Popular GitHub Repositories for Quantitative Trading

Investment Research for Everyone, Everywhere.

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.

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

[?updating ...] AI 自动量化交易机器人(完全本地部署) AI-powered Quantitative Investment Research Platform. ? online docs: https://ufund-me.github.io/Qbot ? :news: qbot-mini: https://github.com/Charmve/iQuant

FinRL: Financial Reinforcement Learning. ?

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

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

? A curated list of awesome LLMs & deep learning strategies & tools in financial market.

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

Quant/Algorithm trading resources with an emphasis on Machine Learning

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

Algorithmic Trading in Python with Machine Learning

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

: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.

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

Using python and scikit-learn to make stock predictions

Use unsupervised and supervised learning to predict stocks

Find big moving stocks before they move using machine learning and anomaly detection