This repository serves as a comprehensive collection of algorithmic trading strategies developed by our team. It includes strategies created during our research phase, those developed for trading competitions, and a curated selection of academic papers that have informed our approach.
├── research-strategies/ # Strategies developed during our research phase
├── competition-strategies/ # Strategies developed for trading competitions
├── papers/ # Academic papers and research materials
├── utils/ # Utility functions and common code
├── data/ # Sample datasets (no sensitive data)
├── backtests/ # Backtesting results and performance metrics
└── docs/ # Documentation on strategy implementation
This section contains algorithmic trading strategies developed during our team's research and study phase. Each strategy includes:
- Detailed implementation code
- Strategy explanation and theoretical foundation
- Parameter optimization approaches
- Performance metrics and analysis
This section showcases the strategies developed specifically for trading competitions. Each competition strategy includes:
- Competition-specific implementations
- Performance results
- Lessons learned and post-competition analysis
- Adaptation considerations for real-market conditions
A curated collection of academic papers, research materials, and study notes that have influenced our strategy development. This includes:
- Academic papers on quantitative finance
- Market microstructure research
- Machine learning applications in finance
- Risk management frameworks
- Team summaries and critical analyses
git clone https://github.com/yourusername/algotrading-strategies.git
cd algotrading-strategies
pip install -r requirements.txt