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Evolution of trading strategies with flexible structures: A configuration comparison

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorMartín, Carlos
dc.contributor.authorQuintana, David
dc.contributor.authorIsasi, Pedro
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2022-01-21T12:02:22Z
dc.date.available2022-01-21T12:02:22Z
dc.date.issued2019-02-28
dc.description.abstractEvolutionary Computation is often used in the domain of automated discovery of trading rules. Within this area, both Genetic Programming and Grammatical Evolution offer solutions with similar structures that have two key advantages in common: they are both interpretable and flexible in terms of their structure. The core algorithms can be extended to use automatically defined functions or mechanisms aimed to promote parsimony. The number of references on this topic is ample, but most of the studies focus on a specific setup. This means that it is not clear which is the best alternative. This work intends to fill that gap in the literature presenting a comprehensive set of experiments using both techniques with similar variations, and measuring their sensitivity to an increase in population size and composition of the terminal set. The experimental work, based on three S&P 500 data sets, suggest that Grammatical Evolution generates strategies that are more profitable, more robust and simpler, especially when a parsimony control technique was applied. As for the use of automatically defined function, it improved the performance in some experiments, but the results were inconclusive. (C) 2018 Elsevier B.V. All rights reserved.en
dc.description.sponsorshipThe authors acknowledge financial support granted by the Spanish Ministry of Science and Innovation under grant ENE2014-56126-C2-2-R.en
dc.identifier.bibliographicCitationMartín, C., Quintana, D., Isasi, P. (2019). Evolution of trading strategies with flexible structures: A configuration comparison. Neurocomputing, 331, pp. 242-262en
dc.identifier.doihttps://doi.org/10.1016/j.neucom.2018.11.062
dc.identifier.issn0925-2312
dc.identifier.publicationfirstpage242
dc.identifier.publicationlastpage262
dc.identifier.publicationtitleNeurocomputingen
dc.identifier.publicationvolume331
dc.identifier.urihttps://hdl.handle.net/10016/33932
dc.identifier.uxxiAR/0000022950
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. ENE2014-56126-C2-2-R
dc.rights© 2018 Elsevier B.V.
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformáticaen
dc.subject.otherEvolutionary computationen
dc.subject.otherGenetic programmingen
dc.subject.otherGrammatical evolutionen
dc.subject.otherTradingen
dc.subject.otherRulesen
dc.subject.otherAlgorithmsen
dc.subject.otherPredictabilityen
dc.subject.otherMarketsen
dc.titleEvolution of trading strategies with flexible structures: A configuration comparisonen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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