Predicting Optimal Trading Actions Using a Genetic Algorithm and Ensemble Method

Author: Kazuma Kuroda

ABSTRACT
Machine learning has been applied to the foreign exchange market for algorithmic trading. However, the selection of trading algorithms is a difficult problem. In this work, an approach that combines trading agents is designed. In the proposed approach, an artificial neural network is used to predict the optimum actions of each agent for USD/JPY currency pairs. The agents are trained using a genetic algorithm and are then combined using an ensemble method. We compare the performance of the combined agent to the average performance of many agents. Simulation results show that the total return is better when the combined agent is used.

Source:

Journal: Intelligent Information Management
DOI: 10.4236/iim.2017.96012 (PDF)
Paper Id: 80106 (metadata)

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