Finite Normal Mixture Models for the Ensemble Learning of Recurrent Neural Networks with Applications to Currency Pairs

The paper proposes the use of related components by the method of the moving separation of mixtures as nontrivial features to expand the feature space in problems of the learning of recurrent neural networks. These features are added based on the approximation of data increments using probabilistic models based on finite normal mixtures. To take into account relationships in the data as well as in related components, the article uses the long short-term memory variant of recurrent architectures. The proposed approach is used to build an automated trading strategy based on an ensemble of the long short-term memory networks for the three most commonly traded currency pairs: euro–US dollar, US dollar–Japanese yen, and euro–pound sterling, for which data are taken from January 2011 to the end of September 2021. It is shown that the profitability of the developed ensemble long short-term memory model using additional features, i.e., information on the probabilistic distribution of data increments, outperforms both the basic methods of algorithmic trading by financial indicators (advantage of up to 32.2% on test data) and well-known approaches based on long short-term memory networks without statistical expansion of the feature space (advantage of up to 23.3%). For the best models within the framework of model trading, the final and annual yields are found to be up to 99% and 54%, respectively.