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Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
The research engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
Performance analysis of predictive (alpha) stock factors
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
Portfolio optimization with deep learning.
Predicts how news-driven supply-chain shocks ripple across S&P 500 stocks, with an event-study backtest and a hash-chained public prediction ledger. Built on MIT open-source SEC supply-graph code.
Deterministic, dependency-free Chan-theory (缠论) structure engine + multi-market trading workstation (A-shares / HK / US / crypto) with teaching chart annotations and a mobile PWA.
Go library containing a collection of financial functions for time value of money (annuities), cash flow, interest rate conversions, bonds and depreciation calculations.
Do stock correlations predict the market? 66 pre-registered tests, the data, and a triple pendulum that explains why not. If you have an idea you want me to try, submit it as a PR and I will test it and get back to you.