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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.
"Vibe-Trading: Your Personal Trading Agent"
Python toolkit for quantitative finance
The research engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
Portfolio analytics for quants, written in Python
TranslatedQuantitative research - replication of brokerage financial engineering research reports
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Performance analysis of predictive (alpha) stock factors
A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
Portfolio optimization with deep learning.
Technical Analysis Library for Golang
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.
Streaming-first technical indicators with a Rust core: native Python, Node.js and WebAssembly bindings plus a C ABI hub for C, C++, C#, Go, Java and R. 514 indicators, O(1) per-tick updates, no system dependencies. Drop-in TA-Lib replacement.