How to build stock chart pattern search without the silent failure modes

Finding historical chart patterns that resemble a current stock's shape is a common feature request, but naive implementations produce misleading results because raw price levels and scale dominate the similarity metric rather than actual shape. The fix involves z-normalizing each candidate window independently before comparison, which also reveals that z-normalized Euclidean distance and Pearson correlation produce identical rankings. Using log returns instead of raw prices is recommended since prices are non-stationary, meaning their variance shifts over time. Window length is another critical variable — short windows capture noise while very long windows are nearly unique, making genuine matches rare. The article stops short of claiming predictive value for any matched patterns, treating the method purely as a retrieval and visualization tool.