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.
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da Costa Pereira, C., Tettamanzi, A.G.B. (2008). Horizontal Generalization Properties of Fuzzy Rule-Based Trading Models. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_10
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DOI: https://doi.org/10.1007/978-3-540-78761-7_10
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