Fingerprint Pattern Recognition: Build labeled datasets from MT5 charts with CSV exports

This is a detailed review of Fingerprint Pattern Recognition written in 2026 that focuses on performance and practical analysis for dataset builders. Fingerprint Pattern Recognition is a non-trading MT5 utility that loads a fingerprint CSV of OHLC bars, scans historical charts for similar price action, and exports each match to a results CSV for later labeling. In this review I examine detection logic, data output, configuration options, and limitations. What makes Fingerprint Pattern Recognition unique is its emphasis on reproducible sample extraction rather than signal execution. The algorithm computes similarity across recent closed candles using configurable distance metrics and a user-set similarity threshold, then records every instance it deems a match. The secondary formation detector flags classic patterns like triangles and head-and-shoulders as complementary outputs. Filip Dockal developed the tool for MT5, and the implementation is lightweight, priced at $30 with a 4.5 out of 5 rating from 100 reviews on the marketplace. The utility performs best in markets with consistent bar structure and sufficient historical depth, such as major FX pairs and liquid futures. It is not a trading EA; risk management sits with the analyst or model that consumes the exported matches. Expected performance characteristics revolve around dataset quality: higher thresholds yield fewer but cleaner matches, while lower thresholds increase recall at the expense of precision. Use Fingerprint Pattern Recognition when you need systematic, auditable sample extraction prior to modeling.