docs: update document for RD-Agent(Q) (#940)

2026-07-28 16:07:46 +00:00 · 2025-06-07 14:41:02 +08:00
parent 63ba3296dd
commit 671771a626
3 changed files with 126 additions and 11 deletions
@@ -61,7 +61,7 @@ R&D-Agent for Quantitative Finance, in short **RD-Agent(Q)**, is the first data-

 Extensive experiments in real stock markets show that, at a cost under $10, RD-Agent(Q) achieves approximately 2× higher ARR than benchmark factor libraries while using over 70% fewer factors. It also surpasses state-of-the-art deep time-series models under smaller resource budgets. Its alternating factor–model optimization further delivers excellent trade-off between predictive accuracy and strategy robustness.

-You can learn more details about **RD-Agent(Q)** through the [paper](https://arxiv.org/abs/2505.15155).
+You can learn more details about **RD-Agent(Q)** through the [paper](https://arxiv.org/abs/2505.15155) and reproduce it through the [documentation](https://rdagent.readthedocs.io/en/latest/scens/quant_agent_fin.html).

 # 📰 News
 | 🗞️ News        | 📝 Description                 |
@@ -13,27 +13,28 @@ In the two key areas of data-driven scenarios, model implementation and data bui
 The supported scenarios are listed below:


-
-.. list-table:: 
+.. list-table::
    :header-rows: 1

    * - Scenario/Target
      - Model Implementation
      - Data Building
    * - 💹 Finance
-      - :ref:`🤖Iteratively Proposing Ideas & Evolving <model_agent_fin>`
-      - :ref:`🦾Auto reports reading & implementation <data_copilot_fin>`
-
+      - :ref:`🥇The First Data-Centric Quant Multi-Agent Framework <quant_agent_fin>`
+      - :ref:`🤖Iteratively Proposing Ideas & Evolving <model_agent_fin>`  
+        
+        :ref:`🦾Auto reports reading & implementation <data_copilot_fin>`  
+        
        :ref:`🤖Iteratively Proposing Ideas & Evolving <data_agent_fin>`
    * - 🩺 Medical
      - :ref:`🤖Iteratively Proposing Ideas & Evolving <model_agent_med>`
-      - 
+      - *(no data yet)*
    * - 🏭 General
-      - :ref:`🦾Auto paper reading & implementation <model_copilot_general>`
-
+      - :ref:`🦾Auto paper reading & implementation <model_copilot_general>`  
+        
        :ref:`🤖Auto Kaggle Model Tuning <kaggle_agent>`
-      - :ref:`🤖Auto Kaggle feature Engineering <kaggle_agent>`
-
+      - :ref:`🤖Auto Kaggle feature Engineering <kaggle_agent>`  
+        
        :ref:`🤖 Data Science <data_science_agent>`


@@ -42,6 +43,7 @@ The supported scenarios are listed below:
    :caption: Doctree:
    :hidden:

+    quant_agent_fin
    data_agent_fin
    data_copilot_fin
    model_agent_fin
@@ -0,0 +1,113 @@
+.. _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.