Genetic algorithm evolved agent-based equity trading using Technical Analysis and the Capital Asset Pricing Model

This paper investigates genetic algorithm evolved agents trading on real historical equity market data using technical analysis, the capital asset pricing model and a hybrid model of the two approaches. Three agent groups are generated, each using solely one of the two approaches or their hybrid to determine trading decisions. Each group consists of ten independently evolved populations over a thousand generations, whose elite’s performances are consequently averaged and used to compare to the other approaches. Results indicated that the technical analysis based approach performed better than the capital asset pricing model based approach, while the hybrid approach in turn outperformed both. As part of ongoing research, the results would suggest significant benefits in performance oriented implementation of hybrid or multi-method based approaches in agent-based systems.