An open-source collection of production-ready quantitative trading strategies.

Each strategy is self-contained, documented, and deployable. Grab a folder, run it, profit (or learn why not).

# Clone the repository
git clone https://github.com/yourusername/arbiterlabs.git
cd arbiterlabs

# Install base dependencies
pip install -r requirements-base.txt

# Navigate to a strategy
cd mean_reversion/pairs_trading_cointegration

# Install strategy-specific dependencies
pip install -r requirements.txt

# Run backtest
python backtest.py

• Pairs Trading (Cointegration) - Statistical pairs trading using cointegration
• Bollinger Mean Reversion - Mean reversion using Bollinger Bands
• Ornstein-Uhlenbeck - Mean reversion based on OU process
• Z-Score Mean Reversion - Standard deviation-based mean reversion

• Dual Momentum - Relative and absolute momentum strategies
• Momentum Breakout - Price momentum breakout strategies
• RSI Divergence - Trading RSI divergence signals
• MACD Crossover Enhanced - Advanced MACD-based momentum
• Rate of Change Momentum - ROC-based momentum trading
• Relative Strength Rotation - Sector/asset rotation based on RS

• Turtle Trading - Classic Turtle Trading System
• Moving Average Crossover - MA-based trend following
• Adaptive Moving Average - Dynamic MA adjustments
• Supertrend Strategy - Supertrend indicator system
• Donchian Breakout - Channel breakout strategy
• Keltner Channel Breakout - Volatility-based breakouts
• Parabolic SAR Trend - SAR-based trend trading

• Pairs Trading (ML) - Machine learning enhanced pairs
• Basket Trading - Multi-asset statistical arbitrage
• Index Arbitrage - Index vs constituents arbitrage
• ETF Arbitrage - ETF creation/redemption arbitrage
• Cross-Exchange Arbitrage - Cross-exchange price differences

• Basic Market Maker - Simple bid-ask market making
• Avellaneda-Stoikov - Optimal market making model
• Inventory-Based MM - Inventory risk management
• Adaptive Spread MM - Dynamic spread adjustment

• Random Forest Classifier - RF-based signal generation
• LSTM Price Prediction - Recurrent neural networks
• XGBoost Signal Generator - Gradient boosting signals
• Reinforcement Learning (DQN) - Deep Q-learning trading
• Transformer Price Forecast - Transformer models
• Ensemble Voting Strategy - Combined ML predictions

• Delta Neutral Hedging - Delta-neutral option positions
• Iron Condor Systematic - Automated iron condor strategy
• Volatility Arbitrage - Trading implied vs realized vol
• Gamma Scalping - Delta hedging for gamma profit
• Covered Call Wheel - Systematic covered call writing

• Volatility Breakout - Trading volatility expansions
• GARCH Volatility Trading - GARCH model-based trading
• VIX Mean Reversion - VIX-based strategies
• Implied vs Realized - IV-RV spread trading
• Volatility Regime Switching - Regime detection strategies

• Order Block Strategy - Institutional order blocks
• Fair Value Gap Trading - FVG identification and trading
• Liquidity Sweep - Liquidity grab patterns
• Market Structure Break - BOS/CHoCH trading
• Optimal Trade Entry - OTE Fibonacci entries
• Institutional Candle Patterns - Smart money patterns

• Order Flow Imbalance - Microstructure imbalances
• Microstructure Alpha - Market microstructure signals
• Latency Arbitrage - Speed-based arbitrage
• Queue Position Strategy - Order book positioning

• News Sentiment NLP - Natural language processing
• Social Media Sentiment - Twitter/Reddit analysis
• Fear & Greed Index - Market sentiment indicators
• Put/Call Ratio Sentiment - Options-based sentiment

• Day of Week Effect - Weekday anomalies
• Month-End Rebalancing - Month-end flows
• Earnings Drift - Post-earnings momentum
• Holiday Effect - Pre/post-holiday patterns
• Sector Rotation (Seasonal) - Calendar-based rotation

• Risk Parity Portfolio - Equal risk contribution
• Black-Litterman Allocation - Views-based allocation
• Hierarchical Risk Parity - HRP portfolio construction
• Momentum Across Assets - Cross-asset momentum

• Genetic Algorithm Evolved - GA-optimized strategies
• Neural Architecture Search - AutoML for trading
• Alternative Data Signals - Non-traditional data sources
• Quantum-Inspired Optimization - Quantum algorithms

• Strategy Development Guide
• Mathematical Foundations
• Backtesting Best Practices
• Deployment Guide

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Copy _templates/strategy_template/ to appropriate category
3. Implement your strategy
4. Include backtest results (minimum 2 years of data)
5. Write tests
6. Submit a pull request

Every strategy must include:

• ✅ Clear mathematical explanation
• ✅ Realistic backtest (no lookahead bias)
• ✅ Risk management implementation
• ✅ Unit tests
• ✅ Documentation with references
• ✅ Sample data or clear data source instructions

This repository is for educational purposes only. All strategies are provided as-is with no guarantees. Past performance does not indicate future results. Always paper trade before deploying real capital. Trading involves substantial risk of loss.

MIT License - see LICENSE for details.

If you find this project useful, please consider giving it a star! ⭐

• GitHub Issues: Report bugs or request features
• Discussions: Join the community

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