docs: refresh indicator counts and modernize TA-Lib install docs
Update all stale indicator counts (193→224, 253→284) across README, index, docs/index.rst, quickstart.md, and indicators.rst. Remove unverifiable hardcoded numbers (59 core indicators, ~45 indicators, 89/98/66 counterparts) in favor of generic descriptions or links to the live matrix. Add tulipy to optional deps list. Pin ta-lib>=0.6.8 in pyproject.toml and document binary wheel availability in install docs — pre-0.6.5 lacks wheels and requires manual C library setup. Pin tulipy>=0.4.0. Remove duplicate Compatibility Matrix section. Replace stale Windows wheel workaround with single cross-platform install command.
This commit is contained in:
xgboosted
@@ -44,10 +44,10 @@ This is the **classic/community maintained version** of the popular pandas-ta li
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### Key Features
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- **253 Unique Indicators & Patterns**: 193 Category indicators + 62 CDL patterns via `cdl_pattern()` = 253 unique (doji and inside appear in both counts; all CDL patterns use native Python — no TA-Lib required)
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- **284 Unique Indicators & Patterns**: 224 Category indicators + 62 CDL patterns via `cdl_pattern()` = 284 unique (doji and inside appear in both counts; all CDL patterns use native Python — no TA-Lib required)
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- **All-Native Candlestick Patterns**: All 62 CDL patterns have native Python implementations — TA-Lib is never used for CDL patterns
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- **Optional TA-Lib Acceleration**: 59 core indicators (EMA, SMA, RSI, MACD, OBV, ATR, etc.) use native implementations by default; pass `talib=True` to use TA-Lib
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- **Compatibility Scope Is Explicit**: Not every TA-Lib/tulipy function has a pandas-ta-classic counterpart. Current mapping includes 67 indicators with TA-Lib counterparts, 71 with tulipy counterparts, and 44 covered by both. Full per-indicator matrix: `docs/indicator_support_matrix.rst`
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- **Optional TA-Lib Acceleration**: Core indicators (EMA, SMA, RSI, MACD, OBV, ATR, etc.) use native implementations by default; pass `talib=True` to use TA-Lib
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- **Compatibility Scope Is Explicit**: Not every TA-Lib/tulipy function has a pandas-ta-classic counterpart. See the full per‑indicator matrix for current coverage: `docs/indicator_support_matrix.rst`
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- **Optional Performance Boost**: Install `numba` for 6–230× speedups on hot-loop indicators (QQE, RSX, HWMA, SSF, PSAR, Supertrend, MCGD)
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- **Automatic Versioning**: Version management via git tags using setuptools-scm
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- **Modern Package Management**: Full support for both `uv` and `pip`
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@@ -142,15 +142,15 @@ df.ta.strategy("CommonStrategy") # Runs commonly used indicators
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## Features
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- **193 Technical Indicators & Utilities** across 9 categories (Candles, Cycles, Momentum, Overlap, Trend, Volume, etc.)
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- **224 Technical Indicators & Utilities** across 10 categories (Candles, Cycles, Math, Momentum, Overlap, Trend, Volume, etc.)
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- **62 Native Candlestick Patterns** — all patterns natively implemented, no TA-Lib required
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- **253 Unique Indicators & Patterns** - 193 category indicators plus 62 CDL patterns via `cdl_pattern()`
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- **284 Unique Indicators & Patterns** - 224 category indicators plus 62 CDL patterns via `cdl_pattern()`
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- **Dynamic Category Discovery** - automatically detects all available indicators from the filesystem
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- **Optional Numba Acceleration** - 6–230× speedups via `pip install pandas-ta-classic[performance]`
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- **Strategy System** with multiprocessing support for bulk indicator processing
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- **Fluent API Chaining**: ``df.ta.chain().sma(20).ta.rsi(14).ta.macd().ta.bbands(20)`` — chain multiple indicators in a single expression
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- **Pandas DataFrame Extension** for seamless integration (`df.ta.indicator()`)
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- **TA-Lib Integration (dual-role)** - **(1) acceleration backend**: 59 core indicators use native implementations by default; pass `talib=True` to use TA-Lib's C implementation. **(2) oracle**: `test_oracle_talib.py` verifies parity against TA-Lib
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- **TA-Lib Integration (dual-role)** - **(1) acceleration backend**: core indicators use native implementations by default; pass `talib=True` to use TA-Lib's C implementation. **(2) oracle**: `test_oracle_talib.py` verifies parity against TA-Lib
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- **tulipy Integration (oracle only)** - parity test oracle; `test_oracle_tulipy.py` verifies native output against tulipy; never used as computation backend
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- **Backtesting.py Integration** — bridge function and runnable SMA crossover example in ``examples/backtesting_py_strategy.py``
