mirror of
https://github.com/NicolasBohn/NexQuant.git
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docs: Updated documentation for all the major scenarios. (#190)
* Fixed some bugs introduced during refactoring. * update data_agent_fin_doc * Updated documentation for the four major scenarios * feat: remove pdfs and enable online pdf readings (#183) * remove pdfs and enable online pdf readings * update doc format * use url as key * feat: add entry for rdagent. (#187) * Add entries * update entry for rdagent * lint * fix typo * docs: Demo links (#188) add demo links * fix: Fix a fail href in readme (#189) * fix a ci bug * doc * feat: remove pdfs and enable online pdf readings (#183) * remove pdfs and enable online pdf readings * update doc format * use url as key * feat: add entry for rdagent. (#187) * Add entries * update entry for rdagent * lint * fix typo * doc * Updated documentation for med_model scenarios. * fix a ci bug --------- Co-authored-by: Xu Yang <peteryang@vip.qq.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com> Co-authored-by: SH-Src <suhan.c@outlook.com>
This commit is contained in:
WinstonLiyt
@@ -10,13 +10,12 @@ Finance Data Agent
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📖 Background
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~~~~~~~~~~~~~~
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In the dynamic world of quantitative trading, **factors** are the secret weapons that traders use to harness market inefficiencies.
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These powerful tools—ranging from straightforward metrics like price-to-earnings ratios to intricate discounted cash flow models—unlock the potential to predict stock prices with remarkable precision.
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By tapping into this rich vein of data, quantitative traders craft sophisticated strategies that not only capitalize on market patterns but also drastically enhance trading efficiency and accuracy.
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Embrace the power of factors, and you're not just trading; you're strategically outsmarting the market.
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In the dynamic world of quantitative trading, **factors** serve as the strategic tools that enable traders to exploit market inefficiencies.
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These factors—ranging from simple metrics like price-to-earnings ratios to complex models like discounted cash flows—are the key to predicting stock prices with a high degree of accuracy.
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By leveraging these factors, quantitative traders can develop sophisticated strategies that not only identify market patterns but also significantly enhance trading efficiency and precision.
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The ability to systematically analyze and apply these factors is what separates ordinary trading from truly strategic market outmaneuvering.
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And this is where the **Finance Model Agent** comes into play.
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🎥 Demo
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~~~~~~~~~~
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@@ -82,7 +81,7 @@ You can try our demo by running the following command:
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- Create a new conda environment with Python (3.10 and 3.11 are well tested in our CI):
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.. code-block:: sh
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conda create -n rdagent python=3.10
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- Activate the environment:
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@@ -91,12 +90,12 @@ You can try our demo by running the following command:
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conda activate rdagent
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- 🛠️ Run Make Files
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- Navigate to the directory containing the MakeFile and set up the development environment:
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- 📦 Install the RDAgent
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- You can directly install the RDAgent package from PyPI:
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.. code-block:: sh
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make dev
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pip install rdagent
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- ⚙️ Environment Configuration
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- Place the `.env` file in the same directory as the `.env.example` file.
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@@ -118,33 +117,12 @@ You can try our demo by running the following command:
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- **Env Config**
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The following environment variables can be set in the `.env` file to customize the application's behavior:
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- **Path to the folder containing private data (default fundamental data in Qlib):**
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.. code-block:: sh
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FACTOR_CODER_DATA_FOLDER=/path/to/data/factor_implementation_source_data_all
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- **Path to the folder containing partial private data (for debugging):**
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.. code-block:: sh
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FACTOR_CODER_DATA_FOLDER_DEBUG=/path/to/data/factor_implementation_source_data_debug
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- **Maximum time (in seconds) for writing factor code:**
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.. code-block:: sh
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FACTOR_CODER_FILE_BASED_EXECUTION_TIMEOUT=300
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- **Maximum number of factors to write in one experiment:**
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.. code-block:: sh
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FACTOR_CODER_SELECT_THRESHOLD=5
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- **Number of developing loops for writing factors:**
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.. code-block:: sh
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FACTOR_CODER_MAX_LOOP=10
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.. autopydantic_settings:: rdagent.app.qlib_rd_loop.conf.FactorBasePropSetting
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:settings-show-field-summary: False
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:exclude-members: Config
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.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorImplementSettings
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:settings-show-field-summary: False
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:members: coder_use_cache, data_folder, data_folder_debug, cache_location, enable_execution_cache, file_based_execution_timeout, select_method, select_threshold, max_loop, knowledge_base_path, new_knowledge_base_path
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:exclude-members: Config, fail_task_trial_limit, v1_query_former_trace_limit, v1_query_similar_success_limit, v2_query_component_limit, v2_query_error_limit, v2_query_former_trace_limit, v2_error_summary, v2_knowledge_sampler
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@@ -17,7 +17,7 @@ Furthermore, rather than hastily replicating factors from a report, it's essenti
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Does the factor capture the essential market dynamics? How unique is it compared to the factors already in your library?
