Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems

Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate...

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Detalles Bibliográficos
Autores: Soltero, Francisco José, Fernández, Pablo, Hidalgo, José Ignacio
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/29335
Acceso en línea:https://hdl.handle.net/10115/29335
Access Level:acceso abierto
Palabra clave:machine learning
trading systems
multiobjective optimization
evolutionary algorithms
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spelling Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading SystemsSoltero, Francisco JoséFernández, PabloHidalgo, José Ignaciomachine learningtrading systemsmultiobjective optimizationevolutionary algorithmsTechnical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate, the size of the time window, and so on. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of some technical financial indicators. We propose the combination of several Multiobjective Evolutionary Algorithms. Unlike other approaches, this paper applies a set of different Multiobjective Evolutionary Algorithms, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of the non-dominated solutions obtained with different MOEAs at the same time. Experimental results show that Collaborative Multiobjective Evolutionary Algorithms obtain up to 22% of profit and increase the returns of the commonly used Buy and Hold strategy and other multi-objective strategies, even for daily operations.Applied Sciences202420242023info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/29335reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/293352026-06-24T12:48:17Z
dc.title.none.fl_str_mv Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
title Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
spellingShingle Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
Soltero, Francisco José
machine learning
trading systems
multiobjective optimization
evolutionary algorithms
title_short Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
title_full Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
title_fullStr Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
title_full_unstemmed Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
title_sort Collaborative Multiobjective Evolutionary Algorithms in the Search of Better Pareto Fronts: An Application to Trading Systems
dc.creator.none.fl_str_mv Soltero, Francisco José
Fernández, Pablo
Hidalgo, José Ignacio
author Soltero, Francisco José
author_facet Soltero, Francisco José
Fernández, Pablo
Hidalgo, José Ignacio
author_role author
author2 Fernández, Pablo
Hidalgo, José Ignacio
author2_role author
author
dc.subject.none.fl_str_mv machine learning
trading systems
multiobjective optimization
evolutionary algorithms
topic machine learning
trading systems
multiobjective optimization
evolutionary algorithms
description Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate, the size of the time window, and so on. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of some technical financial indicators. We propose the combination of several Multiobjective Evolutionary Algorithms. Unlike other approaches, this paper applies a set of different Multiobjective Evolutionary Algorithms, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of the non-dominated solutions obtained with different MOEAs at the same time. Experimental results show that Collaborative Multiobjective Evolutionary Algorithms obtain up to 22% of profit and increase the returns of the commonly used Buy and Hold strategy and other multi-objective strategies, even for daily operations.
publishDate 2023
dc.date.none.fl_str_mv 2023
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/10115/29335
url https://hdl.handle.net/10115/29335
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Applied Sciences
publisher.none.fl_str_mv Applied Sciences
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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