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WinstonLiyt
080d9ca689
* Fixed some bugs introduced during refactoring. * Added docs for four scenrios. * Update factor_from_report_w_sc.py
120 lines
4.4 KiB
ReStructuredText
120 lines
4.4 KiB
ReStructuredText
.. _model_agent_fin:
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=======================
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Finance Model Agent
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=======================
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**🤖 Automated Quantitative Trading & Iterative Model Evolution**
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------------------------------------------------------------------------------------------
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📖 Background
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~~~~~~~~~~~~~~
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TODO
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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, 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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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 financial justification.
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**Step 2 : Model Creation ✨**
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- Transform the hypothesis into a task.
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- Develop, define, and implement a quantitative 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 Qlib 📉**
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- Conduct backtesting using the newly developed model and 20 factors extracted from Alpha158 in Qlib.
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- Evaluate the model's 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 | RDAgent-dev| 20 factors (Alpha158) | +-----------+--------------------------+ |
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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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- 📦 Install Pytorch
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- Install Pytorch and related libraries:
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.. code-block:: sh
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
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pip3 install torch_geometric
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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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- Export each variable in the .env file:
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.. code-block:: sh
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export $(grep -v '^#' .env | xargs)
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- 🚀 Run the Application
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.. code-block:: sh
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python rdagent/app/qlib_rd_loop/model_w_sc.py
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🛠️ Usage of modules
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~~~~~~~~~~~~~~~~~~~~~
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TODO: Show some examples:
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