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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.

Project Overview

TL;DR

  • Role: Quantitative developer and portfolio strategist
  • Stack: Python, pandas, NumPy, Alpha Vantage API, matplotlib, Jupyter

Key Metrics

Backtest Period
2018-Present
Annual Return
12.8%
Sharpe Ratio
1.32
Rebalance Frequency
Monthly
API Reliability
99.5%

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

Gallery

Cross-sectional momentum strategy visualization showing portfolio performance and rebalancing

Links

View SourceLive Demo

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.