detect-fed-unamortized-discount-pattern

Detect financial stress patterns in WUDSHO series by computing correlation, dynamic time warping (DTW), and shape feature similarity against historical crisis windows like COVID_2020, GFC_2008, TAPER_2013, and RATE_HIKE_2022.

Cross-validate Fed balance sheet movements by comparing WUDSHO shape similarity against crisis baselines while checking cross-indicator signals from credit spreads, VIX, yield curve, and Fed balance to reduce false positives.

Yes, this pattern detection approach works with pandas and scipy, using numpy for numerical operations, requests for FRED data retrieval, fastdtw for dynamic time warping, and matplotlib for visualization.

When using dynamic time warping for baseline window comparison, limitations include potential false positives from single-indicator analysis, which is why cross-validation with credit spreads, VIX, yield curve, and Fed balance sheet is required.

Generate a structured JSON report with a composite risk score by running the pattern detection script, which outputs pattern_similarity_score, stress_confirmation, best match details, and interpretation to an output directory.