Why the 50-Day WMA Dominates: A Quantitative Risk-Return Analysis in Python

This article analyzes the performance of different moving average window lengths (10, 20, 50, 100, and 200 days) on the S&P 500 using a quantitative risk-return analysis. The study uses a bias-free backtesting engine to eliminate lookahead bias and scores each strategy on Sharpe ratio, annualized volatility, and maximum drawdown. The results show that the 50-day window outperforms the others, making it an empirically defensible choice. The article provides a complete strategy lab in Python, including a Colab notebook, and covers practical use cases and limitations. Engineers can use this framework to build their own trading strategies.