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A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading
2019
Computation
Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper
doi:10.3390/computation7040067
fatcat:bcyqnar6vvcnpeqbfpb2daxrl4