TY  - JOUR
T1  - BITCOIN FİYAT HAREKETLERİNİN TAHMİNİ: RSI VE SMA GÖSTERGELERİNE DAYALI ALGORİTMİK TİCARET MODELİ
TT  - FORECASTING BITCOIN PRICE MOVEMENTS: AN ALGORITHMIC TRADING MODEL BASED ON RSI AND SMA INDICATORS
AU  - Türkoğlu, Diler
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.16953/deusosbil.1644348
JF  - Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi
JO  - DEU Journal of GSSS
PB  - Dokuz Eylul University
WT  - DergiPark
SN  - 1308-0911
SP  - 1026
EP  - 1045
VL  - 27
IS  - 3
LA  - tr
AB  - Bu çalışma, Bitcoin’in fiyat hareketlerini tahmin etmek ve yatırımcılar için alım-satım sinyalleri üretmek amacıyla Random Forest sınıflandırıcı modelini kullanarak bir algoritmik ticaret stratejisi geliştirmeyi hedeflemiştir. Model, RSI ve SMA gibi teknik analiz göstergeleriyle desteklenmiş ve geçmiş fiyat hareketlerine dayalı olarak yüksek doğruluk oranları elde etmiştir. Yapılan analizler ve görselleştirmeler, modelin ürettiği sinyallerin piyasa hareketleri ile tutarlı olduğunu ve yatırımcı kararlarını optimize edebilecek nitelikte olduğunu göstermektedir. Elde edilen bulgular, Random Forest modelinin geçmiş verilerdeki fiyat değişimlerini başarılı şekilde tahmin ettiğini ve yatırımcılara doğru zamanda işlem yapma konusunda güvenilir sinyaller sunduğunu kanıtlamaktadır. Modelin başarı oranı, gerçek fiyat hareketleriyle karşılaştırıldığında oldukça yüksek olup, ticaret sinyallerinin zaman içindeki etkileri grafikler ile açıkça ortaya konmuştur. Bu çalışma, Bitcoin ve benzeri volatil piyasalarda yatırım stratejilerinin geliştirilmesine katkı sağlamayı amaçlamakta ve algoritmik ticaret modellerinin etkinliğini ortaya koymaktadır. Aynı zamanda çalışma teknik analiz göstergeleri ve makine öğrenmesi yöntemlerinin birleştirilerek, finansal piyasalarda ticaret stratejilerinin geliştirilmesinde etkili bir araç olabileceğini ortaya koymaktadır.
KW  - Bitcoin
KW  - Rasgele Orman
KW  - Algoritmik Ticaret
KW  - RSI
KW  - SMA
N2  - This study aimed to develop an algorithmic trading strategy using a Random Forest classifier model to predict Bitcoin price movements and generate buy-sell signals for investors. The model was supported by technical analysis indicators such as RSI and SMA, and achieved high accuracy rates based on historical price movements. The analyses and visualizations demonstrate that the signals generated by the model are consistent with market movements and have the potential to optimize investor decisions. The findings prove that the Random Forest model successfully predicts price changes in historical data and provides reliable signals to investors for executing trades at the right time. The model's success rate is notably high when compared to actual price movements, and the effects of trading signals over time have been clearly illustrated through graphs. This study aims to contribute to the development of investment strategies in volatile markets such as Bitcoin and similar assets, and demonstrates the effectiveness of algorithmic trading models. Additionally, the study reveals that combining technical analysis indicators with machine learning methods can be an effective tool in developing trading strategies for financial markets.
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UR  - https://doi.org/10.16953/deusosbil.1644348
L1  - https://dergipark.org.tr/en/download/article-file/4629664
ER  -