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Cross-Sectional Momentum Rebalancer
quantitativetradingpythonfinancemomentumrebalancing
Advanced quantitative trading system implementing cross-sectional momentum strategies with automated portfolio rebalancing, backtested from 2018-present using pandas and Alpha Vantage API.
Problem
Traditional momentum strategies suffer from high transaction costs and poor timing. Cross-sectional momentum addresses this by ranking assets relative to their peers, providing more robust and cost-effective signals.
Approach
- Implemented cross-sectional momentum ranking system with percentile scoring
- Built automated monthly rebalancing framework for portfolio optimization
- Integrated Alpha Vantage API for comprehensive market data
- Created volatility-targeted position sizing algorithms
- Developed backtesting engine with realistic transaction costs
Results
- Achieved consistent outperformance with monthly rebalancing strategy
- Successfully managed API rate limits and data quality issues
- Implemented volatility targeting for improved risk-adjusted returns
- Created robust backtesting framework for strategy validation
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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.