YOLO Object Recognition Algorithm and & x201C;Buy-Sell Decision & x201D; Model Over 2D Candlestick Charts

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dc.authoridKose, Utku/0000-0002-9652-6415
dc.authoridTemur, Gunay/0000-0002-7197-5804
dc.authorwosidKose, Utku/C-8683-2009
dc.authorwosidTemur, Gunay/AAC-4219-2020
dc.contributor.authorBirogul, Serdar
dc.contributor.authorTemur, Gunay
dc.contributor.authorKose, Utku
dc.date.accessioned2021-12-01T18:48:17Z
dc.date.available2021-12-01T18:48:17Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractEarning via real-time predictions with the experience in the visible trend directions of an investment instrument in the past requires a different perspective on charts. Indicators and formations within the scope of technical analysis constitute the most significant basis of this perspective. Those who can generate a high income in financial markets and even be more successful than large companies are actually the ones interpreting the data in a different way. In this study, a model which had never been encountered in the literature before, was designed through a different perspective on the same data, enabling the movements of an investment element over the 2D candlestick chart to be recognized as a & x201C;Buy-Sell & x201D; object respectively and to decide on the trend direction as a result. The model is trained by state-of-the-art, real-time object detection system (You Only Look Once) YOLO; for the training, one-year candlestick charts belonging to the stocks traded on Borsa & x0130;stanbul (BIST) between 2000 & x2013;2018 were used. The model, which can make a & x201C;Buy-Sell & x201D; decision without the need for an additional time series except for the views on the visual candlestick charts, is promising in terms of its successful predictions. Its ultimate aim is to provide a foresight strengthening the & x201C;Buy-Sell & x201D; decisions to be made in the decision-making process following the other basic and technical analyses in addition to its stand-alone use in making investment decisions. The effect of this foresight on the success can clearly be seen on the test results received. In the results, the model was found to be successful by 85 & x0025; while a 100 & x0025; profit was generated. Besides, the model can be used for all the time series for which candlestick charts can be created.en_US
dc.identifier.doi10.1109/ACCESS.2020.2994282
dc.identifier.endpage91915en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85085559052en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage91894en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2994282
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10498
dc.identifier.volume8en_US
dc.identifier.wosWOS:000538738800020en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPredictive modelsen_US
dc.subjectInvestmenten_US
dc.subjectTime series analysisen_US
dc.subjectMachine learningen_US
dc.subjectVisualizationen_US
dc.subjectObject recognitionen_US
dc.subjectData modelsen_US
dc.subjectYOLOen_US
dc.subjectobject detection and classificationen_US
dc.subjectdecision support systemsen_US
dc.subjectdeep learningen_US
dc.subjectfinanceen_US
dc.subjecttrend decisionen_US
dc.titleYOLO Object Recognition Algorithm and & x201C;Buy-Sell Decision & x201D; Model Over 2D Candlestick Chartsen_US
dc.typeArticleen_US

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