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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.
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
A curated list of practical financial machine learning tools and applications.
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
modular quant framework.
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
📈 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
Trading Environment(OpenAI Gym) + DDQN (Keras-RL)
Trading Environment(OpenAI Gym) + PPO(TensorForce)
Deep Reinforcement Learning for Financial Trading using Price Trailing @ ICASSP 2019
Build autonomous AI agents that interact with blockchains — in minutes, not months.
Trading environnement for RL agents, backtesting and training.
AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.
:chart_with_upwards_trend: A live cryptocurrency historical trade data blotter. Download live historical trade data from any cryptoexchange, be it for machine learning, backtesting/visualizing trading strategies or for Quantopian/Zipline.
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.
Open BTC 5-minute prediction market sandbox for AI agents — register with one API call, get 1,000 test credits, and place a live order