Quant Sector Rotation Strategy 📈

A sophisticated quantitative trading strategy leveraging momentum and volatility signals for ETF sector rotation, enhanced with LLM-powered strategy analysis.

🚀 Try It Now!

Experience the strategy in action: Quant Sector Rotation App

🚀 Strategy Overview

This project implements a systematic sector rotation strategy using ETFs, combining momentum signals with intelligent risk management. The strategy employs a unique "Moving Average Energy" indicator for momentum measurement and incorporates VIX-based position sizing.

🎯 Key Features

• MA Energy Indicator: Proprietary momentum indicator using multiple timeframe moving averages, normalized by price volatility
• Dynamic Risk Management: VIX-based position sizing with adaptive thresholds
• LLM Strategy Review: AI-powered performance analysis and strategy behavior insights
• Interactive Dashboard: Real-time strategy monitoring and backtesting visualization

📊 Backtest Results (2010-2024)

• Annual Return: 18.5%
• Sharpe Ratio: 1.45
• Information Ratio: 0.82

🛠️ Technical Architecture

• Signal Generation

- Multi-timeframe MA Energy calculation - Cross-asset momentum comparison - Volatility normalization

• Risk Management

- VIX-based position sizing - Trailing stop implementation - Maximum drawdown control

• Strategy Review

- LLM-powered strategy behavior analysis - Historical context integration - Performance attribution

📦 Installation

git clone https://github.com/garroshub/QuantSectorRotation_Strategy.git
cd QuantSectorRotation_Strategy
pip install -r requirements.txt

🚀 Quick Start

streamlit run app.py

AI Strategy Review Configuration

The AI strategy review is optional. To enable it, set a Gemini API key in your runtime environment:

export GOOGLEAPIKEY="yourgeminiapikeyhere"

On Windows PowerShell:

$env:GOOGLEAPIKEY="yourgeminiapikeyhere"

Do not commit real API keys to the repository. If no key is configured, the dashboard still runs and the AI review panel reports that the feature is disabled.

📊 Dashboard Features

• Strategy Parameters

- MA windows customization - Risk thresholds adjustment - Universe selection

• Performance Analytics

- Rolling window analysis - Risk metrics visualization - Position history tracking

• AI Strategy Review

- Strategy behavior analysis - Performance attribution - Improvement suggestions

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

GitHub: @garroshub

⭐ Star History

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Disclaimer: This strategy is for educational purposes only. Past performance does not guarantee future results. Always do your own research and consider your risk tolerance before trading.