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- **backtrader Integration** — precompute-then-feed pattern with dynamic `PandasData` subclass; runnable example in ``examples/backtrader_strategy.py``
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@@ -171,7 +171,7 @@ df.ta.strategy("CommonStrategy") # Runs commonly used indicators
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**Reference Documentation:**
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- [**Usage Guide**](https://xgboosted.github.io/pandas-ta-classic/usage.html) - Programming conventions and basic usage
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- [**Strategy System**](https://xgboosted.github.io/pandas-ta-classic/strategies.html) - Multiprocessing and bulk indicator processing
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- [**Indicators Reference**](https://xgboosted.github.io/pandas-ta-classic/indicators.html) - Complete list of 193 indicators plus 62 CDL patterns (253 unique total)
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- [**Indicators Reference**](https://xgboosted.github.io/pandas-ta-classic/indicators.html) - Complete list of 224 indicators plus 62 CDL patterns (284 unique total)
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- [**DataFrame API**](https://xgboosted.github.io/pandas-ta-classic/dataframe_api.html) - Properties and methods reference
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- [**Performance Metrics**](https://xgboosted.github.io/pandas-ta-classic/performance.html) - Backtesting and performance analysis
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@@ -185,7 +185,7 @@ df.ta.strategy("CommonStrategy") # Runs commonly used indicators
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| Library | Role | Effect when installed |
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|---------|------|-----------------------|
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| TA-Lib | **Acceleration backend + oracle** | 59 core indicators — native by default, opt-in via `talib=True`; also used in `test_oracle_talib.py` for parity checks |
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| TA-Lib | **Acceleration backend + oracle** | Core indicators — native by default, opt-in via `talib=True`; also used in `test_oracle_talib.py` for parity checks |
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| tulipy | **Oracle only** | Never used as computation backend; only `test_oracle_tulipy.py` uses it to verify native output |
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| Area | Behaviour without TA-Lib | Behaviour with TA-Lib |
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@@ -215,7 +215,7 @@ pip install TA-Lib # TA-Lib only (also enables acceleration backend)
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pip install tulipy # tulipy only (oracle test use only)
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```
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> **Note:** Both oracle test suites (`test_oracle_talib.py`, `test_oracle_tulipy.py`) are guarded with `@unittest.skipUnless` and skip automatically when the respective library is not installed. Neither is required for normal use. Installing TA-Lib additionally enables C-library acceleration for 59 indicators via `talib=True`.
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> **Note:** Both oracle test suites (`test_oracle_talib.py`, `test_oracle_tulipy.py`) are guarded with `@unittest.skipUnless` and skip automatically when the respective library is not installed. Neither is required for normal use. Installing TA-Lib additionally enables C-library acceleration for core indicators via `talib=True`.
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**Performance boost:** Install `numba` for 6–230× speedups on computation-heavy indicators:
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- Using `uv`: `uv pip install pandas-ta-classic[performance]`
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+1
-1
@@ -1,7 +1,7 @@
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Pandas TA Classic Documentation
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===============================
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**Pandas TA Classic** is an easy to use library that leverages the Pandas package with **193 indicators and utility functions** plus **62 native candlestick patterns** (**253 unique total**; ``cdl_doji`` and ``cdl_inside`` are counted in both sets). All CDL patterns are natively implemented — no TA-Lib required. This is the **community maintained version** of the popular pandas-ta library.
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**Pandas TA Classic** is an easy to use library that leverages the Pandas package with **224 indicators and utility functions** plus **62 native candlestick patterns** (**284 unique total**; ``cdl_doji`` and ``cdl_inside`` are counted in both sets). All CDL patterns are natively implemented — no TA-Lib required. This is the **community maintained version** of the popular pandas-ta library.
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.. note::
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The library features **dynamic configuration management**:
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@@ -1,6 +1,8 @@
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Indicator Compatibility Matrix
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==============================
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The full per-indicator compatibility table (Native / TA-Lib / tulipy) is maintained here:
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Legend: ``yes`` = indicator has corresponding implementation in that library by direct name or maintained alias mapping.