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Therefore, there is an urgent need for a systematic approach to design a framework that can effectively manage this process.
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This is where our RDAgent comes into play.
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And this is where the **Finance Data Copilot** steps in.
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🎥 Demo
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@@ -91,12 +91,12 @@ You can try our demo by running the following command:
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conda activate rdagent
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- 🛠️ Run Make Files
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- Navigate to the directory containing the MakeFile and set up the development environment:
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- 📦 Install the RDAgent
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- You can directly install the RDAgent package from PyPI:
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.. code-block:: sh
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make dev
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pip install rdagent
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- ⚙️ Environment Configuration
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- Place the `.env` file in the same directory as the `.env.example` file.
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@@ -105,11 +105,21 @@ You can try our demo by running the following command:
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- If you want to change the default environment variables, you can refer to `Env Config`_ below
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- 🚀 Run the Application
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.. code-block:: sh
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- Store the factors you want to extract from the financial reports in your desired folder. Then, save the paths of the reports in the `report_result_json_file_path`. The format should be as follows:
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rdagent fin_factor_report
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.. code-block:: json
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[
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"git_ignore_folder/report/fin_report1.pdf",
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"git_ignore_folder/report/fin_report2.pdf",
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"git_ignore_folder/report/fin_report3.pdf"
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]
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- Run the application using the following command:
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.. code-block:: sh
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rdagent fin_factor_report
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🛠️ Usage of modules
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~~~~~~~~~~~~~~~~~~~~~
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@@ -119,32 +129,13 @@ You can try our demo by running the following command:
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- **Env Config**
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The following environment variables can be set in the `.env` file to customize the application's behavior:
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- **Path to the folder containing research reports:**
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.. code-block:: sh
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.. autopydantic_settings:: rdagent.app.qlib_rd_loop.conf.FactorFromReportPropSetting
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:settings-show-field-summary: False
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:show-inheritance:
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:exclude-members: Config
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QLIB_FACTOR_LOCAL_REPORT_PATH=/path/to/research/reports
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- **Path to the JSON file listing research reports for factor extraction:**
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.. code-block:: sh
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QLIB_FACTOR_REPORT_RESULT_JSON_FILE_PATH=/path/to/reports/list.json
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- **Maximum time (in seconds) for writing factor code:**
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.. code-block:: sh
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FACTOR_CODER_FILE_BASED_EXECUTION_TIMEOUT=300
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- **Maximum number of factors to write in one experiment:**
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.. code-block:: sh
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FACTOR_CODER_SELECT_THRESHOLD=5
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- **Number of developing loops for writing factors:**
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.. code-block:: sh
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FACTOR_CODER_MAX_LOOP=10
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.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorImplementSettings
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:settings-show-field-summary: False
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:members: coder_use_cache, data_folder, data_folder_debug, cache_location, enable_execution_cache, file_based_execution_timeout, select_method, select_threshold, max_loop, knowledge_base_path, new_knowledge_base_path
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:exclude-members: Config, python_bin, fail_task_trial_limit, v1_query_former_trace_limit, v1_query_similar_success_limit, v2_query_component_limit, v2_query_error_limit, v2_query_former_trace_limit, v2_error_summary, v2_knowledge_sampler
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@@ -9,7 +9,12 @@ Finance Model Agent
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📖 Background
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~~~~~~~~~~~~~~
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TODO
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In the realm of quantitative finance, both factor discovery and model development play crucial roles in driving performance.
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While much attention is often given to the discovery of new financial factors, the **models** that leverage these factors are equally important.
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The effectiveness of a quantitative strategy depends not only on the factors used but also on how well these factors are integrated into robust, predictive models.
