microsoft/qlib

What it solves

Qlib is designed to bridge the gap between AI research and production in quantitative investment. It provides a comprehensive platform to handle the entire quantitative pipeline—from data processing and alpha seeking to risk modeling, portfolio optimization, and order execution—reducing the friction of implementing machine learning models for financial trading.

How it works

Qlib uses a modular, loosely-coupled architecture where each component can function independently. It supports multiple machine learning paradigms, including supervised learning for pattern mining, market dynamics modeling to handle concept drift, and reinforcement learning for continuous investment decision-making. The platform includes a full ML pipeline that manages data preparation, model training, back-testing, and online serving with low-cost model rolling.

Who it’s for

It is built for quantitative researchers and investors who want to apply state-of-the-art AI technologies to financial markets, as well as developers building automated trading systems.

Highlights

• Full-Stack Pipeline: Covers the entire chain from data collection to order execution.
• Diverse Learning Paradigms: Supports supervised learning, reinforcement learning, and market dynamics modeling.
• Model Zoo: Includes a wide array of pre-implemented SOTA models like Transformer, Localformer, Tabnet, and TCN.
• Infrastructure Support: Features a Point-in-Time database and support for both offline and online serving modes.
• Autonomous R&D: Integrates with RD-Agent for LLM-based autonomous factor mining and model optimization.