Dynamic Time Warping for Financial Price Pattern Recognition

Summary

The document explains dynamic time warping (DTW) as a way to compare a candidate price sequence with a reference pattern despite differences in timing or pace. It outlines how pairwise distances form a matrix, how cumulative costs identify a minimum-cost alignment, and how a warping path links the sequence points while preserving order. It also reviews distance measures and standard versus asymmetric step patterns, which constrain the possible alignments.

The article describes an MQL5 implementation and demonstrates using it to search price data for a selected pattern. DTW can capture shape similarity that strict point-by-point comparisons may miss, making it a potential tool for financial time series. The document gives no evidence of predictive performance or trading profitability. It cautions that DTW can require substantial computation and memory, is sensitive to noise and outliers, and can be difficult to interpret; results should therefore be treated as exploratory and paired with other analysis.

Key ideas

• DTW compares sequences by finding an ordered alignment that minimizes cumulative distance.
• A lower DTW distance indicates greater similarity between the candidate and reference sequences.
• Step patterns and constraints affect which alignments are permitted and can bias the warping path.
• The article demonstrates implementing DTW in MQL5 for searching price patterns.
• Computational cost, noise sensitivity, and interpretability limit DTW's standalone use in trading.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.