, volume=volume, - length=length, talib=mode_tal - ) - - if momo is None: - return - - np_upper_atr, np_lower_atr = upper_atr.to_numpy(), lower_atr.to_numpy() - - at = nb_alpha(np_lower_atr, np_upper_atr, momo.to_numpy() >= threshold) - at = Series(at, index=close.index) - - atl = at.shift(lag) - - if all(isnan(at)) or all(isnan(atl)): - return # Emergency Break - - # Offset - if offset != 0: - at = at.shift(offset) - atl = atl.shift(offset) - - # Fill - if "fillna" in kwargs: - at.fillna(kwargs["fillna"], inplace=True) - atl.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}_{multiplier}_{threshold}" - at.name = f"ALPHAT{_props}" - atl.name = f"ALPHATl{_props}_{lag}" - at.category = atl.category = "trend" - - data = {at.name: at, atl.name: atl} - df = DataFrame(data, index=close.index) - df.name = at.name - df.category = at.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/amat.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/amat.py deleted file mode 100644 index f5d48c77..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/amat.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series -from .long_run import long_run -from .short_run import short_run - - - -def amat( - close: Series, fast: Int = None, slow: Int = None, - lookback: Int = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Archer Moving Averages Trends - - This indicator, by Kevin Johnson, attempts to identify both long run - and short run trends. - - Sources: - * Kevin Johnson - * [tradingview](https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/) - - Parameters: - close (Series): ```close``` Series - fast (int): Fast MA period. Default: ```8``` - slow (int): Slow MA period. Default: ```21``` - lookback (int): Lookback period for ```long_run``` and ```short_run```. - Default: ```2``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - run_length (int): OBV trend period. Default: ```2``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Note: - Both the long run and short run values are integers, where ```1``` - is a trend and ```0``` is not a trend. - """ - # Validate - fast = v_pos_default(fast, 8) - slow = v_pos_default(slow, 21) - lookback = v_pos_default(lookback, 2) - close = v_series(close, max(fast, slow, lookback)) - - if close is None: - return - - mamode = v_mamode(mamode, "ema") - offset = v_offset(offset) - if "length" in kwargs: - kwargs.pop("length") - - # Calculate - fast_ma = ma(mamode, close, length=fast, **kwargs) - slow_ma = ma(mamode, close, length=slow, **kwargs) - - mas_long = long_run(fast_ma, slow_ma, length=lookback) - mas_short = short_run(fast_ma, slow_ma, length=lookback) - - # Offset - if offset != 0: - mas_long = mas_long.shift(offset) - mas_short = mas_short.shift(offset) - - # Fill - if "fillna" in kwargs: - mas_long.fillna(kwargs["fillna"], inplace=True) - mas_short.fillna(kwargs["fillna"], inplace=True) - - _props = f"_{fast}_{slow}_{lookback}" - data = { - f"AMAT{mamode[0]}_LR{_props}": mas_long, - f"AMAT{mamode[0]}_SR{_props}": mas_short - } - df = DataFrame(data, index=close.index) - - # Name and Category - df.name = f"AMAT{mamode[0]}{_props}" - df.category = "trend" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/aroon.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/aroon.py deleted file mode 100644 index e627029f..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/aroon.py +++ /dev/null @@ -1,100 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - recent_maximum_index, - recent_minimum_index, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib -) - - - -def aroon( - high: Series, low: Series, - length: Int = None, scalar: IntFloat = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Aroon & Aroon Oscillator - - This indicator attempts to identify trends and their magnitude. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/aroon-ar/) - * [tradingview](https://www.tradingview.com/wiki/Aroon) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - length (int): The period. Default: ```14``` - scalar (float): Scalar. Default: ```100``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - length = v_pos_default(length, 14) - high = v_series(high, length + 1) - low = v_series(low, length + 1) - - if high is None or low is None: - return - - scalar = v_scalar(scalar, 100) - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import AROON, AROONOSC - aroon_down, aroon_up = AROON(high, low, length) - aroon_osc = AROONOSC(high, low, length) - else: - periods_from_hh = high.rolling(length + 1) \ - .apply(recent_maximum_index,raw=True) - periods_from_ll = low.rolling(length + 1) \ - .apply(recent_minimum_index,raw=True) - - aroon_up = aroon_down = scalar - aroon_up *= 1 - (periods_from_hh / length) - aroon_down *= 1 - (periods_from_ll / length) - aroon_osc = aroon_up - aroon_down - - # Offset - if offset != 0: - aroon_up = aroon_up.shift(offset) - aroon_down = aroon_down.shift(offset) - aroon_osc = aroon_osc.shift(offset) - - # Fill - if "fillna" in kwargs: - aroon_up.fillna(kwargs["fillna"], inplace=True) - aroon_down.fillna(kwargs["fillna"], inplace=True) - aroon_osc.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - aroon_up.name = f"AROONU_{length}" - aroon_down.name = f"AROOND_{length}" - aroon_osc.name = f"AROONOSC_{length}" - - aroon_down.category = aroon_up.category = aroon_osc.category = "trend" - - data = { - aroon_down.name: aroon_down, - aroon_up.name: aroon_up, - aroon_osc.name: aroon_osc - } - df = DataFrame(data, index=high.index) - df.name = f"AROON_{length}" - df.category = aroon_down.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/chop.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/chop.py deleted file mode 100644 index 7db600d5..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/chop.py +++ /dev/null @@ -1,92 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import log, log10 -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import ( - v_bool, - v_drift, - v_offset, - v_pos_default, - v_scalar, - v_series -) -from pandas_ta.volatility import atr - - - -def chop( - high: Series, low: Series, close: Series, - length: Int = None, atr_length: Int = None, - ln: bool = None, scalar: IntFloat = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Choppiness Index - - This indicator, by E.W. Dreiss, attempts to determine choppiness. - - Sources: - * E.W. Dreiss an Australian Commodity Trader - * [motivewave](https://www.motivewave.com/studies/choppiness_index.htm) - * [tradingview](https://www.tradingview.com/scripts/choppinessindex/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - atr_length (int): ATR period. Default: ```1``` - ln (bool): Use ```ln``` instead of ```log10```. Default: ```False``` - scalar (float): Scalar. Default: ```100``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - * Choppy: ```~ 100``` - * Trending: ```~ 0``` - """ - # Validate - length = v_pos_default(length, 14) - high = v_series(high, length + 1) - low = v_series(low, length + 1) - close = v_series(close, length + 1) - - if high is None or low is None or close is None: - return - - atr_length = v_pos_default(atr_length, 1) - scalar = v_scalar(scalar, 100) - ln = v_bool(ln, False) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - diff = high.rolling(length).max() - low.rolling(length).min() - - atr_ = atr(high=high, low=low, close=close, length=atr_length) - atr_sum = atr_.rolling(length).sum() - - chop = scalar - if ln: - chop *= (log(atr_sum) - log(diff)) / log(length) - else: - chop *= (log10(atr_sum) - log10(diff)) / log10(length) - - # Offset - if offset != 0: - chop = chop.shift(offset) - - # Fill - if "fillna" in kwargs: - chop.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - chop.name = f"CHOP{'ln' if ln else ''}_{length}_{atr_length}_{scalar}" - chop.category = "trend" - - return chop diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/cksp.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/cksp.py deleted file mode 100644 index fb2fa274..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/cksp.py +++ /dev/null @@ -1,101 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import ( - v_mamode, - v_offset, - v_pos_default, - v_series, - v_tradingview -) -from pandas_ta.volatility import atr - - - -def cksp( - high: Series, low: Series, close: Series, - p: Int = None, x: IntFloat = None, q: Int = None, - tvmode: bool = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Chande Kroll Stop - - This indicator, by Tushar Chande and Stanley Kroll, attempts to identify - trends with long and short stops. - - Sources: - * "The New Technical Trader", Wiley 1st ed. ISBN 9780471597803, page 95 - * [multicharts](https://www.multicharts.com/discussion/viewtopic.php?t=48914) - - Parameters: - close (Series): ```close``` Series - p (int): ATR and first stop period; see Note. - Default: ```10``` for both modes - x (float): ATR scalar; see Note. Default: ```1``` or ```3``` - q (int): Second stop period; see Note. Default: ```9``` or ```20``` - tvmode (bool): Trading View mode. Default: ```True``` - mamode (str): See ```help(ta.ma)```. Default: ```None``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Note: Book vs TradingView Defaults - * Book: ```p=10, x=3, q=20, ma="sma"``` - * Trading View: ```p=10, x=1, q=9, ma="rma"``` - """ - # Validate - mode_tv = v_tradingview(tvmode) - p = v_pos_default(p, 10) - # TODO: clean up x and q - x = float(x) if isinstance(x, float) and x > 0 else 1 if tvmode is True else 3 - q = int(q) if isinstance(q, float) and q > 0 else 9 if tvmode is True else 20 - _length = p + q - - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mamode = v_mamode(mamode, "rma") if mode_tv else v_mamode(mamode, "sma") - offset = v_offset(offset) - - # Calculate - atr_ = atr(high=high, low=low, close=close, length=p, mamode=mamode) - if atr_ is None or all(isnan(atr_)): - return - - long_stop_ = high.rolling(p).max() - x * atr_ - long_stop = long_stop_.rolling(q).max() - - short_stop_ = low.rolling(p).min() + x * atr_ - short_stop = short_stop_.rolling(q).min() - - # Offset - if offset != 0: - long_stop = long_stop.shift(offset) - short_stop = short_stop.shift(offset) - - # Fill - if "fillna" in kwargs: - long_stop.fillna(kwargs["fillna"], inplace=True) - short_stop.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{p}_{x}_{q}" - long_stop.name = f"CKSPl{_props}" - short_stop.name = f"CKSPs{_props}" - long_stop.category = short_stop.category = "trend" - - data = {long_stop.name: long_stop, short_stop.name: short_stop} - df = DataFrame(data, index=close.index) - df.name = f"CKSP{_props}" - df.category = long_stop.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/decay.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/decay.py deleted file mode 100644 index 161a3de3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/decay.py +++ /dev/null @@ -1,95 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import float64, zeros_like -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_str - - - -# Exponential Decay - https://tulipindicators.org/edecay -@njit(cache=True) -def nb_exponential_decay(x, n): - m, rate = x.size, 1.0 - (1.0 / n) - - result = zeros_like(x, dtype=float64) - result[0] = x[0] - - for i in range(1, m): - result[i] = max(0, x[i], result[i - 1] * rate) - - return result - - -# Linear Decay - https://tulipindicators.org/decay -@njit(cache=True) -def nb_linear_decay(x, n): - m, rate = x.size, 1.0 / n - - result = zeros_like(x, dtype=float64) - result[0] = x[0] - - for i in range(1, m): - result[i] = max(0, x[i], result[i - 1] - rate) - - return result - - -def decay( - close: Series, length: Int = None, mode: str = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Decay - - This function creates a decay moving forward from prior signals. - - Sources: - * [tulipindicators](https://tulipindicators.org/decay) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```1``` - mode (str): Either ```"linear"``` or ```"exp"``` (exponetional) - Default: ```"linear"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - close = v_series(close, length) - - if close is None: - return - - length = v_pos_default(length, 1) - mode = v_str(mode, "linear") - offset = v_offset(offset) - - # Calculate - _mode, np_close = "L", close.to_numpy() - - if mode in ["exp", "exponential"]: - _mode = "EXP" - result = nb_exponential_decay(np_close, length) - else: # "linear" - result = nb_linear_decay(np_close, length) - - result = Series(result, index=close.index) - - # Offset - if offset != 0: - result = result.shift(offset) - - # Fill - if "fillna" in kwargs: - result.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - result.name = f"{_mode}DECAY_{length}" - result.category = "trend" - - return result diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/decreasing.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/decreasing.py deleted file mode 100644 index 04762e15..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/decreasing.py +++ /dev/null @@ -1,85 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import ( - v_percent, - v_bool, - v_drift, - v_offset, - v_pos_default, - v_series -) - - - -def decreasing( - close: Series, length: Int = None, strict: bool = None, - asint: bool = None, percent: IntFloat = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Decreasing - - This indicator, by Kevin Johnson, attempts to identify decreasing periods. - - Sources: - * Kevin Johnson - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```1``` - strict (bool): Check if continuously increasing. Default: ```False``` - percent (float): Percent, i.e. ```5.0```. Default: ```None``` - asint (bool): Returns as ```Int```. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 1) - close = v_series(close, length) - - if close is None: - return - - strict = v_bool(strict, False) - asint = v_bool(asint, True) - percent = float(percent) if v_percent(percent) else False - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - close_ = (1 - 0.01 * percent) * close if percent else close - if strict: - # Returns value as float64? Have to cast to bool - decreasing = close < close_.shift(drift) - for x in range(3, length + 1): - decreasing &= (close.shift(x - (drift + 1)) < close_.shift(x - drift)) - - decreasing.fillna(0, inplace=True) - decreasing = decreasing.astype(bool) - else: - decreasing = close_.diff(length) < 0 - - if asint: - decreasing = decreasing.astype(int) - - # Offset - if offset != 0: - decreasing = decreasing.shift(offset) - - # Fill - if "fillna" in kwargs: - decreasing.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _percent = f"_{0.01 * percent}" if percent else '' - _props = f"{'S' if strict else ''}DEC{'p' if percent else ''}" - decreasing.name = f"{_props}_{length}{_percent}" - decreasing.category = "trend" - - return decreasing diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/dpo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/dpo.py deleted file mode 100644 index db003497..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/dpo.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import sma -from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series - - - -def dpo( - close: Series, length: Int = None, centered: bool = True, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Detrend Price Oscillator - - This indicator attempts to detrend (remove the trend) and identify cycles. - - Sources: - * [fidelity](https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/dpo) - * [stockcharts](http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci) - * [tradingview](https://www.tradingview.com/scripts/detrendedpriceoscillator/) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - centered (bool): Shift the dpo back by ```int(0.5 * length) + 1```. - Set to ```False``` to remove data leakage. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Danger: Possible Data Leak - Set ```centered=False``` to remove data leakage. See [Issue #60]( https://github.com/twopirllc/pandas-ta/issues/60#). - """ - # Validate - length = v_pos_default(length, 20) - close = v_series(close, length + 1) - - if close is None: - return - - centered = v_bool(centered, True) - offset = v_offset(offset) - - # Calculate - t = int(0.5 * length) + 1 - ma = sma(close, length) - - if centered: - dpo = (close.shift(t) - ma).shift(-t) - else: - dpo = close - ma.shift(t) - - # Offset - if offset != 0: - dpo = dpo.shift(offset) - - # Fill - if "fillna" in kwargs: - dpo.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - dpo.name = f"DPO_{length}" - dpo.category = "trend" - - return dpo diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/ht_trendline.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/ht_trendline.py deleted file mode 100644 index 72c47b74..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/ht_trendline.py +++ /dev/null @@ -1,148 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import arctan, isnan, nan, rad2deg, zeros_like -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib - - - -@njit(cache=True) -def nb_ht_trendline(x): - a, b, m = 0.0962, 0.5769, x.size - - wma4, dt = zeros_like(x), zeros_like(x) - q1, q2 = zeros_like(x), zeros_like(x) - ji, jq = zeros_like(x), zeros_like(x) - i1, i2 = zeros_like(x), zeros_like(x) - re, im = zeros_like(x), zeros_like(x) - period, smp = zeros_like(x), zeros_like(x) - i_trend = zeros_like(x) - - result = zeros_like(x) - result[:13] = x[:13] - - # Ehlers's starts from 6, TALib from 63 - for i in range(6, m): - adj_prev_period = 0.075 * period[i - 1] + 0.54 - - wma4[i] = 0.4 * x[i] + 0.3 * x[i - 1] + 0.2 * x[i - 2] + 0.1 * x[i - 3] - dt[i] = adj_prev_period * (a * wma4[i] + b * wma4[i - 2] - b * wma4[i - 4] - a * wma4[i - 6]) - - q1[i] = adj_prev_period * (a * dt[i] + b * dt[i - 2] - b * dt[i - 4] - a * dt[i - 6]) - i1[i] = dt[i - 3] - - ji[i] = adj_prev_period * (a * i1[i] + b * i1[i - 2] - b * i1[i - 4] - a * i1[i - 6]) - jq[i] = adj_prev_period * (a * q1[i] + b * q1[i - 2] - b * q1[i - 4] - a * q1[i - 6]) - - i2[i] = i1[i] - jq[i] - q2[i] = q1[i] + ji[i] - - i2[i] = 0.2 * i2[i] + 0.8 * i2[i - 1] - q2[i] = 0.2 * q2[i] + 0.8 * q2[i - 1] - - re[i] = i2[i] * i2[i - 1] + q2[i] * q2[i - 1] - im[i] = i2[i] * q2[i - 1] - q2[i] * i2[i - 1] - - re[i] = 0.2 * re[i] + 0.8 * re[i - 1] - im[i] = 0.2 * im[i] + 0.8 * im[i - 1] - - if re[i] != 0 and im[i] != 0: - period[i] = 360.0 / rad2deg(arctan(im[i] / re[i])) - if period[i] > 1.5 * period[i - 1]: - period[i] = 1.5 * period[i - 1] - if period[i] < 0.67 * period[i - 1]: - period[i] = 0.67 * period[i - 1] - if period[i] < 6.0: - period[i] = 6.0 - if period[i] > 50.0: - period[i] = 50.0 - period[i] = 0.2 * period[i] + 0.8 * period[i - 1] - smp[i] = 0.33 * period[i] + 0.67 * smp[i - 1] - - dc_period = int(smp[i] + 0.5) - dcp_avg = 0 - for k in range(dc_period): - dcp_avg += x[i - k] - - if dc_period > 0: - dcp_avg /= dc_period - - i_trend[i] = dcp_avg - - if i > 12: - result[i] = 0.4 * i_trend[i] + 0.3 * i_trend[i - 1] + 0.2 * i_trend[i - 2] + 0.1 * i_trend[i - 3] - - return result - - -def ht_trendline( - close: Series, talib: bool = None, - prenan: Int = None, offset: Int = None, - **kwargs: DictLike -) -> Series: - """Hilbert Transform TrendLine - - This indicator uses the Hilbert Transform to smooth values. - - Sources: - * John F Ehlers's "Rocket Science for Traders" Book - * [mql5](https://c.mql5.com/forextsd/forum/59/023inst.pdf) - * TA-Lib [ta_HT_TRENDLINE](https://github.com/TA-Lib/ta-lib/blob/main/src/ta_func/ta_HT_TRENDLINE.c) - - Parameters: - close (Series): ```close``` Series. - talib (bool): If installed, use TA Lib. Default: ```True``` - prenan (int): Prenans to apply. Ehlers's ```6``` or ```12```, - TALib ```63``` Default: ```63``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9979308363057683)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - prenan = v_pos_default(prenan, 63) - close = v_series(close, prenan) - - if close is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - if Imports["talib"] and mode_tal: - from talib import HT_TRENDLINE - tl = HT_TRENDLINE(close) - else: - np_close = close.to_numpy() - np_tl = nb_ht_trendline(np_close) - - if prenan > 0: - np_tl[:prenan] = nan - tl = Series(np_tl, index=close.index) - - if all(isnan(tl)): - return # Emergency Break - - # Offset - if offset != 0: - trend_line = tl.shift(offset) - - # Fill - if "fillna" in kwargs: - tl.fillna(kwargs["fillna"], inplace=True) - - tl.name = f"HT_TL" - tl.category = "trend" - - return tl diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/increasing.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/increasing.py deleted file mode 100644 index 6d06b19d..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/increasing.py +++ /dev/null @@ -1,85 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import ( - v_percent, - v_bool, - v_drift, - v_offset, - v_pos_default, - v_series -) - - - -def increasing( - close: Series, length: Int = None, strict: bool = None, - asint: bool = None, percent: IntFloat = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Increasing - - This indicator, by Kevin Johnson, attempts to identify increasing periods. - - Sources: - * Kevin Johnson - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```1``` - strict (bool): Check if continuously increasing. Default: ```False``` - percent (float): Percent, i.e. ```5.0```. Default: ```None``` - asint (bool): Returns as ```Int```. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 1) - close = v_series(close, length) - - if close is None: - return - - strict = v_bool(strict, False) - asint = v_bool(asint, True) - percent = float(percent) if v_percent(percent) else False - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - close_ = (1 + 0.01 * percent) * close if percent else close - if strict: - # Returns value as float64? Have to cast to bool - increasing = close > close_.shift(drift) - for x in range(3, length + 1): - increasing &= (close.shift(x - (drift + 1)) > close_.shift(x - drift)) - - increasing.fillna(0, inplace=True) - increasing = increasing.astype(bool) - else: - increasing = close_.diff(length) > 0 - - if asint: - increasing = increasing.astype(int) - - # Offset - if offset != 0: - increasing = increasing.shift(offset) - - # Fill - if "fillna" in kwargs: - increasing.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _percent = f"_{0.01 * percent}" if percent else '' - _props = f"{'S' if strict else ''}INC{'p' if percent else ''}" - increasing.name = f"{_props}_{length}{_percent}" - increasing.category = "trend" - - return increasing diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/long_run.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/long_run.py deleted file mode 100644 index 24a51d20..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/long_run.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series -from .decreasing import decreasing -from .increasing import increasing - - - -def long_run( - fast: Series, slow: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Long Run - - This indicator, by Kevin Johnson, attempts to identify long runs. - - Sources: - * Kevin Johnson - * [tradingview](https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/) - - Parameters: - fast (Series): ```fast``` Series. - slow (Series): ```slow``` Series. - length (int): The ```decreasing``` and ```increasing``` period. - Default: ```2``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 2) - fast = v_series(fast, length) - slow = v_series(slow, length) - - if fast is None or slow is None: - return - - offset = v_offset(offset) - - # Calculate - inc = increasing(fast, length) - - # potential bottom or bottom - pb = inc & decreasing(slow, length) - # fast and slow are increasing - bi = inc & increasing(slow, length) - long_run = pb | bi - - # Offset - if offset != 0: - long_run = long_run.shift(offset) - - # Fill - if "fillna" in kwargs: - long_run.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - long_run.name = f"LR_{length}" - long_run.category = "trend" - - return long_run diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/psar.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/psar.py deleted file mode 100644 index a4b4bcaf..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/psar.py +++ /dev/null @@ -1,154 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import full, nan, zeros -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_pos_default, v_series, zero - - - -def psar( - high: Series, low: Series, close: Series = None, - af0: IntFloat = None, af: IntFloat = None, max_af: IntFloat = None, tv=False, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Parabolic Stop and Reverse - - This indicator, by J. Wells Wilder, attempts to identify trend direction - and potential reversals. - - Sources: - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=66&Name=Parabolic) - * [tradingview](https://www.tradingview.com/pine-script-reference/#fun_sar) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): Optional ```close``` Series - af0 (float): Initial Acceleration Factor. Default: ```0.02``` - af (float): Acceleration Factor. Default: ```0.02``` - max_af (float): Maximum Acceleration Factor. Default: ```0.2``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 4 columns - - Warning: - TA-Lib Correlation: ```np.float64(0.9837617513753181)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - _length = 1 - high = v_series(high, _length) - low = v_series(low, _length) - - if high is None or low is None: - return - - orig_high = high.copy() - orig_low = low.copy() - # Numpy arrays offer some performance improvements - high, low = high.to_numpy(), low.to_numpy() - - paf = v_pos_default(af, 0.02) # paf is used to keep af from parameters - af0 = v_pos_default(af0, paf) - af = af0 - - max_af = v_pos_default(max_af, 0.2) - offset = v_offset(offset) - - # Set up - m = high.size - sar = zeros(m) - long = full(m, nan) - short = full(m, nan) - reversal = zeros(m, dtype=int) - _af = zeros(m) - _af[:2] = af0 - falling = _falling(orig_high.iloc[:2], orig_low.iloc[:2]) - ep = low[0] if falling else high[0] - if close is not None: - close = v_series(close) - sar[0] = close.iloc[0] - else: - sar[0] = high[0] if falling else low[0] - - # Calculate - for i in range(1, m): - sar[i] = sar[i - 1] + af * (ep - sar[i - 1]) - - if falling: - reverse = high[i] > sar[i] - if low[i] < ep: - ep = low[i] - af = min(af + af0, max_af) - sar[i] = max(high[i - 1], sar[i]) - else: - reverse = low[i] < sar[i] - if high[i] > ep: - ep = high[i] - af = min(af + af0, max_af) - sar[i] = min(low[i - 1], sar[i]) - - if reverse: - sar[i] = ep - af = af0 - falling = not falling - ep = low[i] if falling else high[i] - - # Separate long/short SAR based on falling - if falling: - short[i] = sar[i] - else: - long[i] = sar[i] - - _af[i] = af - reversal[i] = int(reverse) - - _af = Series(_af, index=orig_high.index) - long = Series(long, index=orig_high.index) - short = Series(short, index=orig_high.index) - reversal = Series(reversal, index=orig_high.index) - - # Offset - if offset != 0: - _af = _af.shift(offset) - long = long.shift(offset) - short = short.shift(offset) - reversal = reversal.shift(offset) - - # Fill - if "fillna" in kwargs: - _af.fillna(kwargs["fillna"], inplace=True) - long.fillna(kwargs["fillna"], inplace=True) - short.fillna(kwargs["fillna"], inplace=True) - reversal.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _name = f"PSAR" - _props = f"_{af0}_{max_af}" - - data = { - f"{_name}l{_props}": long, - f"{_name}s{_props}": short, - f"{_name}af{_props}": _af, - f"{_name}r{_props}": reversal - } - df = DataFrame(data, index=orig_high.index) - df.name = f"{_name}{_props}" - df.category = long.category = short.category = "trend" - - return df - - -def _falling(high, low, drift: int = 1): - """Returns the last -DM value""" - # Not to be confused with ta.falling() - up = high - high.shift(drift) - dn = low.shift(drift) - low - _dmn = (((dn > up) & (dn > 0)) * dn).apply(zero).iloc[-1] - return _dmn > 0 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/qstick.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/qstick.py deleted file mode 100644 index 7fd14657..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/qstick.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - non_zero_range, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - - -def qstick( - open_: Series, close: Series, length: Int = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Q Stick - - This indicator, by Tushar Chande, attempts to quantify and identify - trends. - - Sources: - * [tradingtechnologies](https://library.tradingtechnologies.com/trade/chrt-ti-qstick.html) - - Parameters: - open_ (Series): ```open``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - open_ = v_series(open_, length) - close = v_series(close, length) - - if open_ is None or close is None: - return - - mamode = v_mamode(mamode, "sma") - offset = v_offset(offset) - - # Calculate - diff = non_zero_range(close, open_) - qstick = ma(mamode, diff, length=length, **kwargs) - - # Offset - if offset != 0: - qstick = qstick.shift(offset) - - # Fill - if "fillna" in kwargs: - qstick.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - qstick.name = f"QS_{length}" - qstick.category = "trend" - - return qstick diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/rwi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/rwi.py deleted file mode 100644 index ed77f181..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/rwi.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.volatility import atr -from pandas_ta.utils import ( - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def rwi( - high: Series, low: Series, close: Series, - length: Int = None, mamode: str = None, talib: bool = None, - drift: Int = None, offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Random Walk Index - - This indicator attempts to identify the difference between a trend and - a random walk. - - Sources: - * [technicalindicators](https://www.technicalindicators.net/indicators-technical-analysis/168-rwi-random-walk-index) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - mamode (str): See ```help(ta.ma)```. Default: ```"rma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - length = v_pos_default(length, 14) - _length = length + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mamode = v_mamode(mamode, "rma") - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - atr_ = atr( - high=high, low=low, close=close, - length=length, mamode=mamode, talib=mode_tal - ) - if all(isnan(atr_)): - return # Emergency Break - - denom = atr_ * (length ** 0.5) - rwi_high = (high - low.shift(length)) / denom - rwi_low = (high.shift(length) - low) / denom - - # Offset - if offset != 0: - rwi_high = rwi_high.shift(offset) - rwi_low = rwi_low.shift(offset) - - # Fill - if "fillna" in kwargs: - rwi_high.fillna(kwargs["fillna"], inplace=True) - rwi_low.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - rwi_high.name = f"RWIh_{length}" - rwi_low.name = f"RWIl_{length}" - rwi_high.category = rwi_low.category = "trend" - - # Prepare DataFrame to return - data = {rwi_high.name: rwi_high, rwi_low.name: rwi_low} - df = DataFrame(data, index=close.index) - df.name = f"RWI_{length}" - df.category = "trend" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/short_run.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/short_run.py deleted file mode 100644 index 3aa86866..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/short_run.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series -from .decreasing import decreasing -from .increasing import increasing - - - -def short_run( - fast: Series, slow: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Short Run - - This indicator, by Kevin Johnson, attempts to identify short runs. - - Sources: - * Kevin Johnson - * [tradingview](https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/) - - Parameters: - fast (Series): ```fast``` Series. - slow (Series): ```slow``` Series. - length (int): The ```decreasing``` and ```increasing``` period. - Default: ```2``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 2) - fast = v_series(fast, length) - slow = v_series(slow, length) - - if fast is None or slow is None: - return - - offset = v_offset(offset) - - # Calculate - dec = decreasing(fast, length) - - # potential top or top - pt = dec & increasing(slow, length) - # fast and slow are decreasing - bd = dec & decreasing(slow, length) - short_run = pt | bd - - # Offset - if offset != 0: - short_run = short_run.shift(offset) - - # Fill - if "fillna" in kwargs: - short_run.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - short_run.name = f"SR_{length}" - short_run.category = "trend" - - return short_run diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/trendflex.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/trendflex.py deleted file mode 100644 index e90a11de..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/trendflex.py +++ /dev/null @@ -1,111 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import cos, exp, nan, sqrt, zeros_like -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -# Ehlers's Trendflex -# http://traders.com/Documentation/FEEDbk_docs/2020/02/TradersTips.html -@njit(cache=True) -def nb_trendflex(x, n, k, alpha, pi, sqrt2): - m, ratio = x.size, 2 * sqrt2 / k - a = exp(-pi * ratio) - b = 2 * a * cos(180 * ratio) - c = a * a - b + 1 - - _f = zeros_like(x) - _ms = zeros_like(x) - result = zeros_like(x) - - for i in range(2, m): - _f[i] = 0.5 * c * (x[i] + x[i - 1]) + b * _f[i - 1] - a * a * _f[i - 2] - - for i in range(n, m): - _sum = 0 - for j in range(1, n): - _sum += _f[i] - _f[i - j] - _sum /= n - - _ms[i] = alpha * _sum * _sum + (1 - alpha) * _ms[i - 1] - if _ms[i] != 0.0: - result[i] = _sum / sqrt(_ms[i]) - - return result - - -def trendflex( - close: Series, length: Int = None, - smooth: Int = None, alpha: IntFloat = None, - pi: IntFloat = None, sqrt2: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Trendflex - - This trend indicator, by John F. Ehlers, complements the "reflex" - indicator. - - Sources: - * [rengel8](https://github.com/rengel8) (2021-08-11) based on the - implementation from "ProRealCode" (2021-08-11) - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/reflex-and-trendflex-indicators-john-f-ehlers/) - * [traders](http://traders.com/Documentation/FEEDbk_docs/2020/02/TradersTips.html) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - smooth (int): Super Smoother period. Default: ```20```` - alpha (float): Alpha weight. Default: ```0.04``` - pi (float): Ehlers's truncated value: ```3.14159```. - Default: ```3.14159``` - sqrt2 (float): Ehlers's truncated value: ```1.414```. - Default: ```1.414``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - John F. Ehlers introduced two indicators within the article - "Reflex: A New Zero-Lag Indicator” in February 2020, TASC magazine. - One of which is Reflex, a lag reduced cycle indicator. Both indicators - (Reflex/Trendflex) are oscillators that complement each other with the - focus for cycle and trend. - """ - # Validate - length = v_pos_default(length, 20) - smooth = v_pos_default(smooth, 20) - close = v_series(close, max(length, smooth) + 1) - - if close is None: - return - - alpha = v_pos_default(alpha, 0.04) - pi = v_pos_default(pi, 3.14159) - sqrt2 = v_pos_default(sqrt2, 1.414) - offset = v_offset(offset) - - # Calculate - np_close = close.to_numpy() - result = nb_trendflex(np_close, length, smooth, alpha, pi, sqrt2) - result[:length] = nan - result = Series(result, index=close.index) - - # Offset - if offset != 0: - result = result.shift(offset) - - # Fill - if "fillna" in kwargs: - result.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - result.name = f"TRENDFLEX_{length}_{smooth}_{alpha}" - result.category = "trend" - - return result diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/ttm_trend.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/ttm_trend.py deleted file mode 100644 index f3578c84..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/ttm_trend.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import hl2 -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def ttm_trend( - high: Series, low: Series, close: Series, - length: Int = None, offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """TTM Trend - - This indicator, by John Carter, labels bars green, ```1```, or - red ```-1```, when above or below the average value. - - Sources: - * John Carter, book “Mastering the Trade” - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/ttm-trend-price/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```6``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 1 column - - Tip: - * Two bars of the opposite color is the signal to get in or out. - * Recommended to stay in trade if colors do not change. - """ - # Validate - length = v_pos_default(length, 6) - high = v_series(high, length) - low = v_series(low, length) - close = v_series(close, length) - - if high is None or low is None or close is None: - return - - offset = v_offset(offset) - - # Calculate - trend_avg = hl2(high, low) - for i in range(1, length): - trend_avg = trend_avg + hl2(high.shift(i), low.shift(i)) - - trend_avg = trend_avg / length - - tm_trend = (close > trend_avg).astype(int) - tm_trend.replace(0, -1, inplace=True) - - # Offset - if offset != 0: - tm_trend = tm_trend.shift(offset) - - # Fill - if "fillna" in kwargs: - tm_trend.