Using Genetic Algorithms for Robust Optimization in Financial Applications

In this study, optimal indicators and strategies for foreign exchange trading models are investigated in the framework of genetic algorithms. We rst explain how the relevant quantities of our application can be encoded in "genes" so as to t the requirements of the genetic evolutionary optimization technique. In nancial problems, sharp peaks of high tness are usually not representative of a general solution but, rather, indicative of some accidental uctuations. Such uctuations may arise out of inherent noise in the time series or due to threshold e ects in the trading model performance. Peaks in such a discontinuous, noisy and multimodal tness space generally correspond to trading models which will not perform well in out-of-sample tests. In this paper we show that standard genetic algorithms will be quickly attracted to one of the accidental peaks of the tness space whereas genetic algorithm for multimodal functions employing clustering and a specially designed tness sharing scheme will nd optimal parameters which correspond to broad regions where the tness function is higher on average. The optimization and the quality tests have been performed over eight years of high frequency data of the main foreign exchange rates.