wehub-resource-sync
151 lines
6.4 KiB
ReStructuredText
151 lines
6.4 KiB
ReStructuredText
.. _model_agent_fin:
|
|
|
|
=======================
|
|
Finance Model Agent
|
|
=======================
|
|
|
|
**🤖 Automated Quantitative Trading & Iterative Model Evolution**
|
|
------------------------------------------------------------------------------------------
|
|
|
|
📖 Background
|
|
~~~~~~~~~~~~~~
|
|
In the realm of quantitative finance, both factor discovery and model development play crucial roles in driving performance.
|
|
While much attention is often given to the discovery of new financial factors, the **models** that leverage these factors are equally important.
|
|
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.
|
|
|
|
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.
|
|
And this is where the **Finance Model Agent** steps in.
|
|
|
|
|
|
🎥 `Demo <https://rdagent.azurewebsites.net/model_loop>`_
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. raw:: html
|
|
|
|
<div style="display: flex; justify-content: center; align-items: center;">
|
|
<video width="600" controls>
|
|
<source src="https://rdagent.azurewebsites.net/media/d85e8cab1da1cd3501d69ce837452f53a971a24911eae7bfa9237137.mp4" type="video/mp4">
|
|
Your browser does not support the video tag.
|
|
</video>
|
|
</div>
|
|
|
|
|
|
🌟 Introduction
|
|
~~~~~~~~~~~~~~~~
|
|
|
|
In this scenario, our automated system proposes hypothesis, constructs model, implements code, conducts back-testing, and utilizes feedback in a continuous, iterative process.
|
|
|
|
The goal is to automatically optimize performance metrics within the Qlib library, ultimately discovering the most efficient code through autonomous research and development.
|
|
|
|
Here's an enhanced outline of the steps:
|
|
|
|
**Step 1 : Hypothesis Generation 🔍**
|
|
|
|
- Generate and propose initial hypotheses based on previous experiment analysis and domain expertise, with thorough reasoning and financial justification.
|
|
|
|
**Step 2 : Model Creation ✨**
|
|
|
|
- Transform the hypothesis into a task.
|
|
- Develop, define, and implement a quantitative model, including its name, description, and formulation.
|
|
|
|
**Step 3 : Model Implementation 👨💻**
|
|
|
|
- Implement the model code based on the detailed description.
|
|
- Evolve the model iteratively as a developer would, ensuring accuracy and efficiency.
|
|
|
|
**Step 4 : Backtesting with Qlib 📉**
|
|
|
|
- Conduct backtesting using the newly developed model and 20 factors extracted from Alpha158 in Qlib.
|
|
- Evaluate the model's effectiveness and performance.
|
|
|
|
+----------------+------------+------------------------+----------------------------------------------------+
|
|
| Dataset | Model | Factors | Data Split |
|
|
+================+============+========================+====================================================+
|
|
| CSI300 | RDAgent-dev| 20 factors (Alpha158) | +-----------+--------------------------+ |
|
|
| | | | | Train | 2008-01-01 to 2014-12-31 | |
|
|
| | | | +-----------+--------------------------+ |
|
|
| | | | | Valid | 2015-01-01 to 2016-12-31 | |
|
|
| | | | +-----------+--------------------------+ |
|
|
| | | | | Test | 2017-01-01 to 2020-08-01 | |
|
|
| | | | +-----------+--------------------------+ |
|
|
+----------------+------------+------------------------+----------------------------------------------------+
|
|
|
|
**Step 5 : Feedback Analysis 🔍**
|
|
|
|
- Analyze backtest results to assess performance.
|
|
- Incorporate feedback to refine hypotheses and improve the model.
|
|
|
|
**Step 6 :Hypothesis Refinement ♻️**
|
|
|
|
- Refine hypotheses based on feedback from backtesting.
|
|
- Repeat the process to continuously improve the model.
|
|
|
|
⚡ Quick Start
|
|
~~~~~~~~~~~~~~~~~
|
|
|
|
Please refer to the installation part in :doc:`../installation_and_configuration` to prepare your system dependency.
|
|
|
|
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 install the RDAgent package from PyPI:
|
|
|
|
.. code-block:: sh
|
|
|
|
pip install rdagent
|
|
|
|
- 🚀 Run the Application
|
|
|
|
- You can directly run the application by using the following command:
|
|
|
|
.. code-block:: sh
|
|
|
|
rdagent fin_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.qlib_rd_loop.conf.ModelBasePropSetting
|
|
:settings-show-field-summary: False
|
|
:exclude-members: Config
|
|
|
|
- **Qlib Config**
|
|
- 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`.
|