Optimization of technical indicators in real time with multiobjective evolutionary algorithms

Technical analysis uses technical indicators to identify changes in market trend. These are composed by a set of parameters and rules, whose values try to determine the future movements of the assets. This paper addresses the optimization of these values depending on the current market, allowing bet...

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Detalles Bibliográficos
Autores: Soltero, Francisco José, Bodas, Diego, Fernández, Pablo, Hidalgo, José Ignacio
Tipo de recurso: artículo
Fecha de publicación:2012
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/29332
Acceso en línea:https://hdl.handle.net/10115/29332
Access Level:acceso embargado
Palabra clave:Algoritmos evolutivos
optimización
indicadores bursátiles
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spelling Optimization of technical indicators in real time with multiobjective evolutionary algorithmsSoltero, Francisco JoséBodas, DiegoFernández, PabloHidalgo, José IgnacioAlgoritmos evolutivosoptimizaciónindicadores bursátilesTechnical analysis uses technical indicators to identify changes in market trend. These are composed by a set of parameters and rules, whose values try to determine the future movements of the assets. This paper addresses the optimization of these values depending on the current market, allowing better returns with less risk. The use of Multi-objective Evolutionary Algorithms (MOEAs) is proposed in this work to obtain the best parameter values in real time belonging to a collection of indicators that will help in the buying and selling of shares. Unlike other previous approaches, the necessity of repeating the parameters optimization process each time a new data enters the system is justified, searching for the best adjustment in every moment. This technique can greatly improve the results of Buy & Hold (B & H) strategy even operating daily. This statement will be demonstrated by comparing the results to those presented in the literature.Association for Computing Machinery New York, NY, United States.202420242012info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/29332reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésinfo:eu-repo/semantics/embargoedAccessoai:burjcdigital.urjc.es:10115/293322026-06-24T12:48:17Z
dc.title.none.fl_str_mv Optimization of technical indicators in real time with multiobjective evolutionary algorithms
title Optimization of technical indicators in real time with multiobjective evolutionary algorithms
spellingShingle Optimization of technical indicators in real time with multiobjective evolutionary algorithms
Soltero, Francisco José
Algoritmos evolutivos
optimización
indicadores bursátiles
title_short Optimization of technical indicators in real time with multiobjective evolutionary algorithms
title_full Optimization of technical indicators in real time with multiobjective evolutionary algorithms
title_fullStr Optimization of technical indicators in real time with multiobjective evolutionary algorithms
title_full_unstemmed Optimization of technical indicators in real time with multiobjective evolutionary algorithms
title_sort Optimization of technical indicators in real time with multiobjective evolutionary algorithms
dc.creator.none.fl_str_mv Soltero, Francisco José
Bodas, Diego
Fernández, Pablo
Hidalgo, José Ignacio
author Soltero, Francisco José
author_facet Soltero, Francisco José
Bodas, Diego
Fernández, Pablo
Hidalgo, José Ignacio
author_role author
author2 Bodas, Diego
Fernández, Pablo
Hidalgo, José Ignacio
author2_role author
author
author
dc.subject.none.fl_str_mv Algoritmos evolutivos
optimización
indicadores bursátiles
topic Algoritmos evolutivos
optimización
indicadores bursátiles
description Technical analysis uses technical indicators to identify changes in market trend. These are composed by a set of parameters and rules, whose values try to determine the future movements of the assets. This paper addresses the optimization of these values depending on the current market, allowing better returns with less risk. The use of Multi-objective Evolutionary Algorithms (MOEAs) is proposed in this work to obtain the best parameter values in real time belonging to a collection of indicators that will help in the buying and selling of shares. Unlike other previous approaches, the necessity of repeating the parameters optimization process each time a new data enters the system is justified, searching for the best adjustment in every moment. This technique can greatly improve the results of Buy & Hold (B & H) strategy even operating daily. This statement will be demonstrated by comparing the results to those presented in the literature.
publishDate 2012
dc.date.none.fl_str_mv 2012
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/29332
url https://hdl.handle.net/10115/29332
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Association for Computing Machinery New York, NY, United States.
publisher.none.fl_str_mv Association for Computing Machinery New York, NY, United States.
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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