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However, the process of developing and optimizing these models can be labor-intensive and complex, requiring continuous refinement and adaptation to ever-changing market conditions.
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And this is where the **Finance Model Agent** steps in.
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🎥 Demo
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~~~~~~~~~~
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@@ -19,9 +24,9 @@ TODO: Here should put a video of the demo.
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🌟 Introduction
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~~~~~~~~~~~~~~~~
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||||
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||||
In this scenario, our automated system proposes hypothesis, constructs model, implements code, receives back-testing, and uses feedbacks.
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Hypothesis is iterated in this continuous process.
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||||
The system aims to automatically optimise performance metrics from Qlib library thereby finding the optimised code through autonomous research and development.
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In this scenario, our automated system proposes hypothesis, constructs model, implements code, conducts back-testing, and utilizes feedback in a continuous, iterative process.
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The goal is to automatically optimize performance metrics within the Qlib library, ultimately discovering the most efficient code through autonomous research and development.
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Here's an enhanced outline of the steps:
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@@ -84,17 +89,25 @@ You can try our demo by running the following command:
|
||||
|
||||
conda activate rdagent
|
||||
|
||||
- 🛠️ Run Make Files
|
||||
- Navigate to the directory containing the MakeFile and set up the development environment:
|
||||
- 📦 Install the RDAgent
|
||||
- You can directly install the RDAgent package from PyPI:
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
make dev
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||||
pip install rdagent
|
||||
|
||||
- ⚙️ Environment Configuration
|
||||
- Place the `.env` file in the same directory as the `.env.example` file.
|
||||
- The `.env.example` file contains the environment variables required for users using the OpenAI API (Please note that `.env.example` is an example file. `.env` is the one that will be finally used.)
|
||||
|
||||
- Export each variable in the .env file:
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||||
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||||
.. code-block:: sh
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||||
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||||
export $(grep -v '^#' .env | xargs)
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||||
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||||
- If you want to change the default environment variables, you can refer to `Env Config`_ below
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||||
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||||
- 🚀 Run the Application
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||||
.. code-block:: sh
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||||
|
||||
@@ -102,4 +115,32 @@ You can try our demo by running the following command:
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||||
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🛠️ Usage of modules
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~~~~~~~~~~~~~~~~~~~~~
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TODO: Show some examples:
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.. _Env Config:
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- **Env Config**
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||||
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||||
The following environment variables can be set in the `.env` file to customize the application's behavior:
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||||
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.. autopydantic_settings:: rdagent.app.qlib_rd_loop.conf.ModelBasePropSetting
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:settings-show-field-summary: False
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:exclude-members: Config
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- **Qlib Config**
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- The `config.yaml` file located in the `model_template` folder contains the relevant configurations for running the developed model in Qlib. The default settings include key information such as:
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- **market**: Specifies the market, which is set to `csi300`.
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- **fields_group**: Defines the fields group, with the value `feature`.
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- **col_list**: A list of columns used, including various indicators such as `RESI5`, `WVMA5`, `RSQR5`, and others.
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- **start_time**: The start date for the data, set to `2008-01-01`.
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- **end_time**: The end date for the data, set to `2020-08-01`.
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- **fit_start_time**: The start date for fitting the model, set to `2008-01-01`.
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- **fit_end_time**: The end date for fitting the model, set to `2014-12-31`.
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- The default hyperparameters used in the configuration are as follows:
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- **n_epochs**: The number of epochs, set to `100`.
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- **lr**: The learning rate, set to `1e-3`.
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- **early_stop**: The early stopping criterion, set to `10`.
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- **batch_size**: The batch size, set to `2000`.
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- **metric**: The evaluation metric, set to `loss`.
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- **loss**: The loss function, set to `mse`.
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- **n_jobs**: The number of parallel jobs, set to `20`.
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@@ -1,5 +1,111 @@
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.. _model_agent_med:
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===================
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=======================
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Medical Model Agent
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===================
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=======================
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**🤖 Automated Medical Predtion Model Evolution**
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------------------------------------------------------------------------------------------
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📖 Background
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~~~~~~~~~~~~~~
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In this scenario, we consider the problem of risk prediction from patients' ICU monitoring data. We use the a public EHR dataset - MIMIC-III and extract a binary classification task for evaluating the framework.
