Optimization of the FX trading rule with dynamic index switching by GA

Abstract

The optimization of trading rules in Foreign Exchange (FX) by using metaheuristics such as a Genetic Algorithm (GA) has been recently proposed. GA can learn trends and generate proper rules for FX. However, trends of exchange fluctuations always change by various factors. In such situations, it is difficult to apply one specific rule generated by GA for gaining benefits. This paper proposes the dynamic switching of generating rules by GA. The similarity between the learning data and the test data is calculated by correlation coefficient. This allows generated rules to adopt complex trends according to the circumstances. By conducting substantial simulations, we found that the medium correlation generated best benefits than strong correlations.