RDAgent

RDAgent is a tool aimed at automating industrial R&D processes, especially data-driven R&D scenarios. By imitating the way human experts conduct research and development, it is able to:

• Reading Comprehension: Extract key information from reports, papers, and other materials, such as formulas, feature descriptions, model structures, etc.
• Code Implementation: Convert extracted information into runnable code, for example, implementing features, factors, or models.
• Iterative Optimization: Continuously improve code performance by learning from feedback and knowledge.
• Innovative Ideas: Propose new R&D ideas based on existing knowledge and observations.

The core idea of RDAgent is to establish an "R&D loop," where "R" (Research) represents proposing new ideas, and "D" (Development) represents implementing these ideas. Through automated R&D loops, RDAgent can help users explore and validate new data science methods more efficiently.

Main Features of RDAgent:

• Data-Driven: Focuses on data-driven R&D processes, making full use of data to drive innovation.
• Automation: Automates key aspects of the R&D process, such as feature engineering, model building, and experimental validation.
• Scalability: Easily integrates new methods and technologies to adapt to different R&D needs.
• Modularity: The framework design is clear, easy to customize and extend.
• Collaboration: Designed to support human-computer collaboration, it can serve as an intelligent assistant for R&D personnel.

Usage Scenarios of RDAgent:

RDAgent is suitable for various data-driven R&D scenarios, especially in the following areas:

• Quantitative Trading (Automatic Quant Factory):

  • Automatically generate quantitative factors (factor).
  • Automatically build and optimize quantitative models (model).

• Data Mining (Data Mining Agent):

  • Iteratively propose new data and models.
  • Improve model performance by learning from data.

• Research Assistant (Research Copilot):

  • Automatically read research papers and financial reports.
  • Extract model structures and dataset construction methods.

• Kaggle Competition (Kaggle Agent):

  • Automatically adjust model parameters.
  • Automatically perform feature engineering to achieve better results in competitions.

• Medical Model Development (Medical Prediction Model Evolution):

  • Automatically optimize medical prediction models.

How to Use RDAgent?

The documentation provides detailed quick-start guides, including:

1. Install Docker: RDAgent relies on Docker to run certain components, so Docker needs to be installed first.
2. Create Conda Environment: It is recommended to create a new Conda environment to install RDAgent and its dependencies.
3. Install RDAgent: Use the pip command to install the RDAgent package.
4. Configure API Keys: Configure corresponding API keys and model parameters according to the API service used (e.g., OpenAI or Azure OpenAI).
5. Run Examples: Use the rdagent command to run predefined examples, such as rdagent fin_factor (quantitative factor), rdagent fin_model (quantitative model), rdagent med_model (medical model), etc.
6. Monitor Results: Use the rdagent ui command to launch the user interface to view running logs and results.

Summary:

RDAgent is a powerful automated R&D tool that helps data scientists and R&D personnel conduct data-driven innovations more efficiently. It has a wide range of application scenarios and provides easy-to-use interfaces and examples. Through automated R&D loops, RDAgent can accelerate the exploration and validation of new methods and technologies, thereby driving innovation.