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In this task, we aim at predicting the whether the patients will suffer from Acute Respiratory Failure (ARF) based their first 12 hours ICU monitoring data.
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🎥 Demo
|
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~~~~~~~~~~
|
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TODO: Here should put a video of the demo.
|
||||
|
||||
|
||||
🌟 Introduction
|
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~~~~~~~~~~~~~~~~
|
||||
|
||||
In this scenario, our automated system proposes hypothesis, constructs model, implements code, receives back-testing, and uses feedbacks.
|
||||
Hypothesis is iterated in this continuous process.
|
||||
The system aims to automatically optimise performance metrics of medical prediction thereby finding the optimised code through autonomous research and development.
|
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|
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Here's an enhanced outline of the steps:
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**Step 1 : Hypothesis Generation 🔍**
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- Generate and propose initial hypotheses based on previous experiment analysis and domain expertise, with thorough reasoning and justification.
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**Step 2 : Model Creation ✨**
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- Transform the hypothesis into a model.
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- Develop, define, and implement a machine learning model, including its name, description, and formulation.
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**Step 3 : Model Implementation 👨💻**
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- Implement the model code based on the detailed description.
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- Evolve the model iteratively as a developer would, ensuring accuracy and efficiency.
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**Step 4 : Backtesting with MIMIC-III 📉**
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- Conduct backtesting using the newly developed model on the extracted task from MIMIC-III.
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- Evaluate the model's effectiveness and performance in terms of AUROC score.
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**Step 5 : Feedback Analysis 🔍**
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- Analyze backtest results to assess performance.
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- Incorporate feedback to refine hypotheses and improve the model.
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**Step 6 :Hypothesis Refinement ♻️**
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- Refine hypotheses based on feedback from backtesting.
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- Repeat the process to continuously improve the model.
|
||||
|
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⚡ Quick Start
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
You can try our demo by running the following command:
|
||||
|
||||
- 🐍 Create a Conda Environment
|
||||
- Create a new conda environment with Python (3.10 and 3.11 are well tested in our CI):
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
conda create -n rdagent python=3.10
|
||||
|
||||
- Activate the environment:
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
conda activate rdagent
|
||||
|
||||
- 📦 Install the RDAgent
|
||||
- You can directly install the RDAgent package from PyPI:
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
pip install rdagent
|
||||
|
||||
- ⚙️ Environment Configuration
|
||||
- Place the `.env` file in the same directory as the `.env.example` file.
|
||||
- The `.env.example` file contains the environment variables required for users using the OpenAI API (Please note that `.env.example` is an example file. `.env` is the one that will be finally used.)
|
||||
|
||||
- Export each variable in the .env file:
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
export $(grep -v '^#' .env | xargs)
|
||||
|
||||
- If you want to change the default environment variables, you can refer to `Env Config`_ below
|
||||
|
||||
- 🚀 Run the Application
|
||||
.. code-block:: sh
|
||||
|
||||
rdagent med_model
|
||||
|
||||
🛠️ Usage of modules
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. _Env Config:
|
||||
|
||||
- **Env Config**
|
||||
|
||||
The following environment variables can be set in the `.env` file to customize the application's behavior:
|
||||
|
||||
.. autopydantic_settings:: rdagent.app.data_mining.conf.PropSetting
|
||||
:settings-show-field-summary: False
|
||||
:exclude-members: Config
|
||||
@@ -9,7 +9,11 @@ General Model Copilot
|
||||
|
||||
📖 Background
|
||||
~~~~~~~~~~~~~~
|
||||
TODO:
|
||||
In the fast-paced field of artificial intelligence, the number of academic papers published each year is skyrocketing.
|
||||
These papers introduce new models, techniques, and approaches that can significantly advance the state of the art.
|
||||
However, reproducing and implementing these models can be a daunting task, requiring substantial time and expertise.
|
||||
Researchers often face challenges in extracting the essential details from these papers and converting them into functional code.
|
||||
And this is where the **General Model Copilot** steps in.
|
||||
|
||||
🎥 Demo
|
||||
~~~~~~~~~~
|
||||
@@ -60,12 +64,13 @@ You can try our demo by running the following command:
|
||||
|
||||
conda activate rdagent
|
||||
|
||||
- 🛠️ Run Make Files
|
||||
- Navigate to the directory containing the MakeFile and set up the development environment:
|
||||
- 📦 Install the RDAgent
|
||||
- You can directly install the RDAgent package from PyPI:
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
make dev
|
||||
pip install rdagent
|
||||
|
||||
|
||||
- ⚙️ Environment Configuration
|
||||
- Place the `.env` file in the same directory as the `.env.example` file.
