mirror of
https://github.com/NicolasBohn/NexQuant.git
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Xu Yang
151 lines
5.6 KiB
ReStructuredText
151 lines
5.6 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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This is where our RDAgent comes into play.
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🎥 Demo
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~~~~~~~~~~
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TODO: Here should put a video of the demo.
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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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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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- 🛠️ Run Make Files
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- Navigate to the directory containing the MakeFile and set up the development environment:
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.. code-block:: sh
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make dev
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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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- 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.)
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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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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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- **Path to the folder containing research reports:**
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.. code-block:: sh
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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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