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.. list-table:: pandas-ta-classic indicator support vs TA-Lib and tulipy
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@@ -381,9 +381,4 @@ Utility / signal functions (accessible directly or via ``df.ta``):
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* *Crossany*: **crossany** (returns Boolean Series that is True on any bar where ``a`` and ``b`` cross in either direction)
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* *Lag*: **lag** (returns a Series offset by ``n`` periods; equivalent to tulipy ``lag``)
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Compatibility Matrix
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--------------------
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The full per-indicator compatibility table (Native / TA-Lib / tulipy) is maintained here:
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.. include:: indicator_support_matrix.rst
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+24
-10
@@ -19,6 +19,7 @@ Optional Dependencies
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For enhanced functionality, consider installing:
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- **TA-Lib**: Optional — all 62 CDL patterns work natively without it (see :ref:`Installing TA-Lib` below)
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- **tulipy**: Optional — oracle-only; used by ``test_oracle_tulipy.py`` for parity verification, never as a computation backend
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- **yfinance**: For downloading stock data with ``df.ta.ticker()``
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- **vectorbt**: For backtesting integration
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- **pytest + Hypothesis**: For running the test suite and property-based tests (``pip install pandas-ta-classic[test]``)
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@@ -123,7 +124,7 @@ Both TA-Lib and tulipy are **fully optional**. They serve different roles:
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- Effect when installed
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* - TA-Lib
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- **Acceleration backend + oracle**
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- 59 core indicators — native by default, opt-in via ``talib=True``; also used by ``test_oracle_talib.py`` for parity checks
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- Core indicators — native by default, opt-in via ``talib=True``; also used by ``test_oracle_talib.py`` for parity checks
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* - tulipy
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- **Oracle only**
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- Never used as computation backend; only ``test_oracle_tulipy.py`` uses it to verify native output
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@@ -133,12 +134,12 @@ Both TA-Lib and tulipy are **fully optional**. They serve different roles:
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Installing TA-Lib
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~~~~~~~~~~~~~~~~~
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TA-Lib has a **dual role**: acceleration backend for 59 core indicators (opt-in via ``talib=True``), and parity oracle. The two behavioural areas affected are:
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TA-Lib has a **dual role**: acceleration backend for core indicators (opt-in via ``talib=True``), and parity oracle. The two behavioural areas affected are:
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**Candlestick patterns (CDL family)**
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All 62 CDL patterns have native Python implementations that are always used. TA-Lib is **never** invoked for CDL patterns — the TA-Lib fallback code path in ``cdl_pattern()`` is only retained for hypothetical future patterns without a native implementation.
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**59 core indicators** (``ema``, ``sma``, ``rsi``, ``macd``, ``obv``, ``atr``, etc.)
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**Core indicators** (``ema``, ``sma``, ``rsi``, ``macd``, ``obv``, ``atr``, etc.)
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The native implementation is used by default. TA-Lib is opt-in — pass ``talib=True`` to any call to use TA-Lib's implementation instead.
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.. code-block:: python
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@@ -151,25 +152,38 @@ TA-Lib has a **dual role**: acceleration backend for 59 core indicators (opt-in
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# Use TA-Lib implementation if installed
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ema = df.ta.ema(length=20, talib=True)
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# CDL patterns — always native, talib= kwarg has no effect here
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df = df.ta.cdl_pattern(name="engulfing") # native
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result = df.ta.cdl_pattern(name="hammer") # native
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# CDL patterns — always native, talib= kwarg has no effect here
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df = df.ta.cdl_pattern(name="engulfing") # native
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result = df.ta.cdl_pattern(name="hammer") # native
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Installing TA-Lib:
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Installation
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^^^^^^^^^^^^
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Binary wheels (v0.6.5+) bundle the TA-Lib C library — no separate C library
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setup needed. Versions before 0.6.5 lack binary wheels and require manual C library
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installation. We require ``>=0.6.8`` so ``pip install ta-lib`` works out of
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the box on Linux, macOS, and Windows (x86\_64, arm64):
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Using ``uv``:
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.. code-block:: bash
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uv pip install TA-Lib
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uv pip install "ta-lib>=0.6.8"
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Using ``pip``:
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.. code-block:: bash
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pip install TA-Lib
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pip install "ta-lib>=0.6.8"
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**Note**: If you encounter installation issues with TA-Lib, refer to the `TA-Lib installation guide <https://github.com/mrjbq7/ta-lib#installation>`_.
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If your platform is not covered by binary wheels, or you prefer a source build, install the `TA-Lib C library <https://ta-lib.org/install/>`_ first. For Conda users:
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.. code-block:: bash
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conda install -c conda-forge libta-lib
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conda install -c conda-forge ta-lib
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See the `TA-Lib Python README <https://github.com/TA-Lib/ta-lib-python#dependencies>`_ for platform-specific instructions and troubleshooting.
