File Download

Conference Paper: Complex stock trading strategy based on particle swarm optimization

TitleComplex stock trading strategy based on particle swarm optimization
Authors
Issue Date2012
Citation
The 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr), New York City, NY., 29-30 March 2012., p. 1-6 How to Cite?
AbstractTrading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this paper, a complex stock trading strategy, namely weight reward strategy (WRS), is proposed. WRS combines the two most popular classes of trading rules-moving average (MA) and trading range break-out (TRB). For both MA and TRB, WRS includes different combinations of the rule parameters to get a universe of 140 component trading rules in all. Each component rule is assigned a start weight and a reward/penalty mechanism based on profit is proposed to update these rules’ weights over time. To determine the best parameter values of WRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by WRS. The experiments show that our proposed WRS optimized by PSO outperforms the best moving average and trading range break-out rules.
DescriptionTechnical Session 1B - Advanced Algorithmic Trading – I: no. 41
Persistent Identifierhttp://hdl.handle.net/10722/164922

 

DC FieldValueLanguage
dc.contributor.authorWang, Fen_US
dc.contributor.authorYu, PLHen_US
dc.contributor.authorCheung, DWLen_US
dc.date.accessioned2012-09-20T08:12:24Z-
dc.date.available2012-09-20T08:12:24Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr), New York City, NY., 29-30 March 2012., p. 1-6en_US
dc.identifier.urihttp://hdl.handle.net/10722/164922-
dc.descriptionTechnical Session 1B - Advanced Algorithmic Trading – I: no. 41-
dc.description.abstractTrading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this paper, a complex stock trading strategy, namely weight reward strategy (WRS), is proposed. WRS combines the two most popular classes of trading rules-moving average (MA) and trading range break-out (TRB). For both MA and TRB, WRS includes different combinations of the rule parameters to get a universe of 140 component trading rules in all. Each component rule is assigned a start weight and a reward/penalty mechanism based on profit is proposed to update these rules’ weights over time. To determine the best parameter values of WRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by WRS. The experiments show that our proposed WRS optimized by PSO outperforms the best moving average and trading range break-out rules.-
dc.languageengen_US
dc.relation.ispartof2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleComplex stock trading strategy based on particle swarm optimizationen_US
dc.typeConference_Paperen_US
dc.identifier.emailWang, F: fwang@cs.hku.hken_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.naturepostprint-
dc.identifier.hkuros208977en_US
dc.identifier.spage1-
dc.identifier.epage6-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats