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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.
Production-grade Rust-native trading engine with deterministic event-driven architecture
Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
TranslatedQUANTAXIS: a purely local quantitative solution for stocks/futures/options that supports task scheduling and distributed deployment, covering data/backtesting/simulation/trading/visualization/multiple accounts
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
Free open source crypto trading bot to automate AI, Grid, DCA and TradingView strategies on Binance, Hyperliquid and 15+ exchanges, with a simple interface.
A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities
11 Claude skills for day trading: scan thousands of charts, track breaking news, time every entry and exit, and replay every outcome before you risk a dollar.
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
A Java library for technical analysis.
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
Python trading framework with AI agents that actually place the trade. Backtest, paper and live trade on 12 brokers: stocks, options, futures, forex, crypto and prediction markets.
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
Say It Simply, Test It Properly. Agent-powered strategy research with independent ledger checks and reproducible reports.
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Batch backtest, import and strategy params optimalization for Gekko Trading Bot. With one command you will run any number of backtests.
Real-time HFT bot for Polymarket crypto Up/Down markets combining CLOB order books, Chainlink feeds and multi-exchange prices with TWAP and Black-Scholes probability models; paper mode and backtests.