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Reinforcement learning applied to Forex trading - 2018

Research Area:  Machine Learning

Abstract:

This paper describes a new system for short-term speculation in the foreign exchange market, based on recent reinforcement learning (RL) developments. Neural networks with three hidden layers of ReLU neurons are trained as RL agents under the Q-learning algorithm by a novel simulated market environment framework which consistently induces stable learning that generalizes to out-of-sample data. This framework includes new state and reward signals, and a method for more efficient use of available historical tick data that provides improved training quality and testing accuracy. In the EUR/USD market from 2010 to 2017 the system yielded, over 10 tests with varying initial conditions, an average total profit of 114.0 ± 19.6% for an yearly average of 16.3 ± 2.8%.

Author(s) Name:  JoãoCarapuço,RuiNeves and NunoHorta

Journal name:  Applied Soft Computing

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.asoc.2018.09.017

Volume Information:  Volume 73, December 2018, Pages 783-794