Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
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Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
The most complete open-source, self-hosted Agentic Trading OS—AI research, Python strategies, factor discovery, backtesting, optimization, paper/live multi-asset execution, risk controls, monitoring, MCP, and multi-tenant SaaS in one auditable stack.
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
📈 Get real-time stocks from TradingView
Automated Binance trading bot with pluggable strategies, historical backtesting, a live dashboard, and connect an AI agent over MCP
MCP server for market analysis in AI assistants: screeners, multi-timeframe technical analysis, backtests and sentiment for crypto, stocks, forex and futures. 39 tools. Self-host free, or use the hosted version, TradingMCP.
Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.
modular quant framework.
Statistical and Algorithmic Investing Strategies for Everyone
面向 A 股日K、分钟K与ETF分钟数据的本地量化引擎,集成增量同步、本地缓存、复权、批量查询、回测与指标计算。
A Java library for technical analysis.
Python Crypto Bot (PyCryptoBot)
为前端设计的无需 Python、无需后端服务、零依赖的获取股票数据 JavaScript SDK。
High-performance technical analysis indicators and backtesting framework in Go. Zero dependencies. ✨ See how! 👀
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
Python AutoML for Trading Systems and Sports Betting
Scalable, event-driven, deep-learning-friendly backtesting library
Trady is a handy library for computing technical indicators, and it targets to be an automated trading system that provides stock data feeding, indicator computing, strategy building and automatic trading. It is built based on .NET Standard 2.0.
Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot
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.
AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.