Popular GitHub Repositories for Investment Strategies

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.

[?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

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

This repository offers a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies. It serves as an essential resource for mastering the art of effectively communicating with and leveraging large language models in AI applications.

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

? 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

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:

Avalanche: an End-to-End Library for Continual Learning based on PyTorch.

Use unsupervised and supervised learning to predict stocks

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

Machine Learning in Asset Management (by @firmai)

Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!