Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/2006
Title: | TN-RSI: Trend-Normalized RSI indicator for Stock Trading Systems with Evolutionary Computation | Authors: | Şahin, Uğur Özbayoğlu, Ahmet Murat |
Keywords: | stock market forecasting RSI genetic algorithms stock trading evolutionary computation trend detection |
Publisher: | ELSEVIER Science BV | Source: | 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. | 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. | Description: | Complex Adaptive Systems (2014 : United States) | URI: | https://www.sciencedirect.com/science/article/pii/S1877050914013350?via%3Dihub https://hdl.handle.net/20.500.11851/2006 |
ISSN: | 1877-0509 |
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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ozbayoglu-TN-RSI.pdf | 344.37 kB | Adobe PDF | View/Open |
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