A quantitative analysis and portfolio optimization framework for the DACH region (Germany, Austria, Switzerland). This system provides tools for strategy research, risk management, and portfolio construction.
| Feature | Description |
|---|---|
| Strategy Research | Modular environment for testing various quantitative strategies. |
| Portfolio Optimization | Implementation of Hierarchical Risk Parity (HRP) for portfolio stability. |
| Market Monitoring | Tools for identifying market regimes and potential instabilities. |
| Backtesting | Framework for evaluating strategy performance with historical data. |
A collection of quantitative strategies including:
- Regime Classification: Identifying market states for DACH indices.
- Trend Analysis: Statistical methods for price prediction.
- Statistical Arbitrage: Analyzing correlations between DAX and international peers.
Implements Hierarchical Risk Parity (HRP) to optimize asset allocation based on correlation clusters, aiming for improved diversification.
Handles data ingestion for XETRA/SIX and provides a backtesting environment for strategy evaluation.
/dach-quant-optimizer
├── README.md # Project documentation
├── LIMITATIONS.md # Disclosure of system boundaries
├── requirements.txt # Python dependencies
├── main.py # Strategy execution CLI
├── backtester.py # Backtesting engine
├── data_fetcher.py # Data ingestion
├── portfolio.py # Portfolio optimizer
└── strategies/ # Modular strategy library
- Clone the repository:
git clone https://github.com/gilhermanns/dach-quant-optimizer.git cd dach-quant-optimizer - Install dependencies:
pip install -r requirements.txt
This project is licensed under the MIT License. Quantitative analysis involves significant risk. This framework is for research and educational purposes only.
Entwickelt mit Unterstützung von Claude Code (Anthropic).