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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.
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
Portfolio optimization with deep learning.
Scalable, event-driven, deep-learning-friendly backtesting library
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.
Trading Environment(OpenAI Gym) + DDQN (Keras-RL)
Trading Environment(OpenAI Gym) + PPO(TensorForce)
Deep Reinforcement Learning for Financial Trading using Price Trailing @ ICASSP 2019
AI crypto trading bot with deep neural network (84.9% accuracy, 25 coins). BiLSTM + Attention trained on GPU. Bybit, Binance, OKX, Gate.io. Free cloud or self-hosted.