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
142991
Keywords: stock market forecasting
RSI
genetic algorithms
stock trading
evolutionary computation
trend detection
Issue Date: 2014
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

Files in This Item:
File Description SizeFormat 
ozbayoglu-TN-RSI.pdf344.37 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

SCOPUSTM   
Citations

11
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

7
checked on Sep 24, 2022

Page view(s)

24
checked on Nov 28, 2022

Download(s)

2
checked on Nov 28, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.