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dc.contributor.authorSahin, Ugur
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.urihttp://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.isoengen_US
dc.publisherELSEVIER Science BVen_US
dc.rightsinfo:eu-repo/semantics/openAccess
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.typeconferenceObjecten_US
dc.relation.journalProcedia Computer Scienceen_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Computer Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume36
dc.identifier.startpage240
dc.identifier.endpage245
dc.contributor.orcidhttps://orcid.org/0000-0001-7998-5735
dc.identifier.wosWOS:000349978000031
dc.identifier.scopus2-s2.0-84938564135
dc.contributor.tobbetuauthorÖzbayoğlu, Ahmet Murat
dc.contributor.YOKid142991
dc.identifier.doi10.1016/j.procs.2014.09.086
dc.contributor.wosresearcherIDH-2328-2011
dc.contributor.ScopusAuthorID6505999525
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıtr_TR


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