Predicting Optimal Trading Actions Using a Genetic Algorithm and Ensemble Method

Author: Kazuma Kuroda

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.


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

See also: Comments to Paper

About scirp

(SCIRP: is an academic publisher of open access journals. It also publishes academic books and conference proceedings. SCIRP currently has more than 200 open access journals in the areas of science, technology and medicine. Readers can download papers for free and enjoy reuse rights based on a Creative Commons license. Authors hold copyright with no restrictions. SCIRP calculates different metrics on article and journal level. Citations of published papers are shown based on Google Scholar and CrossRef. Most of our journals have been indexed by several world class databases. All papers are archived by PORTICO to guarantee their availability for centuries to come.
This entry was posted in IIM. Bookmark the permalink.

Leave a Reply

Your email address will not be published. Required fields are marked *