TY  - JOUR
T1  - Genetic Algorithm Based Approach for Algorithmic Trading in Financial Markets
TT  - Finansal Piyasalarda Algoritmik Ticaret İçin Genetik Algoritma Temeli Yaklaşım
AU  - Santur, Yunus
AU  - Ulaş, Mustafa
AU  - Karabatak, Murat
PY  - 2023
DA  - December
DO  - 10.46460/ijiea.1176888
JF  - International Journal of Innovative Engineering Applications
JO  - ijiea, IJIEA
PB  - Niyazi ÖZDEMİR
WT  - DergiPark
SN  - 2587-1943
SP  - 164
EP  - 169
VL  - 7
IS  - 2
LA  - en
AB  - Software that enables realtime buy and sell transactions in financial markets according to predetermined conditions is called algorithmic trading. When developing algorithmic trading robots, indicators used in technical analysis are generally used. For the strategy selection of the robot, a process called Backtest is performed on the historical time series. The purpose of the Backtest process is the process of obtaining and interpreting values such as the number of successful/unsuccessful trades, the portfolio cash value after the commission to be paid to the intermediary institution, the profit factor and the sharpe ratio. The biggest disadvantage in this process is the selection of the appropriate stock, period, indicator and their parameters. Linear programming approaches are mostly used in the selection of these parameters that optimize the Backtest process optimally. However, according to the strategy to be used, the coding of these algorithms can have a linear, quadratic or polynomial complexity. This requires more long testing times for investors and algorithmic robot developers. Genetic algorithm-based approaches inspired by nature, on the other hand, converge to the optimal solution with much less iteration and require less processing power and time. In this study, a genetic programming-based approach is proposed for the selection of optimal conditions in algorithmic trading. In the experimental studies section, it has been seen that the use of traditional and genetic algorithm-based approaches in algorithmic trading operations has advantages when comparing complexity.
KW  - Algorithmic trading
KW  - genetic algorithm
KW  - optimization
N2  - Finansal piyasalarda önceden belirlenmiş koşullara göre anlık al sat işlemlerinin yapılmasını sağlayan yazılımlara algoritmik ticaret denilmektedir. Algoritmik işlem robotları geliştirilirken genellikle teknik analizde kullanılan göstergeler kullanılmaktadır. Robotun strateji seçimi için geçmiş veriler üzerinde Backtest adı verilen işlem gerçekleştirilmektedir. Backtest işleminin amacı gerçekleştirilen başarılı/başarısız ticaret sayısı, aracı kuruma ödenecek komisyon sonrası portföy kasa değeri, kar faktörü ve sharpe oranı gibi değerlerin elde edilerek yorumlanması işlemidir. Bu süreçte en büyük dezavantaj   uygun stok, periyot, indikatör ve bunlara ait parametrelerin seçimidir. Backtest işlemini optimal olarak en iyileyen bu parametrelerin seçiminde çoğunlukla doğrusal programlama yaklaşımları kullanılmaktadır. Ancak kullanılacak stratejiye göre bu algoritmaların kodlanması lineer bir karmaşıklıktan, quadratic veya polynomial karmaşıklığa sahip olabilmektedir. Bu durum yatırımcılar ve algoritmik robot geliştiriciler için uzun test süreleri gerektirmektedir. Doğadan esinlenerek geliştirilen genetik algoritma tabanlı yaklaşımlar ise çok daha az iterasyon ile optimal çözüme yakınsayarak, daha az işlem gücü ve zaman gerektirmektedir. Bu çalışmada algoritmik ticarette optimal koşulların seçimi için genetik programlama tabanlı bir yaklaşım önerilmiştir. Deneysel çalışmalar bölümünde, geleneksel ve genetik algoritma tabanlı yaklaşımların karmaşıklık, benchmark ve Backtest sonuçları karşılaştırıldığında algoritmik ticaret işlemlerinde kullanılmasının avantajlara sahip olduğu görülmüştür.
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UR  - https://doi.org/10.46460/ijiea.1176888
L1  - http://dergipark.org.tr/tr/download/article-file/2657267
ER  -