Comparative Study of FOREX Trading Systems Built with SVR+GHSOM and Genetic Algorithms Optimization of Technical Indicators

Abstract

Considerable effort has been made by researchers from various areas of science to forecast financial time series such as stock market and foreign exchange market (Forex). Recent studies have shown that the market can be outperformed by trading systems built with computational intelligence techniques. This study applies the Genetic Algorithm (GA) technique to optimize technical indicators parameters in order to maximize profit in the nine most tradable foreign exchange rates. Fifteen trading systems were created by combining four technical indicators optimized by the GA. It is then compared to an SVR+GHSOM model trading system and an analysis is performed to assess the most adaptable model in a period of international economic crisis. We report in the experiments that the GA model was far superior compared to the SVR+GHSOM model in the test period. The comparison considered performance measures such as profitability (ROI) and the maximum draw down (MD). The experiments have also shown that it is possible to increase profit by adjusting the risk parameter (lots size), at the expense of increasing the risk.