Abstract
We investigate the generalization properties of a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct fuzzy predictive models of financial instruments. The models, expressed as fuzzy rule bases, take a number of popular technical indicators on day t as inputs and produce a trading signal for day t∈+∈1 based on a dataset of past observations of which actions would have been most profitable. The approach has been applied to trading several financial instruments (large-cap stocks and indices), in order to study the horizontal, i.e., cross-market, generalization capabilities of the models. © 2008 Springer-Verlag Berlin Heidelberg.
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Da Costa Pereira, C., & Tettamanzi, A. G. B. (2008). Horizontal generalization properties of fuzzy rule-based trading models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4974 LNCS, pp. 93–102). https://doi.org/10.1007/978-3-540-78761-7_10
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