wehub-resource-sync
114 lines
5.1 KiB
ReStructuredText
114 lines
5.1 KiB
ReStructuredText
.. _quant_agent_fin:
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=====================
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Finance Quant Agent
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=====================
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**🥇The First Data-Centric Quant Multi-Agent Framework RD-Agent(Q)**
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---------------------------------------------------------------------
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R&D-Agent for Quantitative Finance, in short **RD-Agent(Q)**, is the first data-centric, multi-agent framework designed to automate the full-stack research and development of quantitative strategies via coordinated factor-model co-optimization.
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You can learn more details about **RD-Agent(Q)** through the `paper <https://arxiv.org/abs/2505.15155>`_.
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⚡ Quick Start
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~~~~~~~~~~~~~~~~~
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Before you start, please make sure you have installed RD-Agent and configured the environment for RD-Agent correctly. If you want to know how to install and configure the RD-Agent, please refer to the `documentation <../installation_and_configuration.html>`_.
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Then, you can run the framework 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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- You can directly run the application by using the following command:
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.. code-block:: sh
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rdagent fin_quant
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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.QuantBasePropSetting
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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.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, 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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- **Qlib Configuration**
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- The `.yaml` files in both the `model_template` and `factor_template` directories contain some configurations for running the corresponding models or factors within the Qlib framework. Below is an overview of their contents and roles:
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- **General Settings**:
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- **provider_uri**: Specifies the local Qlib data path, set to `~/.qlib/qlib_data/cn_data`.
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- **market**: Configured to `csi300`, representing the CSI 300 index constituents.
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- **benchmark**: Set to `SH000300`, used for backtesting evaluation.
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- **Data Handling**:
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- **start_time** and **end_time**: Define the full data range, from `2008-01-01` to `2022-08-01`.
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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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- **features and labels**: Generated via a nested data loader combining `Alpha158DL` (for engineered features such as `RESI5`, `WVMA5`, `RSQR5`, `KLEN`, etc.) and a `StaticDataLoader` that loads precomputed factor files (`combined_factors_df.parquet`).
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- **normalization**: The pipeline includes `RobustZScoreNorm` (with clipping) and `Fillna` for inference, and `DropnaLabel` with `CSZScoreNorm` for training.
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- **Training Configuration**:
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- **Model**: Uses `GeneralPTNN`, a PyTorch-based neural network model.
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- **Dataset Splits**:
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- **train**: `2008-01-01` to `2014-12-31`
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- **valid**: `2015-01-01` to `2016-12-31`
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- **test**: `2017-01-01` to `2020-08-01`
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- **Default Hyperparameters** (can be overridden by command-line arguments):
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- **n_epochs**: `100`
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- **lr**: `2e-4`
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- **early_stop**: `10`
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- **batch_size**: `256`
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- **weight_decay**: `0.0`
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- **metric**: `loss`
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- **loss**: `mse`
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- **n_jobs**: `20`
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- **GPU**: `0` (uses GPU 0 if available)
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- **Backtesting and Evaluation**:
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- **strategy**: `TopkDropoutStrategy`, which selects the top 50 stocks and randomly drops 5 to introduce exploration.
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- **backtest period**: `2017-01-01` to `2020-08-01`
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- **initial capital**: `100,000,000`
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- **cost configuration**: Includes open/close costs, minimum transaction costs, and slippage control.
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- **Recording and Analysis**:
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- **SignalRecord**: Logs predicted signals.
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- **SigAnaRecord**: Performs signal analysis without long-short separation.
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- **PortAnaRecord**: Conducts portfolio analysis using the configured strategy and backtest settings.
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