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Installing tulipy
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~~~~~~~~~~~~~~~~~
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+4
-7
@@ -420,14 +420,11 @@ pip install --upgrade pandas-ta-classic
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**Solution:** TA-Lib is optional and only used for supported core indicators (candlestick patterns are native). Install TA-Lib separately:
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```bash
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# On Linux/Mac
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pip install TA-Lib
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# On Windows, download wheel from:
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# https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
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pip install TA_Lib-0.4.XX-cpXX-cpXX-win_amd64.whl
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pip install "ta-lib>=0.6.8"
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```
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Binary wheels (v0.6.5+) bundle the C library — no separate TA-Lib C installation needed on any platform. Versions before 0.6.5 lack binary wheels and require manual C library setup.
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(next-steps)=
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## Next Steps
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@@ -435,7 +432,7 @@ Now that you've got the basics, explore more:
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1. **[Full Documentation](https://xgboosted.github.io/pandas-ta-classic/)** - Complete API reference
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2. **[Tutorials](https://xgboosted.github.io/pandas-ta-classic/tutorials.html)** - Step-by-step guides for common tasks
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3. **[Indicator Reference](https://xgboosted.github.io/pandas-ta-classic/indicators.html)** - 192 indicators plus 62 CDL patterns (252 unique total)
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3. **[Indicator Reference](https://xgboosted.github.io/pandas-ta-classic/indicators.html)** - 224 indicators plus 62 CDL patterns (284 unique total)
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4. **[Strategy Guide](https://xgboosted.github.io/pandas-ta-classic/strategies.html)** - Advanced strategy system
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5. **[Examples](https://github.com/xgboosted/pandas-ta-classic/tree/main/examples)** - Jupyter notebooks with real examples
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+2
-2
@@ -68,7 +68,7 @@ Requires ``ta-lib`` and ``tulipy`` installed.
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Native Indicator Tests
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----------------------
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**Why:** Cover ~45 indicators that have no TA-Lib alternative, validating
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**Why:** Cover indicators that have no TA-Lib alternative, validating
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return type, non-NaN row count, value finiteness, and mathematical bounds.
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**Files:** ``test_native_indicators.py``.
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@@ -164,7 +164,7 @@ boundary violations.
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* **Mathematical invariants** — Bollinger Band ordering, ATR/STDEV
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non-negativity, MOM/ROC relationship.
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* **Core utilities** — ``verify_series``, ``apply_offset``, ``apply_fill``.
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* **None-guard safety** — 7 indicators return ``None`` for ``None`` input.
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* **None-guard safety** — Indicators return ``None`` for ``None`` input.
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* **NaN propagation** — All-NaN input → all-NaN output, no crash.
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* **Idempotence** — Same args twice → identical result.
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* **Category discovery** — Dynamic discovery stays consistent.
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@@ -5,17 +5,17 @@ title: Pandas TA Classic - Technical Analysis Library
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# Pandas TA Classic
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**Pandas Technical Analysis (Pandas TA Classic)** is an easy to use library that leverages the Pandas package with **193 indicators and utility functions** plus **62 native candlestick patterns** (**253 total unique** — no TA-Lib required). This is the **community maintained version** of the popular pandas-ta library.
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**Pandas Technical Analysis (Pandas TA Classic)** is an easy to use library that leverages the Pandas package with **224 indicators and utility functions** plus **62 native candlestick patterns** (**284 total unique** — no TA-Lib required). This is the **community maintained version** of the popular pandas-ta library.
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## Features
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* **193 indicators and utility functions** across 9 categories
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* **224 indicators and utility functions** across 10 categories
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* **62 native candlestick patterns** — all natively implemented, no TA-Lib required
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* **253 total unique indicators and patterns**
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* **284 total unique indicators and patterns**
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* Dynamic category discovery - automatically detects available indicators
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* **Optional TA-Lib Acceleration**: 34 core indicators auto-use TA-Lib when installed; pass `talib=False` to force native
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* **Optional TA-Lib Acceleration**: Core indicators auto-use TA-Lib when installed; pass `talib=False` to force native
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* **Optional Oracle Libraries**: TA-Lib (acceleration backend + oracle) and tulipy (oracle only) are optional and skip gracefully when not installed
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* Supports both standalone and DataFrame extension usage
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* **Fluent API chaining**: ``df.ta.chain().sma(20).ta.rsi(14).ta.macd()``
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+2
-2
@@ -53,8 +53,8 @@ optional = [
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# External oracle libraries for parity tests
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oracle = [
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"ta-lib",
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"tulipy",
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"ta-lib>=0.6.8",
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"tulipy>=0.4.0",
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]
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# CI code quality tools
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