Awesome

<a href="https://rdagent.azurewebsites.net" target="_blank">🖥️ Live Demo</a> | <a href="https://rdagent.azurewebsites.net/factor_loop" target="_blank">🎥 Demo Video</a> <a href="https://www.youtube.com/watch?v=JJ4JYO3HscM&list=PLALmKB0_N3_i52fhUmPQiL4jsO354uopR" target="_blank">▶️YouTube</a> | <a href="https://rdagent.readthedocs.io/en/latest/index.html" target="_blank">📖 Documentation</a> | <a href="#-paperwork-list"> 📃 Papers </a>

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📰 News

🗞️ News	📝 Description
Kaggle Scenario release	We release Kaggle Agent, try the new features!
Official WeChat group release	We created a WeChat group, welcome to join! (🗪QR Code)
Official Discord release	We launch our first chatting channel in Discord (🗪)
First release	RDAgent is released on GitHub

🌟 Introduction

RDAgent aims to automate the most critical and valuable aspects of the industrial R&D process, and we begin with focusing on the data-driven scenarios to streamline the development of models and data. Methodologically, we have identified a framework with two key components: 'R' for proposing new ideas and 'D' for implementing them. We believe that the automatic evolution of R&D will lead to solutions of significant industrial value.

R&D is a very general scenario. The advent of RDAgent can be your

• 💰 Automatic Quant Factory (🎥Demo Video|▶️YouTube)
• 🤖 Data Mining Agent: Iteratively proposing data & models (🎥Demo Video 1|▶️YouTube) (🎥Demo Video 2|▶️YouTube) and implementing them by gaining knowledge from data.
• 🦾 Research Copilot: Auto read research papers (🎥Demo Video|▶️YouTube) / financial reports (🎥Demo Video|▶️YouTube) and implement model structures or building datasets.
• 🤖 Kaggle Agent: Auto Model Tuning and Feature Engineering(🎥Demo Video Coming Soon...) and implementing them to achieve more in competitions.
• ...

You can click the links above to view the demo. We're continuously adding more methods and scenarios to the project to enhance your R&D processes and boost productivity.

Additionally, you can take a closer look at the examples in our 🖥️ Live Demo.

⚡ Quick start

You can try above demos by running the following command:

🐳 Docker installation.

Users must ensure Docker is installed before attempting most scenarios. Please refer to the official 🐳Docker page for installation instructions.

🐍 Create a Conda Environment

• Create a new conda environment with Python (3.10 and 3.11 are well-tested in our CI):

  conda create -n rdagent python=3.10
  

• Activate the environment:

  conda activate rdagent
  

🛠️ Install the RDAgent

• You can directly install the RDAgent package from PyPI:

  pip install rdagent
  

⚙️ Configuration

• You have to config your GPT model in the .env

  cat << EOF  > .env
  OPENAI_API_KEY=<your_api_key>
  # EMBEDDING_MODEL=text-embedding-3-small
  CHAT_MODEL=gpt-4-turbo
  EOF
  

🚀 Run the Application

The 🖥️ Live Demo is implemented by the following commands(each item represents one demo, you can select the one you prefer):

• Run the Automated Quantitative Trading & Iterative Factors Evolution: Qlib self-loop factor proposal and implementation application

  rdagent fin_factor
  

• Run the Automated Quantitative Trading & Iterative Model Evolution: Qlib self-loop model proposal and implementation application

  rdagent fin_model
  

• Run the Automated Medical Prediction Model Evolution: Medical self-loop model proposal and implementation application

  (1) Apply for an account at PhysioNet. <br /> (2) Request access to FIDDLE preprocessed data: FIDDLE Dataset. <br /> (3) Place your username and password in .env.

  cat << EOF  >> .env
  DM_USERNAME=<your_username>
  DM_PASSWORD=<your_password>
  EOF
  

  rdagent med_model
  

• Run the Automated Quantitative Trading & Factors Extraction from Financial Reports: Run the Qlib factor extraction and implementation application based on financial reports

  # 1. Generally, you can run this scenario using the following command:
  rdagent fin_factor_report --report_folder=<Your financial reports folder path>
  
  # 2. Specifically, you need to prepare some financial reports first. You can follow this concrete example:
  wget https://github.com/SunsetWolf/rdagent_resource/releases/download/reports/all_reports.zip
  unzip all_reports.zip -d git_ignore_folder/reports
  rdagent fin_factor_report --report_folder=git_ignore_folder/reports
  

• Run the Automated Model Research & Development Copilot: model extraction and implementation application

  # 1. Generally, you can run your own papers/reports with the following command:
  rdagent general_model <Your paper URL>
  
  # 2. Specifically, you can do it like this. For more details and additional paper examples, use `rdagent general_model -h`:
  rdagent general_model  "https://arxiv.org/pdf/2210.09789"
  

• Run the Automated Kaggle Model Tuning & Feature Engineering: self-loop model proposal and feature engineering implementation application <br />

  Using sf-crime (San Francisco Crime Classification) as an example. <br />

  1. Register and login on the Kaggle website. <br />
  2. Configuring the Kaggle API. <br /> (1) Click on the avatar (usually in the top right corner of the page) -> Settings -> Create New Token, A file called kaggle.json will be downloaded. <br /> (2) Move kaggle.json to ~/.config/kaggle/ <br /> (3) Modify the permissions of the kaggle.json file. Reference command: chmod 600 ~/.config/kaggle/kaggle.json <br />
  3. Join the competition: Click Join the competition -> I Understand and Accept at the bottom of the competition details page.

