Qlib

:newspaper: What's NEW!   :sparkling_heart:

Recent released features

Introducing <a href="https://github.com/microsoft/RD-Agent"><img src="docs/_static/img/rdagent_logo.png" alt="RD_Agent" style="height: 2em"></a>: LLM-Based Autonomous Evolving Agents for Industrial Data-Driven R&D

We are excited to announce the release of RD-Agent📢, a powerful tool that supports automated factor mining and model optimization in quant investment R&D.

RD-Agent is now available on GitHub, and we welcome your star🌟!

To learn more, please visit our ♾️Demo page. Here, you will find demo videos in both English and Chinese to help you better understand the scenario and usage of RD-Agent.

We have prepared several demo videos for you: | Scenario | Demo video (English) | Demo video (中文) | | -- | ------ | ------ | | Quant Factor Mining | Link | Link | | Quant Factor Mining from reports | Link | Link | | Quant Model Optimization | Link | Link |

• 📃Paper: R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
• 👾Code: https://github.com/microsoft/RD-Agent/

@misc{li2025rdagentquant,
    title={R\&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization},
    author={Yuante Li and Xu Yang and Xiao Yang and Minrui Xu and Xisen Wang and Weiqing Liu and Jiang Bian},
    year={2025},
    eprint={2505.15155},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}

---

| Feature | Status | | -- | ------ | | R&D-Agent-Quant Published | Apply R&D-Agent to Qlib for quant trading | | BPQP for End-to-end learning | 📈Coming soon!(Under review) | | 🔥LLM-driven Auto Quant Factory🔥 | 🚀 Released in ♾️RD-Agent on Aug 8, 2024 | | KRNN and Sandwich models | :chart_with_upwards_trend: Released on May 26, 2023 | | Release Qlib v0.9.0 | :octocat: Released on Dec 9, 2022 | | RL Learning Framework | :hammer: :chart_with_upwards_trend: Released on Nov 10, 2022. #1332, #1322, #1316,#1299,#1263, #1244, #1169, #1125, #1076| | HIST and IGMTF models | :chart_with_upwards_trend: Released on Apr 10, 2022 | | Qlib notebook tutorial | 📖 Released on Apr 7, 2022 | | Ibovespa index data | :rice: Released on Apr 6, 2022 | | Point-in-Time database | :hammer: Released on Mar 10, 2022 | | Arctic Provider Backend & Orderbook data example | :hammer: Released on Jan 17, 2022 | | Meta-Learning-based framework & DDG-DA | :chart_with_upwards_trend: :hammer: Released on Jan 10, 2022 | | Planning-based portfolio optimization | :hammer: Released on Dec 28, 2021 | | Release Qlib v0.8.0 | :octocat: Released on Dec 8, 2021 | | ADD model | :chart_with_upwards_trend: Released on Nov 22, 2021 | | ADARNN model | :chart_with_upwards_trend: Released on Nov 14, 2021 | | TCN model | :chart_with_upwards_trend: Released on Nov 4, 2021 | | Nested Decision Framework | :hammer: Released on Oct 1, 2021. Example and Doc | | Temporal Routing Adaptor (TRA) | :chart_with_upwards_trend: Released on July 30, 2021 | | Transformer & Localformer | :chart_with_upwards_trend: Released on July 22, 2021 | | Release Qlib v0.7.0 | :octocat: Released on July 12, 2021 | | TCTS Model | :chart_with_upwards_trend: Released on July 1, 2021 | | Online serving and automatic model rolling | :hammer: Released on May 17, 2021 | | DoubleEnsemble Model | :chart_with_upwards_trend: Released on Mar 2, 2021 | | High-frequency data processing example | :hammer: Released on Feb 5, 2021 | | High-frequency trading example | :chart_with_upwards_trend: Part of code released on Jan 28, 2021 | | High-frequency data(1min) | :rice: Released on Jan 27, 2021 | | Tabnet Model | :chart_with_upwards_trend: Released on Jan 22, 2021 |

Features released before 2021 are not listed here.

<p align="center"> <img src="docs/_static/img/logo/1.png" /> </p>

Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning.

An increasing number of SOTA Quant research works/papers in diverse paradigms are being released in Qlib to collaboratively solve key challenges in quantitative investment. For example, 1) using supervised learning to mine the market's complex non-linear patterns from rich and heterogeneous financial data, 2) modeling the dynamic nature of the financial market using adaptive concept drift technology, and 3) using reinforcement learning to model continuous investment decisions and assist investors in optimizing their trading strategies.

It contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. For more details, please refer to our paper "Qlib: An AI-oriented Quantitative Investment Platform".

<table> <tbody> <tr> <th>Frameworks, Tutorial, Data & DevOps</th> <th>Main Challenges & Solutions in Quant Research</th> </tr> <tr> <td> <li><a href="#plans"><strong>Plans</strong></a></li> <li><a href="#framework-of-qlib">Framework of Qlib</a></li> <li><a href="#quick-start">Quick Start</a></li> <ul dir="auto"> <li type="circle"><a href="#installation">Installation</a> </li> <li type="circle"><a href="#data-preparation">Data Preparation</a></li> <li type="circle"><a href="#auto-quant-research-workflow">Auto Quant Research Workflow</a></li> <li type="circle"><a href="#building-customized-quant-research-workflow-by-code">Building Customized Quant Research Workflow by Code</a></li></ul> <li><a href="#quant-dataset-zoo"><strong>Quant Dataset Zoo</strong></a></li> <li><a href="#learning-framework">Learning Framework</a></li> <li><a href="#more-about-qlib">More About Qlib</a></li> <li><a href="#offline-mode-and-online-mode">Offline Mode and Online Mode</a> <ul> <li type="circle"><a href="#performance-of-qlib-data-server">Performance of Qlib Data Server</a></li></ul> <li><a href="#related-reports">Related Reports</a><