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https://hdl.handle.net/20.500.11851/2006
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Şahin, Uğur | - |
dc.contributor.author | Özbayoğlu, Ahmet Murat | - |
dc.date.accessioned | 2019-07-10T14:42:45Z | |
dc.date.available | 2019-07-10T14:42:45Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Sahin, 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.issn | 1877-0509 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1877050914013350?via%3Dihub | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/2006 | - |
dc.description | Complex Adaptive Systems (2014 : United States) | |
dc.description.abstract | RSI 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.sponsorship | Missouri S and T, Penn State Online and INCOSE | |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER Science BV | en_US |
dc.relation.ispartof | Procedia Computer Science | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | stock market forecasting | en_US |
dc.subject | RSI | en_US |
dc.subject | genetic algorithms | en_US |
dc.subject | stock trading | en_US |
dc.subject | evolutionary computation | en_US |
dc.subject | trend detection | en_US |
dc.title | TN-RSI: Trend-Normalized RSI indicator for Stock Trading Systems with Evolutionary Computation | en_US |
dc.type | Conference Object | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 36 | |
dc.identifier.startpage | 240 | |
dc.identifier.endpage | 245 | |
dc.authorid | 0000-0001-7998-5735 | - |
dc.identifier.wos | WOS:000349978000031 | en_US |
dc.identifier.scopus | 2-s2.0-84938564135 | en_US |
dc.institutionauthor | Özbayoğlu, Ahmet Murat | - |
dc.identifier.doi | 10.1016/j.procs.2014.09.086 | - |
dc.authorwosid | H-2328-2011 | - |
dc.authorscopusid | 6505999525 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | - | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | Conference Object | - |
item.grantfulltext | open | - |
crisitem.author.dept | 02.1. 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 |
Files in This Item:
File | Description | Size | Format | |
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ozbayoglu-TN-RSI.pdf | 344.37 kB | Adobe PDF | View/Open |
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