  Technical analysis is used to discover investment opportunities. To test this
hypothesis we propose an hybrid system using machine learning techniques
together with genetic algorithms. Using technical analysis there are more ways
to represent a currency exchange time series than the ones it is possible to
test computationally, i.e., it is unfeasible to search the whole input feature
space thus a genetic algorithm is an alternative. In this work, an architecture
for automatic feature selection is proposed to optimize the cross validated
performance estimation of a Naive Bayes model using a genetic algorithm. The
proposed architecture improves the return on investment of the unoptimized
system from 0,43% to 10,29% in the validation set. The features selected and
the model decision boundary are visualized using the algorithm t-Distributed
Stochastic Neighbor embedding.
