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Conference Paper: Combining Technical Trading Rules Using Parallel Particle Swarm Optimization based on Hadoop

TitleCombining Technical Trading Rules Using Parallel Particle Swarm Optimization based on Hadoop
Authors
Issue Date2014
PublisherI E E E.
Citation
International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6-11 July 2014. In International Joint Conference on Neural Networks Proceedings, 2014, p. 3987-3994 How to Cite?
AbstractTechnical trading rules have been utilized in the stock markets to make profit for more than a century. However, no single trading rule can ever be expected to predict the stock price trend accurately. In fact, many investors and fund managers make trading decisions by combining a bunch of technical indicators. In this paper, we consider the complex stock trading strategy, called Performance-based Reward Strategy (PRS), proposed by [1]. Instead of combining two classes of technical trading rules, we expand the scope to combine the seven most popular classes of trading rules in financial markets, resulting in a total of 1059 component trading rules. Each component rule is assigned a starting weight and a reward/penalty mechanism based on rules' recent profit is proposed to update their weights over time. To determine the best parameter values of PRS, we employ an improved time variant particle swarm optimization (TVPSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to a large number of component rules and swarm size, the optimization time is significant. A parallel PSO based on Hadoop, an open source parallel programming model of MapReduce, is employed to optimize PRS more efficiently. The experimental results show that PRS outperforms all of the component rules in the testing period.
Persistent Identifierhttp://hdl.handle.net/10722/203630
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWang, Fen_US
dc.contributor.authorYu, PLHen_US
dc.contributor.authorCheung, DWLen_US
dc.date.accessioned2014-09-19T15:49:07Z-
dc.date.available2014-09-19T15:49:07Z-
dc.date.issued2014en_US
dc.identifier.citationInternational Joint Conference on Neural Networks (IJCNN), Beijing, China, 6-11 July 2014. In International Joint Conference on Neural Networks Proceedings, 2014, p. 3987-3994en_US
dc.identifier.isbn9781479966271-
dc.identifier.urihttp://hdl.handle.net/10722/203630-
dc.description.abstractTechnical trading rules have been utilized in the stock markets to make profit for more than a century. However, no single trading rule can ever be expected to predict the stock price trend accurately. In fact, many investors and fund managers make trading decisions by combining a bunch of technical indicators. In this paper, we consider the complex stock trading strategy, called Performance-based Reward Strategy (PRS), proposed by [1]. Instead of combining two classes of technical trading rules, we expand the scope to combine the seven most popular classes of trading rules in financial markets, resulting in a total of 1059 component trading rules. Each component rule is assigned a starting weight and a reward/penalty mechanism based on rules' recent profit is proposed to update their weights over time. To determine the best parameter values of PRS, we employ an improved time variant particle swarm optimization (TVPSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to a large number of component rules and swarm size, the optimization time is significant. A parallel PSO based on Hadoop, an open source parallel programming model of MapReduce, is employed to optimize PRS more efficiently. The experimental results show that PRS outperforms all of the component rules in the testing period.en_US
dc.languageengen_US
dc.publisherI E E E.-
dc.relation.ispartofInternational Joint Conference on Neural Networks Proceedingsen_US
dc.rightsInternational Joint Conference on Neural Networks Proceedings. Copyright © I E E E.-
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleCombining Technical Trading Rules Using Parallel Particle Swarm Optimization based on Hadoopen_US
dc.typeConference_Paperen_US
dc.identifier.emailYu, PLH: plhyu@hku.hken_US
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_US
dc.identifier.authorityYu, PLH=rp00835en_US
dc.identifier.authorityCheung, DWL=rp00101en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/IJCNN.2014.6889599-
dc.identifier.hkuros237506en_US
dc.identifier.spage3987-
dc.identifier.epage3994-
dc.publisher.placeUnited States-

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