Overview
Qlib is an open-source, AI-oriented quantitative investment platform developed under the Microsoft organization. It aims to empower quant research and production by providing a complete, modular ML pipeline that covers data collection and storage, feature/factor construction, model training, backtesting, analysis and online serving. Qlib is designed to be extensible so researchers can plug in custom datasets, models, and execution environments.
Key Capabilities
- Data pipeline and dataset zoo: compact, efficient storage formats and utilities to build and manage quant datasets (e.g., Alpha158, Alpha360), with tools for data health checks and incremental updates.
- Learning frameworks: support for diverse paradigms including supervised learning (various model zoos) and reinforcement learning (order execution and trading agents). The platform provides workflow primitives and reusable components to build experiments reproducibly.
- Model & benchmark zoo: built-in benchmarks and runnable implementations of many models (LightGBM, XGBoost, various neural models, Transformer variants, TabNet, TCN, ADARNN, IGMTF, HIST, KRNN, Sandwich, etc.), with scripts to run single or multiple models and produce standardized analysis metrics.
- Backtesting & analysis: integrated backtest engine, reporting tools (IC, cumulative returns, risk metrics, group return plots), and a qrun command to execute end-to-end workflows (data build -> train -> backtest -> report).
- Deployment & serving: supports offline/local mode and an online shared data service (Qlib-Server) with guidance for deployment (including Azure CLI scripts), and Docker images for reproducible environments.
- Auto R&D integration: recently integrated RD-Agent (an LLM-driven autonomous agent framework) to automate factor mining and model optimization workflows, demonstrating extensibility to LLM-based automation.
Intended users & use cases
Qlib is targeted at quantitative researchers and engineers who want a flexible, reproducible platform to:
- Rapidly prototype and benchmark predictive models for alpha generation.
- Implement and evaluate portfolio construction and execution strategies.
- Explore adaptive/online approaches for non-stationary market dynamics.
- Integrate RL agents for order execution and decision-making.
- Automate parts of R&D with agent-based tools (e.g., RD-Agent).
Architecture & extensibility
Qlib is modular: data, learning framework, workflow, strategy and execution components are loosely coupled so users can replace or extend components independently. It provides configuration-driven workflows and code-level APIs, plus helper scripts and notebooks for quick starts and tutorials. CI workflows, Docker images, and detailed documentation (ReadTheDocs) assist reproducible experiments and productionization.
Notable facts
- GitHub repository: microsoft/qlib (created 2020-08-14).
- It includes examples, benchmark configs, tutorials and contributions from many authors; Microsoft coordinates the project and maintains documentation and release notes.
References
See the project docs and the original Qlib paper ("Qlib: An AI-oriented Quantitative Investment Platform") for architecture and evaluation details. The repository also links to RD-Agent for automated R&D workflows.