|
||||
@@ -79,31 +84,7 @@ You can try our demo by running the following command:
|
||||
rdagent/scenarios/general_model
|
||||
|
||||
- Run the following command in your terminal within the same virtual environment:
|
||||
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
python rdagent/app/general_model/general_model.py report_file_path
|
||||
|
||||
🛠️ Usage of modules
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
There are mainly two modules in this scenario: one that reads the paper and returns a model card & one that reads the model card and returns functional code. The moduldes can also be used separately as components for developers to build up new scenarios.
|
||||
|
||||
|
||||
- Configurations
|
||||
- The `config.yaml` file located in the `model_template` folder contains the relevant configurations for running the developed model in Qlib. The default settings include key information such as:
|
||||
- **market**: Specifies the market, which is set to `csi300`.
|
||||
- **fields_group**: Defines the fields group, with the value `feature`.
|
||||
- **col_list**: A list of columns used, including various indicators such as `RESI5`, `WVMA5`, `RSQR5`, and others.
|
||||
- **start_time**: The start date for the data, set to `2008-01-01`.
|
||||
- **end_time**: The end date for the data, set to `2020-08-01`.
|
||||
- **fit_start_time**: The start date for fitting the model, set to `2008-01-01`.
|
||||
- **fit_end_time**: The end date for fitting the model, set to `2014-12-31`.
|
||||
|
||||
- The default hyperparameters used in the configuration are as follows:
|
||||
- **n_epochs**: The number of epochs, set to `100`.
|
||||
- **lr**: The learning rate, set to `1e-3`.
|
||||
- **early_stop**: The early stopping criterion, set to `10`.
|
||||
- **batch_size**: The batch size, set to `2000`.
|
||||
- **metric**: The evaluation metric, set to `loss`.
|
||||
- **loss**: The loss function, set to `mse`.
|
||||
- **n_jobs**: The number of parallel jobs, set to `20`.
|
||||
rdagent general_model --report_file_path=<path_to_pdf_file>
|
||||
@@ -1,20 +1,38 @@
|
||||
from pathlib import Path
|
||||
|
||||
from pydantic_settings import BaseSettings
|
||||
|
||||
from rdagent.components.workflow.conf import BasePropSetting
|
||||
|
||||
|
||||
class PropSetting(BasePropSetting):
|
||||
class Config:
|
||||
env_prefix = "DM_" # Use MODEL_CODER_ as prefix for environment variables
|
||||
protected_namespaces = () # Add 'model_' to the protected namespaces
|
||||
env_prefix = "DM_"
|
||||
"""Use `DM_` as prefix for environment variables"""
|
||||
protected_namespaces = ()
|
||||
"""Add 'model_' to the protected namespaces"""
|
||||
|
||||
# 1) overriding the default
|
||||
scen: str = "rdagent.scenarios.data_mining.experiment.model_experiment.DMModelScenario"
|
||||
"""Scenario class for data mining model"""
|
||||
|
||||
hypothesis_gen: str = "rdagent.scenarios.data_mining.proposal.model_proposal.DMModelHypothesisGen"
|
||||
"""Hypothesis generation class"""
|
||||
|
||||
hypothesis2experiment: str = "rdagent.scenarios.data_mining.proposal.model_proposal.DMModelHypothesis2Experiment"
|
||||
"""Hypothesis to experiment class"""
|
||||
|
||||
coder: str = "rdagent.scenarios.data_mining.developer.model_coder.DMModelCoSTEER"
|
||||
"""Coder class"""
|
||||
|
||||
runner: str = "rdagent.scenarios.data_mining.developer.model_runner.DMModelRunner"
|
||||
"""Runner class"""
|
||||
|
||||
summarizer: str = "rdagent.scenarios.data_mining.developer.feedback.DMModelHypothesisExperiment2Feedback"
|
||||
"""Summarizer class"""
