Genetic optimization of a trading algorithm based on pattern recognition

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dc.contributor.authorRuiz-Cruz, Riemann
dc.contributor.authorSedano, Chelsie
dc.contributor.authorFlores, Oscar
dc.date.accessioned2021-04-27T23:46:02Z
dc.date.available2021-04-27T23:46:02Z
dc.date.issued2019-11
dc.descriptionIn the present paper, a trading strategy based onpattern recognition is optimized by means of a genetic algorithm.The genetic algorithm is used to determine decisions of buy/sellbased on the patterns found through time for a portfolio in thestock market. The predominant algorithms used in this workwere theK-means clustering algorithm to find the patterns indifferent time lapses, and the genetic algorithm for optimization.The results are supported by simulations using a selected sharesof the Mexican stock market.es_MX
dc.description.sponsorshipITESO, A.C.es
dc.identifier.citationR. Ruiz-Cruz, C. Sedano and O. Flores. Genetic optimization of a trading algorithm based on pattern recognition. 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Guayaquil, Ecuador, 2019, pp. 1-6, doi: 10.1109/LA-CCI47412.2019.9037052.es_MX
dc.identifier.isbn978-1-7281-5666-8
dc.identifier.urihttps://hdl.handle.net/11117/6574
dc.language.isoenges_MX
dc.publisherIEEEes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectGenetic algorithmes_MX
dc.subjectPortfolio optimizationes_MX
dc.subjectTrading algorithmes_MX
dc.titleGenetic optimization of a trading algorithm based on pattern recognitiones_MX
dc.typeinfo:eu-repo/semantics/articlees_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX

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