.. _quant_agent_fin:

=====================
Finance Quant Agent
=====================


**🥇The First Data-Centric Quant Multi-Agent Framework RD-Agent(Q)**
---------------------------------------------------------------------

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.

You can learn more details about **RD-Agent(Q)** through the `paper <https://arxiv.org/abs/2505.15155>`_.

⚡ Quick Start
~~~~~~~~~~~~~~~~~

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>`_.

Then, you can run the framework 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_quant


🛠️ 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.QuantBasePropSetting
    :settings-show-field-summary: False
    :exclude-members: Config

.. autopydantic_settings:: rdagent.components.coder.factor_coder.config.FactorCoSTEERSettings
    :settings-show-field-summary: False
    :members: coder_use_cache, data_folder, data_folder_debug, file_based_execution_timeout, select_method, max_loop, knowledge_base_path, new_knowledge_base_path
    :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
    :no-index:

- **Qlib Configuration**
    - 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:
        - **General Settings**:
            - **provider_uri**: Specifies the local Qlib data path, set to `~/.qlib/qlib_data/cn_data`.
            - **market**: Configured to `csi300`, representing the CSI 300 index constituents.
            - **benchmark**: Set to `SH000300`, used for backtesting evaluation.
        
        - **Data Handling**:
            - **start_time** and **end_time**: Define the full data range, from `2008-01-01` to `2022-08-01`.
            - **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`.
            - **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`).
            -  **normalization**: The pipeline includes `RobustZScoreNorm` (with clipping) and `Fillna` for inference, and `DropnaLabel` with `CSZScoreNorm` for training.
        
        - **Training Configuration**:
            - **Model**: Uses `GeneralPTNN`, a PyTorch-based neural network model.
            - **Dataset Splits**:
                - **train**: `2008-01-01` to `2014-12-31`
                - **valid**: `2015-01-01` to `2016-12-31`
                - **test**: `2017-01-01` to `2020-08-01`

        - **Default Hyperparameters** (can be overridden by command-line arguments):
            - **n_epochs**: `100`
            - **lr**: `2e-4`
            - **early_stop**: `10`
            - **batch_size**: `256`
            - **weight_decay**: `0.0`
            - **metric**: `loss`
            - **loss**: `mse`
            - **n_jobs**: `20`
            - **GPU**: `0` (uses GPU 0 if available)
            
        - **Backtesting and Evaluation**:
            - **strategy**: `TopkDropoutStrategy`, which selects the top 50 stocks and randomly drops 5 to introduce exploration.
            - **backtest period**: `2017-01-01` to `2020-08-01`
            - **initial capital**: `100,000,000`
            - **cost configuration**: Includes open/close costs, minimum transaction costs, and slippage control.
            
        - **Recording and Analysis**:
            - **SignalRecord**: Logs predicted signals.
            - **SigAnaRecord**: Performs signal analysis without long-short separation.
            - **PortAnaRecord**: Conducts portfolio analysis using the configured strategy and backtest settings.