Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques

K. Theofilatos, S. Likothanassis, A. Karathanasopoulos

Abstract


The present paper aims in investigating the performance of state-of-the-art machine learning techniques in trading with the EUR/USD exchange rate at the ECB fixing. For this purpose, five supervised learning classification techniques (K-Nearest Neighbors algorithm, Naïve Bayesian Classifier, Artificial Neural Networks, Support Vector Machines and Random Forests) were applied in the problem of the one day ahead movement prediction of the EUR/USD exchange rate with only autoregressive terms as inputs. For comparison reasons, the performance of all machine learning techniques was benchmarked by two traditional techniques (Naïve  Strategy and moving average convergence/divergence model). Trading strategies produced by the machine learning techniques of Support Vector Machines and Random Forests clearly outperformed all other strategies in terms of annualized return and sharp ratio. To the best of our knowledge, this is the first application of Random Forests in the problem of trading with the EUR/USD exchange rate providing extremely satisfactory results.


Keywords


EUR/USD Exchange Rate; future direction prediction; naive strategy; MACD strategy; Naive Bayesian Classifier; K-nearest neighbors classifier; SVM; Random Forests; leverage; transaction costs

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References


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