Popular GitHub Repositories for Trading Strategy

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

TradingAgents: Multi-Agents LLM Financial Trading Framework

Code for Machine Learning for Algorithmic Trading, 2nd edition.

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

FinRL: Financial Reinforcement Learning. ?

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

Multi-exchange AI trading platform (Binance/Hyperliquid/Aster) with multi-AI competition(deepseek/qwen/claude), self-evolution, and real-time dashboard

股票AI操盘手：从学习、模拟到实盘，一站式平台。包含股票知识、策略实例、大模型、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等，可以方便学习、模拟及实盘交易

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Quant/Algorithm trading resources with an emphasis on Machine Learning

MiniMax-01 is a simple implementation of the MiniMax algorithm, a widely used strategy for decision-making in two-player turn-based games like Tic-Tac-Toe. The algorithm aims to minimize the maximum possible loss for the player, making it a popular choice for developing AI opponents in various game scenarios.

Algorithmic Trading in Python with Machine Learning

C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage

The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)

TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks

Using python and scikit-learn to make stock predictions

Use unsupervised and supervised learning to predict stocks