High Frequency Foreign Exchange Trading Strategies Based on Genetic Algorithms

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. Trading strategies and their parameters are heuristically or subjectively constructed. Recently, artificial intelligence techniques such as fuzzy logic, neural networks and genetic algorithms are used to solve various problems in trading. In this paper we used genetic algorithms to generate the most profitable trading strategy based on technical indicators on the foreign exchange market. The trading strategies with neutral position generated by genetic algorithms have an annualized return of 3.7% during test period which is better than the trading strategies without neutral position.