Trading strategy in foreign exchange market using reinforcement learning hierarchical neuro-fuzzy systems

Abstract

This paper evaluates the performance of the new hybrid neuro-fuzzy model, Reinforcement Learning Hierarchical Neuro-Fuzzy System (RL-HNFP), in a trade decision application. The proposed model was tested with the Euro/Yen negotiated in Foreign Exchange Market. The main objective of the trading system is to optimize the resource allocation, in order to determine the best investment strategy. The performance of the RL-HNFP was compared with different benchmark models. The results showed that the system was able to detect long term strategies, obtaining more profitability with smaller number of trades. © 2009 Springer Berlin Heidelberg.