wehub-resource-sync
165 lines
6.6 KiB
ReStructuredText
165 lines
6.6 KiB
ReStructuredText
.. _data_copilot_fin:
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=====================
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Finance Data Copilot
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=====================
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**🤖 Automated Quantitative Trading & Factors Extraction from Financial Reports**
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📖 Background
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~~~~~~~~~~~~~~
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**Research reports** are treasure troves of insights, often unveiling potential **factors** that can drive successful quantitative trading strategies.
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Yet, with the sheer volume of reports available, extracting the most valuable insights efficiently becomes a daunting task.
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Furthermore, rather than hastily replicating factors from a report, it's essential to delve into the underlying logic of their construction.
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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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And this is where the **Finance Data Copilot** steps in.
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🎥 `Demo <https://rdagent.azurewebsites.net/report_factor>`_
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. raw:: html
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<div style="display: flex; justify-content: center; align-items: center;">
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<video width="600" controls>
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<source src="https://rdagent.azurewebsites.net/media/7b14b2bd3d8771da9cf7eb799b6d96729cec3d35c8d4f68060f3e2fd.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</div>
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🌟 Introduction
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~~~~~~~~~~~~~~~~
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In this scenario, RDAgent demonstrates the process of extracting factors from financial research reports, implementing these factors, and analyzing their performance through Qlib backtesting.
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This process continually expands and refines the factor library.
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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 insights from financial reports with thorough reasoning and financial justification.
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**Step 2 : Factor Creation ✨**
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- Based on the hypothesis and financial reports, divide the tasks.
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- Each task involves developing, defining, and implementing a new financial factor, including its name, description, formulation, and variables.
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**Step 3 : Factor Implementation 👨💻**
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- Implement the factor code based on the description, evolving it as a developer would.
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- Quantitatively validate the newly created factors.
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**Step 4 : Backtesting with Qlib 📉**
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- Integrate the full dataset into the factor implementation code and prepare the factor library.
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- Conduct backtesting using the Alpha158 plus newly developed factors and LGBModel in Qlib to evaluate the new factors' effectiveness and performance.
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+----------------+------------+----------------+----------------------------------------------------+
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| Dataset | Model | Factors | Data Split |
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+================+============+================+====================================================+
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| CSI300 | LGBModel | Alpha158 Plus | +-----------+--------------------------+ |
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| | | | | Train | 2008-01-01 to 2014-12-31 | |
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| | | | +-----------+--------------------------+ |
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| | | | | Valid | 2015-01-01 to 2016-12-31 | |
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| | | | +-----------+--------------------------+ |
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| | | | | Test | 2017-01-01 to 2020-08-01 | |
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| | | | +-----------+--------------------------+ |
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+----------------+------------+----------------+----------------------------------------------------+
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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
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~~~~~~~~~~~~~~~~~
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Please refer to the installation part in :doc:`../installation_and_configuration` to prepare your system dependency.
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You can try our demo by running the following command:
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- 🐍 Create a Conda Environment
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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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.. code-block:: sh
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conda activate rdagent
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- 📦 Install the RDAgent
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- You can install the RDAgent package from PyPI:
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.. code-block:: sh
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pip install rdagent
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- 🚀 Run the Application
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- Download the financial reports you wish to extract factors from and store them in your preferred folder.
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- Specifically, you can follow this example, or use your own method:
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.. code-block:: sh
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wget https://github.com/SunsetWolf/rdagent_resource/releases/download/reports/all_reports.zip
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unzip all_reports.zip -d git_ignore_folder/reports
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- Run the application with the following command:
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.. code-block:: sh
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rdagent fin_factor_report --report-folder=git_ignore_folder/reports
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- Alternatively, you can store the paths of the reports in `report_result_json_file_path`. The format should be:
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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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- Then, 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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.. _Env Config:
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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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.. 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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.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorCoSTEERSettings
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:settings-show-field-summary: False
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:members: coder_use_cache, data_folder, data_folder_debug, file_based_execution_timeout, select_method, 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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:no-index:
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