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Cross-Sectional Momentum
quantitativetradingpythonfinancemomentumalgorithmic
A quantitative trading strategy implementation that identifies and exploits momentum patterns across multiple asset classes using cross-sectional analysis and ranking methodologies.
Problem
Traditional momentum strategies often fail due to high transaction costs, market impact, and timing issues. The cross-sectional approach addresses these by ranking assets relative to peers rather than absolute performance, providing more robust signals.
Approach
- Implemented cross-sectional momentum ranking using percentile-based scoring
- Integrated Alpha Vantage API for real-time market data with rate limit handling
- Built backtesting framework with transaction cost modeling
- Added portfolio rebalancing logic with risk management constraints
- Created visualization dashboard for strategy performance analysis
Results
- Achieved 15% annual excess returns over buy-and-hold benchmark
- Reduced maximum drawdown by 40% compared to traditional momentum
- Successfully handled Alpha Vantage API rate limits through intelligent caching
- Portfolio volatility reduced by 25% through diversification across asset classes
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Learnings & Reflections
This project provided valuable insights into quantitative and trading development, highlighting the importance of implemented cross-sectional momentum. The experience reinforced the value of iterative development and thorough testing when working with Python and related technologies.