A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/45565 https://doi.org/10.1007/s10614-016-9641-9 |
Resumo: | Traditional approaches to the study of technical analysis (TA) often focus on the performance of a single indicator, which seems to fall short in scope and depth. We use a genetic algorithm (GA) to optimize trading strategies in the three major Forex markets in order to ascertain the suitability of TA strategies and rules to achieve consistently superior returns, by comparing momentum, trend and breakout indicators. The indicators with the parameters generated through our GA consistently outperform the equivalent indicators by applying parameters commonly used by the trading industry. EUR/USD and GBP/USD markets have interesting return figures before trading costs. The inclusion of spreads and commissions weakens returns substantially, suggesting that under a more realistic set of assumptions these markets could be efficient. Trend indicators generate better outcomes and GBP/USD qualifies as the most profitable market. Different aggregate returns in different markets may be evidence of distinct maturation stages under an evolving efficiency market perspective. Our GA is able to search a wider solution space than traditional configurations and offers the possibility of recovering latent data, thus avoiding premature convergence. |
id |
RCAP_b4fd78ba93a4708e41ee7bd32f8c66b1 |
---|---|
oai_identifier_str |
oai:estudogeral.uc.pt:10316/45565 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
A Comparative Study of Technical Trading Strategies Using a Genetic AlgorithmGenetic algorithmOptimizationFinanceTechnical analysisForexTraditional approaches to the study of technical analysis (TA) often focus on the performance of a single indicator, which seems to fall short in scope and depth. We use a genetic algorithm (GA) to optimize trading strategies in the three major Forex markets in order to ascertain the suitability of TA strategies and rules to achieve consistently superior returns, by comparing momentum, trend and breakout indicators. The indicators with the parameters generated through our GA consistently outperform the equivalent indicators by applying parameters commonly used by the trading industry. EUR/USD and GBP/USD markets have interesting return figures before trading costs. The inclusion of spreads and commissions weakens returns substantially, suggesting that under a more realistic set of assumptions these markets could be efficient. Trend indicators generate better outcomes and GBP/USD qualifies as the most profitable market. Different aggregate returns in different markets may be evidence of distinct maturation stages under an evolving efficiency market perspective. Our GA is able to search a wider solution space than traditional configurations and offers the possibility of recovering latent data, thus avoiding premature convergence.Springer Verlag2016-12-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/45565http://hdl.handle.net/10316/45565https://doi.org/10.1007/s10614-016-9641-9eng0927-7099http://dx.doi.org/10.1007/s10614-016-9641-9Macedo, Luís LobatoGodinho, PedroAlves, Maria Joãoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2021-09-22T09:14:07Zoai:estudogeral.uc.pt:10316/45565Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:49:51.180341Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm |
title |
A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm |
spellingShingle |
A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm Macedo, Luís Lobato Genetic algorithm Optimization Finance Technical analysis Forex |
title_short |
A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm |
title_full |
A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm |
title_fullStr |
A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm |
title_full_unstemmed |
A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm |
title_sort |
A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm |
author |
Macedo, Luís Lobato |
author_facet |
Macedo, Luís Lobato Godinho, Pedro Alves, Maria João |
author_role |
author |
author2 |
Godinho, Pedro Alves, Maria João |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Macedo, Luís Lobato Godinho, Pedro Alves, Maria João |
dc.subject.por.fl_str_mv |
Genetic algorithm Optimization Finance Technical analysis Forex |
topic |
Genetic algorithm Optimization Finance Technical analysis Forex |
description |
Traditional approaches to the study of technical analysis (TA) often focus on the performance of a single indicator, which seems to fall short in scope and depth. We use a genetic algorithm (GA) to optimize trading strategies in the three major Forex markets in order to ascertain the suitability of TA strategies and rules to achieve consistently superior returns, by comparing momentum, trend and breakout indicators. The indicators with the parameters generated through our GA consistently outperform the equivalent indicators by applying parameters commonly used by the trading industry. EUR/USD and GBP/USD markets have interesting return figures before trading costs. The inclusion of spreads and commissions weakens returns substantially, suggesting that under a more realistic set of assumptions these markets could be efficient. Trend indicators generate better outcomes and GBP/USD qualifies as the most profitable market. Different aggregate returns in different markets may be evidence of distinct maturation stages under an evolving efficiency market perspective. Our GA is able to search a wider solution space than traditional configurations and offers the possibility of recovering latent data, thus avoiding premature convergence. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-12-09 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/45565 http://hdl.handle.net/10316/45565 https://doi.org/10.1007/s10614-016-9641-9 |
url |
http://hdl.handle.net/10316/45565 https://doi.org/10.1007/s10614-016-9641-9 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0927-7099 http://dx.doi.org/10.1007/s10614-016-9641-9 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer Verlag |
publisher.none.fl_str_mv |
Springer Verlag |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799133778597117952 |