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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.
TranslatedAn open-source quantitative trading platform development framework based on Python
"Vibe-Trading: Your Personal Trading Agent"
Translatedstock. Get stock data, calculate stock indicators, chip distribution, identify stock patterns, comprehensive stock selection, stock selection strategies, stock verification and backtesting, automatic stock trading, supports PC and mobile devices.
The most complete open-source, self-hosted Agentic Trading OS—AI research, Python strategies, factor discovery, backtesting, optimization, paper/live multi-asset execution, risk controls, monitoring, MCP, and multi-tenant SaaS in one auditable stack.
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD
The research engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities
modular quant framework.
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
Python trading framework with AI agents that actually place the trade. Backtest, paper and live trade on 12 brokers: stocks, options, futures, forex, crypto and prediction markets.
Scalable, event-driven, deep-learning-friendly backtesting library
The repository host gives no description.
TranslatedLow-code quant backtesting for China A-shares