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - tm_trend.name = f"TTM_TRND_{length}" - tm_trend.category = "momentum" - - df = DataFrame({tm_trend.name: tm_trend}, index=close.index) - df.name = f"TTMTREND_{length}" - df.category = tm_trend.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/vhf.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/vhf.py deleted file mode 100644 index 7c7814e2..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/vhf.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import inf, fabs, nan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import ( - non_zero_range, - v_drift, - v_offset, - v_pos_default, - v_series -) - - - -def vhf( - close: Series, length: Int = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Vertical Horizontal Filter - - This indicator, by Adam White, attempts to identify trending and - ranging markets. - - Sources: - * [incrediblecharts](https://www.incrediblecharts.com/indicators/vertical_horizontal_filter.php) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```28``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 28) - close = v_series(close, length) - - if close is None: - return - - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - hcp = close.rolling(length).max() - lcp = close.rolling(length).min() - diff = fabs(close.diff(drift)) - vhf = fabs(non_zero_range(hcp, lcp)) / diff.rolling(length).sum() - vhf.replace([inf, -inf], nan, inplace=True) - # np_vhf = where(np_vhf == inf, nan, np_vhf) - - # Offset - if offset != 0: - vhf = vhf.shift(offset) - - # Fill - if "fillna" in kwargs: - vhf.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - vhf.name = f"VHF_{length}" - vhf.category = "trend" - - return vhf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/vortex.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/vortex.py deleted file mode 100644 index ba60ed87..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/vortex.py +++ /dev/null @@ -1,83 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series -from pandas_ta.volatility import true_range - - - -def vortex( - high: Series, low: Series, close: Series, - length: Int = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Vortex - - This indicator attempts to capture positive and negative trend movement - using two oscillators. - - Sources: - * [stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - length = v_pos_default(length, 14) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - _length = max(length, min_periods) - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - tr = true_range(high=high, low=low, close=close) - tr_sum = tr.rolling(length, min_periods=min_periods).sum() - - vmp = (high - low.shift(drift)).abs() - vmm = (low - high.shift(drift)).abs() - - vip = vmp.rolling(length, min_periods=min_periods).sum() / tr_sum - vim = vmm.rolling(length, min_periods=min_periods).sum() / tr_sum - - # Offset - if offset != 0: - vip = vip.shift(offset) - vim = vim.shift(offset) - - # Fill - if "fillna" in kwargs: - vip.fillna(kwargs["fillna"], inplace=True) - vim.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - vip.name = f"VTXP_{length}" - vim.name = f"VTXM_{length}" - vip.category = vim.category = "trend" - - data = {vip.name: vip, vim.name: vim} - df = DataFrame(data, index=close.index) - df.name = f"VTX_{length}" - df.category = "trend" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/zigzag.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/zigzag.py deleted file mode 100644 index 2bcd4103..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/trend/zigzag.py +++ /dev/null @@ -1,335 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import floor, nan, zeros, zeros_like, roll -from pandas import Series, DataFrame -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import ( - v_bool, - v_offset, - v_pos_default, - v_series, -) - - -# Find high and low pivots using a centered rolling window. -@njit(cache=True) -def nb_rolling_hl(np_high, np_low, window_size): - idx = zeros_like(np_high) - swing = zeros_like(np_high) # where a high = 1 and low = -1 - value = zeros_like(np_high) - - extremes = 0 - left = int(floor(window_size / 2)) - right = left + 1 - # sample_array = [*[left-window], *[center], *[right-window]] - - m = np_high.size - for i in range(left, m - right): - low_center = np_low[i] - high_center = np_high[i] - low_window = np_low[i - left: i + right] - high_window = np_high[i - left: i + right] - - if (low_center <= low_window).all(): - idx[extremes] = i - swing[extremes] = -1 - value[extremes] = low_center - extremes += 1 - - if (high_center >= high_window).all(): - idx[extremes] = i - swing[extremes] = 1 - value[extremes] = high_center - extremes += 1 - - return idx[:extremes], swing[:extremes], value[:extremes] - - -# Calculate zigzag points using pre-calculated unfiltered pivots. -@njit(cache=True) -def nb_zz_backtest(idx, swing, value, deviation): - zz_idx = zeros_like(idx) - zz_swing = zeros_like(swing) - zz_value = zeros_like(value) - zz_dev = zeros_like(idx) - - zigzags = 0 - changes = 0 - zz_idx[zigzags] = idx[0] - zz_swing[zigzags] = swing[0] - zz_value[zigzags] = value[0] - zz_dev[zigzags] = 0 - - # print(f'Starting S: {zz_swing[0]}') - - m = idx.size - for i in range(1, m): - last_zz_value = zz_value[zigzags] - current_dev = (value[i] - last_zz_value) / last_zz_value - - # print(f'{i} | P {swing[i]:.0f} : {idx[i]:.0f} , {value[i]}') - # print(f'{len(str(i))*" "} | Last: {zz_swing[zigzags-changes]:.0f} , Dev: %{(current_dev*100):.1f}') - - # Last point in zigzag is bottom - if zz_swing[zigzags-changes] == -1: - if swing[i] == -1: - # If the current pivot is lower than the last ZZ bottom: - # create a new point and log it as a change - if value[i] < zz_value[zigzags]: - if zz_idx[zigzags - changes] == idx[i]: - continue - # print(f'{len(str(i))*" "} | Change -1 : {zz_value[zigzags]} to {value[i]}') - zigzags += 1 - changes += 1 - zz_idx[zigzags] = idx[i] - zz_swing[zigzags] = swing[i] - zz_value[zigzags] = value[i] - zz_dev[zigzags] = 100 * current_dev - else: - # If the deviation between pivot and the last ZZ bottom is - # great enough create new ZZ point. - if current_dev > 0.01 * deviation: - if zz_idx[zigzags - changes] == idx[i]: - continue - # print(f'{len(str(i))*" "} | new ZZ 1 {value[i]}') - zigzags += 1 - zz_idx[zigzags] = idx[i] - zz_swing[zigzags] = swing[i] - zz_value[zigzags] = value[i] - zz_dev[zigzags] = 100 * current_dev - changes = 0 - - # last point in zigzag is top - else: - if swing[i] == 1: - # If the current pivot is higher than the last ZZ top: - # create a new point and log it as a change - if value[i] > zz_value[zigzags]: - if zz_idx[zigzags - changes] == idx[i]: - continue - # print(f'{len(str(i))*" "} | Change 1 : {zz_value[zigzags]} to {value[i]}') - zigzags += 1 - changes += 1 - zz_idx[zigzags] = idx[i] - zz_swing[zigzags] = swing[i] - zz_value[zigzags] = value[i] - zz_dev[zigzags] = 100 * current_dev - else: - # If the deviation between pivot and the last ZZ top is great - # enough create new ZZ point. - if current_dev < -0.01 * deviation: - if zz_idx[zigzags - changes] == idx[i]: - continue - # print(f'{len(str(i))*" "} | new ZZ -1 {value[i]}') - zigzags += 1 - zz_idx[zigzags] = idx[i] - zz_swing[zigzags] = swing[i] - zz_value[zigzags] = value[i] - zz_dev[zigzags] = 100 * current_dev - changes = 0 - - _n = zigzags + 1 - return zz_idx[:_n], zz_swing[:_n], zz_value[:_n], zz_dev[:_n] - - -# Calculate zigzag points using pre-calculated unfiltered pivots. -@njit(cache=True) -def nb_find_zz(idx, swing, value, deviation): - zz_idx = zeros_like(idx) - zz_swing = zeros_like(swing) - zz_value = zeros_like(value) - zz_dev = zeros_like(idx) - - zigzags = 0 - zz_idx[zigzags] = idx[-1] - zz_swing[zigzags] = swing[-1] - zz_value[zigzags] = value[-1] - zz_dev[zigzags] = 0 - - m = idx.size - for i in range(m - 2, -1, -1): - # Next point in zigzag is bottom - if zz_swing[zigzags] == -1: - if swing[i] == -1: - # If the current pivot is lower than the next ZZ bottom in - # time, move it to the pivot. As this lower value invalidates - # the other one - if value[i] < zz_value[zigzags] and zigzags > 1: - current_dev = (zz_value[zigzags - 1] - value[i]) / value[i] - zz_idx[zigzags] = idx[i] - zz_swing[zigzags] = swing[i] - zz_value[zigzags] = value[i] - zz_dev[zigzags - 1] = 100 * current_dev - else: - # If the deviation between pivot and the next ZZ bottom is - # great enough create new ZZ point. - current_dev = (value[i] - zz_value[zigzags]) / value[i] - if current_dev > 0.01 * deviation: - if zz_idx[zigzags] == idx[i]: - continue - zigzags += 1 - zz_idx[zigzags] = idx[i] - zz_swing[zigzags] = swing[i] - zz_value[zigzags] = value[i] - zz_dev[zigzags - 1] = 100 * current_dev - - # Next point in zigzag is top - else: - if swing[i] == 1: - # If the current pivot is greater than the next ZZ top in time, - # move it to the pivot. - # As this higher value invalidates the other one - if value[i] > zz_value[zigzags] and zigzags > 1: - current_dev = (value[i] - zz_value[zigzags - 1]) / value[i] - zz_idx[zigzags] = idx[i] - zz_swing[zigzags] = swing[i] - zz_value[zigzags] = value[i] - zz_dev[zigzags - 1] = 100 * current_dev - else: - # If the deviation between pivot and the next ZZ top is great - # enough create new ZZ point. - current_dev = (zz_value[zigzags] - value[i]) / value[i] - if current_dev > 0.01 * deviation: - if zz_idx[zigzags] == idx[i]: - continue - zigzags += 1 - zz_idx[zigzags] = idx[i] - zz_swing[zigzags] = swing[i] - zz_value[zigzags] = value[i] - zz_dev[zigzags - 1] = 100 * current_dev - - _n = zigzags + 1 - return zz_idx[:_n], zz_swing[:_n], zz_value[:_n], zz_dev[:_n] - - - -# Maps nb_find_zz results back onto the original data indices. -@njit(cache=True) -def nb_map_zz(idx, swing, value, deviation, n): - swing_map = zeros(n) - value_map = zeros(n) - dev_map = zeros(n) - - for j, i in enumerate(idx): - i = int(i) - swing_map[i] = swing[j] - value_map[i] = value[j] - dev_map[i] = deviation[j] - - for i in range(n): - if swing_map[i] == 0: - swing_map[i] = nan - value_map[i] = nan - dev_map[i] = nan - - return swing_map, value_map, dev_map - - - -def zigzag( - high: Series, low: Series, close: Series = None, - legs: int = None, deviation: IntFloat = None, backtest: bool = None, - offset: Int = None, **kwargs: DictLike -): - """Zigzag - - This indicator attempts to filter out smaller movements while identifying - trend direction. It does not predict future trends, but it does identify - swing highs and lows. - - Sources: - * [stockcharts](https://school.stockcharts.com/doku.php?id=technical_indicators:zigzag) - * [tradingview](https://www.tradingview.com/support/solutions/43000591664-zig-zag/#:~:text=Definition,trader%20visual%20the%20price%20action.) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series. Default: ```None``` - legs (int): Number of legs (> 2). Default: ```10``` - deviation (float): Reversal deviation percentage. Default: ```5``` - backtest (bool): Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Note: Deviation - When ```deviation=10```, it shows movements greater than ```10%```. - - Note: Backtest Mode - Ensures the DataFrame is safe for backtesting. By default, swing - points are returned on the pivot index. Intermediate swings are - not returned at all. This mode swing detection is placed on the bar - that would have been detected. Furthermore, changes in swing levels - are also included instead of only the final value. - - * Use the following formula to get the true index of a pivot: - ```p_i = i - int(floor(legs / 2))``` - - Warning: - A Series reversal will create a new line. - """ - # Validate - legs = v_pos_default(legs, 10) - _length = legs + 1 - high = v_series(high, _length) - low = v_series(low, _length) - - if high is None or low is None: - return - - if close is not None: - close = v_series(close,_length) - np_close = close.values - if close is None: - return - - deviation = v_pos_default(deviation, 5.0) - offset = v_offset(offset) - backtest = v_bool(backtest, False) - - if backtest: - offset+=int(floor(legs/2)) - - # Calculation - np_high, np_low = high.to_numpy(), low.to_numpy() - hli, hls, hlv = nb_rolling_hl(np_high, np_low, legs) - - if backtest: - zzi, zzs, zzv, zzd = nb_zz_backtest(hli, hls, hlv, deviation) - else: - zzi, zzs, zzv, zzd = nb_find_zz(hli, hls, hlv, deviation) - - swing, value, dev = nb_map_zz(zzi, zzs, zzv, zzd, np_high.size) - - # Offset - if offset != 0: - swing = roll(swing, offset) - value = roll(value, offset) - dev = roll(dev, offset) - - swing[:offset] = nan - value[:offset] = nan - dev[:offset] = nan - - # Fill - if "fillna" in kwargs: - swing.fillna(kwargs["fillna"], inplace=True) - value.fillna(kwargs["fillna"], inplace=True) - dev.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{deviation}%_{legs}" - data = { - f"ZIGZAGs{_props}": swing, - f"ZIGZAGv{_props}": value, - f"ZIGZAGd{_props}": dev, - } - df = DataFrame(data, index=high.index) - df.name = f"ZIGZAG{_props}" - df.category = "trend" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/__init__.py deleted file mode 100644 index a27ff5ce..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/__init__.py +++ /dev/null @@ -1,28 +0,0 @@ -# -*- coding: utf-8 -*- -from ._candles import * -from ._core import * -from ._math import * -from ._numba import * -from ._signals import * -from ._study import * -from ._time import * -from ._validate import * -from ._candles import __all__ as _candles_all -from ._core import __all__ as _core_all -from ._math import __all__ as _math_all -from ._numba import __all__ as _numba_all -from ._signals import __all__ as _signals_all -from ._study import __all__ as _study_all -from ._time import __all__ as _time_all -from ._validate import __all__ as _validate_all - -__all__ = ( - _candles_all - + _core_all - + _math_all - + _numba_all - + _signals_all - + _study_all - + _time_all - + _validate_all -) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_candles.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_candles.py deleted file mode 100644 index da0537d5..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_candles.py +++ /dev/null @@ -1,54 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta.utils._core import non_zero_range - -__all__ = ["candle_color", "high_low_range", "real_body"] - - - -def candle_color(open_: Series, close: Series) -> Series: - """Candle Change - - If ```close >= open_```, returns ```1```. Otherwise ```-1```. - - Parameters: - open_ (Series): ```open``` Series - close (Series): ```close``` Series - - Returns: - (Series): 1 column - """ - color = close.copy().astype(int) - color[close >= open_] = 1 - color[close < open_] = -1 - return color - - -def high_low_range(high: Series, low: Series) -> Series: - """High Low Range - - Returns a non-zero ```high - low```. - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - - Returns: - (.Series): 1 column - """ - return non_zero_range(high, low) - - -def real_body(open_: Series, close: Series) -> Series: - """Body Range - - Returns a non-zero ```close - open_```. - - Parameters: - open_ (Series): ```open``` Series - close (Series): ```close``` Series - - Returns: - (Series): 1 column - """ - return non_zero_range(close, open_) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_core.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_core.py deleted file mode 100644 index e8ed0ece..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_core.py +++ /dev/null @@ -1,454 +0,0 @@ -# -*- coding: utf-8 -*- -import re as re_ -from contextlib import redirect_stdout -from io import StringIO -from sys import float_info as sflt -from webbrowser import open as webbrowser_open - -from numpy import argmax, argmin -from pandas import DataFrame, Series - -from pandas_ta._typing import ( - Array, - Int, - IntFloat, - ListStr, - TextIO, - Tuple -) -from pandas_ta.utils._validate import ( - v_array, - v_bool, - v_dataframe, - v_pos_default, - v_series, - v_str -) -from pandas_ta.utils._study import Study -from pandas_ta.maps import Category, Imports - -__all__ = [ - "camelCase2Title", - "category_files", - "help", - "ms2secs", - "non_zero_range", - "recent_maximum_index", - "recent_minimum_index", - "pd_rma", - "signed_series", - "simplify_columns", - "speed_test", - "tal_ma", - "unsigned_differences", -] - - - -def camelCase2Title(x: str) -> str | None: - """camelCase2Title - - Converts Camel Case to Title - - Sources: - * [stackoverflow](https://stackoverflow.com/questions/5020906/python-convert-camel-case-to-space-delimited-using-regex-and-taking-acronyms-in) - - Parameters: - x (str): input - - Returns: - (str | None): Title Case string or None - """ - if isinstance(x, str) and len(x): - return re_.sub("([a-z])([A-Z])", r"\g<1> \g<2>", x).title() - return None - - - -def category_files(category: str) -> list: - """Category Files - - Return all filenames in the category directory. - - Parameters: - category (str): String name of a Indicator Category - - Returns: - (list): List of filenames of Category - """ - files = [ - x.stem - for x in list(Path(f"pandas_ta/{category}/").glob("*.py")) - if x.stem != "__init__" - ] - return files - - - -def help(query: str = "") -> None | TextIO: - """Pandas TA Help - - Prints available query words or opens a web browser to relevant - documentation. - - Parameters: - query (str): Query string. Default: ```""``` - - Returns: - (TextIO | None): Available query keywords or opens web browser to - online documentation. - """ - s = v_str(query, "") - - _categories = list(Category.keys()) - _dataframes = ["pandas", "dataframe", "extension"] - _events = ["events", "signals"] - _features = ["bugs", "features", "contribute", "contributing"] - _help = ["help", "support"] - _how2 = ["how2", "how to", "usage"] - _mp = ["custom", "multiprocessing"] - _studies = ["study", "studies"] - KEYWORDS = _dataframes + _events + _features + _help \ - + _how2 + _studies + _categories + _mp - - www = "https://www.pandas-ta.dev" - _url = "" - - if s == "": - out = f'\nSearch words:\n\t{", ".join(sorted(KEYWORDS))}\n' - out += '\nExample: df.ta.help("usage")' - print(out) - elif s in _categories: - _url = f"{www}/api/{s.lower()}" - elif s in _dataframes: - _url = f"{www}/api/ta-extension" - elif s in _events: - _url = f"{www}/api/events" - elif s in _features: - _url = f"{www}/support/bugs-and-features" - elif s in _help: - _url = f"{www}/support" - elif s in _how2: - _url = f"{www}/support/how-to" - elif s in _mp: - _url = f"{www}/getting-started/usage" - elif s in _studies: - _url = f"{www}/api/studies" - else: - _url = f"{www}" - webbrowser_open(_url, new=1) - - - -def ms2secs(ms: IntFloat, p: Int) -> IntFloat: - """ms2secs - - Convert ms to secs - - Parameters: - ms (IntFloat): ms - p (Int): Rounding precision - - Returns: - (IntFloat): seconds - """ - return round(0.001 * ms, p) - - - -def non_zero_range(x: Series, y: Series) -> Series: - """Non-Zero Range - - Calculates the difference of two Series plus epsilon to any zero values. - Technically: ```x - y + epsilon``` - - Parameters: - x (Series): Series of 'x's - y (Series): Series of 'y's - - Returns: - (Series): 1 column - """ - diff = x - y - if diff.eq(0).any().any(): - diff += sflt.epsilon - return diff - - - -def recent_maximum_index(x) -> Int: - """Recent Maximum Index - - Index of the largest value in ```x``` - - Parameters: - x (Series): ```x``` values - - Returns: - (int): Index of the largest value - """ - return int(argmax(x[::-1])) - - - -def recent_minimum_index(x) -> Int: - """Recent Minimum Index - - Index of the smallest value in ```x``` - - Parameters: - x (Series): ```x``` values - - Returns: - (int): Index of the smallest value - """ - return int(argmin(x[::-1])) - - - -def pd_rma(x: Series, n: Int) -> Series: - """RMA (Pandas) - - Pandas Implementation of RMA. - - Parameters: - x (Series): ```x``` Series - n (Int): Bars of lookback. Default: ```0.5``` - - Returns: - (Series): 1 column - """ - x = v_series(x) - if x is None: - return - a = (1.0 / n) if n > 0 else 0.5 - return x.ewm(alpha=a, min_periods=n).mean() - - - -def signed_series(x: Series, initial: Int, lag: Int = None) -> Series: - """Signed Series - - Returns a Signed Series with or without an initial value - - Parameters: - x (Series): Series of 'x's - initial (int): Set inital values of the signed Series. - lag (int): Difference between adjacent items. Default: ```1``` - - Returns: - (Series): 1 column - """ - initial = None - if initial is not None and not isinstance(lag, str): - initial = initial - x = v_series(x) - lag = v_pos_default(lag, 1) - sign = x.diff(lag) - sign[sign > 0] = 1 - sign[sign < 0] = -1 - sign.iloc[0] = initial # sign.iloc[:lag-1] - return sign - - - -def simplify_columns(df: DataFrame, n: Int=3) -> ListStr: - """Simplify Columns - - Helper method for managing columns used by Squeeze and Squeeze Pro. - - Parameters: - df (DataFrame): DataFrame with the columns - n (int): Default: ```3``` - - Returns: - (ListStr): List of string column - """ - df.columns = df.columns.str.lower() - return [c.split("_")[0][n - 1:n] for c in df.columns] - - - -def speed_test(df: DataFrame, - only: ListStr = None, excluded: ListStr = None, - top: Int = None, talib: bool = False, - ascending: bool = False, sortby: str = "secs", - gradient: bool = False, places: Int = 5, stats: bool = False, - verbose: bool = False, silent: bool = False - ) -> DataFrame | Tuple[DataFrame, DataFrame]: - """Speed Test - - Given a standard ohlcv DataFrame, the Speed Test calculates the - speed of each indicator of the DataFrame Extension: df.ta.(). - - Parameters: - df (DataFrame): DataFrame with _ohlcv_ columns - only (list): List of indicators to run. Default: ```None``` - excluded (list): List of indicators to exclude. Default: ```None``` - top (Int): Return a DataFrame the 'top' values. Default: ```None``` - talib (bool): Enable TA Lib. Default: ```False``` - ascending (bool): Ascending Order. Default: ```False``` - sortby (str): Options: "ms", "secs". Default: ```"secs"``` - gradient (bool): Returns a DataFrame the 'top' values with gradient - styling. Default: ```False``` - places (Int): Decimal places. Default: ```5``` - stats (bool): Returns a Tuple of two DataFrames. The second tuple - contains Stats on the performance time. Default: ```False``` - verbose (bool): Display more info. Default: ```False``` - silent (bool): Display nothing. Default: ```False``` - - Returns: - (DataFrame): if ```stats=False``` - (DataFrame, DataFrame): if ```stats=True``` - """ - if df.empty: - print(f"[X] No DataFrame") - return - talib = v_bool(talib, False) - top = int(top) if isinstance(top, int) and top > 0 else None - stats = v_bool(stats, False) - verbose = v_bool(verbose, False) - silent = v_bool(silent, False) - - _ichimoku = ["ichimoku"] - if excluded is None and isinstance(only, list) and len(only) > 0: - _indicators = only - elif only is None and isinstance(excluded, list) and len(excluded) > 0: - _indicators = df.ta.indicators(as_list=True, exclude=_ichimoku + excluded) - else: - _indicators = df.ta.indicators(as_list=True, exclude=_ichimoku) - - if len(_indicators) == 0: return None - - _iname = "Indicator" - if verbose: - print() - data = _speed_group(df.copy(), _indicators, talib, _iname, places) - else: - _this = StringIO() - with redirect_stdout(_this): - data = _speed_group(df.copy(), _indicators, talib, _iname, places) - _this.close() - - tdf = DataFrame.from_dict(data) - tdf.set_index(_iname, inplace=True) - tdf.sort_values(by=sortby, ascending=ascending, inplace=True) - - total_timedf = DataFrame( - tdf.describe().loc[['min', '50%', 'mean', 'max']]).T - total_timedf["total"] = tdf.sum(axis=0).T - total_timedf = total_timedf.T - - _div = "=" * 60 - _observations = f" Bars{'[talib]' if talib else ''}: {df.shape[0]}" - _quick_slow = "Quickest" if ascending else "Slowest" - _title = f" {_quick_slow} Indicators" - _perfstats = f"Time Stats:\n{total_timedf}" - if top: - _title = f" {_quick_slow} {top} Indicators [{tdf.shape[0]}]" - tdf = tdf.head(top) - - if not silent: - print(f"\n{_div}\n{_title}\n{_observations}\n{_div}\n{tdf}\n\n{_div}\n{_perfstats}\n\n{_div}\n") - - if isinstance(gradient, bool) and gradient: - return tdf.style.background_gradient("autumn_r"), total_timedf - - if stats: - return tdf, total_timedf - else: - return tdf - - - -def tal_ma(name: str) -> Int: - """TA Lib MA - - Helper Function that returns the Enum value for TA Lib's MA Type - - Parameters: - name (str): Abbreivated Name of the Moving Average - - Returns: - (int): The equivalent TA Lib MA Enum value for ```name``` - """ - if Imports["talib"] and isinstance(name, str) and len(name) > 1: - from talib import MA_Type - name = name.lower() - if name == "sma": - return MA_Type.SMA # 0 - elif name == "ema": - return MA_Type.EMA # 1 - elif name == "wma": - return MA_Type.WMA # 2 - elif name == "dema": - return MA_Type.DEMA # 3 - elif name == "tema": - return MA_Type.TEMA # 4 - elif name == "trima": - return MA_Type.TRIMA # 5 - elif name == "kama": - return MA_Type.KAMA # 6 - elif name == "mama": - return MA_Type.MAMA # 7 - elif name == "t3": - return MA_Type.T3 # 8 - return 0 # Default: SMA -> 0 - - - -def unsigned_differences( - x: Series, lag: Int = None, asint: bool = None -) -> Tuple[Series, Series]: - """Unsigned Differences - - Returns two Series, an unsigned positive and unsigned negative series based - on the differences of the original series. The positive series are only the - increases and the negative series are only the decreases. - - Parameters: - x (Series): Series of 'x's - lag (int): Difference between adjacent items. Default: ```1``` - asint (bool): Returns as ```Int```. Default: ```False``` - - Returns: - (Series | Series): Positive Series, Negative Series - - Example: - ```py - ta.unsigned_differences(Series([3, 2, 2, 1, 1, 5, 6, 6, 7, 5, 3])) - ``` - """ - asint = v_bool(asint, False) - lag = int(lag) if lag is not None else 1 - negative = x.diff(lag) - negative.fillna(0, inplace=True) - positive = negative.copy() - - positive[positive <= 0] = 0 - positive[positive > 0] = 1 - - negative[negative >= 0] = 0 - negative[negative < 0] = 1 - - if asint: - positive = positive.astype(int) - negative = negative.astype(int) - - return positive, negative - - - -def _speed_group( - df: DataFrame, group: ListStr = [], talib: bool = False, - index_name: str = "Indicator", p: Int = 4 - ) -> ListStr: - result = [] - for i in group: - r = df.ta(i, talib=talib, timed=True) - if r is None: - print(f"[S] {i} skipped due to returning None") - continue # ta.pivots() sometimes returns None - ms = float(r.timed.split(" ")[0].split(" ")[0]) - result.append({index_name: i, "ms": ms, "secs": ms2secs(ms, p)}) - return result diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_math.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_math.py deleted file mode 100644 index 848cd3dc..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_math.py +++ /dev/null @@ -1,811 +0,0 @@ -# -*- coding: utf-8 -*- -from collections.abc import Callable -from functools import reduce -from math import floor as mfloor -from operator import mul -from sys import float_info as sflt - -from numpy import ( - all, array, broadcast_to, concatenate, diff, dot, exp, - fabs, full, isnan, log, logical_and, nan, nanmean, nansum, - ndarray, newaxis, ones, pad, sign, sqrt, sum, triu, zeros -) -from numpy import max as np_max -from numpy import min as np_min -from numpy.lib.stride_tricks import sliding_window_view - -from pandas import DataFrame, Series -from numba import njit -from pandas_ta._typing import ( - Array, - DictLike, - Float, - Int, - IntFloat, - List -) -from pandas_ta.maps import Imports -from pandas_ta.utils._validate import ( - v_array, - v_dataframe, - v_float, - v_int, - v_lowerbound, - v_offset, - v_pos_default, - v_scalar, - v_series -) - -__all__ = [ - "combination", - "cube", - "consecutive_streak", - "df_error_analysis", - "erf", - "fibonacci", - "geometric_mean", - "hpoly", - "ifisher", - "log_geometric_mean", - "pascals_triangle", - "percent_rank", - "remap", - "strided_window", - "sum_signed_rolling_deltas", - "symmetric_triangle", - "weights", - "zero", -] - - - -def combination( - n: Int = 1, r: Int = 0, - repetition: bool = False, multichoose: bool = False -) -> Int: - """Combination - - Combination computation. - - Sources: - * [stackoverflow](https://stackoverflow.com/questions/4941753/is-there-a-math-ncr-function-in-python) - - Parameters: - n (Int): ```n``` - r (Int): ```r``` - repetition (bool): Apply repetition. - multichoose (bool): Apply multichoose. - - Returns: - (Int): Combination value - - Note: - ```n``` Choose ```r```: ```(n r)``` - """ - n, r = int(fabs(n)), int(fabs(r)) - - if repetition or multichoose: - n = n + r - 1 - - # if r < 0: return None - r = min(n, n - r) - if r == 0: - return 1 - - numerator = reduce(mul, range(n, n - r, -1), 1) - denominator = reduce(mul, range(1, r + 1), 1) - return numerator // denominator - - - -def consecutive_streak(x: Array) -> Array: - """Consecutive Streak - - Computes the streak of consecutive value increases or decreases. - - Parameters: - x (Array): Numpy array. - - Returns: - (Array): Streak array of element changes. - - Note: Logic - Yield an array where each value represents the streak value - for that bar. - - 1. Computes the difference between consecutive values. - 2. Assigns 1 for each positive change, -1 for each negative - change -1 and 0 for no change. - - Note: Streaks - * Positive: Consecutive bars of value increases - * Negative: Consecutive bars of value decreases - * Zero: When direction of the value change reverses - - Example: - ```py - x = np.array([100, 101, 102, 100, 100, 101, 102, 103]) - result = consecutive_streak(x) - expected = np.array([0, 1, 1, -1, 0, 1, 1, 1]) - np.array_equal(result, expected) - ``` - """ - return concatenate(([0], sign(diff(x)))) - - - -def cube( - src: Series, pwr: IntFloat = None, signal_offset: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Cube Transform - - This transform, by John Ehlers, is used to compress Svalues near zero for - a normalized oscillator like the Inverse Fisher Transform. - - In other words, a Power Transform/Function: ```result = src ^ pwr``` - - Sources: - * [rengel8](https://github.com/rengel8) based on Markus K. - (cryptocoinserver)'s source - * "Cycle Analytics for Traders", 2014, by John Ehlers, page 200 - - Parameters: - src (Series): Source - pwr (float): The transform power. Default: ```3``` - signal_offset (int): Signal offset. Default: ```-1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Note: - * Values near ```-1``` and ```1``` are nearly unchanged, whereas - values near zero are reduced. - * Input effects of spectral dilation should have been removed - (i.e. roofing filter). - - """ - # Validate - src = v_series(src) - pwr = v_lowerbound(pwr, 3.0, 3.0, strict=False) - signal_offset = v_int(signal_offset, -1, 0) - offset = v_offset(offset) - - # Calculate - result = src ** pwr - ct = Series(result, index=src.index) - ct_signal = Series(result, index=src.index) - - # Offset - if offset != 0: - ct = ct.shift(offset) - ct_signal = ct_signal.shift(offset) - if signal_offset != 0: - ct = ct.shift(signal_offset) - ct_signal = ct_signal.shift(signal_offset) - - if all(isnan(ct)) and all(isnan(ct_signal)): - return # Emergency Break - - # Fill - if "fillna" in kwargs: - ct.fillna(kwargs["fillna"], inplace=True) - ct_signal.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{pwr}_{signal_offset}" - ct.name = f"CUBE{_props}" - ct_signal.name = f"CUBEs{_props}" - ct.category = ct_signal.category = "transform" - - data = {ct.name: ct, ct_signal.name: ct_signal} - df = DataFrame(data, index=src.index) - df.name = f"CUBE{_props}" - df.category = ct.category - - return df - - - -def erf(x: IntFloat) -> Float: - """Error Function - - Computes the erf(x) - - Sources: - * Handbook of Mathematical Functions, formula 7.1.26. - * [stackoverflow](https://stackoverflow.com/questions/457408/is-there-an-easily-available-implementation-of-erf-for-python) - - Parameters: - x (IntFloat): ```x``` value. - - Returns: - (Float): Error value - """ - x_sign = sign(x) - x = abs(x) - - # constants - a1 = 0.254829592 - a2 = -0.284496736 - a3 = 1.421413741 - a4 = -1.453152027 - a5 = 1.061405429 - p = 0.3275911 - - # A&S formula 7.1.26 - t = 1.0 / (1.0 + p * x) - y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) - * t + a1) * t * exp(-x * x) - return x_sign * y # erf(-x) = -erf(x) - - - -@njit(cache=True) -def fibonacci(n: Int = 2, weighted: bool = False) -> Array: - """Fibonacci - - Computes closed form Fibonacci values. - - Parameters: - n (Int): Number of terms (n >= 2). Default: ```2``` - weighted (bool): Return weighted values. Default: ```False``` - - Returns: - (Array): Numpy array results - """ - n = n if n > 1 else 2 - sqrt5 = sqrt(5.0) - phi, psi = 0.5 * (1.0 + sqrt5), 0.5 * (1.0 - sqrt5) - - result = zeros(n) - for i in range(0, n): - result[i] = float(phi ** (i + 1) - psi ** (i + 1)) / sqrt5 - - if weighted: - return result / result.sum() - return result - - - -def geometric_mean(x: Series) -> Float: - """Geometric Mean - - Computes the Geometric Mean of positive values. - - Parameters: - x (Series): Values - - Returns: - (Float): Geometric Mean - """ - n = x.size - if n < 1: - return x.iloc[0] - - has_zeros = 0 in x.to_numpy() - if has_zeros: - x = x.fillna(0) + 1 - if all(x > 0): - mean = x.prod() ** (1 / n) - return mean if not has_zeros else mean - 1 - return 0 - - - -def hpoly(x: Array, v: IntFloat) -> Float: - """Horner's Polynomial - - Evaluates a polynomial with an array of polynomial coefficients, ```x```, - and a value, ```v```, using Horner's Calculation for Polynomial - Evaluation. - - Parameters: - x (Array): Polynomial coefficients as ```np.array``` - v (IntFloat): Value - - Returns: - (Float): Polynomial value. - - Tip: Performance - Use a ```np.array``` for best performance. - - Example: - ```py - coeffs_0 = [4, -3, 0, 1] # 4x^3 - 3x^2 + 0x + 1 - coeffs_1 = np.array(coeffs_0) # Faster - coeffs_2 = pd.Series(coeffs_0).to_numpy() - x = -6.5 - - hpoly(coeffs_0, x) => -1224.25 - hpoly(coeffs_1, x) or hpoly(coeffs_2, x) => -1224.25 # Faster - ``` - """ - if not isinstance(x, ndarray): - x = array(x) - - m, y = x.size, x[0] - - for i in range(1, m): - y = x[i] + v * y - return y - - - -def ifisher( - x: Series, - amp: IntFloat = None, signal_offset: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Inverse Fisher Transform - - This transform function, by John Ehlers, attempts to create clearer - signals by changing the Probability Distribution Function (pdf) for the - results of known oscillator-indicators. - - Sources: - * [rengel8](https://github.com/rengel8) based on Markus K. - (cryptocoinserver)'s source - * "Cycle Analytics for Traders", 2014, by John Ehlers, page 198 - * [mesasoftware](https://www.mesasoftware.com/papers/TheInverseFisherTransform.pdf) - - Parameters: - x (Series): Normalized to range ```[-1, 1]``` - amp (float): Amplifier. Default: ```1``` - signal_offset (int): Signal line offset. Default: ```-1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Note: - * Normalized input, ```x```, with range ```[-1, 1]``` - * Data range of ```[-0.5, 0.5]``` would not have a significant impact - - Example: Preparation Examples - Or use _ta.remap()_ function to prep - - (RSI - 50) * 0.1 RSI [0 to 100] -> -5 to 5 - - (RSI - 50) * 0.02 RSI [0 to 100] -> -1 to 1 (use amp of 5 to match input of example above) - """ - # Validate - x = v_series(x) - amp = v_scalar(amp, 1.0) - signal_offset = v_int(signal_offset, -1, 0) - offset = v_offset(offset) - - # Calculate - np_x = x.to_numpy() - is_remapped = logical_and(np_x >= -1, np_x <= 1) - if not all(is_remapped): - _np_max, _np_min = np_max(np_x), np_min(np_x) - x_map = remap(x, - from_min=_np_min, from_max=_np_max, - to_min=-1, to_max=1 - ) - if x_map is None or all(isnan(x_map.to_numpy())): - return # Emergency Break - np_x = x_map.to_numpy() - - amped = exp(amp * np_x) - result = (amped - 1) / (amped + 1) - - inv_fisher = Series(result, index=x.index) - signal = Series(result, index=x.index) - - # Offset - if offset != 0: - inv_fisher = inv_fisher.shift(offset) - signal = signal.shift(offset) - - if signal_offset != 0: - inv_fisher = inv_fisher.shift(offset) - signal = signal.shift(offset) - - # Fill - if "fillna" in kwargs: - inv_fisher.fillna(kwargs["fillna"], inplace=True) - signal.