Comparison of trade decision strategies in an equity market GA trader

This paper investigates different trade decision strategies under different market conditions so that a genetic algorithm could be designed to use the appropriate decision strategy. A trade decision strategy defines how a single action is decided upon based on a number of signals where each signal is a result of a technical analysis function. Using historical market data, a population is trained using a simple genetic algorithm employing crossover and mutation. Four genetic algorithms are used to evolve agents to trade, where each genetic algorithm uses a different trade decision strategy. The best individual from each evolved population is compared using an out-of-sample data set. Results show a significant difference in performance between the four decision strategies especially within bearish to moderately bullish stocks. Populations evolved using a weighted decision strategy performs better than strategies that are not weighted when trading bearish to moderately bullish stocks. Non-weighted decision strategies appear to out-perform weighted strategies when used on extremely bullish stock. This out-performance could be attributed to fewer trades made by non-weighted strategies compared to weighted ones.