A pattern recognition algorithm for optimal profits in currency trading

Abstract

A key issue in technical analysis is to obtain good and possibly stable profits. Various trading rules for financial markets do exist for this task. This paper describes a pattern recognition algorithm to optimally match training and trading periods for technical analysis rules. Among the filter techniques,we use the Dual Moving Average Crossover (DMAC) rule. This technique is applied to hourly observations of Euro-Dollar exchange rates. The matching method is accomplished using ten chart patterns very popular in technical analysis.Moreover, in order for the results to have a statistical sense, we use the bootstrap technique. The results show that the algorithm proposed is a good starting point to obtain positive and stable profits.