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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
The QuantLib C++ library
TranslatedQuantitative research - replication of brokerage financial engineering research reports
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
High-performance TensorFlow library for quantitative finance.
Transparent and Efficient Financial Analysis
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Portfolio Optimization in Python
Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
Contingency Random Number Generator — numbers with controllable fat tails, volatility clustering, and scale convergence