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{amp}" - inv_fisher.name = f"INVFISHER{_props}" - signal.name = f"INVFISHERs{_props}" - - data = {inv_fisher.name: inv_fisher, signal.name: signal} - df = DataFrame(data, index=x.index) - df.name = f"INVFISHER{_props}" - - return df - - - -def log_geometric_mean(x: Series) -> Float: - """Logarithmic Geometric Mean - - Computes the Logarithmic Geometric Mean of positive values. - - Parameters: - x (Series): Values - - Returns: - (Float): LogGeometric Mean or zero - """ - n = x.size - if n > 1: - x = x.fillna(0) + 1 - if all(x > 0): - return exp(log(x).sum() / n) - 1 - return 0 - - - -def pascals_triangle( - n: Int = None, inverse: bool = False, weighted: bool = False -) -> Array: - """Pascal's Triangle - - The ```n```th row of Pascal's Triangle. - - Parameters: - n (Int): ```n^th``` row of Pascal' Triange - inverse (bool): Return Inverse weighted. - weighted (bool): Return weighted. - - Returns: - (Array): Classical, Weighted, or Inversely - - Example: - ```py - # Classical - pt4 = pascals_triangle(4) - # pt4 = [1, 4, 6, 4, 1] - - # Inverse - invpt4 = pascals_triangle(4, inverse=True) - # invpt4 = [0.9375, 0.75, 0.625, 0.75, 0.9375] - - # Weighted - wpt4 = pascals_triangle(4, weighted=True) - # wpt4 = [0.0625, 0.25, 0.375, 0.25, 0.0625] - ``` - """ - n = int(fabs(n)) if n is not None else 0 - - # Calculation - triangle = array([combination(n=n, r=i) for i in range(0, n + 1)]) - triangle_sum = sum(triangle) - triangle_weights = triangle / triangle_sum - inverse_weights = 1 - triangle_weights - - if weighted and inverse: - return inverse_weights - if weighted: - return triangle_weights - if inverse: - return None - - return triangle - - - -def percent_rank(x: Series, length: int) -> Series: - """Percent Rank - - Percent Rank of values over a specified length. - - Parameters: - x (Series): ```x``` values - length (int): The period. - - Returns: - (Series): Percent Rank values. - - Note: Logic - Yield a Series where the initial part (up to ```length - 1```) is - padded with NaNs, and the rest contains the Percent Rank values. - - 1. Computes the daily percentage returns. - 2. Creates a rolling window of these returns. - 3. Compares each value in the window to the current value (the - last value in each window). - 4. Percent Rank is calculated as the percentage of values in each - window that are less than the current value. - - Example: - ```py - x = Series([100, 80, 75, 123, 140, 80, 70, 40, 100, 120]).to_numpy() - result = percent_rank(x, 3) - expected_result = Series([np.nan, np.nan, np.nan, 66.666667, 66.666667, 0.0, 33.333333, 0.0, 100.0, 66.666667]) - np.allclose(result, expected_result, rtol=1e-6, equal_nan=True) - ``` - """ - np_pctchg = x.pct_change().to_numpy() - - rws = sliding_window_view(np_pctchg, window_shape=(length + 1,)) - comparison_matrix = rws[:, :-1] < rws[:, -1, newaxis] - - prs = 100 * nanmean(comparison_matrix, axis=1) - result = full(len(x), nan) - result[length:] = prs - - # return Series(padded_percent_ranks, index=x.index) - return result - - - -def remap( - x: Series, from_min: IntFloat = None, from_max: IntFloat = None, - to_min: IntFloat = None, to_max: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """remap - - The standard method of transforming from a source range to a target range - using Max-Min. Useful for bounded sources; not unbounded sources - like _ohlcv_ data. - - Sources: - * Linear (Max-Min) Normalization - - Parameters: - x (Series): Series of 'x's - from_min (IntFloat): Input minimum. Default: ```0.0``` - from_max (IntFloat): Input maximum. Default: ```100.0``` - to_min (IntFloat): Output minimum. Default: ```0.0``` - to_max (IntFloat): Output maximum. Default: ```100.0``` - offset (Int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - x = v_series(x) - from_min = v_float(from_min, 0.0, 0.0) - from_max = v_float(from_max, 100.0, 0.0) - to_min = v_float(to_min, -1.0, 0.0) - to_max = v_float(to_max, 1.0, 0.0) - offset = v_offset(offset) - - # Calculate - frange, trange = from_max - from_min, to_max - to_min - if frange <= 0 or trange <= 0: - return - result = to_min + (trange / frange) * (x.to_numpy() - from_min) - result = Series(result, index=x.index) - - # Offset - if offset != 0: - result = result.shift(offset) - - # Fill - if "fillna" in kwargs: - result.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - result.name = f"REMAP_{from_min}_{from_max}_{to_min}_{to_max}" - # result.name = f"{x.name}_{from_min}_{from_max}_{to_min}_{to_max}" # OR - - return result - - - -def strided_window(x: Array, length: Int) -> Array: - """Strided Window - - Creates a strided window view. - - Source: - * [numpy](https://numpy.org/devdocs/reference/generated/numpy.lib.stride_tricks.as_strided.html) - * [Issue #285](https://github.com/twopirllc/pandas-ta/issues/285) - - Parameters: - x (Array): Source - length (Int): Window period. - - Returns: - (Array): Numpy Array of Strided Window Arrays - - Warning: - Use if necessary, otherwise avoid when possible! - """ - from numpy.lib.stride_tricks import as_strided - strides = x.strides + (x.strides[-1],) - shape = x.shape[:-1] + (x.shape[-1] - length + 1, length) - return as_strided(x, shape=shape, strides=strides, writeable=False) - - - -def sum_signed_rolling_deltas( - open_: Series, close: Series, length: Int, exclusive: bool = True -) -> Series: - """Sum of Signed Rolling Series Deltas - - Calculates the sum of signed differences between the current closing bar - and a rolling window of preceding opening bars. This sum is then padded - to match the original series length. - - Parameters: - open_ (Series): ```open``` Series - close (Series): ```close``` Series - length (Int): Window length. Default: ```4``` - exclusive (bool): Exclusive rolling window. Inclusive rolling window - when ```False```. Default: ```True``` - - Returns: - (Series): 1 column - - Notes: Mode - **Exclusive**: Rolling window excludes the current bar in the - lookback period. - - **Inclusive**: Rolling window includes the current bar in the - lookback period. - - Example: - ```py - open_ = Series([95, 83, 71, 132, 129, 145, 133, 101, 68, 96]) - close = Series([100, 110, 140, 80, 90, 60, 50, 40, 90, 110]) - - result = sum_signed_rolling_deltas(close, open_, 4, exclusive=True) - expected_result = Series([np.nan, np.nan, np.nan, np.nan, 0.0, -4.0, -4.0, -4.0, -4.0, 0.0]) - np.allclose(result, expected_result, rtol=1e-6, equal_nan=True) - - result = sum_signed_rolling_deltas(close, open_, 4, exclusive=False) - expected_result = Series([np.nan, np.nan, np.nan, -1.0, 1.0, -3.0, -3.0, -3.0, -3.0, 1.0]) - np.allclose(result, expected_result, rtol=1e-6, equal_nan=True) - ``` - """ - length = v_pos_default(length, 4) - if not exclusive: - length -= 1 - - rolling_open = sliding_window_view(open_, window_shape=length)[:-1] - - close_broadcasted = broadcast_to( - close[length:].to_numpy()[:, newaxis], rolling_open.shape - ) - - signed_deltas = sign(close_broadcasted - rolling_open) - sum_signed_deltas = nansum(signed_deltas, axis=1).astype(float) - - return Series( - pad(sum_signed_deltas, (length, 0), mode="constant", constant_values=nan), - index=close.index, - ) - - -def symmetric_triangle( - n: Int = None, weighted: bool = False -) -> List[IntFloat]: - """Symmetric Triangle - - Creates a Symmetric Triangle of given length. - - Parameters: - n (Int): Array return size - weighted (bool): Return weighted. - - Returns: - (List[IntFloat]): List of Symmetric Triangle values. - - Example: - ```py - # Default - symt4 = ta.symmetric_triangle(4) - # symt4 = [1, 2, 2, 1] - - # Weighted - wsymt4 = ta.symmetric_triangle(4, weighted=True) - # wsymt4 = [0.16666667 0.33333333 0.33333333 0.16666667] - ``` - """ - n = int(fabs(n)) if n is not None else 2 - - triangle = None - if n == 2: - triangle = [1, 1] - - if n > 2: - if n % 2 == 0: - front = [i + 1 for i in range(0, mfloor(n / 2))] - triangle = front + front[::-1] - else: - front = [i + 1 for i in range(0, mfloor(0.5 * (n + 1)))] - triangle = front.copy() - front.pop() - triangle += front[::-1] - - if weighted and isinstance(triangle, list): - return triangle / sum(triangle) - - return triangle - - - -def weights(w: Array) -> Callable: - """Weights - - Prepares weights for the dot product - - Parameters: - w (Array): Input - - Returns: - (Callable): Weights function for dot product. - """ - def _dot(x): - return dot(w, x) - return _dot - - - -def zero(x: IntFloat) -> IntFloat: - """Zero - - Zeros inputs near zero. - - Parameters: - x (IntFloat): Value to attempt to zero - - Returns: - (IntFloat): ```0``` or ```x``` - """ - return 0 if abs(x) < sflt.epsilon else x - - - -# TESTING - - - -def df_error_analysis( - A: DataFrame, B: DataFrame, - plot: bool = False, triangular: bool = False, - method: str = "pearson", -) -> DataFrame: - """DataFrame Correlation Analysis - - Compares two DataFrames using both their difference and their correlations. - - Parameters: - A (DataFrame): DataFrame A - B (DataFrame): DataFrame B - plot (bool): Create a KDE plot of differences. Default: ```False``` - triangular (bool): Return a Triangular Correlation DataFrame. - Default: ```False``` - method (str): Correlation methods: ```"pearson"```, ```"kendall"```, - or ```"spearman"```. Default: ```"pearson"``` - - Returns: - (DataFrame): Correlation DataFrame or a KDE Difference plot - """ - _r_method = ["pearson", "kendall", "spearman"] - corr_method = method if method in _r_method else _r_method[0] - - # Find their differences and correlation - diff = A - B - result = A.corr(B, method=corr_method) - - # For plotting - if plot: - diff.hist() - if diff[diff > 0].any(): - diff.plot(kind="kde") - - if triangular: - return result.where(triu(ones(result.shape)).astype(bool)) - - return result diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_numba.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_numba.py deleted file mode 100644 index 9c0b25db..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_numba.py +++ /dev/null @@ -1,140 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import ( - append, - array, - empty_like, - finfo, - float64, - int64, - isnan, - nan, - roll, - zeros_like -) -from numba import njit -from pandas_ta._typing import Array, Int, IntFloat - -__all__ = [ - "nb_ffill", - "nb_idiff", - "nb_nonzero_range", - "nb_prenan", - "nb_prepend", - "nb_rolling", - "nb_shift", -] - - - -# Numba version of ffill() -@njit(cache=True) -def nb_ffill(x): - mask = isnan(x) - idx = zeros_like(mask, dtype=int64) - last_valid_idx = -1 - - m = mask.size - for i in range(m): - if not mask[i]: - last_valid_idx = i - idx[i] = last_valid_idx - return x[idx] - - -# Indexwise element difference by k indices of array x. -# Similar to Pandas Series/DataFrame diff() -@njit(cache=True) -def nb_idiff(x, k): - n, k = x.size, int(k) - result = zeros_like(x, dtype=float64) - - for i in range(k, n): - result[i] = x[i] - x[i - k] - result[:k] = nan - - return result - - -# Returns the difference of two series and adds epsilon to any zero values. -# This occurs commonly in crypto data when 'high' = 'low'.""" -@njit(cache=True) -def nb_nonzero_range(x, y): - diff = x - y - if diff.any() == 0: - diff += finfo(float64).eps - return diff - - -# Prepend n values, typically np.nan, to array x. -@njit(cache=True) -def nb_prenan(x, n, value = nan): - if n > 0: - x[:n - 1] = value - return x - return x - - -# Prepend n values, typically np.nan, to array x. -@njit(cache=True) -def nb_prepend(x, n, value = nan): - return append(array([value] * n), x) - -# Prepend n values, typically np.nan, to array x. -# @njit(cache=True) -# def nb_prepend2(x, n, value = nan): - # return concatenate(array([value] * n), x) - - -# Like Pandas Rolling Window. x.rolling(n).fn() -@njit(cache=True) -def nb_rolling(x, n, fn = None): - if fn is None: - return x - m = x.size - result = zeros_like(x, dtype=float) - if n <= 0: - return result # TODO: Handle negative rolling windows - - for i in range(0, m): - result[i] = fn(x[i:n + i]) - result = roll(result, n - 1) - result[:n - 1] = nan - return result - - -# np shift -# shift5 - preallocate empty array and assign slice by chrisaycock -# https://stackoverflow.com/questions/30399534/shift-elements-in-a-numpy-array -@njit(cache=True) -def nb_shift(x, n, value = nan): - result = empty_like(x) - if n > 0: - result[:n] = value - result[n:] = x[:-n] - elif n < 0: - result[n:] = value - result[:n] = x[-n:] - else: - result[:] = x - return result - - -# Uncategorized -# @njit(cache=True) -# def nb_roofing_filter(x: Array, n: Int, k: Int, pi: Float, sqrt2: Float): -# """Ehlers's Roofing Filter (INCOMPLETE) -# http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html""" -# m, hp = x.size, np.copy(x) -# # a = exp(-pi * sqrt(2) / n) -# # b = 2 * a * cos(180 * sqrt(2) / n) -# rsqrt2 = 1 / np.sqrt2 -# a = (np.cos(rsqrt2 * 360 / n) + np.sin(rsqrt2 * 360 / n) - 1) -# a /= np.cos(rsqrt2 * 360 / n) -# b, c = 1 - a, (1 - a / 2) - -# for i in range(2, m): -# hp = c * c * (x[i] - 2 * x[i - 1] + x[i - 2]) \ -# + 2 * b * hp[i - 1] - b * b * hp[i - 2] - -# result = nb_ssf(hp, k, pi, rsqrt2) -# return result diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_signals.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_signals.py deleted file mode 100644 index c964d1d2..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_signals.py +++ /dev/null @@ -1,623 +0,0 @@ -# -*- coding: utf-8 -*- -from functools import partial - -from numpy import nan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils._math import zero -from pandas_ta.utils._validate import ( - v_bool, - v_drift, - v_float, - v_int, - v_offset, - v_series -) - - - -__all__ = [ - "above", - "above_value", - "below", - "below_value", - "cross", - "cross_value", - "signals", - "tsignals", - "xsignals" -] - - - -def above( - x: Series, y: Series, asint: bool = True, offset: Int = None, **kwargs -) -> Series: - """Above - - Determines if each ```x``` value is above (or ```>=```) each ```y``` value. - - Parameters: - x (Series): ```x``` - y (Series): ```y``` - asint (bool): Returns as ```Int```. - offset (Int): Post shift. Default: ```0``` - - Returns: - (Series): State where ```x >= y```. - - Example: - ```py - x = Series([4, 2, 0, -1, 1]) - y = Series([1, 1, 1, 1, 1]) - - x_above_y = ta.above(x, y) - # x_above_y = Series([1, 1, 0, 0, 1]) - ``` - """ - return partial(_above_below, above=True)(x, y, asint=asint, offset=offset, **kwargs) - - -def above_value( - x: Series, value: IntFloat, asint: bool = True, - offset: Int = None, **kwargs -) -> Series: - """Above Value - - Determines if each ```x``` value is above (or ```>=```) a - constant ```value```. - - Parameters: - x (Series): ```x``` - value (IntFloat): Value to compare with ```x```. - asint (bool): Returns as ```Int```. - - Returns: - (Series): State where ```x >= y```. - - Example: - ```py - x = Series([4, 2, 0, -1, 1]) - x_above_1 = ta.above_value(x, 1) - # x_above_1 = Series([1, 1, 0, 0, 1]) - ``` - """ - if not isinstance(value, (int, float)): - print("[X] value is not a number") - return - y = Series(value, index=x.index, name=f"{value}".replace(".", "_")) - return partial(_above_below, above=True)(x, y, asint=asint, offset=offset, **kwargs) - - -def below( - x: Series, y: Series, asint: bool = True, offset: Int = None, **kwargs -) -> Series: - """Below - - Determines if each ```x``` value is below (or ```<=```) each ```y``` value. - - Parameters: - x (Series): ```x``` - y (Series): ```y``` - asint (bool): Returns as ```Int```. - offset (Int): Post shift. Default: ```0``` - - Returns: - (Series): State where ```x <= y```. - - Example: - ```py - x = Series([4, 2, 0, -1, 1]) - y = Series([1, 1, 1, 1, 1]) - - x_below_y = ta.below(x, y) - # x_below_y = Series([0, 0, 1, 1, 1]) - ``` - """ - return partial(_above_below, above=False)(x, y, asint=asint, offset=offset, **kwargs) - - -def below_value( - x: Series, value: IntFloat, asint: bool = True, - offset: Int = None, **kwargs -) -> Series: - """Below Value - - Determines if each ```x``` value is below (or ```<=```) a - constant ```value```. - - Parameters: - x (Series): ```x``` - value (IntFloat): Value to compare with ```x```. - asint (bool): Returns as ```Int```. - offset (Int): Post shift. Default: ```0``` - - Returns: - (Series): State where ```x <= y```. - - Example: - ```py - x = Series([4, 2, 0, -1, 1]) - x_below_1 = ta.below_value(x, 1) - # x_below_1 = Series([0, 0, 1, 1, 1]) - ``` - """ - if not isinstance(value, (int, float)): - print("[X] value is not a number") - return - y = Series(value, index=x.index, name=f"{value}".replace(".", "_")) - return partial(_above_below, above=False)(x, y, asint=asint, offset=offset, **kwargs) - - -def cross( - x: Series, y: Series, - above: bool = True, equal: bool = True, - asint: bool = True, offset: Int = None, - **kwargs: DictLike -) -> Series: - """Cross - - Determines where ```x``` crosses ```y```, either _above_ or _below_, - strictly (_equal_) or not. - - Parameters: - x (Series): ```x``` - y (Series): ```y``` - above (bool): Check above. Check below, set ```above=False``` - equal (bool): At least/most, ```=```, check. - asint (bool): Returns as ```Int```. - offset (Int): Post shift. Default: ```0``` - - Returns: - (Series): Values where ```x``` crosses ```y```. - - Example: - ```py - x = Series([4, 2, 0, -1, 1]) - y = Series([1, 1, 1, 1, 1]) - - # Cross Above Examples - x_xae_y = ta.cross(x, y, above=True, equal=True) - # x_xae_y = Series([0, 0, 0, 0, 1]) - - x_xa_y = ta.cross(x, y, above=True, equal=False) - # x_xa_y = Series([0, 0, 0, 0, 0]) - - # Cross Below Examples - x_xbe_y = ta.cross(x, y, above=False, equal=True) - # x_xbe_y = Series([0, 0, 1, 0, 1]) - - x_xb_y = ta.cross(x, y, above=False, equal=False) - # x_xb_y = Series([0, 0, 1, 0, 0]) - ``` - """ - # Validate - x = v_series(x) - y = v_series(y) - offset = v_offset(offset) - - x.apply(zero) - y.apply(zero) - - # Calculate - if above: - current = x >= y if equal else x > y - previous = x.shift(1) < y.shift(1) - else: - current = x <= y if equal else x < y - previous = x.shift(1) > y.shift(1) - - cross = current & previous - # ensure there is no cross on the first entry - cross.iloc[0] = False - - if asint: - cross = cross.astype(int) - - # Offset - if offset != 0: - cross = cross.shift(offset) - - # Name and Category - cross.name = f"{x.name}_{'XA' if above else 'XB'}_{y.name}" - cross.category = "signal" - - return cross - - -def cross_value( - x: Series, value: IntFloat, - above: bool = True, equal: bool = True, - asint: bool = True, offset: Int = None, - **kwargs -) -> Series: - """Cross Value - - Determines where ```x``` crosses a constant ```value```, either _above_ - or _below_, strictly (_equal_) or not. - - Parameters: - x (Series): ```x``` - value (IntFloat): Value to compare with ```x```. - above (bool): Check above. Check below, set ```above=False``` - equal (bool): At least/most, ```=```, check. - asint (bool): Returns as ```Int```. - offset (Int): Post shift. Default: ```0``` - - Returns: - (Series): Values where ```x``` crosses ```y```. - - Example: - ```py - x = Series([4, 2, 0, -1, 1]) - - # Cross Above Examples - x_xae_y = ta.cross_value(x, 1, above=True, equal=True) - # x_xae_y = Series([0, 0, 0, 0, 1]) - - x_xa_y = ta.cross_value(x, 1, above=True, equal=False) - # x_xa_y = Series([0, 0, 0, 0, 0]) - - # Cross Below Examples - x_xbe_y = ta.cross_value(x, 1, above=False, equal=True) - # x_xbe_y = Series([0, 0, 1, 0, 1]) - - x_xb_y = ta.cross_value(x, 1, above=False, equal=False) - # x_xb_y = Series([0, 0, 1, 0, 0]) - ``` - """ - y = Series(value, index=x.index, name=f"{value}".replace(".", "_")) - return cross(x, y, above, equal, asint, offset, **kwargs) - - - -def signals( - indicator: Series, xa: IntFloat = None, xb: IntFloat = None, - cross_values: bool = None, xseries: Series = None, - xseries_a: Series = None, xseries_b: Series = None, - cross_series: bool = None, offset: Int = None -) -> DataFrame: - """Signals - - Mulitfuncational signal checker that determines whether an - indicator crosses above/below value or Series. - - Parameters: - indicator (Series): Indicator to check for signal crossings. - cross_values (bool): Check if crossed value. - xseries (Series): Cross Series - xseries_a (Series): Cross Above Series - xseries_b (Series): Cross Below Series - cross_series (bool): Check if crossed ```xseries```. - - Other Parameters: - xa (IntFloat): Crossing above value. - xb (IntFloat): Crossing below value. - offset (Int): Post shift. Default: ```0``` - - Returns: - (DataFrame): 2 columns - - Note: - See sources of: ```er```, ```macd```, ```rsi```, and ```rsx``` - for examples of use. - """ - df = DataFrame() - - if xa is not None and isinstance(xa, (int, float)): - if cross_values: - xa_start = cross_value(indicator, xa, above=True, offset=offset) - xa_end = cross_value(indicator, xa, above=False, offset=offset) - - df[xa_start.name] = xa_start - df[xa_end.name] = xa_end - else: - xd_above = above_value(indicator, xa, offset=offset) - df[xd_above.name] = xd_above - - if xb is not None and isinstance(xb, (int, float)): - if cross_values: - xb_start = cross_value(indicator, xb, above=True, offset=offset) - xb_end = cross_value(indicator, xb, above=False, offset=offset) - - df[xb_start.name] = xb_start - df[xb_end.name] = xb_end - else: - xd_below = below_value(indicator, xb, offset=offset) - df[xd_below.name] = xd_below - - # xseries is the default value for both xseries_a and xseries_b - if xseries_a is None: - xseries_a = xseries - if xseries_b is None: - xseries_b = xseries - - if xseries_a is not None and v_series(xseries_a): - if cross_series: - xsa = cross(indicator, xseries_a, above=True, offset=offset) - else: - xsa = above(indicator, xseries_a, offset=offset) - - df[xsa.name] = xsa - - if xseries_b is not None and v_series(xseries_b): - if cross_series: - xsb = cross(indicator, xseries_b, above=False, offset=offset) - else: - xsb = below(indicator, xseries_b, offset=offset) - - df[xsb.name] = xsb - - return df - - -def _above_below( - x: Series, y: Series, - above: bool = True, asint: bool = True, - offset: Int = None, **kwargs -) -> Series: - """Above / Below - - Determines if ```x``` is above or below ```y```. - - Parameters: - x (Series): ```x``` - y (Series): ```y``` - above (bool): Above check. Below: ```above=False``` - equal (bool): At least/most, ```=```, check. - asint (bool): Returns as ```Int```. - offset (Int): Post shift. Default: ```0``` - - Returns: - (Series): Values where ```x``` values are above/below ```y``` values. - """ - # Verify - x = v_series(x) - y = v_series(y) - offset = v_offset(offset) - - x.apply(zero) - y.apply(zero) - - # Calculate - if above: - current = x >= y - else: - current = x <= y - - if asint: - current = current.astype(int) - - # Offset - if offset != 0: - current = current.shift(offset) - - # Name and Category - current.name = f"{x.name}_{'A' if above else 'B'}_{y.name}" - current.category = "signal" - - return current - - - -def tsignals( - trend: Series, asbool: bool = None, - trade_offset: Int = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Trend Signals - - This function creates Trend, Trades, Entries and Exit values per bar when - given a trend condition e.g. ```trend = close > sma(close, 50)```. - - Source: - * Kevin Johnson - - Parameters: - trend (Series): ```trend``` Series. Boolean or integer values of - ```0``` and ```1``` - asbool (bool): Return booleans. Default: ```False``` - trade_offset (value): Shift trade entries/exits with live: ```0``` and - backesting: ```1```. Default: ```0``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 4 columns - - Note: Column Detail - * Trends (trend: 1, no trend: 0) - * Trades (Enter: 1, Exit: -1, Otherwise: 0) - * Entries (entry: 1, nothing: 0) - * Exits (exit: 1, nothing: 0) - - Note: Details - A ```trend``` is a state or condition, that is as simple - as ```Close > MA``` or something more complex that has boolean - or integer (trend: 1, no trend: 0) values. - - Tip: VectorBT - * For backtesting, set ```trade_offset=1```. - * Setting ```asbool=True``` is useful for backtesting with vectorbt's - ```Portfolio.from_signal(close, entries, exits)``` method. - - Example: - These are two different outcomes for each (long/short) position and - depends on the source and it's behavior. - - Signals when ```Close > SMA50(Close)``` - - ta.tsignals(close > ta.sma(close, 50), asbool=False) - - Signals when ```EMA(Close, 8) > EMA(Close, 21)``` - - ta.tsignals(ta.ema(close, 8) > ta.ema(close, 21), asbool=True) - - Warning: - Check ALL outcomes BEFORE making an Issue - """ - # Validate - trend = v_series(trend) - if trend is None: - return - - asbool = v_bool(asbool, False) - trade_offset = v_int(trade_offset, 0) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - trends = trend.astype(int) - trades = trends.diff(drift).shift(trade_offset).fillna(0).astype(int) - entries = (trades > 0).astype(int) - exits = (trades < 0).abs().astype(int) - - if asbool: - trends = trends.astype(bool) - entries = entries.astype(bool) - exits = exits.astype(bool) - - data = { - f"TS_Trends": trends, - f"TS_Trades": trades, - f"TS_Entries": entries, - f"TS_Exits": exits, - } - df = DataFrame(data, index=trends.index) - - # Offset - if offset != 0: - df = df.shift(offset) - - # Fill - if "fillna" in kwargs: - df.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - df.name = f"TS" - df.category = "trend" - - return df - - - -def xsignals( - source: Series, - xa: IntFloat | Series, - xb: IntFloat | Series, - above: bool = True, long: bool = True, - asbool: bool = None, trade_offset: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Cross Signals - - This function creates Trend, Trades, Entries and Exits values per bar - for crossing events. - - Sources: - * Kevin Johnson - - Parameters: - source (Series): ```source``` Signal - xa (Series): Series the Signal crosses above if ```above=True``` - xb (Series): Series the Signal crosses below if ```above=True``` - above (bool): The ```source``` crossing; below is ```False```. - Default: ```True``` - long (bool): The ```source``` position; short is ```False```. - Default: ```True``` - offset (int): Post shift. Default: ```0``` - asbool (bool): Return booleans. Default: ```False``` - trade_offset (value): Shift trade entries/exits with live: ```0``` and - backesting: ```1```. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 4 columns - - Note: Column Detail - * Trends (trend: 1, no trend: 0) - * Trades (Enter: 1, Exit: -1, Otherwise: 0) - * Entries (entry: 1, nothing: 0) - * Exits (exit: 1, nothing: 0) - - Tip: VectorBT - * For backtesting, set ```trade_offset=1```. - * Setting ```asbool=True``` is useful for backtesting with vectorbt's - ```Portfolio.from_signal(close, entries, exits)``` method. - - Example: - These are two different outcomes for each (long/short) position and - depends on the source and it's behavior. - - rsi = df.ta.rsi() - - When RSI crosses above 20 and then below 80 in a long position: - - ta.xsignals(source=rsi, xa=20, xb=80, above=True, long=True) - # Simpler - # ta.xsignals(rsi, 20, 80, True, True) - - When RSI crosses below 20 and then above 80 in a long position: - - ta.xsignals(source=rsi, xa=20, xb=80, above=False, long=True) - # Simpler - # ta.xsignals(rsi, 20, 80, False, True) - - * Similarly, short positions (```long=False```) also differ depending - on ```above``` state. - - Warning: - Check ALL parameter combination outcomes BEFORE making an Issue. - """ - # Validate - source = v_series(source) - if source is None: - return - - offset = v_offset(offset) - - # Calculate - if above: - entries = cross_value(source, xa) - exits = -cross_value(source, xb, above=False) - else: - entries = cross_value(source, xa, above=False) - exits = -cross_value(source, xb) - trades = entries + exits - - # Modify trades to fill gaps for trends - trades.replace({0: nan}, inplace=True) - trades.ffill(limit_area="inside", inplace=True) # or trades.bfill(limit_area="inside", inplace=True) - trades.fillna(0, inplace=True) - - trends = (trades > 0).astype(int) - if not long: - trends = 1 - trends - - tskwargs = { - "asbool": asbool, - "trade_offset": trade_offset, - "offset": offset - } - df = tsignals(trends, **tskwargs) - - # Offset handled by tsignals - DataFrame({ - f"XS_LONG": df.TS_Trends, - f"XS_SHORT": 1 - df.TS_Trends - }) - - # Fill - if "fillna" in kwargs: - df.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - df.name = f"XS" - df.category = "trend" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_study.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_study.py deleted file mode 100644 index 16d01e6c..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_study.py +++ /dev/null @@ -1,144 +0,0 @@ -# -*- coding: utf-8 -*- -from multiprocessing import cpu_count -from dataclasses import dataclass, field - -from pandas_ta._typing import Int, List -from pandas_ta.utils._time import get_time - -__all__ = [ - "Study", - "AllStudy", - "CommonStudy" -] - - - -# Study DataClass -@dataclass -class Study: - """Study DataClass - Class to name and group indicators for processing. - - Parameters: - name (str): Name. - ta (list of dicts): i.e [{"kind": "ema", "length", 50}] - cores (int): The number cores to use for multiprocessing. - Default: ```cpu_count()``` - description (str): Description of what the Study. Default: ```""``` - created (str): DateTime String at creation. - Default: Automatically generated. - - Returns: - (DataClass): The Study to be processed by ```df.ta.study()``` - - Example: All or Common Study - Run - ```py - # Run "All" - df.ta.study(ta.AllStudy, **kwargs) - - # Run "Common" - df.ta.study(ta.CommonStudy, **kwargs) - ``` - - Example: Custom Study - Create - ```py - DemoStudy = ta.Study( - name="Demo Study", - description="Example Study Group", - cores=0, # Usually faster than multiprocessing - ta = [ - {"kind": "sma", "length": 200}, - {"kind": "sma", "close": "volume", "length": 50}, - {"kind": "bbands", "length": 20}, - {"kind": "rsi"}, - {"kind": "macd", "fast": 8, "slow": 21}, - {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"} - ] - ``` - - Run - ```py - df.ta.study(DemoStudy, **kwargs) - ``` - - Note: - * See [also](../getting-started/usage.md) the Pandas TA - "Study" Examples - * Case-insensitive "All" is reserved. - - Warning: Multiprocessing - **Not recommended** for: - - * Small sets of indicators - * Indicator chains - """ - name: str - ta: List = field(default_factory=list) - cores: Int = cpu_count() - description: str = "" - created: str = get_time(to_string=True) - - - def __post_init__(self): - if isinstance(self.cores, int) and self.cores >= 0 and self.cores <= cpu_count(): - self.cores = int(self.cores) - - req_args = ["[X] Study requires the following argument(s):"] - - if self._is_name(): - req_args.append( - ' - name. Must be a string. Example: "My TA". Note: "all" is reserved.') - - if self.ta is None: - self.ta = None - elif not self._is_ta(): - s = " - ta. Format is a list of dicts. Example: [{'kind': 'sma', 'length': 10}]" - s += "\n Check the indicator for the correct arguments if you receive this error." - req_args.append(s) - - if len(req_args) > 1: - [print(_) for _ in req_args] - return None - - - def _is_name(self): - return self.name is None or not isinstance(self.name, str) - - - def _is_ta(self): - if isinstance(self.ta, list) and self.total_ta() > 0: - # Check that all elements of the list are dicts. - # Does not check if the dicts values are valid indicator kwargs - # User must check indicator documentation for all indicators args. - return all([isinstance(_, dict) and len(_.keys()) > 0 for _ in self.ta]) - - return False - - - def total_ta(self): - return len(self.ta) if self.ta is not None else 0 - - - -# All Study -AllStudy = Study( - name="All", - description="All the indicators with their default settings. Pandas TA default.", - ta=None, -) - -# Default (Example) Study. -CommonStudy = Study( - name="Common Price and Volume SMAs", - description="Common Price SMAs: 10, 20, 50, 200 and Volume SMA: 20.", - cores=0, - ta=[ - {"kind": "sma", "length": 10}, - {"kind": "sma", "length": 20}, - {"kind": "sma", "length": 50}, - {"kind": "sma", "length": 200}, - {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOL"} - ] -) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_time.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_time.py deleted file mode 100644 index bff01f05..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_time.py +++ /dev/null @@ -1,152 +0,0 @@ -# -*- coding: utf-8 -*- -from datetime import datetime -from time import localtime, perf_counter - -from pandas import DataFrame, Series, Timestamp, to_datetime -from pandas_ta._typing import Float, MaybeSeriesFrame, Optional, Tuple -from pandas_ta.maps import EXCHANGE_TZ - -__all__ = [ - "df_dates", - "df_month_to_date", - "df_quarter_to_date", - "df_year_to_date", - "final_time", - "get_time", - "mtd", - "qtd", - "to_utc", - "total_time", - "unix_convert", - "ytd", -] - - - -def df_dates( - df: DataFrame, dates: Tuple[str, list] = None -) -> MaybeSeriesFrame: - """Yields the DataFrame with the given dates""" - if dates is None: - return None - if not isinstance(dates, list): - dates = [dates] - return df[df.index.isin(dates)] - - -def df_month_to_date(df: DataFrame) -> DataFrame: - """Yields the Month-to-Date (MTD) DataFrame""" - in_mtd = df.index >= Timestamp.now().strftime("%Y-%m-01") - if any(in_mtd): - return df[in_mtd] - return df - - -def df_quarter_to_date(df: DataFrame) -> DataFrame: - """Yields the Quarter-to-Date (QTD) DataFrame""" - now = Timestamp.now() - for m in [1, 4, 7, 10]: - if now.month <= m: - in_qtr = df.index >= datetime(now.year, m, 1).strftime("%Y-%m-01") - if any(in_qtr): - return df[in_qtr] - return df[df.index >= now.strftime("%Y-%m-01")] - - -def df_year_to_date(df: DataFrame) -> DataFrame: - """Yields the Year-to-Date (YTD) DataFrame""" - in_ytd = df.index >= Timestamp.now().strftime("%Y-01-01") - if any(in_ytd): - return df[in_ytd] - return df - - -def final_time(stime: Float) -> str: - """Human readable elapsed time. Calculates the final time elapsed since - stime and returns a string with microseconds and seconds.""" - time_diff = perf_counter() - stime - return f"{time_diff * 1000:2.4f} ms ({time_diff:2.4f} s)" - - -def get_time( - exchange: str = "NYSE", full: bool = True, to_string: bool = False -) -> Optional[str]: - """Returns Current Time, Day of the Year and Percentage, and the current - time of the selected Exchange.""" - tz = EXCHANGE_TZ["NYSE"] # Default is NYSE (Eastern Time Zone) - if isinstance(exchange, str): - exchange = exchange.upper() - tz = EXCHANGE_TZ[exchange] - - # today = Timestamp.utcnow() - today = Timestamp.now() - date = f"{today.day_name()} {today.month_name()} {today.day}, {today.year}" - - _today = today.timetuple() - exchange_time = f"{(_today.tm_hour + tz) % 24}:{_today.tm_min:02d}:{_today.tm_sec:02d}" - - if full: - lt = localtime() - local_ = f"Local: {lt.tm_hour}:{lt.tm_min:02d}:{lt.tm_sec:02d} {lt.tm_zone}" - doy = f"Day {today.dayofyear}/365 ({100 * round(today.dayofyear/365, 2):.2f}%)" - exchange_ = f"{exchange}: {exchange_time}" - - s = f"{date}, {exchange_}, {local_}, {doy}" - else: - s = f"{date}, {exchange}: {exchange_time}" - - return s if to_string else print(s) - - -def total_time(df: DataFrame, tf: str = "years") -> Float: - """Calculates the total time of a DataFrame. Difference of the Last and - First index. Options: 'months', 'weeks', 'days', 'hours', 'minutes' - and 'seconds'. Default: 'years'. - Useful for annualization.""" - time_diff = df.index[-1] - df.index[0] - TimeFrame = { - "years": time_diff.days / 365.242199074074074, # PR 602 - "months": time_diff.days / 30.417, - "weeks": time_diff.days / 7, - "days": time_diff.days, - "hours": time_diff.days * 24, - "minutes": time_diff.total_seconds() / 60, - "seconds": time_diff.total_seconds() - } - - if isinstance(tf, str) and tf in TimeFrame.keys(): - return TimeFrame[tf] - return TimeFrame["years"] - - -def to_utc(df: DataFrame) -> DataFrame: - """Either localizes the DataFrame Index to UTC or it applies tz_convert to - set the Index to UTC. - """ - if not df.empty: - try: - df.index = df.index.tz_localize("UTC") - except TypeError: - df.index = df.index.tz_convert("UTC") - return df - - -def unix_convert(s: int | Series) -> datetime | str: - """unix_convert - - Convert timestamps from Polygon to readable datetime strings. - - Parameters: - s (int | Series): The timestamp(s). An integer posix timestamp - or a Series of timestamps. - - Returns: - (datetime | str): Converted datetime - """ - return to_datetime(s, unit="ms") - - -# Aliases -mtd = df_month_to_date -qtd = df_quarter_to_date -ytd = df_year_to_date diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_validate.