The effect of varying parameters on performance for adaptive agents in technical equity market trading

This paper investigates the impact of varying the quantity of data and the number of generations run for every re-training step in an adaptive trading system. Using historical equity data, populations of agents are continuously retrained and assessed based on their performance across an out-of-sample data set. Comparison was performed using three test sets, for which each had one variable altered for every run. Results showed significant differences in performance when varying the number of trading days, while no difference was found when varying generations.