|
||||
|
||||
evolving_n: int = 10
|
||||
"""Number of evolutions"""
|
||||
|
||||
evolving_n: int = 10
|
||||
|
||||
@@ -22,7 +40,10 @@ class PropSetting(BasePropSetting):
|
||||
# physionet account
|
||||
# NOTE: You should apply the account in https://physionet.org/
|
||||
username: str = ""
|
||||
"""Physionet account username"""
|
||||
|
||||
password: str = ""
|
||||
"""Physionet account password"""
|
||||
|
||||
|
||||
PROP_SETTING = PropSetting()
|
||||
|
||||
@@ -1,45 +1,79 @@
|
||||
from pydantic_settings import BaseSettings
|
||||
|
||||
from rdagent.components.workflow.conf import BasePropSetting
|
||||
|
||||
|
||||
class ModelBasePropSetting(BasePropSetting):
|
||||
class Config:
|
||||
env_prefix = "QLIB_MODEL_" # Use MODEL_CODER_ as prefix for environment variables
|
||||
protected_namespaces = () # Add 'model_' to the protected namespaces
|
||||
env_prefix = "QLIB_MODEL_"
|
||||
"""Use `QLIB_MODEL_` as prefix for environment variables"""
|
||||
protected_namespaces = ()
|
||||
"""Add 'model_' to the protected namespaces"""
|
||||
|
||||
# 1) override base settings
|
||||
scen: str = "rdagent.scenarios.qlib.experiment.model_experiment.QlibModelScenario"
|
||||
"""Scenario class for Qlib Model"""
|
||||
|
||||
hypothesis_gen: str = "rdagent.scenarios.qlib.proposal.model_proposal.QlibModelHypothesisGen"
|
||||
"""Hypothesis generation class"""
|
||||
|
||||
hypothesis2experiment: str = "rdagent.scenarios.qlib.proposal.model_proposal.QlibModelHypothesis2Experiment"
|
||||
"""Hypothesis to experiment class"""
|
||||
|
||||
coder: str = "rdagent.scenarios.qlib.developer.model_coder.QlibModelCoSTEER"
|
||||
"""Coder class"""
|
||||
|
||||
runner: str = "rdagent.scenarios.qlib.developer.model_runner.QlibModelRunner"
|
||||
"""Runner class"""
|
||||
|
||||
summarizer: str = "rdagent.scenarios.qlib.developer.feedback.QlibModelHypothesisExperiment2Feedback"
|
||||
"""Summarizer class"""
|
||||
|
||||
evolving_n: int = 10
|
||||
"""Number of evolutions"""
|
||||
|
||||
|
||||
class FactorBasePropSetting(BasePropSetting):
|
||||
class Config:
|
||||
env_prefix = "QLIB_FACTOR_" # Use MODEL_CODER_ as prefix for environment variables
|
||||
protected_namespaces = () # Add 'model_' to the protected namespaces
|
||||
env_prefix = "QLIB_FACTOR_"
|
||||
"""Use `QLIB_FACTOR_` as prefix for environment variables"""
|
||||
protected_namespaces = ()
|
||||
"""Add 'factor_' to the protected namespaces"""
|
||||
|
||||
# 1) override base settings
|
||||
# TODO: model part is not finished yet
|
||||
scen: str = "rdagent.scenarios.qlib.experiment.factor_experiment.QlibFactorScenario"
|
||||
"""Scenario class for Qlib Factor"""
|
||||
|
||||
hypothesis_gen: str = "rdagent.scenarios.qlib.proposal.factor_proposal.QlibFactorHypothesisGen"
|
||||
"""Hypothesis generation class"""
|
||||
|
||||
hypothesis2experiment: str = "rdagent.scenarios.qlib.proposal.factor_proposal.QlibFactorHypothesis2Experiment"
|
||||
"""Hypothesis to experiment class"""
|
||||
|
||||
coder: str = "rdagent.scenarios.qlib.developer.factor_coder.QlibFactorCoSTEER"
|
||||
"""Coder class"""
|
||||
|
||||
runner: str = "rdagent.scenarios.qlib.developer.factor_runner.QlibFactorRunner"
|
||||
"""Runner class"""
|
||||
|
||||
summarizer: str = "rdagent.scenarios.qlib.developer.feedback.QlibFactorHypothesisExperiment2Feedback"
|
||||
"""Summarizer class"""
|
||||
|
||||
evolving_n: int = 10
|
||||
|
||||
# 2) sub task specific:
|
||||
report_result_json_file_path: str = "git_ignore_folder/report_list.json"
|
||||
max_factors_per_exp: int = 10000
|
||||