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_validate.py deleted file mode 100644 index 6a05bf78..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/utils/_validate.py +++ /dev/null @@ -1,202 +0,0 @@ -# -*- coding: utf-8 -*- -from functools import partial -from numpy import ndarray, zeros -from pandas import DataFrame, Series, isnull -from pandas.api.types import is_datetime64_any_dtype -from pandas_ta._typing import ( - Array, - Float, - Int, - IntFloat, - List, - MaybeSeriesFrame, - Optional, - SeriesFrame, - np_floating, - np_integer -) - -__all__ = [ - "v_array", - "v_ascending", - "v_bool", - "v_dataframe", - "v_datetime_ordered", - "v_drift", - "v_float", - "v_int", - "v_list", - "v_lowerbound", - "v_mamode", - "v_null", - "v_offset", - "v_percent", - "v_pos_default", - "v_scalar", - "v_series", - "v_str", - "v_talib", - "v_tradingview", - "v_upperbound" -] - - -def v_array(var: Array, default: Array = zeros(0)) -> Array: - """Returns zeros(0) if not a valid Array""" - if isinstance(var, ndarray) and var.size > 0: - return var - return default - - -def v_ascending(var: bool) -> bool: - """Returns True by default""" - return partial(v_bool, default=True)(var=var) - - -def v_bool(var: bool, default: bool = True) -> bool: - """Returns default=True if var is not a bool.""" - if isinstance(var, bool): - return bool(var) - return default - - -def v_dataframe(df: MaybeSeriesFrame) -> None: - """Returns None if df is not a DataFrame.""" - if isinstance(df, DataFrame) and df.size > 0: - return df - return None - - - -def v_datetime_ordered(df: SeriesFrame) -> bool: - if df.shape[0] < 2: - return False - if is_datetime64_any_dtype(df.index): - np_dt_index = df.index.to_numpy() - if np_dt_index[0] < np_dt_index[-1]: - return True - return False - - -def v_drift(var: Int) -> Int: - """Defaults to 1""" - return partial(v_int, default=1, ne=0)(var=var) - - -def v_float( - var: IntFloat, default: IntFloat, ne: Optional[IntFloat] = 0.0 -) -> Float: - """Returns the default if var is not equal to the ne value.""" - _types = (float, int, np_floating, np_integer) - if isinstance(ne, _types) and isinstance(var, _types): - if float(var) != float(ne): - return float(var) - return float(default) - - -def v_int(var: Int, default: Int, ne: Optional[Int] = 0) -> Int: - """Returns the default if var is not equal to the ne value.""" - if isinstance(var, int) and int(var) != int(ne): - return int(var) - if isinstance(var, np_integer) and var.item() != int(ne): - return var.item() - return int(default) - - -def v_list(var: List, default: List = []) -> List: - """Returns [] if not a valid list""" - if isinstance(var, list) and len(var) > 0: - return var - return default - - -def v_lowerbound( - var: IntFloat, bound: IntFloat = 0, - default: IntFloat = 0, strict: bool = True, complement: bool = False -) -> IntFloat: - """Returns the default if var(iable) not greater(equal) than bound.""" - var_type = None - if isinstance(var, (float, np_floating)): var_type = float - if isinstance(var, (int, np_integer)): var_type = int - - if var_type is None: - return default - - valid = False - if strict: - valid = var_type(var) > var_type(bound) - else: - valid = var_type(var) >= var_type(bound) - - if complement: valid = not valid - - if valid: - return var_type(var) - return default - - -def v_mamode(var: str, default: str) -> str: # Could be an alias. - return v_str(var, default) - - -def v_null(var: IntFloat, default: IntFloat) -> IntFloat: - """Returns the var if not null else returns the default""" - return default if isnull(var) else var - - -def v_offset(var: Int) -> Int: - """Defaults to 0""" - return partial(v_int, default=0, ne=0)(var=var) - - -def v_percent(x: IntFloat) -> bool: - if isinstance(x, (float, int, np_floating, np_integer)): - return x is not None and 0 <= x <= 100 - return False - - -def v_pos_default( - var: IntFloat, default: IntFloat = 0, strict: bool = True, complement: bool = False -) -> IntFloat: - return partial(v_lowerbound, bound=0) \ - (var=var, default=default, strict=strict, complement=complement) - - -def v_scalar(var: IntFloat, default: Optional[IntFloat] = 1) -> Float: - """Returns the default if var is not an IntFloat.""" - if isinstance(var, (float, int, np_floating, np_integer)): - return float(var) - return float(default) - - -def v_series(series: Series, length: Optional[IntFloat] = 0) -> Optional[Series]: - """Returns None if the series does not meet the required minimum length.""" - if series is not None and isinstance(series, Series): - if series.size >= v_pos_default(length, 0): - return series - return None - - -def v_str(var: str, default: str) -> str: - """"Returns the default value if var is not a empty str""" - if isinstance(var, str) and len(var) > 0: - return f"{var}" - return f"{default}" - - -def v_talib(var: bool) -> bool: - """Returns True by default""" - return partial(v_bool, default=True)(var=var) - - -def v_tradingview(var: bool) -> bool: - """Returns True by default""" - return partial(v_bool, default=True)(var=var) - - -def v_upperbound( - var: IntFloat, bound: IntFloat = 0, - default: IntFloat = 0, strict: bool = True -) -> IntFloat: - return partial(v_lowerbound, complement=True)\ - (var=var, bound=bound, default=default, strict=strict) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/__init__.py deleted file mode 100644 index 8f804ca4..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -# -*- coding: utf-8 -*- -from .aberration import aberration -from .accbands import accbands -from .atr import atr -from .atrts import atrts -from .bbands import bbands -from .chandelier_exit import chandelier_exit -from .donchian import donchian -from .hwc import hwc -from .kc import kc -from .massi import massi -from .natr import natr -from .pdist import pdist -from .rvi import rvi -from .thermo import thermo -from .true_range import true_range -from .ui import ui - -__all__ = [ - "aberration", - "accbands", - "atr", - "atrts", - "bbands", - "chandelier_exit", - "donchian", - "hwc", - "kc", - "massi", - "natr", - "pdist", - "rvi", - "thermo", - "true_range", - "ui", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/aberration.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/aberration.py deleted file mode 100644 index 6f78982e..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/aberration.py +++ /dev/null @@ -1,85 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import hlc3, sma -from pandas_ta.utils import v_offset, v_pos_default, v_series -from .atr import atr - - - -def aberration( - high: Series, low: Series, close: Series, - length: Int = None, atr_length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Aberration - - Similar to Keltner Channels. - - Sources: - * [Request #46](https://github.com/twopirllc/pandas-ta/issues/46) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```5``` - atr_length (int): ATR period. Default: ```15``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 4 columns - """ - # Validate - length = v_pos_default(length, 5) - atr_length = v_pos_default(atr_length, 15) - _length = max(atr_length, length) + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - offset = v_offset(offset) - - # Calculate - atr_ = atr(high=high, low=low, close=close, length=atr_length) - jg = hlc3(high=high, low=low, close=close) - - zg = sma(jg, length) - sg = zg + atr_ - xg = zg - atr_ - - # Offset - if offset != 0: - zg = zg.shift(offset) - sg = sg.shift(offset) - xg = xg.shift(offset) - atr_ = atr_.shift(offset) - - # Fill - if "fillna" in kwargs: - zg.fillna(kwargs["fillna"], inplace=True) - sg.fillna(kwargs["fillna"], inplace=True) - xg.fillna(kwargs["fillna"], inplace=True) - atr_.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}_{atr_length}" - zg.name = f"ABER_ZG{_props}" - sg.name = f"ABER_SG{_props}" - xg.name = f"ABER_XG{_props}" - atr_.name = f"ABER_ATR{_props}" - zg.category = sg.category = "volatility" - xg.category = atr_.category = zg.category - - data = {zg.name: zg, sg.name: sg, xg.name: xg, atr_.name: atr_} - df = DataFrame(data, index=close.index) - df.name = f"ABER{_props}" - df.category = zg.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/accbands.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/accbands.py deleted file mode 100644 index c11ac70f..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/accbands.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.utils import ( - non_zero_range, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - - -def accbands( - high: Series, low: Series, close: Series, length: Int = None, - c: IntFloat = None, drift: Int = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Acceleration Bands - - This indicator, by Price Headley, creates lower and upper bands centered - around a moving average based on a ratio of it's High-Low range. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/acceleration-bands-abands/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```10``` - c (int): Multiplier. Default: ```4``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - length = v_pos_default(length, 20) - high = v_series(high, length) - low = v_series(low, length) - close = v_series(close, length) - - if high is None or low is None or close is None: - return - - c = v_pos_default(c, 4) - mamode = v_mamode(mamode, "sma") - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - high_low_range = non_zero_range(high, low) - hl_ratio = high_low_range / (high + low) - hl_ratio *= c - _lower = low * (1 - hl_ratio) - _upper = high * (1 + hl_ratio) - - lower = ma(mamode, _lower, length=length) - mid = ma(mamode, close, length=length) - upper = ma(mamode, _upper, length=length) - - # Offset - if offset != 0: - lower = lower.shift(offset) - mid = mid.shift(offset) - upper = upper.shift(offset) - - # Fill - if "fillna" in kwargs: - lower.fillna(kwargs["fillna"], inplace=True) - mid.fillna(kwargs["fillna"], inplace=True) - upper.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - lower.name = f"ACCBL_{length}" - mid.name = f"ACCBM_{length}" - upper.name = f"ACCBU_{length}" - mid.category = upper.category = lower.category = "volatility" - - data = {lower.name: lower, mid.name: mid, upper.name: upper} - df = DataFrame(data, index=close.index) - df.name = f"ACCBANDS_{length}" - df.category = mid.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/atr.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/atr.py deleted file mode 100644 index 397e2ad6..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/atr.py +++ /dev/null @@ -1,107 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan, nan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_bool, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib -) -from .true_range import true_range - - - -def atr( - high: Series, low: Series, close: Series, length: Int = None, - mamode: str = None, talib: bool = None, - prenan: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Average True Range - - This indicator attempts to quantify volatility with a focus on gaps or - limit moves. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Average_True_Range_(ATR)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - mamode (str): See ```help(ta.ma)```. Default: ```"rma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - prenan (bool): Sets initial values to ```np.nan``` based - on ```drift```. Default: ```False``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - percent (bool): Return as percent. Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - _length = length + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mamode = v_mamode(mamode, "rma") - mode_tal = v_talib(talib) - prenan = v_bool(prenan, False) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import ATR - atr = ATR(high, low, close, length) - else: - tr = true_range( - high=high, low=low, close=close, - talib=mode_tal, prenan=prenan, drift=drift - ) - if all(isnan(tr)): - return # Emergency Break - - presma = kwargs.pop("presma", True) - if presma: - sma_nth = tr[0:length].mean() - tr[:length - 1] = nan - tr.iloc[length - 1] = sma_nth - atr = ma(mamode, tr, length=length, talib=mode_tal) - - if all(isnan(atr)): - return # Emergency Break - - percent = kwargs.pop("percent", False) - if percent: - atr *= 100 / close - - # Offset - if offset != 0: - atr = atr.shift(offset) - - # Fill - if "fillna" in kwargs: - atr.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - atr.name = f"ATR{mamode[0]}{'p' if percent else ''}_{length}" - atr.category = "volatility" - - return atr diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/atrts.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/atrts.py deleted file mode 100644 index ea880287..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/atrts.py +++ /dev/null @@ -1,140 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import isnan, nan, uintc, zeros_like -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma as _ma -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib -) -from pandas_ta.volatility import atr - - - -@njit(cache=True) -def nb_atrts(x, ma, atr_, length, ma_length): - m = x.size - k = max(length, ma_length) - - result = x.copy() - up = zeros_like(x, dtype=uintc) - dn = zeros_like(x, dtype=uintc) - - expn = x > ma - up[expn], dn[~expn] = 1, 1 - up[:k], dn[:k] = 0, 0 - result[:k] = nan - - for i in range(k, m): - pr = result[i - 1] - if up[i]: - result[i] = x[i] - atr_[i] - if result[i] < pr: - result[i] = pr - if dn[i]: - result[i] = x[i] + atr_[i] - if result[i] > pr: - result[i] = pr - - long, short = result * up, result * dn - long[long == 0], short[short == 0] = nan, nan - - return result, long, short - - -def atrts( - high: Series, low: Series, close: Series, length: Int = None, - ma_length: Int = None, k: IntFloat = None, - mamode: str = None, talib: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """ATR Trailing Stop - - This indicator attempts to identify exits for long and short positions. - To determine trend, it uses a moving average with a scalable ATR. - - Sources: - * [motivewave](https://www.motivewave.com/studies/atr_trailing_stops.htm) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - ma_length (int): MA Length. Default: ```20``` - k (int): ATR multiplier. Default: ```3``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - percent (bool): Return as percent. Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - ma_length = v_pos_default(ma_length, 20) - _length = length + ma_length - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - k = v_pos_default(k, 3.0) - mamode = v_mamode(mamode, "ema") - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import ATR - atr_ = ATR(high, low, close, length) - else: - atr_ = atr( - high=high, low=low, close=close, length=length, - mamode=mamode, drift=drift, talib=mode_tal, - offset=offset, **kwargs - ) - - if all(isnan(atr_)): - return # Emergency Break - - atr_ *= k - ma_ = _ma(mamode, close, length=ma_length, talib=mode_tal) - - np_close, np_ma, np_atr = close.to_numpy(), ma_.to_numpy(), atr_.to_numpy() - np_atrts_, _, _ = nb_atrts(np_close, np_ma, np_atr, length, ma_length) - - percent = kwargs.pop("percent", False) - if percent: - np_atrts_ *= 100 / np_close - - atrts = Series(np_atrts_, index=close.index) - - # Offset - if offset != 0: - atrts = atrts.shift(offset) - - # Fill - if "fillna" in kwargs: - atrts.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"ATRTS{mamode[0]}{'p' if percent else ''}" - atrts.name = f"{_props}_{length}_{ma_length}_{k}" - atrts.category = "volatility" - - return atrts diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/bbands.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/bbands.py deleted file mode 100644 index aa235360..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/bbands.py +++ /dev/null @@ -1,124 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.maps import Imports -from pandas_ta.statistics import stdev -from pandas_ta.utils import ( - non_zero_range, - tal_ma, - v_mamode, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def bbands( - close: Series, length: Int = None, - lower_std: IntFloat = None, upper_std: IntFloat = None, - ddof: Int = None, mamode: str = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Bollinger Bands - - This indicator, by John Bollinger, attempts to quantify volatility by - creating lower and upper bands centered around a moving average. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Bollinger_Bands_(BB)) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```5``` - lower_std (IntFloat): Lower standard deviation. Default: ```2.0``` - upper_std (IntFloat): Upper standard deviation. Default: ```2.0``` - ddof (int): Degrees of Freedom to use. Default: ```0``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - ddof (int): By default, uses Pandas ```ddof=1```. - For Numpy calculation, use ```0```. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 5 columns - - Note: - * TA Lib does not have a ```ddof``` parameter. - * The divisor used in calculations is: ```N - ddof```, where ```N``` - is the number of elements. To use ```ddof```, set ```talib=False```. - """ - # Validate - length = v_pos_default(length, 5) - close = v_series(close, length) - - if close is None: - return - - lower_std = v_pos_default(lower_std, 2.0) - upper_std = v_pos_default(upper_std, 2.0) - ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1 - mamode = v_mamode(mamode, "sma") - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import BBANDS - upper, mid, lower = BBANDS(close, length, upper_std, lower_std, tal_ma(mamode)) - else: - std_dev = stdev(close=close, length=length, ddof=ddof, talib=mode_tal) - lower_deviations = lower_std * std_dev - upper_deviations = upper_std * std_dev - - mid = ma(mamode, close, length=length, talib=mode_tal, **kwargs) - lower = mid - lower_deviations - upper = mid + upper_deviations - - ulr = non_zero_range(upper, lower) - bandwidth = 100 * ulr / mid - percent = non_zero_range(close, lower) / ulr - - # Offset - if offset != 0: - lower = lower.shift(offset) - mid = mid.shift(offset) - upper = upper.shift(offset) - bandwidth = bandwidth.shift(offset) - percent = percent.shift(offset) - - # Fill - if "fillna" in kwargs: - lower.fillna(kwargs["fillna"], inplace=True) - mid.fillna(kwargs["fillna"], inplace=True) - upper.fillna(kwargs["fillna"], inplace=True) - bandwidth.fillna(kwargs["fillna"], inplace=True) - percent.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}_{lower_std}_{upper_std}" - lower.name = f"BBL{_props}" - mid.name = f"BBM{_props}" - upper.name = f"BBU{_props}" - bandwidth.name = f"BBB{_props}" - percent.name = f"BBP{_props}" - upper.category = lower.category = "volatility" - mid.category = bandwidth.category = upper.category - - data = { - lower.name: lower, - mid.name: mid, - upper.name: upper, - bandwidth.name: bandwidth, - percent.name: percent - } - df = DataFrame(data, index=close.index) - df.name = f"BBANDS{_props}" - df.category = mid.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/chandelier_exit.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/chandelier_exit.py deleted file mode 100644 index 84433bfe..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/chandelier_exit.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan, nan -from pandas import Series, DataFrame -from pandas_ta.volatility import atr -from pandas_ta._typing import Int, IntFloat, DictLike -from pandas_ta.utils import ( - v_bool, - v_drift, - v_mamode, - v_pos_default, - v_offset, - v_series, - v_talib -) - - - -def chandelier_exit( - high: Series, low: Series, close: Series, - high_length: Int = None, low_length: Int = None, - atr_length: Int = None, multiplier: IntFloat = None, - mamode: str = None, talib: bool = None, use_close: bool = None, - drift: Int = None, offset: Int = None, **kwargs: DictLike -): - """Chandelier Exit - - This indicator attempts to identify trailing stop-losses based on ATR. - - Sources: - * [stockcharts](https://school.stockcharts.com/doku.php?id=technical_indicators:chandelier_exit) - * [tradingview](https://in.tradingview.com/scripts/chandelier/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - high_length (int): Highest high period. Default: ```22``` - low_length (int): Lowest low period. Default: ```22``` - atr_length (int) : ATR length. Default: ```14``` - multiplier (float): Lower & Upper Bands scalar. Default: ```2.0``` - mamode (str): See ```help(ta.ma)```. Default: ```"rma"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - use_close (bool): Use ```max(high_length, low_length)``` for - the ```close```. Default: ```False``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - atr_length = v_pos_default(atr_length, 14) - high_length = v_pos_default(high_length, 22) - low_length = v_pos_default(low_length, 22) - roll_length = max(high_length, low_length) - _length = max(atr_length, roll_length) + 1 - - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - multiplier = v_pos_default(multiplier, 2.0) - mamode = v_mamode(mamode, "rma") - mode_tal = v_talib(talib) - use_close = v_bool(use_close, False) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - atr_ = atr( - high=high, low=low, close=close, length=atr_length, - mamode=mamode, talib=mode_tal, drift=drift, offset=offset - ) - if atr_ is None or all(isnan(atr_)): - return - - atr_mult = atr_ * multiplier - - if use_close: - long = close.rolling(roll_length, min_periods=1).max() - atr_mult - short = close.rolling(roll_length, min_periods=1).min() + atr_mult - else: - long = high.rolling(high_length, min_periods=1).max() - atr_mult - short = low.rolling(low_length, min_periods=1).min() + atr_mult - - uptrend = (close > long.shift(drift)).astype(int) - downtrend = -(close < short.shift(drift)).astype(int) - - direction = uptrend + downtrend - if direction.iloc[0] == 0: - direction.iloc[0] = 1 - direction = direction.replace(0, nan).ffill() - - # Offset - if offset != 0: - long = long.shift(offset) - short = short.shift(offset) - direction = direction.shift(offset) - - # Fill - if "fillna" in kwargs: - long.fillna(kwargs["fillna"], inplace=True) - short.fillna(kwargs["fillna"], inplace=True) - direction.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _name = "CHDLREXT" - _props = f"_{high_length}_{low_length}_{atr_length}_{multiplier}" - if use_close: - _props = f"_CLOSE_{_props}" - - data = { - f"{_name}l{_props}": long, - f"{_name}s{_props}": short, - f"{_name}d{_props}": direction - } - df = DataFrame(data, index=close.index) - df.name = f"{_name}{_props}" - df.category = "volatility" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/donchian.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/donchian.py deleted file mode 100644 index 3355f408..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/donchian.py +++ /dev/null @@ -1,77 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def donchian( - high: Series, low: Series, - lower_length: Int = None, upper_length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Donchian Channels - - This indicator attempt to quantify volatility similarily to - Bollinger Bands and Keltner Channels. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Donchian_Channels_(DC)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - lower_length (int): Lower period. Default: ```20``` - upper_length (int): Upper period. Default: ```20``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - lower_length = v_pos_default(lower_length, 20) - upper_length = v_pos_default(upper_length, 20) - lmin_periods = int(kwargs.pop("lmin_periods", lower_length)) - umin_periods = int(kwargs.pop("umin_periods", upper_length)) - - _length = max(lower_length, lmin_periods, upper_length, umin_periods) - high = v_series(high, _length) - low = v_series(low, _length) - - if high is None or low is None: - return - - offset = v_offset(offset) - - # Calculate - lower = low.rolling(lower_length, min_periods=lmin_periods).min() - upper = high.rolling(upper_length, min_periods=umin_periods).max() - mid = 0.5 * (lower + upper) - - # Fill - if "fillna" in kwargs: - lower.fillna(kwargs["fillna"], inplace=True) - mid.fillna(kwargs["fillna"], inplace=True) - upper.fillna(kwargs["fillna"], inplace=True) - - # Offset - if offset != 0: - lower = lower.shift(offset) - mid = mid.shift(offset) - upper = upper.shift(offset) - - # Name and Category - lower.name = f"DCL_{lower_length}_{upper_length}" - mid.name = f"DCM_{lower_length}_{upper_length}" - upper.name = f"DCU_{lower_length}_{upper_length}" - mid.category = upper.category = lower.category = "volatility" - - data = {lower.name: lower, mid.name: mid, upper.name: upper} - df = DataFrame(data, index=high.index) - df.name = f"DC_{lower_length}_{upper_length}" - df.category = mid.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/hwc.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/hwc.py deleted file mode 100644 index a450dc0f..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/hwc.py +++ /dev/null @@ -1,142 +0,0 @@ -# -*- coding: utf-8 -*- -from sys import float_info as sflt -from numpy import sqrt -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series - - - -def hwc( - close: Series, scalar: IntFloat = None, channels: bool = None, - na: IntFloat = None, nb: IntFloat = None, - nc: IntFloat = None, nd: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Holt-Winter Channel - - This indicator creates a three-parameter moving average using the - "Holt-Winters" method. - - Sources: - * [rengel8](https://github.com/rengel8) (2021-08-11) based on the - implementation from "MetaTrader 5" - * [mql5](https://www.mql5.com/en/code/20857) - - Parameters: - close (Series): ```close``` Series - scalar (float): Channel scalar. Default: ```1``` - channels (bool): Return width and percentage columns. - Default: ```True``` - na (float): Smoothed series in range ```[0, 1]```. Default: ```0.2``` - nb (float): Trend value in range ```[0, 1]```. Default: ```0.1``` - nc (float): Seasonality value in range ```[0, 1]```. Default: ```0.1``` - nd (float): Channel value in range ```[0, 1]```. Default: ```0.1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - close = v_series(close, 1) - scalar = v_pos_default(scalar, 1) - channels = v_bool(channels, True) - na = v_pos_default(na, 0.2) - nb = v_pos_default(nb, 0.1) - nc = v_pos_default(nc, 0.1) - nd = v_pos_default(nd, 0.1) - offset = v_offset(offset) - - if close is None: - return - - # Calculate Result - last_a = last_v = last_var = 0 - last_f = last_price = last_result = close.iloc[0] - lower, result, upper = [], [], [] - chan_pct_width, chan_width = [], [] - - m = close.size - for i in range(m): - F = (1.0 - na) * (last_f + last_v + 0.5 * last_a) + na * close.iloc[i] - V = (1.0 - nb) * (last_v + last_a) + nb * (F - last_f) - A = (1.0 - nc) * last_a + nc * (V - last_v) - result.append((F + V + 0.5 * A)) - - var = (1.0 - nd) * last_var + \ - nd * (last_price - last_result) * (last_price - last_result) - stddev = sqrt(last_var) - upper.append(result[i] + scalar * stddev) - lower.append(result[i] - scalar * stddev) - - if channels: - # channel width - chan_width.append(upper[i] - lower[i]) - # channel percentage price position - chan_pct_width.append( - (close.iloc[i] - lower[i]) / (upper[i] - lower[i] + sflt.epsilon) - ) - - # update values - last_price = close.iloc[i] - last_a = A - last_f = F - last_v = V - last_var = var - last_result = result[i] - - # Aggregate - hwc = Series(result, index=close.index) - hwc_upper = Series(upper, index=close.index) - hwc_lower = Series(lower, index=close.index) - if channels: - hwc_width = Series(chan_width, index=close.index) - hwc_pctwidth = Series(chan_pct_width, index=close.index) - - # Offset - if offset != 0: - hwc = hwc.shift(offset) - hwc_upper = hwc_upper.shift(offset) - hwc_lower = hwc_lower.shift(offset) - if channels: - hwc_width = hwc_width.shift(offset) - hwc_pctwidth = hwc_pctwidth.shift(offset) - - # Fill - if "fillna" in kwargs: - hwc.fillna(kwargs["fillna"], inplace=True) - hwc_upper.fillna(kwargs["fillna"], inplace=True) - hwc_lower.fillna(kwargs["fillna"], inplace=True) - if channels: - hwc_width.fillna(kwargs["fillna"], inplace=True) - hwc_pctwidth.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{scalar}" - hwc.name = f"HWM{_props}" - hwc_upper.name = f"HWU{_props}" - hwc_lower.name = f"HWL{_props}" - hwc.category = hwc_upper.category = hwc_lower.category = "volatility" - - if channels: - data = { - hwc.name: hwc, - hwc_lower.name: hwc_lower, - hwc_upper.name: hwc_upper, - f"HWW{_props}": hwc_width, - f"HWPCT{_props}": hwc_pctwidth - } - else: - data = { - hwc.name: hwc, - hwc_lower.name: hwc_lower, - hwc_upper.name: hwc_upper - } - df = DataFrame(data, index=close.index) - df.name = f"HWC_{scalar}" - df.category = hwc.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/kc.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/kc.py deleted file mode 100644 index 366c7622..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/kc.py +++ /dev/null @@ -1,95 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.utils import ( - high_low_range, - v_bool, - v_mamode, - v_offset, - v_pos_default, - v_series -) -from .true_range import true_range - - - -def kc( - high: Series, low: Series, close: Series, - length: Int = None, scalar: IntFloat = None, - tr: bool = None, mamode: str = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Keltner Channels - - This indicator attempts to identify volatility similarily to - Bollinger Bands and Donchian Channels. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Keltner_Channels_(KC)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - scalar (float): Band scalar. Default: ```2``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - tr (bool): Use True Range calculation. Otherwise use ```high - low``` - for range computation. Default: ```True``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - length = v_pos_default(length, 20) - _length = length + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - scalar = v_pos_default(scalar, 2) - tr = v_bool(tr, True) - mamode = v_mamode(mamode, "ema") - offset = v_offset(offset) - - # Calculate - range_ = true_range(high, low, close) if tr else high_low_range(high, low) - basis = ma(mamode, close, length=length) - band = ma(mamode, range_, length=length) - - lower = basis - scalar * band - upper = basis + scalar * band - - # Offset - if offset != 0: - lower = lower.shift(offset) - basis = basis.shift(offset) - upper = upper.shift(offset) - - # Fill - if "fillna" in kwargs: - lower.fillna(kwargs["fillna"], inplace=True) - basis.fillna(kwargs["fillna"], inplace=True) - upper.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"{mamode.lower()[0] if len(mamode) else ''}_{length}_{scalar}" - lower.name = f"KCL{_props}" - basis.name = f"KCB{_props}" - upper.name = f"KCU{_props}" - basis.category = upper.category = lower.category = "volatility" - - data = {lower.name: lower, basis.name: basis, upper.name: upper} - df = DataFrame(data, index=close.index) - df.name = f"KC{_props}" - df.category = basis.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/massi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/massi.py deleted file mode 100644 index 3a86b65b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/massi.py +++ /dev/null @@ -1,77 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import ema -from pandas_ta.utils import non_zero_range, v_offset, v_pos_default, v_series - - - -def massi( - high: Series, low: Series, fast: Int = None, slow: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Mass Index - - This indicator attempts to use a High-Low Range to identify trend - reversals based on range expansions. - - Sources: - * [stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - fast (int): Fast period. Default: ```9``` - slow (int): Slow period. Default: ```25``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - fast = v_pos_default(fast, 9) - slow = v_pos_default(slow, 25) - if slow < fast: - fast, slow = slow, fast - _length = 2 * max(fast, slow) - min(fast, slow) - high = v_series(high, _length) - low = v_series(low, _length) - - if high is None or low is None: - return - - offset = v_offset(offset) - if "length" in kwargs: - kwargs.pop("length") - - # Calculate - high_low_range = non_zero_range(high, low) - hl_ema1 = ema(close=high_low_range, length=fast, **kwargs) - if all(isnan(hl_ema1)): - return # Emergency Break - hl_ema2 = ema(close=hl_ema1, length=fast, **kwargs) - if all(isnan(hl_ema2)): - return # Emergency Break - - hl_ratio = hl_ema1 / hl_ema2 - massi = hl_ratio.rolling(slow, min_periods=slow).sum() - if all(isnan(massi)): - return # Emergency Break - - # Offset - if offset != 0: - massi = massi.shift(offset) - - # Fill - if "fillna" in kwargs: - massi.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - massi.name = f"MASSI_{fast}_{slow}" - massi.category = "volatility" - - return massi diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/natr.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/natr.py deleted file mode 100644 index f59f9880..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/natr.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_bool, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_scalar, - v_series, - v_talib -) -from pandas_ta.volatility import atr - - - -def natr( - high: Series, low: Series, close: Series, - length: Int = None, scalar: IntFloat = None, mamode: str = None, - talib: bool = None, prenan: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Normalized Average True Range - - This indicator applies a normalizer to Average True Range. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/normalized-average-true-range-natr/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```20``` - scalar (float): Scalar. Default: ```100``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - talib (bool): If installed, use TA Lib. Default: ```True``` - prenan (bool): Sets initial values to ```np.nan``` based - on ```drift```. Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9506743353852364)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 14) - _length = length + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - scalar = v_scalar(scalar, 100) - mamode = v_mamode(mamode, "ema") - mode_tal = v_talib(talib) - prenan = v_bool(prenan, False) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import NATR - natr = NATR(high, low, close, length) - else: - natr = (scalar / close) * \ - atr( - high=high, low=low, close=close, length=length, - mamode=mamode, drift=drift, talib=mode_tal, - prenan=prenan, offset=offset, **kwargs - ) - - # Offset - if offset != 0: - natr = natr.shift(offset) - - # Fill - if "fillna" in kwargs: - natr.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - natr.name = f"NATR_{length}" - natr.category = "volatility" - - return natr diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/pdist.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/pdist.py deleted file mode 100644 index f4193288..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/pdist.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import non_zero_range, v_drift, v_offset, v_series - - - -def pdist( - open_: Series, high: Series, low: Series, close: Series, - drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Price Distance - - This indicator attempts to quantify the magnitude covered by - price movements. - - Sources: - * [prorealcode](https://www.prorealcode.com/prorealtime-indicators/pricedistance/) - - Parameters: - open_ (Series): ```open``` Series - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - drift = v_drift(drift) - open_ = v_series(open_) - high = v_series(high) - low = v_series(low) - close = v_series(close) - offset = v_offset(offset) - - # Calculate - pdist = 2 * non_zero_range(high, low) - if all(isnan(pdist)): - return # Emergency Break - - pdist += non_zero_range(open_, close.shift(drift)).abs() - pdist -= non_zero_range(close, open_).abs() - - if all(isnan(pdist)): - return # Emergency Break - - # Offset - if offset != 0: - pdist = pdist.shift(offset) - - # Fill - if "fillna" in kwargs: - pdist.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - pdist.name = "PDIST" - pdist.category = "volatility" - - return pdist diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/rvi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/rvi.py deleted file mode 100644 index 1f58af32..