How to Find Stock-Specific Moves in the S&P 500 with Python

A step-by-step guide to building a Python scanner that identifies stock-specific (idiosyncratic) moves in the S&P 500 by separating market-driven returns from stock-specific residuals. The approach uses rolling OLS regression via statsmodels to estimate each stock's beta and alpha against SPY over a 60-day window, computes drift-corrected z-scores on residuals, confirms signals with volume ratios, and checks news headlines via the EODHD API. Multi-day compounded residuals (1, 3, 5-day windows) help distinguish one-day spikes from sustained moves. Real examples from June 12, 2026 illustrate the scanner in action, including SATS (-11%, 6x volume), LEN (z-score -3.9), and a TKO ticker collision edge case. Visualizations include actual vs. expected return scatter plots, z-score bar charts with volume labels, and a trailing abnormal returns heatmap.

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