  # Generally, you can run the Kaggle competition program with the following command:
  rdagent kaggle --competition <your competition name>
  
  # Specifically, you will need to first prepare some competition description files and configure the competition description file path, which you can follow for this specific example:
  
  # 1. Prepare the competition description files
  wget https://github.com/SunsetWolf/rdagent_resource/releases/download/kaggle_data/kaggle_data.zip
  unzip kaggle_data.zip -d git_ignore_folder/kaggle_data
  
  # 2. Add the competition description file path to the `.env` file.
  dotenv set KG_LOCAL_DATA_PATH "$(pwd)/git_ignore_folder/kaggle_data"
  
  # 3. run the application
  rdagent kaggle --competition sf-crime
  

  Description of the above example: <br />

  • Kaggle competition data, contains two parts: competition description file (json file) and competition dataset (zip file). We prepare the competition description file for you, the competition dataset will be downloaded automatically when you run the program, as in the example. <br />
  • If you want to download the competition description file automatically, you need to install chromedriver, The instructions for installing chromedriver can be found in the documentation. <br />
  • The Competition List Available can be found here. <br />

🖥️ Monitor the Application Results

• You can serve our demo app to monitor the RD loop by running the following command:

  rdagent ui --port 80 --log_dir <your log folder like "log/">
  

🏭 Scenarios

We have applied RD-Agent to multiple valuable data-driven industrial scenarios.

🎯 Goal: Agent for Data-driven R&D

In this project, we are aiming to build an Agent to automate Data-Driven R&D that can

• 📄 Read real-world material (reports, papers, etc.) and extract key formulas, descriptions of interested features and models, which are the key components of data-driven R&D .
• 🛠️ Implement the extracted formulas (e.g., features, factors, and models) in runnable codes.
  • Due to the limited ability of LLM in implementing at once, build an evolving process for the agent to improve performance by learning from feedback and knowledge.
• 💡 Propose new ideas based on current knowledge and observations.

📈 Scenarios/Demos

In the two key areas of data-driven scenarios, model implementation and data building, our system aims to serve two main roles: 🦾Copilot and 🤖Agent.

• The 🦾Copilot follows human instructions to automate repetitive tasks.
• The 🤖Agent, being more autonomous, actively proposes ideas for better results in the future.

The supported scenarios are listed below:

• RoadMap: Currently, we are working hard to add new features to the Kaggle scenario.

Different scenarios vary in entrance and configuration. Please check the detailed setup tutorial in the scenarios documents.

Here is a gallery of successful explorations (5 traces showed in 🖥️ Live Demo). You can download and view the execution trace using the command below:

rdagent ui --port 80 --log_dir ./demo_traces

Please refer to 📖readthedocs_scen for more details of the scenarios.

⚙️ Framework

Automating the R&D process in data science is a highly valuable yet underexplored area in industry. We propose a framework to push the boundaries of this important research field.

The research questions within this framework can be divided into three main categories:

We believe that the key to delivering high-quality solutions lies in the ability to evolve R&D capabilities. Agents should learn like human experts, continuously improving their R&D skills.

More documents can be found in the 📖 readthedocs.

📃 Paper/Work list

📊 Benchmark

• Towards Data-Centric Automatic R&D

@misc{chen2024datacentric,
    title={Towards Data-Centric Automatic R&D},
    author={Haotian Chen and Xinjie Shen and Zeqi Ye and Wenjun Feng and Haoxue Wang and Xiao Yang and Xu Yang and Weiqing Liu and Jiang Bian},
    year={2024},
    eprint={2404.11276},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}

🔍 Research

In a data mining expert's daily research and development process, they propose a hypothesis (e.g., a model structure like RNN can capture patterns in time-series data), design experiments (e.g., finance data contains time-series and we can verify the hypothesis in this scenario), implement the experiment as code (e.g., Pytorch model structure), and then execute the code to get feedback (e.g., metrics, loss curve, etc.). The experts learn from the feedback and improve in the next iteration.

Based on the principles above, we have established a basic method framework that continuously proposes hypotheses, verifies them, and gets feedback from the real-world practice. This is the first scientific research automation framework that supports linking with real-world verification.

For more detail, please refer to our 🖥️ Live Demo page.

🛠️ Development

• Collaborative Evolving Strategy for Automatic Data-Centric Development

@misc{yang2024collaborative,
    title={Collaborative Evolving Strategy for Automatic Data-Centric Development},
    author={Xu Yang and Haotian Chen and Wenjun Feng and Haoxue Wang and Zeqi Ye and Xinjie Shen and Xiao Yang and Shizhao Sun and Weiqing Liu and Jiang Bian},
    year={2024},
    eprint={2407.18690},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}

🤝 Contributing

📝 Guidelines

This project welcomes contributions and suggestions. Contributing to this project is straightforward and rewarding. Whether it's solving an issue, addressing a bug, enhancing documentation, or even correcting a typo, every contribution is valuable and helps improve RDAgent.

To get started, you can explore the issues list, or search for TODO: comments in the codebase by running the command grep -r "TODO:".

Before we released RD-Agent as an open-source project on GitHub, it was an internal project within our group. Unfortunately, the internal commit history was not preserved when we removed some confidential code. As a result, some contributions from our group members, including Haotian Chen, Wenjun Feng, Haoxue Wang, Zeqi Ye, Xinjie Shen, and Jinhui Li, were not included in the public commits.

⚖️ Legal disclaimer