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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.
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
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.
Contingency Random Number Generator — numbers with controllable fat tails, volatility clustering, and scale convergence