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Technical analysis is used to discover investment opportunities. To test thishypothesis we propose an hybrid system using machine learning techniquestogether with genetic algorithms. Using technical analysis there are more waysto represent a currency exchange time series than the ones it is possible totest computationally, i.e., it is unfeasible to search the whole input featurespace thus a genetic algorithm is an alternative. In this work, an architecturefor automatic feature selection is proposed to optimize the cross validatedperformance estimation of a Naive Bayes model using a genetic algorithm. Theproposed architecture improves the return on investment of the unoptimizedsystem from 0,43% to 10,29% in the validation set. The features selected andthe model decision boundary are visualized using the algorithm t-DistributedStochastic Neighbor embedding.

Currency exchange prediction using machine learning, genetic algorithms and technical analysis