Evolution of trading strategies with flexible structures: A configuration comparison

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dc.contributor.author Martín, Carlos
dc.contributor.author Quintana, David
dc.contributor.author Isasi, Pedro
dc.date.accessioned 2022-01-21T12:02:22Z
dc.date.available 2022-01-21T12:02:22Z
dc.date.issued 2019-02-28
dc.identifier.bibliographicCitation Martín, C., Quintana, D., Isasi, P. (2019). Evolution of trading strategies with flexible structures: A configuration comparison. Neurocomputing, 331, pp. 242-262
dc.identifier.issn 0925-2312
dc.identifier.uri http://hdl.handle.net/10016/33932
dc.description.abstract Evolutionary 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.
dc.description.sponsorship The authors acknowledge financial support granted by the Spanish Ministry of Science and Innovation under grant ENE2014-56126-C2-2-R.
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2018 Elsevier B.V.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Evolutionary computation
dc.subject.other Genetic programming
dc.subject.other Grammatical evolution
dc.subject.other Trading
dc.subject.other Rules
dc.subject.other Algorithms
dc.subject.other Predictability
dc.subject.other Markets
dc.title Evolution of trading strategies with flexible structures: A configuration comparison
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1016/j.neucom.2018.11.062
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ENE2014-56126-C2-2-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 242
dc.identifier.publicationlastpage 262
dc.identifier.publicationtitle Neurocomputing
dc.identifier.publicationvolume 331
dc.identifier.uxxi AR/0000022950
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
dc.affiliation.dpto UC3M. Departamento de Informática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)
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