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/rvi.py +++ /dev/null @@ -1,117 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.ma import ma -from pandas_ta.statistics import stdev -from pandas_ta.utils import ( - unsigned_differences, - v_bool, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - - -def _rvi(source, length, scalar, mode, drift): - std = stdev(source, length) - pos, neg = unsigned_differences(source, drift) - - pos_std = pos * std - neg_std = neg * std - - pos_avg = ma(mode, pos_std, length=length) - neg_avg = ma(mode, neg_std, length=length) - - result = scalar * pos_avg / (pos_avg + neg_avg) - return result - - -def rvi( - close: Series, high: Series = None, low: Series = None, - length: Int = None, scalar: IntFloat = None, - refined: bool = None, thirds: bool = None, - mamode: str = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Relative Volatility Index - - This indicator attempts to quantify volatility using standard deviation. - - Sources: - * [motivewave](https://www.motivewave.com/studies/relative_volatility_index.htm) - * [tradingview A](https://www.tradingview.com/script/mLZJqxKn-Relative-Volatility-Index/) - * [tradingview B](https://www.tradingview.com/support/solutions/43000594684-relative-volatility-index/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - scalar (float): Bands scalar. Default: ```100``` - refined (bool): Use 'refined' calculation which is the average of - RVI(high) and RVI(low) instead of RVI(close). Default: ```False``` - thirds (bool): Average of ```high```, ```low``` and ```close```. - Default: ```False``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - length = v_pos_default(length, 14) - close = v_series(close, length + 2) - - if close is None: - return - - scalar = v_pos_default(scalar, 100) - refined = v_bool(refined, False) - thirds = v_bool(thirds, False) - mamode = v_mamode(mamode, "ema") - drift = v_drift(drift) - offset = v_offset(offset) - - if refined or thirds: - high = v_series(high) - low = v_series(low) - - # Calculate - _mode = "" - if refined: - high_rvi = _rvi(high, length, scalar, mamode, drift) - low_rvi = _rvi(low, length, scalar, mamode, drift) - rvi = 0.5 * (high_rvi + low_rvi) - _mode = "r" - elif thirds: - high_rvi = _rvi(high, length, scalar, mamode, drift) - low_rvi = _rvi(low, length, scalar, mamode, drift) - close_rvi = _rvi(close, length, scalar, mamode, drift) - rvi = (high_rvi + low_rvi + close_rvi) / 3.0 - _mode = "t" - else: - rvi = _rvi(close, length, scalar, mamode, drift) - - if all(isnan(rvi)): - return # Emergency Break - - # Offset - if offset != 0: - rvi = rvi.shift(offset) - - # Fill - if "fillna" in kwargs: - rvi.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - rvi.name = f"RVI{_mode}_{length}" - rvi.category = "volatility" - - return rvi diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/thermo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/thermo.py deleted file mode 100644 index e44bcdbf..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/thermo.py +++ /dev/null @@ -1,111 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - v_bool, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - - -def thermo( - high: Series, low: Series, length: Int = None, - long: Int = None, short: Int = None, - mamode: str = None, asint: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Elders Thermometer - - This indicator, by Dr Alexander Elder, attempts to quantify volatility. - - Sources: - * [motivewave](https://www.motivewave.com/studies/elders_thermometer.htm) - * [tradingview](https://www.tradingview.com/script/HqvTuEMW-Elder-s-Market-Thermometer-LazyBear/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - length (int): The period. Default: ```20``` - long (int): Buy factor. Default: ```2``` - short (float): Sell factor. Default: ```0.5``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - asint (int): Returns as int. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 4 columns - """ - # Validate - length = v_pos_default(length, 20) - high = v_series(high, length + 1) - low = v_series(low, length + 1) - - if high is None or low is None: - return - - long = v_pos_default(long, 2) - short = v_pos_default(short, 0.5) - mamode = v_mamode(mamode, "ema") - asint = v_bool(asint, True) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - thermoL = (low.shift(drift) - low).abs() - thermoH = (high - high.shift(drift)).abs() - - thermo = thermoL - thermo = thermo.where(thermoH < thermoL, thermoH) - thermo.index = high.index - - thermo_ma = ma(mamode, thermo, length=length) - thermo_long = thermo < (thermo_ma * long) - thermo_short = thermo > (thermo_ma * short) - - if asint: - thermo_long = thermo_long.astype(int) - thermo_short = thermo_short.astype(int) - - # Offset - if offset != 0: - thermo = thermo.shift(offset) - thermo_ma = thermo_ma.shift(offset) - thermo_long = thermo_long.shift(offset) - thermo_short = thermo_short.shift(offset) - - # Fill - if "fillna" in kwargs: - thermo.fillna(kwargs["fillna"], inplace=True) - thermo_ma.fillna(kwargs["fillna"], inplace=True) - thermo_long.fillna(kwargs["fillna"], inplace=True) - thermo_short.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}_{long}_{short}" - thermo.name = f"THERMO{_props}" - thermo_ma.name = f"THERMOma{_props}" - thermo_long.name = f"THERMOl{_props}" - thermo_short.name = f"THERMOs{_props}" - thermo.category = thermo_ma.category = "volatility" - thermo_long.category = thermo_short.category = thermo.category - - data = { - thermo.name: thermo, - thermo_ma.name: thermo_ma, - thermo_long.name: thermo_long, - thermo_short.name: thermo_short - } - df = DataFrame(data, index=high.index) - df.name = f"THERMO{_props}" - df.category = thermo.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/true_range.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/true_range.py deleted file mode 100644 index daac99cb..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/true_range.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan, nan -from pandas import Series, concat -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - non_zero_range, - v_bool, - v_drift, - v_offset, - v_series, - v_talib -) - - - -def true_range( - high: Series, low: Series, close: Series, - talib: bool = None, prenan: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """True Range - - This indicator attempts to quantify a High-Low range including potential - gap scenarios. - - Sources: - * [macroption](https://www.macroption.com/true-range/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - talib (bool): If installed, use TA Lib. Default: ```True``` - prenan (bool): Sets initial values to ```nan``` based - on ```drift```. Default: ```False``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9999999999999999)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - _length = 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - - if high is None or low is None or close is None: - return - - mode_tal = v_talib(talib) - prenan = v_bool(prenan, False) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import TRANGE - true_range = TRANGE(high, low, close) - else: - hl_range = non_zero_range(high, low) - pc = close.shift(drift) - ranges = [hl_range, high - pc, pc - low] - true_range = concat(ranges, axis=1) - true_range = true_range.abs().max(axis=1) - if prenan: - true_range.iloc[:drift] = nan - - if all(isnan(true_range)): - return # Emergency Break - - # Offset - if offset != 0: - true_range = true_range.shift(offset) - - # Fill - if "fillna" in kwargs: - true_range.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - true_range.name = f"TRUERANGE_{drift}" - true_range.category = "volatility" - - return true_range diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/ui.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/ui.py deleted file mode 100644 index 494bca8b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volatility/ui.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import sqrt -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import sma -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def ui( - close: Series, length: Int = None, scalar: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Ulcer Index - - This indicator, by Peter Martin, attempts to quantify downside volatility - with a Quadratic Mean. - - Sources: - * [tangotools](http://www.tangotools.com/ui/ui.htm) - * [tradingtechnologies](https://library.tradingtechnologies.com/trade/chrt-ti-ulcer-index.html) - * [wikipedia](https://en.wikipedia.org/wiki/Ulcer_index) - - Parameters: - close (Series): ```close``` Series - length (int): The period. Default: ```14``` - scalar (float): Bands scalar. Default: ```100``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - everget (value): Use Evergets' TradingView SMA. - Default: ```False``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - scalar = v_pos_default(scalar, 100) - close = v_series(close, 2 * length - 1) - - if close is None: - return - - offset = v_offset(offset) - - # Calculate - highest_close = close.rolling(length).max() - downside = scalar * (close - highest_close) / highest_close - d2 = downside * downside - - everget = kwargs.pop("everget", False) - if everget: - # Everget uses SMA instead of SUM for calculation - _ui = sma(d2, length) - else: - _ui = d2.rolling(length).sum() - ui = sqrt(_ui / length) - - # Offset - if offset != 0: - ui = ui.shift(offset) - - # Fill - if "fillna" in kwargs: - ui.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - ui.name = f"UI{'' if not everget else 'e'}_{length}" - ui.category = "volatility" - - return ui diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/__init__.py deleted file mode 100644 index 6f6575e9..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/__init__.py +++ /dev/null @@ -1,44 +0,0 @@ -# -*- coding: utf-8 -*- -from .ad import ad -from .adosc import adosc -from .aobv import aobv -from .cmf import cmf -from .efi import efi -from .eom import eom -from .kvo import kvo -from .mfi import mfi -from .nvi import nvi -from .obv import obv -from .pvi import pvi -from .pvo import pvo -from .pvol import pvol -from .pvr import pvr -from .pvt import pvt -from .tsv import tsv -from .vhm import vhm -from .vp import vp -from .vwap import vwap -from .vwma import vwma - -__all__ = [ - "ad", - "adosc", - "aobv", - "cmf", - "efi", - "eom", - "kvo", - "mfi", - "nvi", - "obv", - "pvi", - "pvo", - "pvol", - "pvr", - "pvt", - "tsv", - "vhm", - "vp", - "vwap", - "vwma", -] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/ad.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/ad.py deleted file mode 100644 index e7f4a678..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/ad.py +++ /dev/null @@ -1,72 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import non_zero_range, v_offset, v_series, v_talib - - - -def ad( - high: Series, low: Series, close: Series, volume: Series, - open_: Series = None, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Accumulation/Distribution - - This indicator attempts to quantify accumulation/distribution from a - relative position within it's High-Low range and volume. - - Sources: - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/accumulationdistribution-ad/) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - volume (Series): ```volume``` Series - open_ (Series): Optional ```open``` Series - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - high = v_series(high) - low = v_series(low) - close = v_series(close) - volume = v_series(volume) - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal and volume.size: - from talib import AD - ad = AD(high, low, close, volume) - else: - if open_ is not None: - open_ = v_series(open_) - ad = non_zero_range(close, open_) # AD with Open - else: - ad = 2 * close - (high + low) # AD with High, Low, Close - - high_low_range = non_zero_range(high, low) - ad *= volume / high_low_range - ad = ad.cumsum() - - # Offset - if offset != 0: - ad = ad.shift(offset) - - # Fill - if "fillna" in kwargs: - ad.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - ad.name = "AD" if open_ is None else "ADo" - ad.category = "volume" - - return ad diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/adosc.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/adosc.py deleted file mode 100644 index f7dccd47..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/adosc.py +++ /dev/null @@ -1,95 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.overlap import ema -from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib -from pandas_ta.volume import ad - - - -def adosc( - high: Series, low: Series, close: Series, volume: Series, - open_: Series = None, fast: Int = None, slow: Int = None, - talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Accumulation/Distribution Oscillator - - This indicator is an AD oscillator. It is interpreted similarly - to MACD and APO. - - Sources: - * [investopedia](https://www.investopedia.com/articles/active-trading/031914/understanding-chaikin-oscillator.asp) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - open_ (Series): ```open``` Series - volume (Series): ```volume``` Series - fast (int): Fast MA period. Default: ```12``` - slow (int): Slow MA period. Default: ```26``` - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - Also known as Chaikin Oscillator - - Warning: - TA-Lib Correlation: ```np.float64(0.9989721423605135)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - fast = v_pos_default(fast, 3) - slow = v_pos_default(slow, 10) - _length = max(fast, slow) - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - volume = v_series(volume, _length) - - if high is None or low is None or close is None or volume is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import ADOSC - adosc = ADOSC(high, low, close, volume, fast, slow) - else: - # remove length so it doesn't override ema length - if "length" in kwargs: - kwargs.pop("length") - - ad_ = ad( - high=high, low=low, close=close, volume=volume, - open_=open_, talib=mode_tal - ) - fast_ad = ema(close=ad_, length=fast, **kwargs, talib=mode_tal) - slow_ad = ema(close=ad_, length=slow, **kwargs, talib=mode_tal) - adosc = fast_ad - slow_ad - - # Offset - if offset != 0: - adosc = adosc.shift(offset) - - # Fill - if "fillna" in kwargs: - adosc.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - adosc.name = f"ADOSC_{fast}_{slow}" - adosc.category = "volume" - - return adosc diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/aobv.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/aobv.py deleted file mode 100644 index f850c05c..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/aobv.py +++ /dev/null @@ -1,113 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.trend.long_run import long_run -from pandas_ta.trend.short_run import short_run -from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series -from .obv import obv - - - -def aobv( - close: Series, volume: Series, fast: Int = None, slow: Int = None, - max_lookback: Int = None, min_lookback: Int = None, - mamode: str = None, run_length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Archer On Balance Volume - - This indicator, by Kevin Johnson, attempts to identify OBV trends using - two moving averages. It also attempts to identify if the moving averages - are in a long_run or short_run. Finally, it also calculates the rolling - maximum and minimum of OBV. - - Sources: - * Kevin Johnson - * [tradingview](https://www.tradingview.com/script/Co1ksara-Trade-Archer-On-balance-Volume-Moving-Averages-v1/) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - fast (int): Fast MA period. Default: ```4``` - slow (int): Slow MA period. Default: ```12``` - max_lookback (int): Maximum OBV period. Default: ```2``` - min_lookback (int): Minimum OBV period. Default: ```2``` - run_length (int): Long and short run period. Default: ```2``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 6 columns - - Note: - * [long_run](../api/trend.md/#src.pandas_ta.trend.long_run.long_run) - * [short_run](../api/trend.md/#src.pandas_ta.trend.short_run.short_run) - """ - # Validate - fast = v_pos_default(fast, 4) - slow = v_pos_default(slow, 12) - min_lookback = v_pos_default(min_lookback, 2) - max_lookback = v_pos_default(max_lookback, 2) - - if slow < fast: - fast, slow = slow, fast - _length = max(max_lookback, min_lookback) + slow - - close = v_series(close, _length) - volume = v_series(volume, _length) - - if close is None or volume is None: - return - - mamode = v_mamode(mamode, "ema") - run_length = v_pos_default(run_length, 2) - offset = v_offset(offset) - # remove length so it doesn't override ema length - if "length" in kwargs: - kwargs.pop("length") - - # Calculate - obv_ = obv(close=close, volume=volume, **kwargs) - maf = ma(mamode, obv_, length=fast, **kwargs) - mas = ma(mamode, obv_, length=slow, **kwargs) - - obv_long = long_run(maf, mas, length=run_length) - obv_short = short_run(maf, mas, length=run_length) - - # Offset - if offset != 0: - obv_ = obv_.shift(offset) - maf = maf.shift(offset) - mas = mas.shift(offset) - obv_long = obv_long.shift(offset) - obv_short = obv_short.shift(offset) - - # Fill - if "fillna" in kwargs: - obv_.fillna(kwargs["fillna"], inplace=True) - maf.fillna(kwargs["fillna"], inplace=True) - mas.fillna(kwargs["fillna"], inplace=True) - obv_long.fillna(kwargs["fillna"], inplace=True) - obv_short.fillna(kwargs["fillna"], inplace=True) - - _mode = mamode.lower()[0] if len(mamode) else "" - data = { - obv_.name: obv_, - f"OBV_min_{min_lookback}": obv_.rolling(min_lookback).min(), - f"OBV_max_{max_lookback}": obv_.rolling(max_lookback).max(), - f"OBV{_mode}_{fast}": maf, - f"OBV{_mode}_{slow}": mas, - f"AOBV_LR_{run_length}": obv_long, - f"AOBV_SR_{run_length}": obv_short - } - df = DataFrame(data, index=close.index) - - # Name and Category - df.name = f"AOBV{_mode}_{fast}_{slow}_{min_lookback}_{max_lookback}_{run_length}" - df.category = "volume" - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/cmf.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/cmf.py deleted file mode 100644 index 2c83b4b3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/cmf.py +++ /dev/null @@ -1,79 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import non_zero_range, v_offset, v_pos_default, v_series - - - -def cmf( - high: Series, low: Series, close: Series, volume: Series, - open_: Series = None, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Chaikin Money Flow - - This indicator attempts to quantify money flow. - - Sources: - * [stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf) - * [tradingview](https://www.tradingview.com/wiki/Chaikin_Money_Flow_(CMF)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - volume (Series): ```volume``` Series - length (int): The period. Default: ```20``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - open_ (Series): Optional ```open``` Series. Default: ```None``` - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - Commonly used with Accumulation/Distribution [ad](volume.md/#src.pandas_ta.volume.ad.ad) - """ - # Validate - length = v_pos_default(length, 20) - if "min_periods" in kwargs and kwargs["min_periods"] is not None: - min_periods = int(kwargs["min_periods"]) - else: - min_periods = length - _length = max(length, min_periods) - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - volume = v_series(volume, _length) - - if high is None or low is None or close is None or volume is None: - return - - offset = v_offset(offset) - - # Calculate - if open_ is not None: - open_ = v_series(open_) - ad = non_zero_range(close, open_) # AD with Open - else: - ad = 2 * close - (high + low) # AD with High, Low, Close - - ad *= volume / non_zero_range(high, low) - cmf = ad.rolling(length, min_periods=min_periods).sum() \ - / volume.rolling(length, min_periods=min_periods).sum() - - # Offset - if offset != 0: - cmf = cmf.shift(offset) - - # Fill - if "fillna" in kwargs: - cmf.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - cmf.name = f"CMF_{length}" - cmf.category = "volume" - - return cmf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/efi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/efi.py deleted file mode 100644 index 314007fe..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/efi.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - - -def efi( - close: Series, volume: Series, length: Int = None, - mamode: str = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Elder's Force Index - - This indicator attempts to quantify movement magnitude as well as - potential reversals and price corrections. - - Sources: - * [motivewave](https://www.motivewave.com/studies/elders_force_index.htm) - * [tradingview](https://www.tradingview.com/wiki/Elder%27s_Force_Index_(EFI)) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - length (int): The period. Default: ```13``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 13) - close = v_series(close, length) - volume = v_series(volume, length) - - if close is None or volume is None: - return - - mamode = v_mamode(mamode, "ema") - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - pv_diff = close.diff(drift) * volume - efi = ma(mamode, pv_diff, length=length) - - # Offset - if offset != 0: - efi = efi.shift(offset) - - # Fill - if "fillna" in kwargs: - efi.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - efi.name = f"EFI_{length}" - efi.category = "volume" - - return efi diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/eom.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/eom.py deleted file mode 100644 index 31dd49ed..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/eom.py +++ /dev/null @@ -1,82 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap import hl2, sma -from pandas_ta.utils import ( - non_zero_range, - v_drift, - v_pos_default, - v_offset, - v_series -) - - - -def eom( - high: Series, low: Series, close: Series, volume: Series, - length: Int = None, divisor: IntFloat= None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Ease of Movement - - This indicator is an oscillator that attempts to quantify the relationship - with HLC and volume. - - Sources: - * [motivewave](https://www.motivewave.com/studies/ease_of_movement.htm) - * [stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ease_of_movement_emv) - * [tradingview](https://www.tradingview.com/wiki/Ease_of_Movement_(EOM)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - volume (Series): ```volume``` Series - length (int): The period. Default: ```14``` - divisor (float): Divisor. Default: ```100_000_000``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 14) - _length = length + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - volume = v_series(volume, _length) - - if high is None or low is None or close is None or volume is None: - return - - divisor = v_pos_default(divisor, 100_000_000) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - hl_range = non_zero_range(high, low) - distance = hl2(high=high, low=low) - distance -= hl2(high=high.shift(drift), low=low.shift(drift)) - box_ratio = volume / divisor - box_ratio /= hl_range - eom = distance / box_ratio - eom = sma(eom, length=length) - - # Offset - if offset != 0: - eom = eom.shift(offset) - - # Fill - if "fillna" in kwargs: - eom.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - eom.name = f"EOM_{length}_{divisor}" - eom.category = "volume" - - return eom diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/kvo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/kvo.py deleted file mode 100644 index 83c41057..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/kvo.py +++ /dev/null @@ -1,100 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.overlap import hlc3 -from pandas_ta.utils import ( - signed_series, - v_drift, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - - -def kvo( - high: Series, low: Series, close: Series, volume: Series, - fast: Int = None, slow: Int = None, signal: Int = None, - mamode: str = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Klinger Volume Oscillator - - This indicator, by Stephen J. Klinger., attempts to predict - price reversals. - - Sources: - * [daytrading](https://www.daytrading.com/klinger-volume-oscillator) - * [investopedia](https://www.investopedia.com/terms/k/klingeroscillator.asp) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - volume (Series): ```volume``` Series - fast (int): Fast MA period. Default: ```34``` - slow (int): Slow MA period. Default: ```55``` - signal (int): Signal period. Default: ```13``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - """ - # Validate - fast = v_pos_default(fast, 34) - slow = v_pos_default(slow, 55) - signal = v_pos_default(signal, 13) - _length = max(fast, slow) + signal - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - volume = v_series(volume, _length) - - if high is None or low is None or close is None or volume is None: - return - - mamode = v_mamode(mamode, "ema") - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - signed_volume = volume * signed_series(hlc3(high, low, close), -1) - sv = signed_volume.loc[signed_volume.first_valid_index():, ] - - kvo = ma(mamode, sv, length=fast) - ma(mamode, sv, length=slow) - if kvo is None or all(isnan(kvo.to_numpy())): - return # Emergency Break - - kvo_signal = ma(mamode, kvo.loc[kvo.first_valid_index():, ], length=signal) - if kvo_signal is None or all(isnan(kvo_signal.to_numpy())): - return # Emergency Break - - # Offset - if offset != 0: - kvo = kvo.shift(offset) - kvo_signal = kvo_signal.shift(offset) - - # Fill - if "fillna" in kwargs: - kvo.fillna(kwargs["fillna"], inplace=True) - kvo_signal.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{fast}_{slow}_{signal}" - kvo.name = f"KVO{_props}" - kvo_signal.name = f"KVOs{_props}" - kvo.category = kvo_signal.category = "volume" - - data = {kvo.name: kvo, kvo_signal.name: kvo_signal} - df = DataFrame(data, index=close.index) - df.name = f"KVO{_props}" - df.category = kvo.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/mfi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/mfi.py deleted file mode 100644 index 927943a2..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/mfi.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- -from sys import float_info as sflt -from numpy import convolve, maximum, nan, ones, roll, where -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import ( - v_drift, - v_offset, - v_pos_default, - v_series, - v_talib -) - - - -def mfi( - high: Series, low: Series, close: Series, volume: Series, - length: Int = None, talib: bool = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Money Flow Index - - This indicator is an oscillator that attempts to quantify buying and - selling pressure. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Money_Flow_(MFI)) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - volume (Series): ```volume``` Series - length (int): The period. Default: ```14``` - talib (bool): If installed, use TA Lib. Default: ```True``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Warning: - TA-Lib Correlation: ```np.float64(0.9959302104966524)``` - - Tip: - Corrective contributions welcome! - """ - # Validate - length = v_pos_default(length, 14) - _length = length + 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - volume = v_series(volume, _length) - - if high is None or low is None or close is None or volume is None: - return - - mode_tal = v_talib(talib) - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import MFI - mfi = MFI(high, low, close, volume, length) - else: - m, _ones = close.size, ones(length) - - tp = (high.to_numpy() + low.to_numpy() + close.to_numpy()) / 3.0 - smf = tp * volume.to_numpy() * where(tp > roll(tp, shift=drift), 1, -1) - - pos, neg = maximum(smf, 0), maximum(-smf, 0) - avg_gain, avg_loss = convolve(pos, _ones)[:m], convolve(neg, _ones)[:m] - - _mfi = (100.0 * avg_gain) / (avg_gain + avg_loss + sflt.epsilon) - _mfi[:length] = nan - - mfi = Series(_mfi, index=close.index) - - # Offset - if offset != 0: - mfi = mfi.shift(offset) - - # Fill - if "fillna" in kwargs: - mfi.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - mfi.name = f"MFI_{length}" - mfi.category = "volume" - - return mfi diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/nvi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/nvi.py deleted file mode 100644 index 19678912..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/nvi.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.momentum import roc -from pandas_ta.utils import signed_series, v_offset, v_pos_default, v_series - - - -def nvi( - close: Series, volume: Series, length: Int = None, initial: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Negative Volume Index - - This indicator attempts to identify where smart money is active. - - Sources: - * [motivewave](https://www.motivewave.com/studies/negative_volume_index.htm) - * [stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - length (int): The period. Default: ```13``` - initial (int): Initial value. Default: ```1000``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: - Commonly paired with [pvi](volume.md/#src.pandas_ta.volume.pvi.pvi) - """ - # Validate - length = v_pos_default(length, 1) - close = v_series(close, length + 1) - volume = v_series(volume, length + 1) - - if close is None or volume is None: - return - - initial = v_pos_default(initial, 1000) - offset = v_offset(offset) - - # Calculate - roc_ = roc(close=close, length=length) - signed_volume = signed_series(volume, 1) - nvi = signed_volume[signed_volume < 0].abs() * roc_ - nvi.fillna(0, inplace=True) - nvi.iloc[0] = initial - nvi = nvi.cumsum() - - # Offset - if offset != 0: - nvi = nvi.shift(offset) - - # Fill - if "fillna" in kwargs: - nvi.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - nvi.name = f"NVI_{length}" - nvi.category = "volume" - - return nvi diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/obv.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/obv.py deleted file mode 100644 index 3f60057e..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/obv.py +++ /dev/null @@ -1,65 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.maps import Imports -from pandas_ta.utils import signed_series, v_offset, v_series, v_talib - - - -def obv( - close: Series, volume: Series, talib: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """On Balance Volume - - This indicator attempts to quantify buying and selling pressure. - - Sources: - * [motivewave](https://www.motivewave.com/studies/on_balance_volume.htm) - * [tradingtechnologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/) - * [tradingview](https://www.tradingview.com/wiki/On_Balance_Volume_(OBV)) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - talib (bool): If installed, use TA Lib. Default: ```True``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - _length = 1 - close = v_series(close, _length) - volume = v_series(volume, _length) - - if close is None or volume is None: - return - - mode_tal = v_talib(talib) - offset = v_offset(offset) - - # Calculate - if Imports["talib"] and mode_tal: - from talib import OBV - obv = OBV(close, volume) - else: - sv = signed_series(close, initial=1) * volume - obv = sv.cumsum() - - # Offset - if offset != 0: - obv = obv.shift(offset) - - # Fill - if "fillna" in kwargs: - obv.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - obv.name = f"OBV" - obv.category = "volume" - - return obv diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvi.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvi.py deleted file mode 100644 index 522529b2..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvi.py +++ /dev/null @@ -1,111 +0,0 @@ -# -*- coding: utf-8 -*- -from numba import njit -from numpy import float64, zeros_like -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - v_bool, - v_mamode, - v_offset, - v_pos_default, - v_series -) - - -@njit(cache=True) -def nb_pvi(np_close, np_volume, initial): - result = zeros_like(np_close, dtype=float64) - result[0] = initial - - m = np_close.size - for i in range(1, m): - if np_volume[i] > np_volume[i - 1]: - result[i] = result[i - i] * (np_close[i] / np_close[i - 1]) - else: - result[i] = result[i - i] - - return result - - - -def pvi( - close: Series, volume: Series, length: Int = None, initial: Int = None, - mamode: str = None, overlay: bool = None, offset: Int = None, - **kwargs: DictLike -) -> DataFrame: - """Positive Volume Index - - This indicator attempts to identify where smart money is active. - - Sources: - * [investopedia](https://www.investopedia.com/terms/p/pvi.asp) - * [sierrachart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=101) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - length (int): The period. Default: ```255``` - initial (int): Initial value. Default: ```100``` - mamode (str): See ```help(ta.ma)```. Default: ```"ema"``` - overlay (bool): Overlay ```initial```. Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 2 columns - - Note: - Commonly paired with [nvi](volume.md/#src.pandas_ta.volume.nvi.nvi) - """ - # Validate - length = v_pos_default(length, 255) - close = v_series(close, length + 1) - volume = v_series(volume, length + 1) - - if close is None or volume is None: - return - - mamode = v_mamode(mamode, "ema") - overlay = v_bool(overlay, False) - if overlay: - initial = close.iloc[0] - initial = v_pos_default(initial, 100) - offset = v_offset(offset) - - # Calculate - np_close, np_volume = close.to_numpy(), volume.to_numpy() - _pvi = nb_pvi(np_close, np_volume, initial) - - pvi = Series(_pvi, index=close.index) - pvi_ma = ma(mamode, pvi, length=length) - - # Offset - if offset != 0: - pvi = pvi.shift(offset) - pvi_ma = pvi_ma.shift(offset) - - # Fill - if "fillna" in kwargs: - pvi.fillna(kwargs["fillna"], inplace=True) - pvi_ma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _mode = mamode.lower()[0] if len(mamode) else "" - _props = f"{_mode}_{length}" - pvi.name = f"PVI" - pvi_ma.name = f"PVI{_props}" - pvi.category = pvi_ma.category = "volume" - - data = { pvi.name: pvi} - if np_close.size > length + 1: - data[pvi_ma.name] = pvi_ma - df = DataFrame(data, index=close.index) - - # Name and Category - df.name = pvi.name - df.category = pvi.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvo.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvo.py deleted file mode 100644 index 52df1424..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvo.py +++ /dev/null @@ -1,81 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, IntFloat -from pandas_ta.overlap import ema -from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series - - - -def pvo( - volume: Series, fast: Int = None, slow: Int = None, - signal: Int = None, scalar: IntFloat = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Percentage Volume Oscillator - - This indicator is a volume momentum oscillator. - - Sources: - * [fmlabs](https://www.fmlabs.com/reference/default.htm?url=PVO.htm) - - Parameters: - volume (Series): ```volume``` Series - fast (int): Fast MA period. Default: ```12``` - slow (int): Slow MA period. Default: ```26``` - signal (int): Signal period. Default: ```9``` - scalar (float): Scalar. Default: ```100``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - """ - # Validate - fast = v_pos_default(fast, 12) - slow = v_pos_default(slow, 26) - signal = v_pos_default(signal, 9) - if slow < fast: - fast, slow = slow, fast - volume = v_series(volume, max(fast, slow, signal)) - - if volume is None: - return - - scalar = v_scalar(scalar, 100) - offset = v_offset(offset) - - # Calculate - fastma = ema(volume, length=fast) - slowma = ema(volume, length=slow) - pvo = scalar * (fastma - slowma) / slowma - - signalma = ema(pvo, length=signal) - histogram = pvo - signalma - - # Offset - if offset != 0: - pvo = pvo.shift(offset) - histogram = histogram.shift(offset) - signalma = signalma.shift(offset) - - # Fill - if "fillna" in kwargs: - pvo.fillna(kwargs["fillna"], inplace=True) - histogram.fillna(kwargs["fillna"], inplace=True) - signalma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{fast}_{slow}_{signal}" - pvo.name = f"PVO{_props}" - histogram.name = f"PVOh{_props}" - signalma.name = f"PVOs{_props}" - pvo.category = histogram.category = signalma.category = "momentum" - - data = {pvo.name: pvo, histogram.name: histogram, signalma.name: signalma} - df = DataFrame(data, index=volume.index) - df.name = pvo.name - df.category = pvo.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvol.