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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.
FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive exploration and data-driven decision-making in a user-friendly environment.
Production-grade Rust-native trading engine with deterministic event-driven architecture
TranslatedAbu Quantitative Trading System (stocks, options, futures, Bitcoin, machine learning) An open-source quantitative trading and quantitative investment architecture based on Python
Translated[🔥updating ...] AI automated quantitative trading robot (fully local deployment) 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. 🔥
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
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
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.