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We compare the performance of four (4) different special-purpose pattern recognition methods designed for trading systems in forex markets. Each of thesemethodshasbeenproperlydesignedtoexploithighlycompute-intensive aggregation calculations of complex but efficient distributed SQL queries on the relational databases holding the market data. Specifically, we have implemented several methods for discovering hidden price patterns in forex marketstime-seriesdatathatsignaltheshort-termdirectionofpricechangesof the market.We encoded the time series forex data using (a) Binary Rule based Approximation (BRA), appropriate for candlestick-based pattern recognition methods, (b) a Symbolic Aggregate approximation (SAX) technique, (c) a modified SAX technique, calledVolatility-Sized SAX algorithm (VSAX) and (d) a heuristic based on capturing local peaks and valleys in the time-series (called TP-SAX). Then, after processing the codified patterns (training and validating), we extracted those patterns whose accuracy exceeds a certain threshold, and stored them in a database. In the final stage, we use these discovered patterns in a trading system which only accepts as inputs only the estimatedprobabilitiesoffuturechangeinprices.Inourexperiments,weused

Intraday Business Model Strategies onForex Markets: Comparing the Performanceof Price Pattern Recognition Methods