Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2006
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dc.contributor.authorŞahin, Uğur-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.date.accessioned2019-07-10T14:42:45Z
dc.date.available2019-07-10T14:42:45Z
dc.date.issued2014
dc.identifier.citationSahin, U., & Ozbayoglu, A. M. (2014). TN-RSI: Trend-normalized RSI indicator for stock trading systems with evolutionary computation. Procedia Computer Science, 36, 240-245.en_US
dc.identifier.issn1877-0509
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050914013350?via%3Dihub-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2006-
dc.descriptionComplex Adaptive Systems (2014 : United States)
dc.description.abstractRSI is a commonly used indicator preferred by stock traders. However, even though it works well when the market is trendless, during bull or bear market conditions (when there is a clear trend) its performance degrades. In this study, we developed a trading model using a modified RSI using trend-removed stock data. The model has several parameters including, the trend detection period, RSI buy-sell trigger levels and periods. These parameters are optimized using genetic algorithms; then the trading performance is compared against B&H and standard RSI indicator usage. 9 different ETFs are selected for evaluating trading performance. The results indicate there is a performance improvement both in profit and success rates using this new model. As future work, other indicators might be modelled in a similar fashion in order to see if it is possible to find one indicator that can work under any market condition. (C) 2014 The Authors. Published by Elsevier B.V.en_US
dc.description.sponsorshipMissouri S and T, Penn State Online and INCOSE
dc.language.isoenen_US
dc.publisherELSEVIER Science BVen_US
dc.relation.ispartofProcedia Computer Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectstock market forecastingen_US
dc.subjectRSIen_US
dc.subjectgenetic algorithmsen_US
dc.subjectstock tradingen_US
dc.subjectevolutionary computationen_US
dc.subjecttrend detectionen_US
dc.titleTN-RSI: Trend-Normalized RSI indicator for Stock Trading Systems with Evolutionary Computationen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume36
dc.identifier.startpage240
dc.identifier.endpage245
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000349978000031en_US
dc.identifier.scopus2-s2.0-84938564135en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.contributor.YOKid142991-
dc.identifier.doi10.1016/j.procs.2014.09.086-
dc.authorwosidH-2328-2011-
dc.authorscopusid6505999525-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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