qlib: an AI-oriented quantitative investment platform for the full pipeline from alpha seeking to order execution

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 trading pipeline, including data processing, model training, back-testing, alpha seeking, risk modeling, portfolio optimization, and order execution.

How it works

Qlib uses a modular, loosely-coupled architecture where each component can be used 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 data server for efficient data handling and supports both offline and online serving modes for model deployment.

Who it’s for

Quantitative researchers, data scientists, and investors who want to implement AI-driven trading strategies, from exploring initial ideas to deploying them into production.

Highlights

• Full ML Pipeline: Covers everything from data preparation to order execution.
• Diverse Learning Paradigms: Supports supervised learning, reinforcement learning, and market dynamics modeling.
• Model Zoo: Includes a collection of SOTA Quant research models (e.g., Transformer, Tabnet, TCN) to solve forecasting and market adaptation challenges.
• Integrated Infrastructure: Provides tools for data collection, health checking, and online serving with automatic model rolling.
• Autonomous R&D: Integrates with RD-Agent for automated factor mining and model optimization.