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvol.py deleted file mode 100644 index 87e65d7b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvol.py +++ /dev/null @@ -1,51 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import signed_series, v_bool, v_offset, v_series - - - -def pvol( - close: Series, volume: Series, signed: bool = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Price-Volume - - This indicator returns the product of Price & Volume (Price * Volume). - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - signed (bool): Return with signs. Default: ```False``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - close = v_series(close) - volume = v_series(volume) - signed = v_bool(signed, False) - offset = v_offset(offset) - - # Calculate - pvol = close * volume - if signed: - pvol *= signed_series(close, 1) - - # Offset - if offset != 0: - pvol = pvol.shift(offset) - - # Fill - if "fillna" in kwargs: - pvol.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - pvol.name = f"PVOL" - pvol.category = "volume" - - return pvol diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvr.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvr.py deleted file mode 100644 index 30060cb5..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvr.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import nan -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import v_drift, v_series - - - -def pvr( - close: Series, volume: Series, - drift: Int = None, **kwargs: DictLike -) -> Series: - """Price Volume Rank - - This indicator, by Anthony J. Macek, is a simple rank computation with - close and volume values. - - Sources: - * Anthony J. Macek, June, 1994 issue of Technical Analysis of - Stocks & Commodities (TASC) Magazine - * [fmlabs](https://www.fmlabs.com/reference/default.htm?url=PVrank.htm) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - drift (int): Difference amount. Default: ```1``` - - Returns: - (Series): 1 column - - Note: Signals - - Buy < 2.5 - - Sell > 2.5 - """ - # Validate - drift = v_drift(drift) - close = v_series(close, drift) - volume = v_series(volume, drift) - - if close is None or volume is None: - return - - # Calculate - close_diff = close.diff(drift).fillna(0) - volume_diff = volume.diff(drift).fillna(0) - - pvr = Series(nan, index=close.index) - - pvr.loc[(close_diff >= 0) & (volume_diff >= 0)] = 1 - pvr.loc[(close_diff >= 0) & (volume_diff < 0)] = 2 - pvr.loc[(close_diff < 0) & (volume_diff >= 0)] = 3 - pvr.loc[(close_diff < 0) & (volume_diff < 0)] = 4 - - # Name and Category - pvr.name = f"PVR" - pvr.category = "volume" - - return pvr diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvt.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvt.py deleted file mode 100644 index 5eb43ed3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/pvt.py +++ /dev/null @@ -1,59 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.momentum import roc -from pandas_ta.utils import v_drift, v_offset, v_series - - - -def pvt( - close: Series, volume: Series, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Price-Volume Trend - - This indicator attempts to quantify money flow. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Price_Volume_Trend_(PVT)) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - drift = v_drift(drift) - _drift = drift + 1 - close = v_series(close, _drift) - volume = v_series(volume, _drift) - - if close is None or volume is None: - return - - offset = v_offset(offset) - - # Calculate - pv = roc(close=close, length=drift) * volume - pvt = pv.cumsum() - - # Offset - if offset != 0: - pvt = pvt.shift(offset) - - # Fill - if "fillna" in kwargs: - pvt.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - pvt.name = f"PVT" - pvt.category = "volume" - - return pvt diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/tsv.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/tsv.py deleted file mode 100644 index 322ec695..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/tsv.py +++ /dev/null @@ -1,104 +0,0 @@ -# -*- coding: utf-8 -*- -from numpy import isnan -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - signed_series, - v_drift, - v_mamode, - v_pos_default, - v_offset, - v_series, - zero -) - - - -def tsv( - close: Series, volume: Series, - length: Int = None, signal: Int = None, - mamode: str = None, drift: Int = None, - offset: Int = None, **kwargs: DictLike -) -> DataFrame: - """Time Segmented Value - - This indicator, by Worden Brothers Inc., attempts to quantify the amount - of money flowing at various time segments of price and time; similar to - On Balance Volume. - - Sources: - * [tc2000](https://help.tc2000.com/m/69404/l/747088-time-segmented-volume) - * [tradingview](https://www.tradingview.com/script/6GR4ht9X-Time-Segmented-Volume/) - * [usethinkscript](https://usethinkscript.com/threads/time-segmented-volume-for-thinkorswim.519/) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - length (int): The period. Default: ```18``` - signal (int): Signal period. Default: ```10``` - mamode (str): See ```help(ta.ma)```. Default: ```"sma"``` - drift (int): Difference amount. Default: ```1``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 3 columns - - Note: - * The zero line is called the baseline. - * Entries and exits signals occur when crossing the baseline. - """ - # Validate - length = v_pos_default(length, 18) - signal = v_pos_default(signal, 10) - _length = max(length, signal) + 1 - close = v_series(close, _length) - - if close is None: - return - - mamode = v_mamode(mamode, "sma") - drift = v_drift(drift) - offset = v_offset(offset) - - # Calculate - signed_volume = volume * signed_series(close, 1) # > 0 - signed_volume[signed_volume < 0] = -signed_volume # < 0 - signed_volume.apply(zero) # ~ 0 - cvd = signed_volume * close.diff(drift) - - tsv = cvd.rolling(length).sum() - if all(isnan(tsv)): - return # Emergency Break - - signal_ = ma(mamode, tsv, length=signal) - ratio = tsv / signal_ - - # Offset - if offset != 0: - tsv = tsv.shift(offset) - signal_ = signal.shift(offset) - ratio = ratio.shift(offset) - - # Fill - if "fillna" in kwargs: - tsv.fillna(kwargs["fillna"], inplace=True) - signal_.fillna(kwargs["fillna"], inplace=True) - ratio.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"_{length}_{signal}" - tsv.name = f"TSV{_props}" - signal_.name = f"TSVs{_props}" - ratio.name = f"TSVr{_props}" - tsv.category = signal_.category = ratio.category = "volume" - - data = {tsv.name: tsv, signal_.name: signal_, ratio.name: ratio} - df = DataFrame(data, index=close.index) - df.name = f"TSV{_props}" - df.category = tsv.category - - return df diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vhm.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vhm.py deleted file mode 100644 index 1e039b5a..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vhm.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from statistics import pstdev -from pandas_ta._typing import DictLike, Int -from pandas_ta.ma import ma -from pandas_ta.utils import ( - v_mamode, - v_offset, - v_pos_default, - v_series -) - - - -def vhm( - volume: Series, length: Int = None, std_length = None, - mamode: str = None, offset: Int = None, **kwargs: DictLike - ) -> Series: - """Volume Heatmap - - This indicator attempts to quantify volume trend strength of - specified length. - - Sources: - * [tradingview](https://www.tradingview.com/script/unWex8N4-Heatmap-Volume-xdecow/) - - Parameters: - volume (Series): ```volume``` Series - length (int): The period. Default: ```610``` - std_length (int): Standard devation. Default: ```610``` - mamode (str): Mean MA. See ```help(ta.ma)```. Default: ```"sma"``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - - Note: Signals - - Extremely Cold: ```vhm <= -0.5``` - - Cold: ```-0.5 < vhm <= 1.0``` - - Medium: ```1.0 < vhm <= 2.5``` - - Hot: ```2.5 < vhm <= 4.0``` - - Extremely Hot: ```vhm >= 4``` - """ - # Validate - length = v_pos_default(length, 610) - std_length = v_pos_default(std_length, length) - _length = max(length, std_length) - volume = v_series(volume, _length) - - if volume is None: - return - - mamode = v_mamode(mamode, "sma") - offset = v_offset(offset) - - # Calculate - mu = ma(mamode, volume, length=length) - vhm = (volume - mu) / pstdev(volume, std_length) - - # Offset - if offset != 0: - vhm = vhm.shift(offset) - - # Fill - if "fillna" in kwargs: - vhm.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - _props = f"VHM_{length}" - vhm.name = _props if length == std_length else f"{_props}_{std_length}" - vhm.category = "volume" - - return vhm diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vp.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vp.py deleted file mode 100644 index 52083bb3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vp.py +++ /dev/null @@ -1,128 +0,0 @@ -# -*- coding: utf-8 -*- -from warnings import simplefilter -from numpy import array_split, mean, sum -from pandas import DataFrame, Series, concat, cut -from pandas_ta._typing import DictLike, Int -from pandas_ta.utils import signed_series, v_bool, v_pos_default, v_series - - - -def vp( - close: Series, volume: Series, - width: Int = None, sort: bool = None, - **kwargs: DictLike -) -> DataFrame: - """Volume Profile - - This indicator attempts to quantify volume across binned price ranges of - certain width. - - Sources: - * [ranchodinero](http://www.ranchodinero.com/volume-tpo-essentials/) - * [stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:volume_by_price) - * [tradingtechnologies](https://www.tradingtechnologies.com/blog/2013/05/15/volume-at-price/) - * [tradingview](https://www.tradingview.com/wiki/Volume_Profile) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - width (int): Source distrubution count. Default: ```10``` - sort (value): Sort ```close``` before splitting into ranges. - Default: ```False``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (DataFrame): 5 columns - - Note: - * By default, sorts by date index or chronological. - * Value Area is not calculated. - - Warning: - **Volume Profile** not a Time ```Series```. It is a volume distribution - snapshot for an arbitrary ```DateTime``` Index and thus can not be - concatenated onto the existing ```DataFrame```. - - """ - # Validate - width = v_pos_default(width, 10) - close = v_series(close, width) - volume = v_series(volume, width) - - if close is None or volume is None: - return - - sort = v_bool(sort, False) - - # Calculate - signed_price = signed_series(close, 1) - pos_volume = volume * signed_price[signed_price > 0] - pos_volume.name = volume.name - neg_volume = -volume * signed_price[signed_price < 0] - neg_volume.name = volume.name - neut_volume = volume + signed_price[signed_price == 0] - neut_volume.name = volume.name - vp = concat([close, pos_volume, neg_volume, neut_volume], axis=1) - - close_col = f"{vp.columns[0]}" - high_price_col = f"high_{close_col}" - low_price_col = f"low_{close_col}" - mean_price_col = f"mean_{close_col}" - - volume_col = f"{vp.columns[1]}" - pos_volume_col = f"pos_{volume_col}" - neg_volume_col = f"neg_{volume_col}" - neut_volume_col = f"neut_{volume_col}" - total_volume_col = f"total_{volume_col}" - vp.columns = [close_col, pos_volume_col, neg_volume_col, neut_volume_col] - - simplefilter(action="ignore", category=FutureWarning) - # sort: Sort by close before splitting into ranges. Default: False - # If False, it sorts by date index or chronological versus by price - if sort: - vp[mean_price_col] = vp[close_col] - - vpdf = vp.groupby( - cut(vp[close_col], width, include_lowest=True, precision=2), - observed=False - ).agg({ - mean_price_col: mean, - pos_volume_col: sum, - neg_volume_col: sum, - neut_volume_col: sum - }) - - vpdf[low_price_col] = [x.left for x in vpdf.index] - vpdf[high_price_col] = [x.right for x in vpdf.index] - vpdf = vpdf.reset_index(drop=True) - - vpdf = vpdf[[ - low_price_col, mean_price_col, high_price_col, - pos_volume_col, neg_volume_col, neut_volume_col - ]] - else: - vp_ranges = array_split(vp, width) - result = list({ - low_price_col: r[close_col].min(), - mean_price_col: r[close_col].mean(), - high_price_col: r[close_col].max(), - pos_volume_col: r[pos_volume_col].sum(), - neg_volume_col: r[neg_volume_col].sum(), - neut_volume_col: r[neut_volume_col].sum(), - } for r in vp_ranges) - - vpdf = DataFrame(result) - - vpdf[total_volume_col] = vpdf[pos_volume_col] + vpdf[neg_volume_col] - - # Fill - if "fillna" in kwargs: - vpdf.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - vpdf.name = f"VP_{width}" - vpdf.category = "volume" - - return vpdf diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vwap.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vwap.py deleted file mode 100644 index b6ca3d4c..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vwap.py +++ /dev/null @@ -1,120 +0,0 @@ -# -*- coding: utf-8 -*- -from warnings import simplefilter -from pandas import DataFrame, Series -from pandas_ta._typing import DictLike, Int, List -from pandas_ta.overlap import hlc3 -from pandas_ta.utils import v_datetime_ordered, v_list, v_offset, v_series - - - -def vwap( - high: Series, low: Series, close: Series, volume: Series, - anchor: str = None, bands: List = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Volume Weighted Average Price - - This indicator computes the Volume Weighted Average Price. - - Sources: - * [tradingview](https://www.tradingview.com/wiki/Volume_Weighted_Average_Price_(VWAP)) - * [Trading Technologies](https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/volume-weighted-average-price-vwap/) - * [Stockcharts](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vwap_intraday) - * [Sierra Chart](https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=108&Name=Volume_Weighted_Average_Price_-_VWAP_-_with_Standard_Deviation_Lines) - - Parameters: - high (Series): ```high``` Series - low (Series): ```low``` Series - close (Series): ```close``` Series - volume (Series): ```volume``` Series - anchor (str): VWAP Anchor. Default: ```"D"```. - bands (list): List of positive ```IntFloat``` deviations. - Default: ```[]``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series | pd.DataFrame): ```DataFrame``` when ```bands``` is set. - Default: ```Series``` - - Note: - * Commonly used with intraday charts to identify general direction. - * Depending on the index values, it will implement various - [Timeseries Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases) - - Tip: - * Negative bands are computed automatically. - """ - # Validate - _length = 1 - high = v_series(high, _length) - low = v_series(low, _length) - close = v_series(close, _length) - volume = v_series(volume, _length) - - if high is None or low is None or close is None or volume is None: - return - - bands = v_list(bands) - offset = v_offset(offset) - - if anchor and isinstance(anchor, str) and len(anchor) >= 1: - anchor = anchor.upper() - else: - anchor = "D" - - typical_price = hlc3(high=high, low=low, close=close) - if not v_datetime_ordered(volume) or \ - not v_datetime_ordered(typical_price): - print("[!] VWAP requires an ordered DatetimeIndex.") - return - - # Calculate - _props = f"VWAP_{anchor}" - wp = typical_price * volume - simplefilter(action="ignore", category=UserWarning) - vwap = wp.groupby(wp.index.to_period(anchor)).cumsum() \ - / volume.groupby(volume.index.to_period(anchor)).cumsum() - - if bands and len(bands): - # Calculate vwap stdev bands - vwap_var = volume * (typical_price - vwap) ** 2 - vwap_var_sum = vwap_var \ - .groupby(vwap_var.index.to_period(anchor)).cumsum() - vwap_volume_sum = volume \ - .groupby(volume.index.to_period(anchor)).cumsum() - std_volume_weighted = (vwap_var_sum / vwap_volume_sum) ** 0.5 - - # Name and Category - vwap.name = _props - vwap.category = "overlap" - - if bands: - df = DataFrame({vwap.name: vwap}, index=close.index) - for i in bands: - df[f"{_props}_L_{i}"] = vwap - i * std_volume_weighted - df[f"{_props}_U_{i}"] = vwap + i * std_volume_weighted - df[f"{_props}_L_{i}"].name = df[f"{_props}_U_{i}"].name = _props - df[f"{_props}_L_{i}"].category = "overlap" - df[f"{_props}_U_{i}"].category = "overlap" - df.name = _props - df.category = "overlap" - - # Offset - if offset != 0: - if bands and not df.empty: - df = df.shift(offset) - vwap = vwap.shift(offset) - - # Fill - if "fillna" in kwargs: - if bands and not df.empty: - df.fillna(kwargs["fillna"], inplace=True) - else: - vwap.fillna(kwargs["fillna"], inplace=True) - - if bands and not df.empty: - return df - return vwap diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vwma.py b/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vwma.py deleted file mode 100644 index d1fad3c4..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/src/pandas_ta/volume/vwma.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series -from pandas_ta._typing import DictLike, Int -from pandas_ta.overlap import sma -from pandas_ta.utils import v_offset, v_pos_default, v_series - - - -def vwma( - close: Series, volume: Series, length: Int = None, - offset: Int = None, **kwargs: DictLike -) -> Series: - """Volume Weighted Moving Average - - Computes a weighted average using price and volume. - - Sources: - * [motivewave](https://www.motivewave.com/studies/volume_weighted_moving_average.htm) - - Parameters: - close (Series): ```close``` Series - volume (Series): ```volume``` Series - length (int): The period. Default: ```10``` - offset (int): Post shift. Default: ```0``` - - Other Parameters: - fillna (value): ```pd.DataFrame.fillna(value)``` - - Returns: - (Series): 1 column - """ - # Validate - length = v_pos_default(length, 10) - close = v_series(close, length) - volume = v_series(volume, length) - - if close is None or volume is None: - return - - offset = v_offset(offset) - - # Calculate - pv = close * volume - vwma = sma(close=pv, length=length) / sma(close=volume, length=length) - - # Offset - if offset != 0: - vwma = vwma.shift(offset) - - # Fill - if "fillna" in kwargs: - vwma.fillna(kwargs["fillna"], inplace=True) - - # Name and Category - vwma.name = f"VWMA_{length}" - vwma.category = "overlap" - - return vwma diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/__init__.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/config.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/config.py deleted file mode 100644 index 893e14a3..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/config.py +++ /dev/null @@ -1,96 +0,0 @@ -# -*- coding: utf-8 -*- -import datetime -from pathlib import Path - -from numpy import array -from pandas import DataFrame, DatetimeIndex, read_csv - -import pandas_ta -from pandas_ta._typing import DictLike, IntFloat - - - -sample_adx_data = read_csv( - f"data/ADX_D.csv", index_col=0, - parse_dates=True, date_format="%f" -) - -ALERT: str = f"[!]" -INFO: str = f"[i]" -TEST: str = f"[T]" - -CORRELATION: str = "corr" # "sem" -CORRELATION_THRESHOLD: IntFloat = 0.99 # Less than 0.99 is undesirable -VERBOSE: bool = False - -welles_wilder_df = DataFrame({ - "open": array([50, 50.7, 51.7, 52.5, 53.6, 54.4, 52.9, 52]), - "high": array([51.2, 51.8, 52.9, 53.7, 54.8, 54.4, 53.2, 52.7]), - "low": array([49.8, 50.3, 51.7, 52.3, 53.5, 52.9, 52, 52]), - "close": array([50.9, 51.5, 52.8, 53.5, 54.7, 53, 52, 52.2]) -}) - - -def error_analysis( - df: DataFrame, kind: str, msg: str, - icon: str = INFO, newline: bool = True -): - if VERBOSE: - s = f"{icon} {df.name}['{kind}']: {msg}" - if newline: - s = f"\n{s}" - print(s) - - -def load(**kwargs: DictLike): - kwargs.setdefault("ticker", "SPY") - kwargs.setdefault("fpath", "data/TEST_D.csv") - kwargs.setdefault("interval", "d") - - kwargs.setdefault("index_col", 0) - kwargs.setdefault("parse_dates", True) - - kwargs.setdefault("verbose", False) - - print(f"\n{TEST} Pandas TA on {datetime.datetime.now()}") - fpath = Path(kwargs["fpath"]) - - df = read_csv( - fpath, - index_col=kwargs["index_col"], - parse_dates=kwargs["parse_dates"], - ) - - kwargs.setdefault("n", None) - if isinstance(kwargs["n"], int): - if kwargs["n"] > 0: - df = df[:kwargs["n"]] - elif kwargs['n'] < 0: - df = df[kwargs["n"]:] - - df.columns = df.columns.str.lower() - if kwargs["verbose"]: - print(f"{INFO} Loading {kwargs['ticker']}{df.shape} from {fpath}") - print(f"{INFO} From {df.index[0]} to {df.index[-1]}\n{df}\n") - return df - -_tdpy = pandas_ta.RATE["TRADING_DAYS_PER_YEAR"] -# At least 90 (88 with trix with default values) bars/rows/observations are -# needed to test All indicators individually and within the DataFrame -# extension. A larger sample may be required because of the Unstable Period -sample_data = load( - fpath="data/TEST_D.csv", - n = [ - -2 * _tdpy, -_tdpy, - -200, -89, 0, 89, 200, - _tdpy, 2 * _tdpy - ][4], - verbose=VERBOSE - ) - - -# Example multiindex download code -# _df = DataFrame() -# tickers =["spy", "mint"] -# data = {t:_df.ta.ticker(t, period="1y", timed=True) for t in tickers if len(t) > 1} -# assets = concat(data, names=["ticker", "datetime"], verify_integrity=True) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/conftest.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/conftest.py deleted file mode 100644 index de5bf5f7..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/conftest.py +++ /dev/null @@ -1,155 +0,0 @@ -# -*- coding: utf-8 -*- -import sys -sys.dont_write_bytecode = True - -from os import system as os_system - -import pytest -import pandas_ta as ta - -from pandas import read_csv - - - -TEST_ROWS = 200 -TEST_CSV = [f"data/TEST_D.csv", f"data/ALT_D.csv"][0] - -BEEP = False -PLAY_BEEP = f"osascript -e beep" - - - -@pytest.fixture(name="df", scope="function") -def testdf(): - """Yields a truncated df from TEST_CSV file""" - df = read_csv(TEST_CSV, index_col=0, parse_dates=True) - df.columns = df.columns.str.lower() - df.drop(columns=["dividends", "stock splits", "vwap"], errors="ignore", inplace=True) - df = df.iloc[:,:5] - # print(f"{df.columns=}") - - n = df.shape[0] - if n < TEST_ROWS: - print(f"{n=} {TEST_ROWS=}") - yield df.iloc[:TEST_ROWS] - - del df - if BEEP: os_system(PLAY_BEEP) - - -@pytest.fixture(scope="function") -def all_study(): - """Returns the All Study""" - return ta.AllStudy - - -@pytest.fixture(scope="function") -def common_study(): - """Returns the Common Study""" - return ta.CommonStudy - - -@pytest.fixture(scope="function") -def custom_study_a(): - """Returns a Custom Study with a chained/composed indicator: 'ema(CUMLOGRET_1, 5)'. - This Study only works when cores=0. When using mulptiprocessing (cores > 0), - the multiprocesser might miss the results of the indicator - 'CUMLOGRET_1 = log_return(cumulative=True)' - """ - _ta = [ - {"kind": "cdl_pattern", "name": "tristar"}, # 1 - {"kind": "rsi"}, # 1 - {"kind": "macd"}, # 3 - {"kind": "sma", "length": 50}, # 1 - {"kind": "trix"}, # 2 - {"kind": "bbands", "length": 20}, # 5 - {"kind": "log_return", "cumulative": True}, # 1 - {"kind": "ema", "close": "CUMLOGRET_1", "length": 5, "suffix": "CLR"} # 1 - ] - return ta.Study( - name="Commons with Cumulative Log Return EMA Chain", - ta=_ta, - # cores=0, - description="Common indicators with specific lengths and a chained indicator" - ) - - -@pytest.fixture(scope="function") -def custom_study_b(): - """Returns a Custom Study that allows setting indicator values by - parameter index as a tuple instead of using a named parameter""" - _ta = [ - {"kind": "ema", "params": (5,)}, # 1 - {"kind": "fisher", "params": (13, 7)} # 2 - ] - return ta.Study( - name="Custom Args Tuple", - ta=_ta, - description="Allow for easy filling in indicator arguments by argument placement" - ) - - -@pytest.fixture(scope="function") -def custom_study_c(): - """Returns a Custom Study that makes it easy to rename individual - indicator resultant column names""" - return ta.Study( - name="Custom Col Numbers Tuple", - ta=[{"kind": "bbands", "col_names": ("LB", "MB", "UB", "BW", "BP")}], - description="Allow for easy renaming of resultant columns" - ) - - -@pytest.fixture(scope="function") -def custom_study_d(): - """Returns a Custom Study that makes it easily return individual - indicator resultant columns by column number (col_numbers) as a tuple""" - return ta.Study( - name="Custom Col Numbers Tuple", - ta=[ - {"kind": "macd", "col_numbers": (1,)}, # macd histogram - {"kind": "bbands", "col_numbers": (0,2)} # bbands lower and upper - ], - description="Allow for easy selection of resultant columns" - ) - - -@pytest.fixture(scope="function") -def custom_study_e(): - """Returns a Custom Study that has non default indicator parameters and - an example of indicator composition/chaining: 'ema(CUMLOGRET_1, 5)'""" - _ta = [ - {"kind": "amat", "fast": 20, "slow": 50 }, # 2 - {"kind": "log_return", "cumulative": True}, # 1 - {"kind": "ema", "close": "CUMLOGRET_1", "length": 5} # 1 - ] - - return ta.Study( - name="AMAT Log Returns", - ta=_ta, - cores=0, - description="AMAT Log Returns" - ) - - -@pytest.fixture(scope="function") -def custom_study_f(): - """ - Scaling Study. Chained Indicators are unsupported. - """ - _ta = [ - {"kind": "ichimoku"}, - {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"}, - {"kind": "sma", "length": 10}, - {"kind": "rsi"}, - {"kind": "macd"}, - {"kind": "bbands", "length": 20}, - {"kind": "cci"}, - {"kind": "uo"}, - ] - return ta.Study( - name="Scaling Study", - ta=_ta, - cores=0, - description="Example Scaling Group" - ) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_candle.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_candle.py deleted file mode 100644 index 88c1631b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_candle.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -import pandas.testing as pdt -import talib as tal - -import pandas_ta as ta - -from .config import CORRELATION, CORRELATION_THRESHOLD, error_analysis - - - -# TA Lib style Tests -def test_cdl_doji(df): - result = ta.cdl_doji(df.open, df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "CDL_DOJI_10_0.1" - - try: - expected = tal.CDLDOJI(df.open, df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False, check_dtype=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - -def test_cdl_inside(df): - result = ta.cdl_inside(df.open, df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "CDL_INSIDE" - - result = ta.cdl_inside(df.open, df.high, df.low, df.close, asbool=True) - assert isinstance(result, Series) - assert result.name == "CDL_INSIDE" - - -def test_cdl_pattern(df): - result = ta.cdl_pattern(df.open, df.high, df.low, df.close, name="all") - assert isinstance(result, DataFrame) - assert len(result.columns) == len(ta.CDL_PATTERN_NAMES) - - result = ta.cdl_pattern(df.open, df.high, df.low, df.close, name="doji") - assert isinstance(result, DataFrame) - - result = ta.cdl_pattern(df.open, df.high, df.low, df.close, name=["doji", "inside"]) - assert isinstance(result, DataFrame) - - -def test_cdl_z(df): - result = ta.cdl_z(df.open, df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "CDL_Z_30_1" - - -def test_ha(df): - result = ta.ha(df.open, df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "Heikin-Ashi" - - - -# DataFrame Extension Tests -def test_ext_cdl_doji(df): - df.ta.cdl_pattern("doji", append=True) - assert df.columns[-1] == "CDL_DOJI_10_0.1" - - -def test_ext_cdl_inside(df): - df.ta.cdl_pattern("inside", append=True) - assert df.columns[-1] == "CDL_INSIDE" - - -def test_ext_cdl_z(df): - df.ta.cdl_z(append=True) - assert list(df.columns[-4:]) == [ - "open_Z_30_1", "high_Z_30_1", "low_Z_30_1", "close_Z_30_1" - ] - - -def test_ext_ha(df): - df.ta.ha(append=True) - assert list(df.columns[-4:]) == [ - "HA_open", "HA_high", "HA_low", "HA_close" - ] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_cycles.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_cycles.py deleted file mode 100644 index 8f15fffd..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_cycles.py +++ /dev/null @@ -1,30 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import Series - -import pandas_ta as ta - - - -# TA Lib style Tests -def test_ebsw(df): - result = ta.ebsw(df.close) - assert isinstance(result, Series) - assert result.name == "EBSW_40_10" - - -def test_reflex(df): - result = ta.reflex(df.close) - assert isinstance(result, Series) - assert result.name == "REFLEX_20_20_0.04" - - - -# DataFrame Extension Tests -def test_ext_ebsw(df): - df.ta.ebsw(append=True) - assert df.columns[-1] == "EBSW_40_10" - - -def test_ext_reflex(df): - df.ta.reflex(append=True) - assert df.columns[-1] == "REFLEX_20_20_0.04" diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_momentum.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_momentum.py deleted file mode 100644 index dde84698..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_momentum.py +++ /dev/null @@ -1,845 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series, concat -import pandas.testing as pdt -from pytest import mark -import talib as tal - -import pandas_ta as ta - -from .config import CORRELATION, CORRELATION_THRESHOLD, error_analysis - - - -# TA Lib style Tests -def test_ao(df): - result = ta.ao(df.high, df.low) - assert isinstance(result, Series) - assert result.name == "AO_5_34" - - -def test_apo(df): - result = ta.apo(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "APO_12_26" - - try: - expected = tal.APO(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - assert corr > CORRELATION_THRESHOLD - print(f"{corr=}") - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.apo(df.close) - assert isinstance(result, Series) - assert result.name == "APO_12_26" - - -def test_bias(df): - result = ta.bias(df.close) - assert isinstance(result, Series) - assert result.name == "BIAS_SMA_26" - - -def test_bop(df): - result = ta.bop(df.open, df.high, df.low, df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "BOP" - - try: - expected = tal.BOP(df.open, df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - assert corr > CORRELATION_THRESHOLD - print(f"{corr=}") - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.bop(df.open, df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "BOP" - - -def test_brar(df): - result = ta.brar(df.open, df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "BRAR_26" - - -def test_cci(df): - result = ta.cci(df.high, df.low, df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "CCI_14_0.015" - - try: - expected = tal.CCI(df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - assert corr > CORRELATION_THRESHOLD - print(f"{corr=}") - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.cci(df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "CCI_14_0.015" - - -def test_cfo(df): - result = ta.cfo(df.close) - assert isinstance(result, Series) - assert result.name == "CFO_9" - - -def test_cg(df): - result = ta.cg(df.close) - assert isinstance(result, Series) - assert result.name == "CG_10" - - -def test_cmo(df): - result = ta.cmo(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "CMO_14" - - try: - expected = tal.CMO(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - assert corr > CORRELATION_THRESHOLD - print(f"{corr=}") - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.cmo(df.close) - assert isinstance(result, Series) - assert result.name == "CMO_14" - - -def test_coppock(df): - result = ta.coppock(df.close) - assert isinstance(result, Series) - assert result.name == "COPC_11_14_10" - - -def test_crsi(df): - result = ta.crsi(df.close) - assert isinstance(result, Series) - assert result.name == "CRSI_3_2_100" - - -def test_cti(df): - result = ta.cti(df.close) - assert isinstance(result, Series) - assert result.name == "CTI_12" - - -def test_dm(df): - result = ta.dm(df.high, df.low, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "DM_14" - - try: - expected_pos = tal.PLUS_DM(df.high, df.low) - expected_neg = tal.MINUS_DM(df.high, df.low) - expecteddf = DataFrame({"DMP_14": expected_pos, "DMN_14": expected_neg}) - pdt.assert_frame_equal(result, expecteddf) - except AssertionError: - try: - dmp_corr = ta.utils.df_error_analysis(result.iloc[:, 0], expecteddf.iloc[:, 0]) - assert dmp_corr > CORRELATION_THRESHOLD - print(f"{dmp_corr=}") - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - try: - dmn_corr = ta.utils.df_error_analysis(result.iloc[:, 1], expecteddf.iloc[:, 1]) - assert dmn_corr > CORRELATION_THRESHOLD - print(f"{dmn_corr=}") - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.dm(df.high, df.low) - assert isinstance(result, DataFrame) - assert result.name == "DM_14" - - -def test_er(df): - result = ta.er(df.close) - assert isinstance(result, Series) - assert result.name == "ER_10" - - -def test_eri(df): - result = ta.eri(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "ERI_13" - - -def test_exhc(df): - result = ta.exhc(df.close) - assert isinstance(result, DataFrame) - assert result.name == "EXHCa" - - result = ta.exhc(df.close, show_all=False) - assert isinstance(result, DataFrame) - assert result.name == "EXHC" - - -def test_fisher(df): - result = ta.fisher(df.high, df.low) - assert isinstance(result, DataFrame) - assert result.name == "FISHERT_9_1" - - -def test_inertia(df): - result = ta.inertia(df.close) - assert isinstance(result, Series) - assert result.name == "INERTIA_20_14" - - result = ta.inertia(df.close, df.high, df.low, refined=True) - assert isinstance(result, Series) - assert result.name == "INERTIAr_20_14" - - result = ta.inertia(df.close, df.high, df.low, thirds=True) - assert isinstance(result, Series) - assert result.name == "INERTIAt_20_14" - - -def test_kdj(df): - result = ta.kdj(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "KDJ_9_3" - - -def test_kst(df): - result = ta.kst(df.close) - assert isinstance(result, DataFrame) - assert result.name == "KST_10_15_20_30_10_10_10_15_9" - - -def test_macd(df): - result = ta.macd(df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "MACD_12_26_9" - - try: - expected = tal.MACD(df.close) - expecteddf = DataFrame({ - "MACDh_12_26_9": expected[2], - "MACDs_12_26_9": expected[1], - "MACD_12_26_9": expected[0] - }) - pdt.assert_frame_equal(result, expecteddf) - except AssertionError: - try: - macd_corr = ta.utils.df_error_analysis(result, expected) - print(f"{macd_corr=}") - assert macd_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 0], CORRELATION, ex) - - try: - history_corr = ta.utils.df_error_analysis(result, expected) - print(f"{history_corr=}") - assert history_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 1], CORRELATION, ex) - - try: - signal_corr = ta.utils.df_error_analysis(result, expected) - print(f"{signal_corr=}") - assert signal_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 2], CORRELATION, ex) - - result = ta.macd(df.close) - assert isinstance(result, DataFrame) - assert result.name == "MACD_12_26_9" - - result = ta.macd(df.close, asmode=True) - assert isinstance(result, DataFrame) - assert result.name == "MACDAS_12_26_9" - - -def test_mom(df): - result = ta.mom(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "MOM_10" - - try: - expected = tal.MOM(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.mom(df.close) - assert isinstance(result, Series) - assert result.name == "MOM_10" - - -def test_pgo(df): - result = ta.pgo(df.high, df.low, df.close, asmode=True) - assert isinstance(result, Series) - assert result.name == "PGO_14" - - -def test_ppo(df): - result = ta.ppo(df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "PPO_12_26_9" - - try: - expected = tal.PPO(df.close) - pdt.assert_series_equal(result.iloc[:,0], expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.ppo(df.close) - assert isinstance(result, DataFrame) - assert result.name == "PPO_12_26_9" - - -def test_psl(df): - result = ta.psl(df.close, asmode=True) - assert isinstance(result, Series) - assert result.name == "PSL_12" - - -def test_qqe(df): - result = ta.qqe(df.close, asmode=True) - assert isinstance(result, DataFrame) - assert result.name == "QQE_14_5_4.236" - - -def test_roc(df): - result = ta.roc(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "ROC_10" - - try: - expected = tal.ROC(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.roc(df.close) - assert isinstance(result, Series) - assert result.name == "ROC_10" - - -def test_rsi(df): - result = ta.rsi(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "RSI_14" - - try: - expected = tal.RSI(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.rsi(df.close) - assert isinstance(result, Series) - assert result.name == "RSI_14" - - -def test_rsx(df): - result = ta.rsx(df.close) - assert isinstance(result, Series) - assert result.name == "RSX_14" - - -def test_rvgi(df): - result = ta.rvgi(df.open, df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "RVGI_14_4" - - -def test_slope(df): - result = ta.slope(df.close) - assert isinstance(result, Series) - assert result.name == "SLOPE_1" - - result = ta.slope(df.close, as_angle=True) - assert isinstance(result, Series) - assert result.name == "ANGLEr_1" - - result = ta.slope(df.close, as_angle=True, to_degrees=True) - assert isinstance(result, Series) - assert