A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm

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 verify the adequacy of TA strategies and rules to attain consistent superior returns, by comparing momentum, trend and breakout indicators. The indicators with the parameters generated through our GA consistently outperform the equivalent indicators applying parameters commonly used by the trading industry. EUR/USD and GBP/USD markets present interesting return figures before trading costs. The inclusion of spreads and commissions deteriorates returns substantially, suggesting these markets, under a more realistic set of assumptions, may be efficient. Trend indicators generate better outcomes and GBP/USD qualifies as the most profitable market. Different aggregate returns in different markets may stand as 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 presents the possibility of recovering latent data, avoiding premature convergence