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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
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
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
Statistical and Algorithmic Investing Strategies for Everyone
A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
QuantMind is an open source agent-native knowledge extraction and retrieval framework for quantitative finance.
A comprehensive quantitative trading system with AI-powered analysis, real-time data processing, and advanced risk management
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau
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.
TranslatedScorecard Development in R, scorecard
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.
Financial Market Intelligence MCP Server — stock quotes, technical analysis, crypto data, and portfolio insights for AI agents
Contingency Random Number Generator — numbers with controllable fat tails, volatility clustering, and scale convergence