A preliminary investigation into multi-agent trading simulations using a genetic algorithm

This paper investigates the effect of using varying amounts of training data on the specificity and robustness of a multi-agent based solution for use in trading simulations using historical equity market data. Three separate amounts of training data were used in five experiments to evolve 15 groups of agents under varying conditions. These groups were then exposed to three separate test environments to determine whether differences in performance could be related back to their training environment. The results indicate that larger training data sets lead to more general solutions and overall better performance when tested in environments with varying conditions. This will lead onto future research focusing on agent behavior and decisions in financial markets