TODO: Add badges.
# News
| 🗞️News | 📝Description |
| -- | ------ |
| First release | RDAgent are release on Github |
# Introduction
RDAgent aims to automate the most critical and valuable aspects of the industrial R&D process, and we begins 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]()
- Data mining copilot: iteratively proposing [🎥data]() & [models]() and implementing them by gaining knowledge from data.
- Research copilot: Auto read [🎥research papers]()/[🎥reports]() and implement model structures or building datasets.
- ...
You can click the [🎥link]() above to view the demo. More methods and scenarios are being added to the project to empower your R&D processes and boost productivity.
We have a quick 🎥demo for one use case of RDAgent.
- TODO: Demo
# ⚡Quick start
You can try our demo by running the following command:
```bash
# TODO:
# prepare environment
# installation
# App entrance
```
The [🎥demo]() is implemented by the above commands.
# 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 a 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, features, factors and models in runnable codes.
+ Due the limited ability for LLM in implementing in once, evolving the agent to be able to extend abilities by learn from feedback and knowledge and improve the agent's ability to implement more complex models.
+ 💡Propose **new ideas** based on current knowledge and observations.
## 📈 Scenarios Matrix
Here is our supported scenarios
| Scenario/Target | Model Implementation | Data Building |
| -- | -- | -- |
| 💹Finance | Iteratively Proposing Ideas & Evolving | Auto reports reading & implementation
Iteratively Proposing Ideas & Evolving |
| 🩺Medical | Iteratively Proposing Ideas & Evolving | - |
| General | Auto paper reading & implementation | - |
Different scenarios vary in entrance and configuration. Please check the detailed setup tutorial in the scenarios documents.
# ⚙️Framework
- TODOs:
- Framework introdution
- Research problems.
# 📃Paper list
TODO: under review. Please check the.
# Contributing
More documents can be found in the [📚readthedocs](). TODO: add link
## Guidance
This project welcomes contributions and suggestions.
You can find issues in the issues list or simply running `grep -r "TODO:"`.
Making contributions is not a hard thing. Solving an issue(maybe just answering a question raised in issues list ), fixing/issuing a bug, improving the documents and even fixing a typo are important contributions to RDAgent.
