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.
It contains the full ML pipeline of data processing, model training, and back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.
With Qlib, users can easily try ideas to create better quant investment strategies.
Framework of Qlib
The high-level framework of Qlib can be found above(users can find the detailed framework of Qlib’s design when getting into the nitty-gritty). The components are designed as loose-coupled modules, and each component could be used stand-alone.
Stars: 10.2k
License: MIT
Languages: Python(99.5%)
Link: https://github.com/microsoft/qlibQlib provides a strong infrastructure to support Quant research. Data is always an important part. A strong learning framework is designed to support diverse learning paradigms (e.g. reinforcement learning, supervised learning) and patterns at different levels(e.g. market dynamic modeling). By modeling the market, trading strategies will generate trade decisions that will…