"""Number of evolutions"""
|
||||
|
||||
|
||||
class FactorFromReportPropSetting(FactorBasePropSetting):
|
||||
# Override the scen attribute
|
||||
# 1) override the scen attribute
|
||||
scen: str = "rdagent.scenarios.qlib.experiment.factor_from_report_experiment.QlibFactorFromReportScenario"
|
||||
"""Scenario class for Qlib Factor from Report"""
|
||||
|
||||
# 2) sub task specific:
|
||||
report_result_json_file_path: str = "git_ignore_folder/report_list.json"
|
||||
"""Path to the JSON file listing research reports for factor extraction"""
|
||||
|
||||
max_factors_per_exp: int = 10000
|
||||
"""Maximum number of factors implemented per experiment"""
|
||||
|
||||
|
||||
FACTOR_PROP_SETTING = FactorBasePropSetting()
|
||||
|
||||
@@ -8,19 +8,23 @@ SELECT_METHOD = Literal["random", "scheduler"]
|
||||
|
||||
class FactorImplementSettings(BaseSettings):
|
||||
class Config:
|
||||
env_prefix = "FACTOR_CODER_" # Use FACTOR_CODER_ as prefix for environment variables
|
||||
env_prefix = "FACTOR_CODER_"
|
||||
"""Use `FACTOR_CODER_` as prefix for environment variables"""
|
||||
|
||||
coder_use_cache: bool = False
|
||||
data_folder: str = str(
|
||||
(Path().cwd() / "git_ignore_folder" / "factor_implementation_source_data").absolute(),
|
||||
)
|
||||
data_folder_debug: str = str(
|
||||
(Path().cwd() / "git_ignore_folder" / "factor_implementation_source_data_debug").absolute(),
|
||||
)
|
||||
cache_location: str = str(
|
||||
(Path().cwd() / "git_ignore_folder" / "factor_implementation_execution_cache").absolute(),
|
||||
)
|
||||
enable_execution_cache: bool = True # whether to enable the execution cache
|
||||
"""Indicates whether to use cache for the coder"""
|
||||
|
||||
data_folder: str = "git_ignore_folder/factor_implementation_source_data"
|
||||
"""Path to the folder containing financial data (default is fundamental data in Qlib)"""
|
||||
|
||||
data_folder_debug: str = "git_ignore_folder/factor_implementation_source_data_debug"
|
||||
"""Path to the folder containing partial financial data (for debugging)"""
|
||||
|
||||
cache_location: str = "git_ignore_folder/factor_implementation_execution_cache"
|
||||
"""Path to the cache location"""
|
||||
|
||||
enable_execution_cache: bool = True
|
||||
"""Indicates whether to enable the execution cache"""
|
||||
|
||||
# TODO: the factor implement specific settings should not appear in this settings
|
||||
# Evolving should have a method specific settings
|
||||
@@ -36,17 +40,26 @@ class FactorImplementSettings(BaseSettings):
|
||||
v2_error_summary: bool = False
|
||||
v2_knowledge_sampler: float = 1.0
|
||||
|
||||
file_based_execution_timeout: int = 120 # seconds for each factor implementation execution
|
||||
file_based_execution_timeout: int = 120
|
||||
"""Timeout in seconds for each factor implementation execution"""
|
||||
|
||||
select_method: str = "random"
|
||||
"""Method for the selection of factors implementation"""
|
||||
|
||||
select_method: SELECT_METHOD = "random"
|
||||
select_threshold: int = 10
|
||||
"""Threshold for the number of factor selections"""
|
||||
|
||||
max_loop: int = 10
|
||||
"""Maximum number of task implementation loops"""
|
||||
|
||||
knowledge_base_path: Union[str, None] = None
|
||||
"""Path to the knowledge base"""
|
||||
|
||||
new_knowledge_base_path: Union[str, None] = None
|
||||
"""Path to the new knowledge base"""
|
||||
|
||||
python_bin: str = "python"
|
||||
"""Path to the Python binary"""
|
||||
|
||||
|
||||
FACTOR_IMPLEMENT_SETTINGS = FactorImplementSettings()
|
||||
|
||||
Reference in New Issue
Block a user