result.name == "ANGLEd_1" - - -def test_smc(df): - result = ta.smc(df.open, df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "SMC_14_50_20_5" - - -def test_smi(df): - result = ta.smi(df.close) - assert isinstance(result, DataFrame) - assert result.name == "SMI_5_20_5_1.0" - - result = ta.smi(df.close, scalar=10) - assert isinstance(result, DataFrame) - assert result.name == "SMI_5_20_5_10.0" - - -def test_squeeze(df): - result = ta.squeeze(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "SQZ_20_2.0_20_1.5" - - result = ta.squeeze(df.high, df.low, df.close, tr=False) - assert isinstance(result, DataFrame) - assert result.name == "SQZhlr_20_2.0_20_1.5" - - result = ta.squeeze(df.high, df.low, df.close, lazybear=True) - assert isinstance(result, DataFrame) - assert result.name == "SQZ_20_2.0_20_1.5_LB" - - result = ta.squeeze(df.high, df.low, df.close, tr=False, lazybear=True) - assert isinstance(result, DataFrame) - assert result.name == "SQZhlr_20_2.0_20_1.5_LB" - - -def test_squeeze_pro(df): - result = ta.squeeze_pro(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "SQZPRO_20_2.0_20_2.0_1.5_1.0" - - result = ta.squeeze_pro(df.high, df.low, df.close, tr=False) - assert isinstance(result, DataFrame) - assert result.name == "SQZPROhlr_20_2.0_20_2.0_1.5_1.0" - - result = ta.squeeze_pro(df.high, df.low, df.close, 20, 2, 20, 1, 2, 3) - assert isinstance(result, DataFrame) - assert result.name == "SQZPRO_20_2_20_3.0_2.0_1.0" - - result = ta.squeeze_pro(df.high, df.low, df.close, 20, 2, 20, 1, 2, 3, tr=False) - assert isinstance(result, DataFrame) - assert result.name == "SQZPROhlr_20_2_20_3.0_2.0_1.0" - - -def test_stc(df): - result = ta.stc(df.close) - assert isinstance(result, DataFrame) - assert result.name == "STC_10_12_26_0.5" - - -def test_stoch(df): - result = ta.stoch(df.high, df.low, df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "STOCH_14_3_3" - - try: - expected = tal.STOCH(df.high, df.low, df.close, 14, 3, 0, 3, 0) - expecteddf = DataFrame({"STOCHk_14_3_0_3_0": expected[0], "STOCHd_14_3_0_3": expected[1]}) - pdt.assert_frame_equal(result, expecteddf) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.stoch(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "STOCH_14_3_3" - - -def test_stochf(df): - result = ta.stochf(df.high, df.low, df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "STOCHF_14_3" - - try: - expected = tal.STOCHF(df.high, df.low, df.close, 14, 3, 0) - expecteddf = DataFrame({"STOCHFk_14_3_0": expected[0], "STOCHFd_14_3_0": expected[1]}) - pdt.assert_frame_equal(result, expecteddf) - except AssertionError: - try: - stochk_corr = ta.utils.df_error_analysis(result.iloc[:, 0], expected.iloc[:, 0]) - print(f"{stochk_corr=}") - assert stochk_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 0], CORRELATION, ex) - - try: - stochd_corr = ta.utils.df_error_analysis(result.iloc[:, 1], expected.iloc[:, 1]) - print(f"{stochd_corr=}") - assert stochd_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 1], CORRELATION, ex) - - result = ta.stochf(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "STOCHF_14_3" - - -def test_stochrsi(df): - # TV Correlation - result = ta.stochrsi(df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "STOCHRSI_14_14_3_3" - - try: - expected = tal.STOCHRSI(df.close, 14, 14, 3, 0) - expecteddf = DataFrame({ - "STOCHRSIk_14_14_0_3": expected[0], - "STOCHRSId_14_14_3_0": expected[1] - }) - pdt.assert_frame_equal(result, expecteddf) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result.iloc[:, 0], expecteddf.iloc[:, 1]) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 0], CORRELATION, ex, newline=False) - - -def test_tmo(df): - result = ta.tmo(df.open, df.close) - assert isinstance(result, DataFrame) - assert result.name == "TMO_14_5_3" - - result = ta.tmo(df.open, df.close, compute_momentum=True) - assert isinstance(result, DataFrame) - assert result.name == "TMO_14_5_3" - - -def test_trix(df): - result = ta.trix(df.close) - assert isinstance(result, DataFrame) - assert result.name == "TRIX_30_9" - - -def test_tsi(df): - result = ta.tsi(df.close) - assert isinstance(result, DataFrame) - assert result.name == "TSI_13_25_13" - - -def test_uo(df): - result = ta.uo(df.high, df.low, df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "UO_7_14_28" - - try: - expected = tal.ULTOSC(df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.uo(df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "UO_7_14_28" - - -def test_willr(df): - result = ta.willr(df.high, df.low, df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "WILLR_14" - - try: - expected = tal.WILLR(df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.willr(df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "WILLR_14" - - - -# DataFrame Extension Tests -def test_ext_ao(df): - df.ta.ao(append=True) - assert df.columns[-1] == "AO_5_34" - - -def test_ext_apo(df): - df.ta.apo(append=True) - assert df.columns[-1] == "APO_12_26" - - -def test_ext_bias(df): - df.ta.bias(append=True) - assert df.columns[-1] == "BIAS_SMA_26" - - -def test_ext_bop(df): - df.ta.bop(append=True) - assert df.columns[-1] == "BOP" - - -def test_ext_brar(df): - df.ta.brar(append=True) - assert df.columns[-1] == "BR_26" - - -def test_ext_cci(df): - df.ta.cci(append=True) - assert df.columns[-1] == "CCI_14_0.015" - - -def test_ext_cfo(df): - df.ta.cfo(append=True) - assert df.columns[-1] == "CFO_9" - - -def test_ext_cg(df): - df.ta.cg(append=True) - assert df.columns[-1] == "CG_10" - - -def test_ext_cmo(df): - df.ta.cmo(append=True) - assert df.columns[-1] == "CMO_14" - - -def test_ext_coppock(df): - df.ta.coppock(append=True) - assert df.columns[-1] == "COPC_11_14_10" - - -def test_ext_crsi(df): - df.ta.crsi(append=True) - assert df.columns[-1] == "CRSI_3_2_100" - - -def test_ext_crsi(df): - df.ta.crsi(append=True) - assert df.columns[-1] == "CRSI_3_2_100" - - -def test_ext_cti(df): - df.ta.cti(append=True) - assert df.columns[-1] == "CTI_12" - - -def test_ext_dm(df): - df.ta.dm(append=True) - assert list(df.columns[-2:]) == ["DMP_14", "DMN_14"] - - -def test_ext_er(df): - df.ta.er(append=True) - assert df.columns[-1] == "ER_10" - - -def test_ext_eri(df): - df.ta.eri(append=True) - assert list(df.columns[-2:]) == ["BULLP_13", "BEARP_13"] - - -def test_ext_exhc(df): - df.ta.exhc(append=True) - assert list(df.columns[-2:]) == ["EXHC_DNa", "EXHC_UPa"] - - -def test_ext_fisher(df): - df.ta.fisher(append=True) - assert list(df.columns[-2:]) == ["FISHERT_9_1", "FISHERTs_9_1"] - - -def test_ext_inertia(df): - df.ta.inertia(append=True) - assert df.columns[-1] == "INERTIA_20_14" - - -def test_ext_kdj(df): - df.ta.kdj(append=True) - assert list(df.columns[-3:]) == ["K_9_3", "D_9_3", "J_9_3"] - - -def test_ext_kst(df): - df.ta.kst(append=True) - assert list(df.columns[-2:]) == ["KST_10_15_20_30_10_10_10_15", "KSTs_9"] - - -def test_ext_macd(df): - df.ta.macd(append=True) - columns = ["MACD_12_26_9", "MACDh_12_26_9", "MACDs_12_26_9"] - assert list(df.columns[-3:]) == columns - - -def test_ext_mom(df): - df.ta.mom(append=True) - assert df.columns[-1] == "MOM_10" - - -def test_ext_pgo(df): - df.ta.pgo(append=True) - assert df.columns[-1] == "PGO_14" - - -def test_ext_ppo(df): - df.ta.ppo(append=True) - assert list(df.columns[-3:]) == [ - "PPO_12_26_9", "PPOh_12_26_9", "PPOs_12_26_9" - ] - - -def test_ext_psl(df): - df.ta.psl(append=True) - assert df.columns[-1] == "PSL_12" - - -def test_ext_qqe(df): - df.ta.qqe(append=True) - columns = [ - "QQE_14_5_4.236", "QQE_14_5_4.236_RSIMA", - "QQEl_14_5_4.236", "QQEs_14_5_4.236" - ] - assert list(df.columns[-4:]) == columns - - -def test_ext_roc(df): - df.ta.roc(append=True) - assert df.columns[-1] == "ROC_10" - - -def test_ext_rsi(df): - df.ta.rsi(append=True) - assert df.columns[-1] == "RSI_14" - - -def test_ext_rsx(df): - df.ta.rsx(append=True) - assert df.columns[-1] == "RSX_14" - - -def test_ext_rvgi(df): - df.ta.rvgi(append=True) - assert list(df.columns[-2:]) == ["RVGI_14_4", "RVGIs_14_4"] - - -def test_ext_slope(df): - df.ta.slope(append=True) - assert df.columns[-1] == "SLOPE_1" - - -def test_ext_smc(df): - df.ta.smc(append=True) - columns = [ - "SMChv_14_50_20_5", - "SMCbf_14_50_20_5", "SMCbi_14_50_20_5", "SMCbp_14_50_20_5", - "SMCtf_14_50_20_5", "SMCti_14_50_20_5", "SMCtp_14_50_20_5" - ] - assert list(df.columns[-7:]) == columns - - -def test_ext_smi(df): - df.ta.smi(append=True) - columns = ["SMI_5_20_5_1.0", "SMIs_5_20_5_1.0", "SMIo_5_20_5_1.0"] - assert list(df.columns[-3:]) == columns - - -def test_ext_squeeze(df): - df.ta.squeeze(append=True) - columns = ["SQZ_20_2.0_20_1.5", "SQZ_ON", "SQZ_OFF", "SQZ_NO"] - assert list(df.columns[-4:]) == columns - - -def test_ext_squeeze_pro(df): - df.ta.squeeze_pro(append=True) - columns = [ - "SQZPRO_ON_NORMAL", "SQZPRO_ON_NARROW", "SQZPRO_OFF", "SQZPRO_NO" - ] - assert list(df.columns[-4:]) == columns - - -def test_ext_stc(df): - df.ta.stc(append=True) - columns = [ - "STC_10_12_26_0.5", "STCmacd_10_12_26_0.5", "STCstoch_10_12_26_0.5" - ] - assert list(df.columns[-3:]) == columns - - -def test_ext_stoch(df): - df.ta.stoch(append=True) - columns = ["STOCHk_14_3_3", "STOCHd_14_3_3", "STOCHh_14_3_3"] - assert list(df.columns[-3:]) == columns - - -def test_ext_stochf(df): - df.ta.stochf(append=True) - assert list(df.columns[-2:]) == ["STOCHFk_14_3", "STOCHFd_14_3"] - - -def test_ext_stochrsi(df): - df.ta.stochrsi(append=True) - assert list(df.columns[-2:]) == [ - "STOCHRSIk_14_14_3_3", "STOCHRSId_14_14_3_3" - ] - - -def test_ext_tmo(df): - df.ta.tmo(append=True) - columns = ["TMO_14_5_3", "TMOs_14_5_3", "TMOM_14_5_3", "TMOMs_14_5_3"] - assert list(df.columns[-4:]) == columns - - -def test_ext_trix(df): - df.ta.trix(append=True) - assert list(df.columns[-2:]) == ["TRIX_30_9", "TRIXs_30_9"] - - -def test_ext_tsi(df): - df.ta.tsi(append=True) - assert list(df.columns[-2:]) == ["TSI_13_25_13", "TSIs_13_25_13"] - - -def test_ext_uo(df): - df.ta.uo(append=True) - assert df.columns[-1] == "UO_7_14_28" - - -def test_ext_willr(df): - df.ta.willr(append=True) - assert df.columns[-1] == "WILLR_14" diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_overlap.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_overlap.py deleted file mode 100644 index 2077711c..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_overlap.py +++ /dev/null @@ -1,779 +0,0 @@ -# -*- coding: utf-8 -*- -import numpy as np -from pandas import DataFrame, Series -import pandas.testing as pdt -from pytest import mark -import talib as tal - -import pandas_ta as ta - -from .config import CORRELATION, CORRELATION_THRESHOLD, error_analysis - - - -# TA Lib style Tests -def test_alligator(df): - result = ta.alligator(df.high, df.low) - assert isinstance(result, DataFrame) - assert result.name == "AG_13_8_5" - - -def test_alma(df): - result = ta.alma(df.close) - assert isinstance(result, Series) - assert result.name == "ALMA_9_6.0_0.85" - - -def test_dema(df): - result = ta.dema(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "DEMA_10" - - try: - expected = tal.DEMA(df.close, 10) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.dema(df.close) - assert isinstance(result, Series) - assert result.name == "DEMA_10" - - -def test_ema(df): - result = ta.ema(df.close, talib=False, presma=True) - assert isinstance(result, Series) - assert result.name == "EMA_10" - - try: - expected = tal.EMA(df.close, 10) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.ema(df.close, talib=True) - assert isinstance(result, Series) - assert result.name == "EMA_10" - - result = ta.ema(df.close, talib=True, presma=False, adjust=False) - assert isinstance(result, Series) - assert result.name == "EMA_10" - - result = ta.ema(df.close, talib=True, presma=False, adjust=True) - assert isinstance(result, Series) - assert result.name == "EMA_10" - - result = ta.ema(df.close, talib=True, presma=True, adjust=True) - assert isinstance(result, Series) - assert result.name == "EMA_10" - - -def test_fwma(df): - result = ta.fwma(df.close) - assert isinstance(result, Series) - assert result.name == "FWMA_10" - - -def test_hilo(df): - result = ta.hilo(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "HILO_13_21" - - -def test_hl2(df): - result = ta.hl2(df.high, df.low) - assert isinstance(result, Series) - assert result.name == "HL2" - - -def test_hlc3(df): - result = ta.hlc3(df.high, df.low, df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "HLC3" - - try: - expected = tal.TYPPRICE(df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.hlc3(df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "HLC3" - - -def test_hma(df): - result = ta.hma(df.close) - assert isinstance(result, Series) - assert result.name == "HMA_10" - - result = ta.hma(df.close, mamode="ema") - assert isinstance(result, Series) - assert result.name == "HMAe_10" - - result = ta.hma(df.close, mamode="sma") - assert isinstance(result, Series) - assert result.name == "HMAs_10" - - -def test_hwma(df): - result = ta.hwma(df.close) - assert isinstance(result, Series) - assert result.name == "HWMA_0.2_0.1_0.1" - - -def test_ichimoku(df): - result_ichimoku, result_span = ta.ichimoku(df.high, df.low, df.close) - assert isinstance(result_ichimoku, DataFrame) - assert isinstance(result_span, DataFrame) - assert result_ichimoku.name == "ICHIMOKU_9_26_52" - assert result_span.name == "ICHISPAN_9_26" - - -def test_jma(df): - result = ta.jma(df.close) - assert isinstance(result, Series) - assert result.name == "JMA_7_0.0" - - -def test_kama(df): - result = ta.kama(df.close) - assert isinstance(result, Series) - assert result.name == "KAMA_10_2_30" - - -def test_linreg(df): - result = ta.linreg(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "LINREG_14" - - try: - expected = tal.LINEARREG(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.linreg(df.close, talib=True) - assert isinstance(result, Series) - assert result.name == "LINREG_14" - - -def test_linreg_angle(df): - result = ta.linreg(df.close, angle=True, talib=False) - assert isinstance(result, Series) - assert result.name == "LINREGa_14" - - try: - expected = tal.LINEARREG_ANGLE(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.linreg(df.close, angle=True, talib=True) - assert isinstance(result, Series) - assert result.name == "LINREGa_14" - - -def test_linreg_intercept(df): - result = ta.linreg(df.close, intercept=True, talib=False) - assert isinstance(result, Series) - assert result.name == "LINREGb_14" - - try: - expected = tal.LINEARREG_INTERCEPT(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.linreg(df.close, intercept=True, talib=True) - assert isinstance(result, Series) - assert result.name == "LINREGb_14" - - -def test_linreg_r(df): - result = ta.linreg(df.close, r=True) - assert isinstance(result, Series) - assert result.name == "LINREGr_14" - - -def test_linreg_slope(df): - result = ta.linreg(df.close, slope=True, talib=False) - assert isinstance(result, Series) - assert result.name == "LINREGm_14" - - try: - expected = tal.LINEARREG_SLOPE(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.linreg(df.close, slope=True, talib=True) - assert isinstance(result, Series) - assert result.name == "LINREGm_14" - - -@mark.parametrize("kind,name", [ - ("dema", "DEMA_10"), ("ema", "EMA_10"), ("fwma", "FWMA_10"), - ("hma", "HMA_10"), ("linreg", "LINREG_14"), ("midpoint", "MIDPOINT_2"), - ("pwma", "PWMA_10"), ("rma", "RMA_10"), ("sinwma", "SINWMA_14"), - ("sma", "SMA_10"), ("ssf", "SSF_20"), ("swma", "SWMA_10"), - ("t3", "T3_10_0.7"), ("tema", "TEMA_10"), ("trima", "TRIMA_10"), - ("vidya", "VIDYA_14"), ("wma", "WMA_10") -]) -def test_ma(df, kind, name): -# def test_ma(df): - result = ta.ma() - assert isinstance(result, list) - assert len(result) > 0 - - result = ta.ma(kind, df.close) - assert isinstance(result, Series) - assert result.name == name - - -def test_mama(df): - result = ta.mama(df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "MAMA_0.5_0.05" - - try: - expected = tal.MAMA(df.close) - expecteddf = DataFrame({ - "MAMA_0.5_0.05": expected[0], - "FAMA_0.5_0.05": expected[1] - }) - pdt.assert_frame_equal(result, expecteddf) - except AssertionError: - try: - mama_corr = ta.utils.df_error_analysis(result.iloc[:, 0], expecteddf.iloc[:, 0]) - assert mama_corr > CORRELATION_THRESHOLD - print(f"{mama_corr=}") - except Exception as ex: - error_analysis(result.iloc[:, 0], CORRELATION, ex) - - try: - fama_corr = ta.utils.df_error_analysis(result.iloc[:, 1], expecteddf.iloc[:, 1]) - assert fama_corr > CORRELATION_THRESHOLD - print(f"{fama_corr=}") - except Exception as ex: - error_analysis(result.iloc[:, 1], CORRELATION, ex) - - result = ta.mama(df.close) - assert isinstance(result, DataFrame) - assert result.name == "MAMA_0.5_0.05" - - -def test_mcgd(df): - result = ta.mcgd(df.close) - assert isinstance(result, Series) - assert result.name == "MCGD_10" - - -def test_midpoint(df): - result = ta.midpoint(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "MIDPOINT_2" - - try: - expected = tal.MIDPOINT(df.close, 2) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.midpoint(df.close) - assert isinstance(result, Series) - assert result.name == "MIDPOINT_2" - - -def test_midprice(df): - result = ta.midprice(df.high, df.low, talib=False) - assert isinstance(result, Series) - assert result.name == "MIDPRICE_2" - - try: - expected = tal.MIDPRICE(df.high, df.low, 2) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.midprice(df.high, df.low) - assert isinstance(result, Series) - assert result.name == "MIDPRICE_2" - - -def test_ohlc4(df): - result = ta.ohlc4(df.open, df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "OHLC4" - - -@mark.parametrize("method,name,columns", [ - (None, "PIVOTS_TRAD_D", 9), ("camarilla", "PIVOTS_CAMA_D", 9), - ("classic", "PIVOTS_CLAS_D", 9), ("demark", "PIVOTS_DEMA_D", 3), - ("fibonacci", "PIVOTS_FIBO_D", 7), ("traditional", "PIVOTS_TRAD_D", 9), - ("woodie", "PIVOTS_WOOD_D", 9) -]) -def test_pivots(df, method, name, columns): - result = ta.pivots(df.open, df.high, df.low, df.close, method=method) - assert isinstance(result, DataFrame) - assert result.name == name - assert result.columns.size == columns - - -def test_pwma(df): - result = ta.pwma(df.close) - assert isinstance(result, Series) - assert result.name == "PWMA_10" - - -def test_rma(df): - result = ta.rma(df.close) - assert isinstance(result, Series) - assert result.name == "RMA_10" - - -def test_sinwma(df): - result = ta.sinwma(df.close) - assert isinstance(result, Series) - assert result.name == "SINWMA_14" - - -def test_sma(df): - result = ta.sma(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "SMA_10" - - try: - expected = tal.SMA(df.close, 10) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.sma(df.close, talib=True) - assert isinstance(result, Series) - assert result.name == "SMA_10" - - -def test_smma(df): - result = ta.smma(df.close) - assert isinstance(result, Series) - assert result.name == "SMMA_7" - - -def test_ssf(df): - result = ta.ssf(df.close) - assert isinstance(result, Series) - assert result.name == "SSF_20" - - result = ta.ssf(df.close, pi=np.pi, sqrt2=np.sqrt(2), everget=True) - assert isinstance(result, Series) - assert result.name == "SSFe_20" - - -def test_ssf3(df): - result = ta.ssf3(df.close) - assert isinstance(result, Series) - assert result.name == "SSF3_20" - - -def test_supertrend(df): - result = ta.supertrend(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "SUPERT_7_3.0" - - -def test_swma(df): - result = ta.swma(df.close) - assert isinstance(result, Series) - assert result.name == "SWMA_10" - - -def test_t3(df): - result = ta.t3(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "T3_10_0.7" - - try: - expected = tal.T3(df.close, 10) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.t3(df.close, talib=True) - assert isinstance(result, Series) - assert result.name == "T3_10_0.7" - - -def test_tema(df): - result = ta.tema(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "TEMA_10" - - try: - expected = tal.TEMA(df.close, 10) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.tema(df.close, talib=True) - assert isinstance(result, Series) - assert result.name == "TEMA_10" - - -def test_trima(df): - result = ta.trima(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "TRIMA_10" - - try: - expected = tal.TRIMA(df.close, 10) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.trima(df.close, talib=True) - assert isinstance(result, Series) - assert result.name == "TRIMA_10" - - -def test_tsf(df): - result = ta.linreg(df.close, tsf=True, talib=False) - assert isinstance(result, Series) - assert result.name == "LINREG_14" - - try: - expected = tal.TSF(df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.linreg(df.close, tsf=True, talib=True) - assert isinstance(result, Series) - assert result.name == "LINREG_14" - - -def test_vidya(df): - result = ta.vidya(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "VIDYA_14" - - try: - expected = ta.vidya(df.close, talib=True) - assert isinstance(expected, Series) - assert expected.name == "VIDYA_14" - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - -def test_wcp(df): - result = ta.wcp(df.high, df.low, df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "WCP" - - try: - expected = tal.WCLPRICE(df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.wcp(df.high, df.low, df.close, talib=True) - assert isinstance(result, Series) - assert result.name == "WCP" - - -def test_wma(df): - result = ta.wma(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "WMA_10" - - try: - expected = tal.WMA(df.close, 10) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.wma(df.close, talib=True) - assert isinstance(result, Series) - assert result.name == "WMA_10" - - -def test_zlma(df): - result = ta.zlma(df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "ZL_EMA_10" - - - -# DataFrame Extension Tests -def test_ext_alligator(df): - df.ta.alligator(append=True) - assert list(df.columns[-3:]) == [ - "AGj_13_8_5", "AGt_13_8_5", "AGl_13_8_5" - ] - - -def test_ext_alma(df): - df.ta.alma(append=True) - assert df.columns[-1] == "ALMA_9_6.0_0.85" - - -def test_ext_dema(df): - df.ta.dema(append=True) - assert df.columns[-1] == "DEMA_10" - - -def test_ext_ema(df): - df.ta.ema(append=True) - assert df.columns[-1] == "EMA_10" - - -def test_ext_fwma(df): - df.ta.fwma(append=True) - assert df.columns[-1] == "FWMA_10" - - -def test_ext_hilo(df): - df.ta.hilo(append=True) - assert list(df.columns[-3:]) == [ - "HILO_13_21", "HILOl_13_21", "HILOs_13_21" - ] - - -def test_ext_hl2(df): - df.ta.hl2(append=True) - assert df.columns[-1] == "HL2" - - -def test_ext_hlc3(df): - df.ta.hlc3(append=True) - assert df.columns[-1] == "HLC3" - - -def test_ext_hma(df): - df.ta.hma(append=True) - assert df.columns[-1] == "HMA_10" - - -def test_ext_hwma(df): - df.ta.hwma(append=True) - assert df.columns[-1] == "HWMA_0.2_0.1_0.1" - - -def test_ext_ichimoku(df): - df.ta.ichimoku(append=True) - columns = ["ISA_9", "ISB_26", "ITS_9", "IKS_26", "ICS_26"] - assert list(df.columns[-5:]) == columns - - -def test_ext_jma(df): - df.ta.jma(append=True) - assert df.columns[-1] == "JMA_7_0.0" - - -def test_ext_kama(df): - df.ta.kama(append=True) - assert df.columns[-1] == "KAMA_10_2_30" - - -def test_ext_linreg(df): - df.ta.linreg(append=True) - assert df.columns[-1] == "LINREG_14" - - -def test_ext_mama(df): - df.ta.mama(append=True) - assert list(df.columns[-2:]) == ["MAMA_0.5_0.05", "FAMA_0.5_0.05"] - - -def test_ext_mcgd(df): - df.ta.mcgd(append=True) - assert df.columns[-1] == "MCGD_10" - - -def test_ext_midpoint(df): - df.ta.midpoint(append=True) - assert df.columns[-1] == "MIDPOINT_2" - - -def test_ext_midprice(df): - df.ta.midprice(append=True) - assert df.columns[-1] == "MIDPRICE_2" - - -def test_ext_ohlc4(df): - df.ta.ohlc4(append=True) - assert df.columns[-1] == "OHLC4" - - -def test_ext_pivots(df): - df.ta.pivots(append=True) - assert len(df.columns[-9:]) == 9 - - -def test_ext_pwma(df): - df.ta.pwma(append=True) - assert df.columns[-1] == "PWMA_10" - - -def test_ext_rma(df): - df.ta.rma(append=True) - assert df.columns[-1] == "RMA_10" - - -def test_ext_sinwma(df): - df.ta.sinwma(append=True) - assert df.columns[-1] == "SINWMA_14" - - -def test_ext_sma(df): - df.ta.sma(append=True) - assert df.columns[-1] == "SMA_10" - - -def test_ext_smma(df): - df.ta.smma(append=True) - assert df.columns[-1] == "SMMA_7" - - -def test_ext_ssf(df): - df.ta.ssf(append=True) - assert df.columns[-1] == "SSF_20" - - -def test_ext_ssf3(df): - df.ta.ssf3(append=True) - assert df.columns[-1] == "SSF3_20" - - -def test_ext_supertrend(df): - df.ta.supertrend(append=True) - columns = [ - "SUPERT_7_3.0", "SUPERTd_7_3.0", "SUPERTl_7_3.0", "SUPERTs_7_3.0" - ] - assert list(df.columns[-4:]) == columns - - -def test_ext_swma(df): - df.ta.swma(append=True) - assert df.columns[-1] == "SWMA_10" - - -def test_ext_t3(df): - df.ta.t3(append=True) - assert df.columns[-1] == "T3_10_0.7" - - -def test_ext_tema(df): - df.ta.tema(append=True) - assert df.columns[-1] == "TEMA_10" - - -def test_ext_trima(df): - df.ta.trima(append=True) - assert df.columns[-1] == "TRIMA_10" - - -def test_ext_vidya(df): - df.ta.vidya(append=True) - assert df.columns[-1] == "VIDYA_14" - - -def test_ext_wcp(df): - df.ta.wcp(append=True) - assert df.columns[-1] == "WCP" - - -def test_ext_wma(df): - df.ta.wma(append=True) - assert df.columns[-1] == "WMA_10" - - -def test_ext_zlma(df): - df.ta.zlma(append=True) - assert df.columns[-1] == "ZL_EMA_10" diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_performance.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_performance.py deleted file mode 100644 index 6ee86a39..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_performance.py +++ /dev/null @@ -1,43 +0,0 @@ -# -*- coding: utf-8 -*- -import pandas_ta as ta - - - -# TA Lib style Tests -def test_drawdown(df): - result = ta.drawdown(df.close) - assert result.name == "DD" - - -def test_log_return(df): - result = ta.log_return(df.close) - assert result.name == "LOGRET_1" - - result = ta.log_return(df.close, cumulative=True) - assert result.name == "CUMLOGRET_1" - - -def test_percent_return(df): - result = ta.percent_return(df.close, cumulative=False) - assert result.name == "PCTRET_1" - - result = ta.percent_return(df.close, cumulative=True) - assert result.name == "CUMPCTRET_1" - - - -# DataFrame Extension Tests -def test_ext_log_return(df): - df.ta.log_return(append=True) - assert df.columns[-1] == "LOGRET_1" - - df.ta.log_return(cumulative=True, append=True) - assert df.columns[-1] == "CUMLOGRET_1" - - -def test_ext_percent_return(df): - df.ta.percent_return(append=True) - assert df.columns[-1] == "PCTRET_1" - - df.ta.percent_return(cumulative=True, append=True) - assert df.columns[-1] == "CUMPCTRET_1" diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_statistics.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_statistics.py deleted file mode 100644 index 42b588b1..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_statistics.py +++ /dev/null @@ -1,161 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -import pandas.testing as pdt -from pytest import mark -import talib as tal - -import pandas_ta as ta - -from .config import CORRELATION, CORRELATION_THRESHOLD, error_analysis - - - -# TA Lib style Tests -def test_entropy(df): - result = ta.entropy(df.close) - assert isinstance(result, Series) - assert result.name == "ENTP_10" - - -def test_kurtosis(df): - result = ta.kurtosis(df.close) - assert isinstance(result, Series) - assert result.name == "KURT_30" - - -def test_mad(df): - result = ta.mad(df.close) - assert isinstance(result, Series) - assert result.name == "MAD_30" - - -def test_median(df): - result = ta.median(df.close) - assert isinstance(result, Series) - assert result.name == "MEDIAN_30" - - -def test_quantile(df): - result = ta.quantile(df.close) - assert isinstance(result, Series) - assert result.name == "QTL_30_0.5" - - -def test_skew(df): - result = ta.skew(df.close) - assert isinstance(result, Series) - assert result.name == "SKEW_30" - - -def test_stdev(df): - result = ta.stdev(df.close, talib=False, ddof=0) - assert isinstance(result, Series) - assert result.name == "STDEV_30" - - try: - expected = tal.STDDEV(df.close, 30) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - -@mark.parametrize("length,stds,name,columns", [ - (None, None, "TOS_STDEVALL", 7), - (30, None, "TOS_STDEVALL_30", 7), - (30, [1, 2], "TOS_STDEVALL_30", 5), -]) -def test_tos_stdevall(df, length, stds, name, columns): - result = ta.tos_stdevall(df.close, length=length, stds=stds) - assert isinstance(result, DataFrame) - assert result.name == name - assert len(result.columns) == columns - - -def test_variance(df): - result = ta.variance(df.close, talib=False, ddof=0) - assert isinstance(result, Series) - assert result.name == "VAR_30" - - try: - expected = tal.VAR(df.close, 30) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.variance(df.close) - assert isinstance(result, Series) - assert result.name == "VAR_30" - - -def test_zscore(df): - result = ta.zscore(df.close) - assert isinstance(result, Series) - assert result.name == "ZS_30" - - - -# DataFrame Extension Tests -def test_ext_entropy(df): - df.ta.entropy(append=True) - assert df.columns[-1] == "ENTP_10" - - -def test_ext_kurtosis(df): - df.ta.kurtosis(append=True) - assert df.columns[-1] == "KURT_30" - - -def test_ext_mad(df): - df.ta.mad(append=True) - assert df.columns[-1] == "MAD_30" - - -def test_ext_median(df): - df.ta.median(append=True) - assert df.columns[-1] == "MEDIAN_30" - - -def test_ext_quantile(df): - df.ta.quantile(append=True) - assert df.columns[-1] == "QTL_30_0.5" - - -def test_ext_skew(df): - df.ta.skew(append=True) - assert df.columns[-1] == "SKEW_30" - - -def test_ext_stdev(df): - df.ta.stdev(append=True) - assert df.columns[-1] == "STDEV_30" - - -def test_ext_tos_stdevall(df): - df.ta.tos_stdevall(append=True) - columns = [ - "TOS_STDEVALL_LR", - "TOS_STDEVALL_L_1", "TOS_STDEVALL_U_1", - "TOS_STDEVALL_L_2", "TOS_STDEVALL_U_2", - "TOS_STDEVALL_L_3", "TOS_STDEVALL_U_3" - ] - assert list(df.columns[-7:]) == columns - - -def test_ext_variance(df): - df.ta.variance(append=True) - assert df.columns[-1] == "VAR_30" - - -def test_ext_zscore(df): - df.ta.zscore(append=True) - assert df.columns[-1] == "ZS_30" diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_trend.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_trend.py deleted file mode 100644 index 76660452..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_trend.py +++ /dev/null @@ -1,436 +0,0 @@ -# -*- coding: utf-8 -*- -import numpy as np -from pandas import DataFrame, Series, read_csv -import pandas.testing as pdt -import talib as tal - -import pandas_ta as ta - -from .config import CORRELATION, CORRELATION_THRESHOLD, error_analysis - - - -sample_adx_data = read_csv( - f"data/ADX_D.csv", - index_col=0, - parse_dates=True, - # date_format="%f" - date_format="%m/%d/%Y %I:%M:%S `%p" -) - -expected_tv_adx = DataFrame({ - "ADX_14": [ - None, None, None, None, None, None, - None, None, None, None, None, None, - None, None, 9.874338, 10.408195, 10.799274, - ], - "DMP_14": [ - None, 13.686598, 14.247809, 13.436449, 17.946530, 17.193874, - 19.214901, 17.860325, 16.899406, 16.207983, 15.998908, 15.202702, - 14.621306, 14.303707, 13.451093, 12.932243, 12.840198 - ], - "DMN_14": [ - None, 21.954010, 21.055379, 23.102292, 21.120552, 20.234781, - 18.827331, 19.298744, 19.029033, 18.250478, 17.414460, 16.772558, - 16.131126, 15.780731, 19.097781, 18.361121, 17.689287 - ] -}) - - -# TA Lib style Tests -def test_adx(df): - result = ta.adx(df.high, df.low, df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "ADX_14" - assert len(result.columns) == 4 - - try: - expected = tal.ADX(df.high, df.low, df.close) - pdt.assert_series_equal(result.iloc[0], expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.adx(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "ADX_14" - - # TV Test - df = sample_adx_data - result = ta.adx(df.high, df.low, df.close, tvmode=True) - assert isinstance(result, DataFrame) - assert result.name == "ADX_14" - - result = result.iloc[13:] - result.drop(result.columns[1], axis=1, inplace=True) - result.reset_index(drop=True, inplace=True) - pdt.assert_frame_equal(result, expected_tv_adx) - - -def test_alphatrend(df): - result = ta.alphatrend(df.open, df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "ALPHAT_14_1_50" - - -def test_amat(df): - result = ta.amat(df.close) - assert isinstance(result, DataFrame) - assert result.name == "AMATe_8_21_2" - - -def test_aroon(df): - result = ta.aroon(df.high, df.low, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "AROON_14" - - try: - expected = tal.AROON(df.high, df.low) - expecteddf = DataFrame({"AROOND_14": expected[0], "AROONU_14": expected[1]}) - pdt.assert_frame_equal(result, expecteddf) - except AssertionError: - try: - aroond_corr = ta.utils.df_error_analysis(result.iloc[:, 0], expected.iloc[:, 0]) - print(f"{aroond_corr=}") - assert aroond_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 0], CORRELATION, ex) - - try: - aroonu_corr = ta.utils.df_error_analysis(result.iloc[:, 1], expected.iloc[:, 1]) - print(f"{aroonu_corr=}") - assert aroonu_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 1], CORRELATION, ex) - - result = ta.aroon(df.high, df.low) - assert isinstance(result, DataFrame) - assert result.name == "AROON_14" - - -def test_aroon_osc(df): - result = ta.aroon(df.high, df.low, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "AROON_14" - - try: - expected = tal.AROONOSC(df.high, df.low) - pdt.assert_series_equal(result.iloc[:, 2], expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected.iloc[:, 2]) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - -def test_chop(df): - result = ta.chop(df.high, df.low, df.close, ln=False) - assert isinstance(result, Series) - assert result.name == "CHOP_14_1_100.0" - - result = ta.chop(df.high, df.low, df.close, ln=True) - assert isinstance(result, Series) - assert result.name == "CHOPln_14_1_100.0" - - -def test_cksp(df): - result = ta.cksp(df.high, df.low, df.close, tvmode=False) - assert isinstance(result, DataFrame) - assert result.name == "CKSP_10_3_20" - - result = ta.cksp(df.high, df.low, df.close, tvmode=True) - assert isinstance(result, DataFrame) - assert result.name == "CKSP_10_1_9" - - -def test_decay(df): - result = ta.decay(df.close) - assert isinstance(result, Series) - assert result.name == "LDECAY_1" - - result = ta.decay(df.close, mode="exp") - assert isinstance(result, Series) - assert result.name == "EXPDECAY_1" - - tulip = Series([0, 0, 0, 1, 0, 0, 0, 1, 0, 0]) - expected = Series([0, 0, 0, 1, 0.75, 0.5, 0.25, 1, 0.75, 0.5]) - result = ta.decay(tulip, length=4, mode="linear") - assert isinstance(result, Series) - assert result.name == "LDECAY_4" - pdt.assert_series_equal(result, expected, check_names=False) - - -def test_decreasing(df): - result = ta.decreasing(df.close) - assert isinstance(result, Series) - assert result.name == "DEC_1" - - result = ta.decreasing(df.close, length=3, strict=True) - assert isinstance(result, Series) - assert result.name == "SDEC_3" - - -def test_dpo(df): - result = ta.dpo(df.close) - assert isinstance(result, Series) - assert result.name == "DPO_20" - - -def test_ht_trendline(df): - result = ta.ht_trendline(df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "HT_TL" - - try: - expected = tal.HT_TRENDLINE(df.close) - corr = ta.utils.df_error_analysis(result, expected) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.ht_trendline(df.close) - assert isinstance(result, Series) - assert result.name == "HT_TL" - - -def test_increasing(df): - result = ta.increasing(df.close) - assert isinstance(result, Series) - assert result.name == "INC_1" - - result = ta.increasing(df.close, length=3, strict=True) - assert isinstance(result, Series) - assert result.name == "SINC_3" - - -def test_long_run(df): - result = ta.long_run(df.open, df.close) - assert isinstance(result, Series) - assert result.name == "LR_2" - - -def test_psar(df): - result = ta.psar(df.high, df.low, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "PSAR_0.02_0.2" - - # Combine Long and Short SAR"s into one SAR value - psar = result[result.columns[:2]].fillna(0) - psar = psar[psar.columns[0]] + psar[psar.columns[1]] - psar.iloc[0] = np.nan - psar.name = result.name - - try: - expected = tal.SAR(df.high, df.low) - pdt.assert_series_equal(psar, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(psar, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - -def test_qstick(df): - result = ta.qstick(df.open, df.close) - assert isinstance(result, Series) - assert result.name == "QS_10" - - -def test_rwi(df): - result = ta.rwi(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "RWI_14" - - -def test_short_run(df): - result = ta.short_run(df.close, df.open) - assert isinstance(result, Series) - assert result.name == "SR_2" - - -def test_trendflex(df): - result = ta.trendflex(df.close) - assert isinstance(result, Series) - assert result.name == "TRENDFLEX_20_20_0.04" - - -def test_vhf(df): - result = ta.vhf(df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "VHF_28" - - -def test_vortex(df): - result = ta.vortex(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "VTX_14" - - -def test_zigzag(df): - result = ta.zigzag(df.high, df.low) - assert isinstance(result, DataFrame) - assert result.name == "ZIGZAG_5.0%_10" - - result = ta.zigzag(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "ZIGZAG_5.0%_10" - - notna = result.iloc[:,0].notna() - high_pivotsdf = result[notna & (result["ZIGZAGs_5.0%_10"]==1)] - assert isinstance(high_pivotsdf, DataFrame) - assert high_pivotsdf.shape[0] == 1 - - low_pivotsdf = result[notna & (result["ZIGZAGs_5.0%_10"]==-1)] - assert isinstance(low_pivotsdf, DataFrame) - assert low_pivotsdf.shape[0] == 1 - - all_pivotsdf = result[notna] - assert isinstance(all_pivotsdf, DataFrame) - total_length = low_pivotsdf.shape[0] + high_pivotsdf.shape[0] - assert all_pivotsdf.shape[0] == total_length - - - -# DataFrame Extension Tests -def test_ext_adx(df): - df.ta.adx(append=True) - assert list(df.columns[-4:]) == [ - "ADX_14", "ADXR_14_2", "DMP_14", "DMN_14" - ] - - -def test_ext_alphatrend(df): - df.ta.alphatrend(append=True) - assert list(df.columns[-2:]) == ["ALPHAT_14_1_50", "ALPHATl_14_1_50_2"] - - -def test_ext_amat(df): - df.ta.amat(append=True) - assert list(df.columns[-2:]) == ["AMATe_LR_8_21_2", "AMATe_SR_8_21_2"] - - -def test_ext_aroon(df): - df.ta.aroon(append=True) - assert list(df.columns[-3:]) == ["AROOND_14", "AROONU_14", "AROONOSC_14"] - - -def test_ext_chop(df): - df.ta.chop(ln=False, append=True) - assert df.columns[-1] == "CHOP_14_1_100.0" - - df.ta.chop(ln=True, append=True) - assert df.columns[-1] == "CHOPln_14_1_100.0" - - -def test_ext_cksp(df): - df.ta.cksp(tvmode=False, append=True) - assert list(df.columns[-2:]) == ["CKSPl_10_3_20", "CKSPs_10_3_20"] - - df.ta.cksp(tvmode=True, append=True) - assert list(df.columns[-2:]) == ["CKSPl_10_1_9", "CKSPs_10_1_9"] - - -def test_ext_decay(df): - df.ta.decay(append=True) - assert df.columns[-1] == "LDECAY_1" - - df.ta.decay(mode="exp", append=True) - assert df.columns[-1] == "EXPDECAY_1" - - -def test_ext_decreasing(df): - df.ta.decreasing(append=True) - assert df.columns[-1] == "DEC_1" - - df.ta.decreasing(length=3, strict=True, append=True) - assert df.columns[-1] == "SDEC_3" - - -def test_ext_dpo(df): - df.ta.dpo(append=True) - assert df.columns[-1] == "DPO_20" - - -def test_ext_ht_trendline(df): - df.ta.ht_trendline(append=True) - assert df.columns[-1] == "HT_TL" - - -def test_ext_increasing(df): - df.ta.increasing(append=True) - assert df.columns[-1] == "INC_1" - - df.ta.increasing(length=3, strict=True, append=True) - assert df.columns[-1] == "SINC_3" - - -def test_ext_long_run(df): - result = df.ta.long_run(append=True) - assert df.shape == result.shape - - fast, slow = df.ta.ema(8), df.ta.ema(21) - df.ta.long_run(fast, slow, append=True) - assert df.columns[-1] == "LR_2" - - -def test_ext_psar(df): - df.ta.psar(append=True) - expected = [ - "PSARl_0.02_0.2", "PSARs_0.02_0.2", - "PSARaf_0.02_0.2", "PSARr_0.02_0.2" - ] - assert list(df.columns[-4:]) == expected - - -def test_ext_qstick(df): - df.ta.qstick(append=True) - assert df.columns[-1] == "QS_10" - - -def test_ext_rwi(df): - df.ta.rwi(append=True) - assert list(df.columns[-2:]) == ["RWIh_14", "RWIl_14"] - - -def test_ext_short_run(df): - result = df.ta.short_run(append=True) - assert df.shape == result.shape - - fast, slow = df.ta.ema(8), df.ta.ema(21) - df.ta.short_run(fast, slow, append=True) - assert df.columns[-1] == "SR_2" - - -def test_ext_trendflex(df): - df.ta.trendflex(append=True) - assert df.columns[-1] == "TRENDFLEX_20_20_0.04" - - -def test_ext_vhf(df): - df.ta.vhf(append=True) - assert df.columns[-1] == "VHF_28" - - -def test_ext_vortex(df): - df.ta.vortex(append=True) - assert list(df.columns[-2:]) == ["VTXP_14", "VTXM_14"] - - -def test_ext_zigzag(df): - df.ta.zigzag(append=True) - assert list(df.columns[-3:]) == [ - "ZIGZAGs_5.0%_10", "ZIGZAGv_5.0%_10", "ZIGZAGd_5.0%_10" - ] diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_volatility.