An Intelligent System for Determination of Stop – Loss and Take – Profit Limits: A Dynamic Decision Learning Approach

Mahmut Sami Sivri, Ahmet Berkay Gultekin, Alp Ustundag, Omer Faruk Beyca, Emre Ari*, Omer Faruk Gurcan

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

Stock market prices are notoriously difficult to predict for traders and investors alike. However, accurate stock market price predictions can result in high returns and substantial percentages of returns for investors and traders. Unfortunately, stock price data is inherently complex, noisy, and nonlinear, making it a challenging task. As technology continues to advance, trading strategies are beginning to adapt to automated systems instead of relying on manual analysis. Dynamically determining buying and selling levels in automated systems has become increasingly important. Many traders and investors seek to minimize losses and maximize profits by utilizing technical analysis methods and implementing stop-loss and take-profit strategies. Technical analysis methods are commonly used by traders and investors to determine and set predetermined thresholds for existing positions, as well as enter positions with stop-loss and take-profit orders. In this study, the main objective is to determine stop-loss and take-profit levels dynamically by analysing historical data using standard deviation and Sharp Ratios. To decide on the selling (short) or buying (long) position, TP\SL levels have been divided into two separate parts with different approaches. The approaches in this study aim to compare the end-of-day Open to Close returns with TP\SL level returns to determine the best course of action. Overall, this study aims to develop effective trading strategies that can minimize losses and maximize profits in the volatile world of stock market trading.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıIntelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference
EditörlerCengiz Kahraman, Irem Ucal Sari, Basar Oztaysi, Sezi Cevik Onar, Selcuk Cebi, A. Çağrı Tolga
YayınlayanSpringer Science and Business Media Deutschland GmbH
Sayfalar617-624
Sayfa sayısı8
ISBN (Basılı)9783031397769
DOI'lar
Yayın durumuYayınlandı - 2023
EtkinlikIntelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference - Istanbul, Turkey
Süre: 22 Ağu 202324 Ağu 2023

Yayın serisi

AdıLecture Notes in Networks and Systems
Hacim759 LNNS
ISSN (Basılı)2367-3370
ISSN (Elektronik)2367-3389

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???event.eventtypes.event.conference???Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot22/08/2324/08/23

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Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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