"Vibe-Trading: Your Personal Trading Agent"
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Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
- c-sharp
- algorithmic-trading-engine
- quantconnect
- lean-engine
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
- trading-strategies
- quantitative-trading
- trading-bot
- quantitative-finance
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
- trading-strategies
- trading-robots
- trading-platform
- algorithmic-trading-engine
The research engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
- backtesting
- trading
- algorithmic-trading
- trading-strategies
TranslatedTSP: a self-hosted, zero-maintenance quantitative workbench for A-shares covering "custom strategies + monitoring + backtesting" | LLM capabilities drive strategy customization + individual stock analysis + post-trade review | Freely connect third-party data sources and personalized extended data | Personal open-source project
- a-stock
- ai-agent
- aigc
- backtesting
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.
- cpp
- jupyter-notebook
- keras
- python
Automated Binance trading bot with pluggable strategies, historical backtesting, a live dashboard, and connect an AI agent over MCP
- binance
- binance-api
- trading-bot
- binance-trading-bot
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.
- claude-desktop
- cryptocurrency
- mcp-server
- mcp-tools
Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books(Level-2 and Level-3), with real-world crypto trading examples for Binance and Bybit
- backtesting
- high-frequency-trading
- market-making
- backtesting-engine
Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.
- stocks
- stock-market
- finance
- python
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-share
- algorithmic-trading
- backtesting
- china-stock-market
Open-source Rust framework for building event-driven live-trading & backtesting systems
- algo-trading
- algorithmic-trading
- algotrading
- backtesting
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.
High-performance technical analysis indicators and backtesting framework in Go. Zero dependencies. ✨ See how! 👀
- indicators
- technical-analysis
- stock-analysis
- stock-market
QuantaAlpha transforms how you discover quantitative alpha factors by combining LLM intelligence with evolutionary strategies. Just describe your research direction, and watch as factors are automatically mined, evolved, and validated through self-evolving trajectories.
- code
- codeagent
- factor-mining
- self-evolving
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
- reinforcement-learning
- supervised-learning
- stock-data
- trading
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.
- strategy
- technical
- analysis
- stock
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.