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_volatility.py deleted file mode 100644 index af3b7f7e..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_volatility.py +++ /dev/null @@ -1,332 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -import pandas.testing as pdt -import talib as tal - -import pandas_ta as ta - -from .config import CORRELATION, CORRELATION_THRESHOLD, error_analysis - - - -# TA Lib style Tests -def test_aberration(df): - result = ta.aberration(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "ABER_5_15" - - -def test_accbands(df): - result = ta.accbands(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "ACCBANDS_20" - - -def test_atr(df): - result = ta.atr(df.high, df.low, df.close, talib=False, prenan=True) - assert isinstance(result, Series) - assert result.name == "ATRr_14" - - try: - expected = tal.ATR(df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.atr(df.high, df.low, df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "ATRr_14" - - -def test_atrts(df): - result = ta.atrts(df.high, df.low, df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "ATRTSe_14_20_3.0" - - -def test_bbands(df): - result = ta.bbands(df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "BBANDS_5_2.0_2.0" - - try: - expected = tal.BBANDS(df.close) - expecteddf = DataFrame({ - "BBL_5_2.0_2.0": expected[2], - "BBM_5_2.0_2.0": expected[1], - "BBU_5_2.0_2.0": expected[0] - }) - pdt.assert_frame_equal(result, expecteddf) - except AssertionError: - try: - bbl_corr = ta.utils.df_error_analysis(result, expected) - print(f"{bbl_corr=}") - assert bbl_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 0], CORRELATION, ex) - - try: - bbm_corr = ta.utils.df_error_analysis(result, expected) - print(f"{bbm_corr=}") - assert bbm_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 1], CORRELATION, ex) - - try: - bbu_corr = ta.utils.df_error_analysis(result, expected) - print(f"{bbu_corr=}") - assert bbu_corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result.iloc[:, 2], CORRELATION, ex) - - result = ta.bbands(df.close, ddof=0) - assert isinstance(result, DataFrame) - assert result.name == "BBANDS_5_2.0_2.0" - - result = ta.bbands(df.close, ddof=1) - assert isinstance(result, DataFrame) - assert result.name == "BBANDS_5_2.0_2.0" - - result = ta.bbands(df.close, lower_std=3, upper_std=5) - assert isinstance(result, DataFrame) - assert result.name == "BBANDS_5_3_5" - - -def test_chandelier_exit(df): - result = ta.chandelier_exit(df.high, df.low, df.close, talib=False) - assert isinstance(result, DataFrame) - assert result.name == "CHDLREXT_22_22_14_2.0" - - -def test_donchian(df): - result = ta.donchian(df.high, df.low) - assert isinstance(result, DataFrame) - assert result.name == "DC_20_20" - - result = ta.donchian(df.high, df.low, lower_length=10, upper_length=5) - assert isinstance(result, DataFrame) - assert result.name == "DC_10_5" - - -def test_hwc(df): - result = ta.hwc(df.close, channels=True) - assert isinstance(result, DataFrame) - assert result.name == "HWC_1" - - result = ta.hwc(df.close, channels=False) - assert isinstance(result, DataFrame) - assert result.name == "HWC_1" - - -def test_kc(df): - result = ta.kc(df.high, df.low, df.close) - assert isinstance(result, DataFrame) - assert result.name == "KCe_20_2" - - result = ta.kc(df.high, df.low, df.close, mamode="sma") - assert isinstance(result, DataFrame) - assert result.name == "KCs_20_2" - - -def test_massi(df): - result = ta.massi(df.high, df.low) - assert isinstance(result, Series) - assert result.name == "MASSI_9_25" - - -def test_natr(df): - result = ta.natr(df.high, df.low, df.close, talib=False, prenan=True) - assert isinstance(result, Series) - assert result.name == "NATR_14" - - try: - expected = tal.NATR(df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.natr(df.high, df.low, df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "NATR_14" - - -def test_pdist(df): - result = ta.pdist(df.open, df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "PDIST" - - -def test_rvi(df): - result = ta.rvi(df.close) - assert isinstance(result, Series) - assert result.name == "RVI_14" - - result = ta.rvi(df.close, df.high, df.low, refined=True) - assert isinstance(result, Series) - assert result.name == "RVIr_14" - - result = ta.rvi(df.close, df.high, df.low, thirds=True) - assert isinstance(result, Series) - assert result.name == "RVIt_14" - - -def test_thermo(df): - result = ta.thermo(df.high, df.low) - assert isinstance(result, DataFrame) - assert result.name == "THERMO_20_2_0.5" - - -def test_true_range(df): - result = ta.true_range(df.high, df.low, df.close, talib=False) - assert isinstance(result, Series) - assert result.name == "TRUERANGE_1" - - try: - expected = tal.TRANGE(df.high, df.low, df.close) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.true_range(df.high, df.low, df.close) - assert isinstance(result, Series) - assert result.name == "TRUERANGE_1" - - -def test_ui(df): - result = ta.ui(df.close) - assert isinstance(result, Series) - assert result.name == "UI_14" - - result = ta.ui(df.close, everget=True) - assert isinstance(result, Series) - assert result.name == "UIe_14" - - - -# DataFrame Extension Tests -def test_ext_aberration(df): - df.ta.aberration(append=True) - columns = [ - "ABER_ZG_5_15", "ABER_SG_5_15", "ABER_XG_5_15", "ABER_ATR_5_15" - ] - assert list(df.columns[-4:]) == columns - - -def test_ext_accbands(df): - df.ta.accbands(append=True) - assert list(df.columns[-3:]) == ["ACCBL_20", "ACCBM_20", "ACCBU_20"] - - -def test_ext_atr(df): - df.ta.atr(append=True) - assert df.columns[-1] == "ATRr_14" - - -def test_ext_atrts(df): - df.ta.atrts(append=True) - assert df.columns[-1] == "ATRTSe_14_20_3.0" - - -def test_ext_bbands(df): - df.ta.bbands(append=True) - columns = [ - "BBL_5_2.0_2.0", "BBM_5_2.0_2.0", - "BBU_5_2.0_2.0", "BBB_5_2.0_2.0", "BBP_5_2.0_2.0" - ] - assert list(df.columns[-5:]) == columns - - result = df.ta.bbands(lower_std=3, upper_std=5) - assert isinstance(result, DataFrame) - assert result.name == "BBANDS_5_3_5" - columns = ["BBL_5_3_5", "BBM_5_3_5", "BBU_5_3_5", "BBB_5_3_5", "BBP_5_3_5"] - assert list(result.columns) == columns - - -def test_ext_chandelier_exit(df): - df.ta.chandelier_exit(append=True) - columns = [ - "CHDLREXTl_22_22_14_2.0", - "CHDLREXTs_22_22_14_2.0", - "CHDLREXTd_22_22_14_2.0" - ] - assert list(df.columns[-3:]) == columns - - -def test_ext_donchian(df): - df.ta.donchian(append=True) - assert list(df.columns[-3:]) == ["DCL_20_20", "DCM_20_20", "DCU_20_20"] - - -def test_ext_hwc(df): - df.ta.hwc(append=True) - assert list(df.columns[-5:]) == [ - "HWM_1", "HWL_1", "HWU_1", "HWW_1", "HWPCT_1" - ] - - -def test_ext_kc(df): - df.ta.kc(append=True) - assert list(df.columns[-3:]) == ["KCLe_20_2", "KCBe_20_2", "KCUe_20_2"] - - -def test_ext_massi(df): - df.ta.massi(append=True) - assert df.columns[-1] == "MASSI_9_25" - - -def test_ext_natr(df): - df.ta.natr(append=True) - assert df.columns[-1] == "NATR_14" - - -def test_ext_pdist(df): - df.ta.pdist(append=True) - assert df.columns[-1] == "PDIST" - - -def test_ext_rvi(df): - df.ta.rvi(append=True) - assert df.columns[-1] == "RVI_14" - - df.ta.rvi(refined=True, append=True) - assert df.columns[-1] == "RVIr_14" - - df.ta.rvi(thirds=True, append=True) - assert df.columns[-1] == "RVIt_14" - - -def test_ext_thermo(df): - df.ta.thermo(append=True) - columns = [ - "THERMO_20_2_0.5", "THERMOma_20_2_0.5", - "THERMOl_20_2_0.5", "THERMOs_20_2_0.5" - ] - assert list(df.columns[-4:]) == columns - - -def test_ext_true_range(df): - df.ta.true_range(append=True) - assert df.columns[-1] == "TRUERANGE_1" - - -def test_ext_ui(df): - df.ta.ui(append=True) - assert df.columns[-1] == "UI_14" - - df.ta.ui(everget=True, append=True) - assert df.columns[-1] == "UIe_14" diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_volume.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_volume.py deleted file mode 100644 index d239f344..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_indicator_volume.py +++ /dev/null @@ -1,324 +0,0 @@ -# -*- coding: utf-8 -*- -from pandas import DataFrame, Series -import pandas.testing as pdt -from pytest import mark -import talib as tal - -import pandas_ta as ta - -from .config import CORRELATION, CORRELATION_THRESHOLD, error_analysis - - - -# TA Lib style Tests -def test_ad(df): - result = ta.ad(df.high, df.low, df.close, df.volume, talib=False) - assert isinstance(result, Series) - assert result.name == "AD" - - try: - expected = tal.AD(df.high, df.low, df.close, df.volume) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.ad(df.high, df.low, df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "AD" - - -def test_ad_open(df): - result = ta.ad(df.high, df.low, df.close, df.volume, df.open) - assert isinstance(result, Series) - assert result.name == "ADo" - - -def test_adosc(df): - result = ta.adosc(df.high, df.low, df.close, df.volume, talib=False) - assert isinstance(result, Series) - assert result.name == "ADOSC_3_10" - - try: - expected = tal.ADOSC(df.high, df.low, df.close, df.volume) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.adosc(df.high, df.low, df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "ADOSC_3_10" - - -def test_aobv(df): - result = ta.aobv(df.close, df.volume) - assert isinstance(result, DataFrame) - assert result.name == "AOBVe_4_12_2_2_2" - - -def test_cmf(df): - result = ta.cmf(df.high, df.low, df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "CMF_20" - - -def test_efi(df): - result = ta.efi(df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "EFI_13" - - -def test_eom(df): - result = ta.eom(df.high, df.low, df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "EOM_14_100000000" - - -def test_kvo(df): - result = ta.kvo(df.high, df.low, df.close, df.volume) - assert isinstance(result, DataFrame) - assert result.name == "KVO_34_55_13" - - -def test_mfi(df): - result = ta.mfi(df.high, df.low, df.close, df.volume, talib=False) - assert isinstance(result, Series) - assert result.name == "MFI_14" - - try: - expected = tal.MFI(df.high, df.low, df.close, df.volume) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.mfi(df.high, df.low, df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "MFI_14" - - -def test_nvi(df): - result = ta.nvi(df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "NVI_1" - - -def test_obv(df): - result = ta.obv(df.close, df.volume, talib=False) - assert isinstance(result, Series) - assert result.name == "OBV" - - try: - expected = tal.OBV(df.close, df.volume) - pdt.assert_series_equal(result, expected, check_names=False) - except AssertionError: - try: - corr = ta.utils.df_error_analysis(result, expected) - print(f"{corr=}") - assert corr > CORRELATION_THRESHOLD - except Exception as ex: - error_analysis(result, CORRELATION, ex) - - result = ta.obv(df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "OBV" - - -def test_pvi(df): - result = ta.pvi(df.close, df.volume, length=10) - assert isinstance(result, DataFrame) - assert result.name == "PVI" - - result = ta.pvi(df.close, df.volume, length=10, overlay=True) - assert isinstance(result, DataFrame) - assert df.close.iloc[0] == result.iloc[0,0] - - -def test_pvo(df): - result = ta.pvo(df.volume) - assert isinstance(result, DataFrame) - assert result.name == "PVO_12_26_9" - - -def test_pvol(df): - result = ta.pvol(df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "PVOL" - - -def test_pvr(df): - result = ta.pvr(df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "PVR" - - -def test_pvt(df): - result = ta.pvt(df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "PVT" - - -def test_tsv(df): - result = ta.tsv(df.close, df.volume) - assert isinstance(result, DataFrame) - assert result.name == "TSV_18_10" - - -def test_vhm(df): - result = ta.vhm(df.volume, 30, 30) - assert isinstance(result, Series) - assert result.name == "VHM_30" - - result = ta.vhm(df.volume, 10, 20) - assert isinstance(result, Series) - assert result.name == "VHM_10_20" - - -def test_vp(df): - result = ta.vp(df.close, df.volume) - assert isinstance(result, DataFrame) - assert result.name == "VP_10" - - result = ta.vp(df.close, df.volume, sort=True) - assert isinstance(result, DataFrame) - assert result.name == "VP_10" - - -@mark.parametrize("bands,dtype", [ - (None, Series), ([1], DataFrame), ([-1, 1], DataFrame), - ([1, 2, 4, 8], DataFrame), ([1, 2.5, 4.13], DataFrame), -]) -def test_vwap(df, bands, dtype): - result = ta.vwap(df.high, df.low, df.close, df.volume, bands=bands) - assert isinstance(result, dtype) - assert result.name == "VWAP_D" - - -def test_vwma(df): - result = ta.vwma(df.close, df.volume) - assert isinstance(result, Series) - assert result.name == "VWMA_10" - - - - -# DataFrame Extension Tests -def test_ext_ad(df): - df.ta.ad(talib=False, append=True) - assert df.columns[-1] == "AD" - - df.ta.ad(open_=df.open, append=True) - assert df.columns[-1] == "ADo" - - -def test_ext_adosc(df): - df.ta.adosc(append=True) - assert df.columns[-1] == "ADOSC_3_10" - - -def test_ext_aobv(df): - df.ta.aobv(append=True) - columns = [ - "OBV", "OBV_min_2", "OBV_max_2", "OBVe_4", - "OBVe_12", "AOBV_LR_2", "AOBV_SR_2" - ] - assert list(df.columns[-7:]) == columns - - -def test_ext_cmf(df): - df.ta.cmf(append=True) - assert df.columns[-1] == "CMF_20" - - -def test_ext_efi(df): - df.ta.efi(append=True) - assert df.columns[-1] == "EFI_13" - - -def test_ext_eom(df): - df.ta.eom(append=True) - assert df.columns[-1] == "EOM_14_100000000" - - -def test_ext_kvo(df): - df.ta.kvo(append=True) - assert list(df.columns[-2:]) == ["KVO_34_55_13", "KVOs_34_55_13"] - - -def test_ext_mfi(df): - df.ta.mfi(append=True) - assert df.columns[-1] == "MFI_14" - - -def test_ext_nvi(df): - df.ta.nvi(append=True) - assert df.columns[-1] == "NVI_1" - - -def test_ext_obv(df): - df.ta.obv(append=True) - assert df.columns[-1] == "OBV" - - -def test_ext_pvi(df): - df.ta.pvi(length=10, append=True) - assert list(df.columns[-2:]) == ["PVI", "PVIe_10"] - - -def test_ext_pvo(df): - df.ta.pvo(append=True) - assert list(df.columns[-3:]) == [ - "PVO_12_26_9", "PVOh_12_26_9", "PVOs_12_26_9" - ] - - -def test_ext_pvol(df): - df.ta.pvol(append=True) - assert df.columns[-1] == "PVOL" - - -def test_ext_pvr(df): - df.ta.pvr(append=True) - assert df.columns[-1] == "PVR" - - -def test_ext_pvt(df): - df.ta.pvt(append=True) - assert df.columns[-1] == "PVT" - - -def test_ext_tsv(df): - df.ta.tsv(append=True) - assert list(df.columns[-3:]) == ["TSV_18_10", "TSVs_18_10", "TSVr_18_10"] - - -def test_ext_vhm(df): - df.ta.vhm(length=30, slength=30, append=True) - assert df.columns[-1] == "VHM_30" - - -# Volume Profile can not be appended to the df - - -def test_ext_vwap(df): - df.ta.vwap(append=True) - assert df.columns[-1] == "VWAP_D" - - -def test_ext_vwma(df): - df.ta.vwma(append=True) - # print(f"\n{df.columns=}\n") - assert df.columns[-1] == "VWMA_10" diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_numba.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_numba.py deleted file mode 100644 index ba7692eb..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_numba.py +++ /dev/null @@ -1,35 +0,0 @@ -# -*- coding: utf-8 -*- -import numpy as np -from pytest import mark - -import pandas_ta as ta - - - -@mark.parametrize("array,n,result", [ - (np.ones(5), 2, np.array([np.nan, 1.0, 1.0, 1.0, 1.0])), - (np.ones(5), -2, np.ones(5)) -]) -def test_nb_prenan(array, n, result): - np.testing.assert_array_equal(ta.nb_prenan(array, n), result) - - -@mark.parametrize("array,n,result", [ - (np.ones(5), 2, np.array([np.nan, np.nan, 1.0, 1.0, 1.0, 1.0, 1.0])), - (np.ones(5), -2, np.ones(5)) -]) -def test_nb_prepend(array, n, result): - np.testing.assert_array_equal(ta.nb_prepend(array, n), result) - - -@mark.parametrize("array,n,fn,result", [(np.ones(5), 2, None, np.ones(5))]) -def test_nb_rolling(array, n, fn, result): - np.testing.assert_array_equal(ta.nb_rolling(array, n, fn), result) - - -@mark.parametrize("array,n,result", [ - (np.ones(5), 2, np.array([np.nan, np.nan, 1.0, 1.0, 1.0])), - (np.ones(5), -2, np.array([1.0, 1.0, 1.0, np.nan, np.nan])) -]) -def test_nb_shift(array, n, result): - np.testing.assert_array_equal(ta.nb_shift(array, n), result) diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_studies.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_studies.py deleted file mode 100644 index 17dbf544..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_studies.py +++ /dev/null @@ -1,125 +0,0 @@ -# -*- coding: utf-8 -*- -from multiprocessing import cpu_count - -from pandas import DataFrame -import pytest - -import pandas_ta as ta - -categories = DataFrame().ta.categories() + \ -[pytest.param(ta.CommonStudy, id="common"), pytest.param(ta.AllStudy, id="all")] - -# +/- when adding/removing indicators -ALL_COLUMNS = 329 - - - -def test_all_study_props(all_study): - s = all_study - assert s.name == "All" - assert isinstance(s.description, str) - assert s.total_ta() == 0 # Only 'study' that is None - assert len(s.created) > 0 - assert s.cores == cpu_count() - - -def test_common_study_props(common_study): - s = common_study - assert s.name == "Common Price and Volume SMAs" - assert isinstance(s.description, str) - assert s.total_ta() == 5 - assert len(s.created) > 0 - assert s.cores == 0 - - -@pytest.mark.parametrize("category,columns", [ - ("candle", 70), ("cycle", 2), ("momentum", 85), ("overlap", 56), - ("performance", 2), ("statistics", 16), ("trend", 33), - ("volatility", 38), ("volume", 27), - pytest.param(ta.AllStudy, ALL_COLUMNS, id=f"all-{ALL_COLUMNS}"), - pytest.param(ta.CommonStudy, 5, id="common-5"), -]) -def test_study_category_columns(df, category, columns): - initial_columns = df.shape[1] - df.ta.study(category, cores=0) - assert df.shape[1] == initial_columns + columns - - -@pytest.mark.parametrize("talib", [False, True]) -@pytest.mark.parametrize("category", categories) -def test_study_category_talib(df, category, talib): - initial_columns = df.shape[1] - df.ta.study(category, cores=0, talib=talib) - assert df.shape[1] > initial_columns - - -@pytest.mark.parametrize("talib", [False, True]) -def test_study_custom_a_talib(df, custom_study_a, talib): - initial_columns = df.shape[1] - df.ta.study(custom_study_a, cores=0, talib=talib) - assert df.shape[1] > initial_columns - - -@pytest.mark.parametrize("talib", [False, True]) -def test_study_custom_b_talib(df, custom_study_b, talib): - initial_columns = df.shape[1] - df.ta.study(custom_study_b, cores=0, talib=talib) - assert df.shape[1] - initial_columns == 3 - - -@pytest.mark.parametrize("talib", [False, True]) -def test_study_custom_c_talib(df, custom_study_c, talib): - initial_columns = df.shape[1] - df.ta.study(custom_study_c, cores=0, talib=talib) - assert df.shape[1] - initial_columns == 5 - - -@pytest.mark.parametrize("talib", [False, True]) -def test_study_custom_d_talib(df, custom_study_d, talib): - initial_columns = df.shape[1] - df.ta.study(custom_study_d, cores=0, talib=talib) - assert df.shape[1] - initial_columns == 3 - - -@pytest.mark.parametrize("talib", [False, True]) -def test_study_custom_e_talib(df, custom_study_e, talib): - initial_columns = df.shape[1] - df.ta.study(custom_study_e, cores=0, talib=talib) - df.ta.tsignals(trend=df["AMATe_LR_20_50_2"], append=True) - assert df.shape[1] - initial_columns == 8 - - -@pytest.mark.parametrize("talib", [False, True]) -def test_study_all_multirun_talib(df, all_study, talib): - new_columns = 617 # +/- when adding/removing indicators - initial_columns = df.shape[1] - df.ta.study(all_study, length=10, cores=0, talib=talib) - df.ta.study(all_study, length=50, cores=0, talib=talib) - df.ta.study(all_study, fast=5, slow=10, cores=0, talib=talib) - - assert df.shape[1] == new_columns + initial_columns - - -# Note: As expected, it will print a VWAP datetime ordered index warning -# when less than 2 rows -@pytest.mark.parametrize("talib", [False, True]) -def test_study_all_incremental_rows_talib(df, all_study, talib): - MAX_ROWS = 77 - df = df.iloc[:MAX_ROWS] # Trim for this test - - for i in range(0, MAX_ROWS): - _df = df.iloc[:i] - _df.ta.study(all_study, cores=0, talib=talib) - # Break when max columns reached - if _df.shape[1] - df.shape[1] == ALL_COLUMNS: - assert _df.shape[1] > df.shape[1] - break - - -@pytest.mark.parametrize("talib", [False, True]) -@pytest.mark.parametrize("category", categories) -def test_study_mp_category_talib(df, category, talib): - cores = cpu_count() - 2 - initial_columns = df.shape[1] - df.ta.study(category, cores=cores, talib=talib) - assert df.shape[1] > initial_columns diff --git a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_utils.py b/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_utils.py deleted file mode 100644 index dddc8d0b..00000000 --- a/temp_pandas_ta/pandas_ta-0.4.71b0/tests/test_utils.py +++ /dev/null @@ -1,439 +0,0 @@ -# -*- coding: utf-8 -*- -from sys import platform as sys_platform - -import numpy as np -from pandas import DataFrame, Series -from pandas.api.types import is_datetime64_ns_dtype -from pytest import mark, param - -from tests.conftest import all_study, common_study - -import pandas_ta as ta - -CROSS_E1 = { - "zero": [0, 0], - "a": [0, 1], - "b": [1, 0], - "c": [1, 1], - "crossed": [0, 1], -} -crosseddf = DataFrame(CROSS_E1) - -SCALARS = [ - np.ones(0), np.ones(1), np.array([1, 2]), np.array([1.5, 2.5]), - np.array([2, 3, 4]), np.array([0.01, 0.1, 10, 100]), - np.array([-10, -1, 10]) -] - - -def test_above(): - result = ta.utils.above(crosseddf["a"], crosseddf["zero"]) - assert isinstance(result, Series) - assert result.name == "a_A_zero" - np.testing.assert_array_equal(result, crosseddf["c"]) - - result = ta.utils.above(crosseddf["zero"], crosseddf["a"]) - assert isinstance(result, Series) - assert result.name == "zero_A_a" - np.testing.assert_array_equal(result, crosseddf["b"]) - - - -def test_above_value(): - result = ta.utils.above_value(crosseddf["a"], 0) - assert isinstance(result, Series) - assert result.name == "a_A_0" - np.testing.assert_array_equal(result, crosseddf["c"]) - - result = ta.utils.above_value(crosseddf["a"], crosseddf["zero"]) - assert result is None - - - -def test_add_df_prefix(df): - result = df.ta.hl2(prefix="pre") - assert result.name == "pre_HL2" - - result = df.ta.hl2(suffix="suf") - assert result.name == "HL2_suf" - - result = df.ta.hl2(prefix="pre", suffix="suf") - assert result.name == "pre_HL2_suf" - - result = df.ta.hl2(prefix=1, suffix=2) - assert result.name == "1_HL2_2" - - result = df.ta.macd(prefix="pre", suffix="suf") - for col in result.columns: - assert col.startswith("pre_") and col.endswith("_suf") - - - -def test_baseline_ohlc(df): - result = df.ta.baseline() - print(f"\n{result}\n") - # assert result.name == "pre_HL2" - - - -def test_below(): - result = ta.utils.below(crosseddf["zero"], crosseddf["a"]) - assert isinstance(result, Series) - assert result.name == "zero_B_a" - np.testing.assert_array_equal(result, crosseddf["c"]) - - result = ta.utils.below(crosseddf["zero"], crosseddf["a"]) - assert isinstance(result, Series) - assert result.name == "zero_B_a" - np.testing.assert_array_equal(result, crosseddf["c"]) - - - -def test_below_value(): - result = ta.utils.below_value(crosseddf["a"], 0) - assert isinstance(result, Series) - assert result.name == "a_B_0" - np.testing.assert_array_equal(result, crosseddf["b"]) - - result = ta.utils.below_value(crosseddf["a"], crosseddf["zero"]) - assert result is None - - - -def test_combination(): - assert ta.utils.combination() != None - - assert ta.utils.combination() == 1 - assert ta.utils.combination(r=-1) == 1 - - assert ta.utils.combination(n=10, r=4, repetition=False) == 210 - assert ta.utils.combination(n=10, r=4, repetition=True) == 715 - - - -def test_cross_above(): - result = ta.utils.cross(crosseddf["a"], crosseddf["b"]) - assert isinstance(result, Series) - np.testing.assert_array_equal(result, crosseddf["crossed"]) - - result = ta.utils.cross(crosseddf["a"], crosseddf["b"], above=True) - assert isinstance(result, Series) - np.testing.assert_array_equal(result, crosseddf["crossed"]) - - result = ta.utils.cross(crosseddf["a"], crosseddf["b"], equal=False) - assert isinstance(result, Series) - np.testing.assert_array_equal(result, crosseddf["crossed"]) - - - -def test_cross_below(): - result = ta.utils.cross(crosseddf["b"], crosseddf["a"], above=False) - assert isinstance(result, Series) - np.testing.assert_array_equal(result, crosseddf["crossed"]) - - result = ta.utils.cross(crosseddf["b"], crosseddf["a"], above=False, equal=False) - assert isinstance(result, Series) - np.testing.assert_array_equal(result, crosseddf["crossed"]) - - # result = ta.utils.cross(crosseddf["a"], crosseddf["b"], above=False) - # assert result[0] == 0 - - - -def test_cube(df): - result = ta.cube(Series([100, 50, 0, 25, 0, 60])) - assert result.name == "CUBE_3.0_-1" - - - -def test_datetime_ordered(df): - # Test if datetime64 index and ordered - # result = df.ta.datetime_ordered() - # assert result is True - assert df.ta.datetime_ordered() is True - - # Test if not ordered - original = df.copy() - original.ta.reverse() - result = original.ta.datetime_ordered() - assert result is False - - # Test a non-datetime64 index - original = df.copy() - original.reset_index(inplace=True) - result = original.ta.datetime_ordered() - assert result is False - - - -def test_df_dates(df): - result = ta.utils.df_dates(df) - assert result is None - - - -def test_fibonacci(): - np.testing.assert_array_equal(ta.utils.fibonacci(0, False), np.array([1, 1])) - np.testing.assert_array_equal(ta.utils.fibonacci(5, False), np.array([1, 1, 2, 3, 5])) - - assert isinstance(ta.utils.fibonacci(2, False), np.ndarray) - - - -def test_fibonacci_weighted(): - np.testing.assert_array_equal(ta.utils.fibonacci(0, True), np.array([0.5, 0.5])) - np.testing.assert_allclose(ta.utils.fibonacci(5, True), np.array([1 / 12, 1 / 12, 1 / 6, 1 / 4, 5 / 12])) - - assert isinstance(ta.utils.fibonacci(2, True), np.ndarray) - - - -def test_geometric_mean(df): - returns = ta.percent_return(df.close) - result = ta.utils.geometric_mean(returns) - assert isinstance(result, (float, int)) - - result = ta.utils.geometric_mean(Series([12, 14, 11, 8])) - assert isinstance(result, float) - - result = ta.utils.geometric_mean(Series([100, 50, 0, 25, 0, 60])) - assert isinstance(result, float) - - series = Series([0, 1, 2, 3]) - result = ta.utils.geometric_mean(series) - assert isinstance(result, float) - - result = ta.utils.geometric_mean(-series) - assert isinstance(result, int) - assert result == 0 - - - -def test_ifisher(): - result = ta.ifisher(Series([1, 2, 3, 4, 5, 6, 7, 4, 2, 1, 3])) - assert result.name == "INVFISHER_1.0" - - - -# def test_linear_regression(): -# x = Series([1, 2, 3, 4, 5]) -# y = Series([1.8, 2.1, 2.7, 3.2, 4]) - -# result = ta.utils.linear_regression(x, y) -# assert isinstance(result, dict) -# assert isinstance(result["a"], float) -# assert isinstance(result["b"], float) -# assert isinstance(result["r"], float) -# assert isinstance(result["t"], float) -# assert isinstance(result["line"], Series) - - - -def test_log_geometric_mean(df): - returns = ta.percent_return(df.close) - result = ta.utils.log_geometric_mean(returns) - assert isinstance(result, float) - - result = ta.utils.log_geometric_mean(Series([12, 14, 11, 8])) - assert isinstance(result, float) - - result = ta.utils.log_geometric_mean(Series([100, 50, 0, 25, 0, 60])) - assert isinstance(result, float) - - series = Series([0, 1, 2, 3]) - result = ta.utils.log_geometric_mean(series) - assert isinstance(result, float) - - result = ta.utils.log_geometric_mean(-series) - assert isinstance(result, int) - assert result == 0 - - - -def test_reverse(df): - original = df.copy() - df.ta.reverse() - - # Check if first and last time are reversed - assert df.index[-1] == original.index[0] - assert df.index[0] == original.index[-1] - - - -def test_pascals_triangle(): - assert ta.utils.pascals_triangle(inverse=True) is None - - array = np.array([1]) - np.testing.assert_array_equal(ta.utils.pascals_triangle(), array) - np.testing.assert_array_equal(ta.utils.pascals_triangle(weighted=True), array) - np.testing.assert_array_equal(ta.utils.pascals_triangle(weighted=True, inverse=True), np.array([0])) - - - -@mark.parametrize("value", [-5, -1, 0, 1, 5]) -def test_pascals_triangle_n(value): - array = ta.utils.pascals_triangle(n=value) - array_w = array / np.sum(array) - array_iw = 1 - array_w - np.testing.assert_array_equal(ta.utils.pascals_triangle(n=value), array) - np.testing.assert_array_equal(ta.utils.pascals_triangle(n=value, weighted=True), array_w) - np.testing.assert_array_equal(ta.utils.pascals_triangle(n=value, weighted=True, inverse=True), array_iw) - - - -@mark.parametrize("value", [param(None, marks=mark.xfail), "NYSE", "NZSX", "SSE"]) -def test_get_time_(value): - print(f"{value=}") - result = ta.utils.get_time(value, to_string=True) - assert isinstance(result, str) - assert value in result - - - -@mark.parametrize("array,degree,result", [ - ([1], 1, 1), ([1, 1], 1, 2), ([1, 0, -1], 1, 0), - ([1, 0, 1], 1, 2), ([1, 1, 1], 1, 3) -]) -def test_hpoly(array, degree, result): - assert ta.utils.hpoly(array, degree) == result - - - -def test_remap(): - result = ta.remap(Series([100, 50, 0, 25, 0, 60])) - assert result.name == "REMAP_0.0_100.0_-1.0_1.0" - - - -def test_signals(df): - _ = df.ta.er(signal_indicators=True) - assert _.shape[1] > 0 - - _ = df.ta.macd(signal_indicators=True) - assert _.shape[1] > 0 - - _ = df.ta.rsi(signal_indicators=True) - assert _.shape[1] > 0 - - _ = df.ta.rsx(signal_indicators=True) - assert _.shape[1] > 0 - - - -def test_symmetric_triangle(): - np.testing.assert_array_equal(ta.utils.symmetric_triangle(), np.array([1,1])) - np.testing.assert_array_equal(ta.utils.symmetric_triangle(weighted=True), np.array([0.5, 0.5])) - - - -@mark.parametrize("value", [2, 3, 10]) -def test_symmetric_triangle_n(value): - array = ta.utils.symmetric_triangle(n=value) - array_w = array / np.sum(array) - np.testing.assert_array_equal(ta.utils.symmetric_triangle(n=value), array) - np.testing.assert_array_equal(ta.utils.symmetric_triangle(n=value, weighted=True), array_w) - - - -@mark.parametrize("value,result", [ - ("sma", 0), ("Sma", 0), ("ema", 1), ("wma", 2), ("dema", 3), ("tema", 4), - ("trima", 5), ("kama", 6), ("mama", 7), ("t3", 8) -]) -def test_tal_ma(value, result): - assert ta.utils.tal_ma(value) == result - - - -def test_to_utc(df): - result = ta.utils.to_utc(df) - assert is_datetime64_ns_dtype(result.index) - # assert is_datetime64tz_dtype(result.index) # Depreciation Warning but no mention in 2.2 docs - - - -def test_version(): - assert isinstance(ta.version, str) - print(f"{ta.version=}") - - - -def test_v_drift_type(): - _instances = [0, None, "", [], {}, np.int8(5), np.int16(5), np.int32(5), np.int64(5)] - for _ in _instances: - assert isinstance(ta.utils.v_drift(_), int) - - - -@mark.parametrize("value,result", [ - (-1.1, 1), (0, 1), (1.1, 1), (5, 5), - (np.int64(-1.1), -1), (np.int64(0), 1), - (np.int64(1.1), 1), (np.int64(5), 5) -]) -def test_v_drift_value(value, result): - assert ta.utils.v_drift(value) == result - - - -@mark.parametrize("value", [None, "", [], {}, -1.1, -1, 0.0, 0, 0.1, 1.0, 1]) -@mark.parametrize("strict", [False, True]) -def test_v_lowerbound_type(value, strict): - assert isinstance(ta.v_lowerbound(value, strict=strict), (float, int)) - - - -@mark.parametrize("value,result", [ - (-1.1, 0), (-1, 0), (0.0, 0), (0, 0), (0.1, 0.1), (1.0, 1.0), (1, 1), -]) -def test_v_lowerbound_value_strict(value, result): - assert ta.utils.v_lowerbound(value) == result - - - -@mark.parametrize("value,result", [ - (-1.1, 0), (-1, 0), (0.0, 0), (0, 0), (0.1, 0.1), (1.0, 1), (1, 1), -]) -def test_v_lowerbound_value_strict_false(value, result): - assert ta.utils.v_lowerbound(value, strict=False) == result - - - -@mark.parametrize("value", [ - 0, None, "", [], {}, np.int8(5), np.int16(5), np.int32(5), np.int64(5) -]) -def test_v_offset_types(value): - assert isinstance(ta.utils.v_offset(value), int) - - - -@mark.parametrize("value,result", [ - (None, 0), (-1.1, 0), (-1, -1), (0, 0), (1.1, 0), (1, 1), (2, 2), - (np.int64(-1), -1), (np.int64(0), 0), (np.int64(1.1), 1), - (np.int64(1), 1), (np.int64(2), 2), (np.int64(-1.1), -1), - (np.int64(1.1), 1), -]) -def test_v_offset_value(value,result): - assert ta.utils.v_offset(value) == result - - - 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