Domenico Laurenza
A holistic auto-configurable ensemble machine learning strategy for financial trading
Carta S.;Corriga A.;Ferreira A.;Recupero D. R.;Saia R.
2019-01-01
Abstract
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 introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions.File | Size | Format | |
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computation-07-00067-v2.pdf open access
Type: versione post-print
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716.53 kB | Adobe PDF | View/Open |
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