Qlib: Quantitative Platform



Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

With Qlib, users can easily try their ideas to create better Quant investment strategies.



At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone.

Name Description
Infrastructure layer Infrastructure layer provides underlying support for Quant research. DataServer provides high-performance infrastructure for users to manage and retrieve raw data. Trainer provides flexible interface to control the training process of models which enable algorithms controlling the training process.
Workflow layer Workflow layer covers the whole workflow of quantitative investment. Information Extractor extracts data for models. Forecast Model focuses on producing all kinds of forecast signals (e.g. alpha, risk) for other modules. With these signals Decision Generator will generate the target trading decisions(i.e. portfolio, orders) to be executed by Execution Env (i.e. the trading market). There may be multiple levels of Trading Agent and Execution Env (e.g. an order executor trading agent and intraday order execution environment could behave like an interday trading environment and nested in daily portfolio management trading agent and interday trading environment )
Interface layer Interface layer tries to present a user-friendly interface for the underlying system. Analyser module will provide users detailed analysis reports of forecasting signals, portfolios and execution results
  • The modules with hand-drawn style are under development and will be released in the future.
  • The modules with dashed borders are highly user-customizable and extendible.