A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm

Abstract

Traditional approaches to the study of technical analysis (TA) often focus on the performance of a single indicator, which seems to fall short in scope and depth. We use a genetic algorithm (GA) to optimize trading strategies in the three major Forex markets in order to ascertain the suitability of TA strategies and rules to achieve consistently superior returns, by comparing momentum, trend and breakout indicators. The indicators with the parameters generated through our GA consistently outperform the equivalent indicators by applying parameters commonly used by the trading industry. EUR/USD and GBP/USD markets have interesting return figures before trading costs. The inclusion of spreads and commissions weakens returns substantially, suggesting that under a more realistic set of assumptions these markets could be efficient. Trend indicators generate better outcomes and GBP/USD qualifies as the most profitable market. Different aggregate returns in different markets may be evidence of distinct maturation stages under an evolving efficiency market perspective. Our GA is able to search a wider solution space than traditional configurations and offers the possibility of recovering latent data, thus avoiding premature convergence.