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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.
TranslatedAbu Quantitative Trading System (stocks, options, futures, Bitcoin, machine learning) An open-source quantitative trading and quantitative investment architecture based on Python
Bindu: The identity, communication, and payments layer for AI agents.
The research engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
TranslatedStock AI Trader: 1-stop platform for learning, sim & live trading. Covers: stock basics, strategies, LLMs, factor mining, ML/DL/RL, graph nets, HFT, C++ deploy & JoinQuant code.
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.
modular quant framework.
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
Algorithmic Trading in Python with Machine Learning
Statistical and Algorithmic Investing Strategies for Everyone
基于深度强化学习的开源自动因子工厂。
A comprehensive quantitative trading system with AI-powered analysis, real-time data processing, and advanced risk management
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
📈 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 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.