BITCOIN FİYAT HAREKETLERİNİN TAHMİNİ: RSI VE SMA GÖSTERGELERİNE DAYALI ALGORİTMİK TİCARET MODELİ

Öz

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.

Anahtar Kelimeler

• Bitcoin
• Rasgele Orman
• Algoritmik Ticaret
• RSI
• SMA

FORECASTING BITCOIN PRICE MOVEMENTS: AN ALGORITHMIC TRADING MODEL BASED ON RSI AND SMA INDICATORS

Öz

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.

Anahtar Kelimeler

• Bitcoin
• Random Forest
• Algorithmic Trading
• RSI
• SMA

Kaynakça

1. Achelis, S. (2001). Technical analysis from A to Z.
2. Alotaibi, S. (2021). Ensemble technique with optimal feature selection for Saudi stock market prediction: A novel hybrid red deer-grey algorithm. IEEE Access, 9, 64929–64944. https://doi.org/10.1109/ACCESS.2021.3073507
3. Anbalagan, T., & Maheswari, S. (2015). Classification and prediction of stock market index based on fuzzy metagraph. Procedia Computer Science, 47, 214–221. https://doi.org/10.1016/j.procs.2015.03.200
4. Arslan, M., Shahzad, A., Shafique, A., & Ahmed, W. (2025). Forecasting Bitcoin: A comparative analysis of traditional versus machine learning approach. In Transformations in Banking, Finance and Regulation.
5. Bâra, A., & Oprea, S.-V. (2024). An ensemble learning method for Bitcoin price prediction based on volatility indicators and trend. Engineering Applications of Artificial Intelligence, 133, 107991. https://doi.org/10.1016/j.engappai.2024.107991
6. Bozorgtabar, B., Aghajannashtaei, Z., & Gholizadeh, M. (2025). Credit risk modeling of cryptocurrency market using machine learning: Application to money laundering detection in Bitcoin transactions. Journal of Investment Knowledge, 14 (55), 531–556. https://doi.org/10.30495/jik.2025.23609
7. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
8. Büyükkör, Y. (2024). Derin öğrenme ve ekonometrik model ile Bitcoin fiyat tahmini: LSTM ve ARIMA. KMÜ Sosyal ve Ekonomik Araştırmalar Dergisi, 26 (47), 978–993.

9. Caliciotti, A., Corazza, M., & Fasano, G. (2024). From regression models to machine learning approaches for long-term Bitcoin price forecast. Annals of Operations Research, 336 (1), 359–381. https://doi.org/10.1007/s10479-023-05444-w
10. Chihab, Y., Bousbaa, Z., Chihab, M., Bencharef, O., & Ziti, S. (2019). Algo-trading strategy for intraweek foreign exchange speculation based on random forest and probit regression. Journal of Applied Mathematics, 1–13. https://doi.org/10.1155/2019/8342461
11. El Fadl, M., Abbey, B., & Choi, K. (2015). Effect of IT trading platform on financial risk-taking and portfolio performance. In 48th Hawaii International Conference on System Sciences (pp. 3298–3306). IEEE.
12. Escobar, A., Moreno, J., & Munera, S. (2013). A technical analysis indicator based on fuzzy logic. Electronic Notes in Theoretical Computer Science, 292, 27–37. https://doi.org/10.1016/j.entcs.2013.02.003
13. Farouk, M., Ragaba, N., Salama, D., Elrashidy, O., Mandour, L., Ahmed, M., Attia R., Ahmed N., Elazab, R. (2024). Bitcoin_ML: An efficient framework for Bitcoin price prediction using machine learning. Journal of Computing and Communication, 3 (1), 70–87.
14. Göğen, E., & Güney, S. (2024). Radyosonde rasatları ile makine öğrenmesi tabanlı hava durumu kestirimi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39 (4).
15. Grindsted, T. (2020). Trading on earthquakes: Algorithmic financialization of tectonic events at global stock exchanges. Geoforum, 111, 80–87. https://doi.org/10.1016/j.geoforum.2019.11.019
16. Islam, Z., Islam, S. I., Das, B. C., Reza, S. A., Bhowmik, P. K., Bishnu, K. K., Rahman M. S., Chowdhury, R., Pant, L. (2025). Machine learning-based detection and analysis of suspicious activities in Bitcoin wallet transactions in the USA. Journal of Ecohumanism, 4 (1), 3714–3734. https://doi.org/10.62754/joe.v4i1.6214
17. Jain, R., Srivastava, S., & Shukla, P. (2025). Predicting Bitcoin prices: A machine learning approach for accurate forecasting. In A. Tiwari & M. Darbari (Eds.), Emerging trends in computer science and its application (pp. 377–381). CRC Press.
18. Jia, X., & Lau, R. (2018). The control strategies for high frequency algorithmic trading. In 2018 IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE) (pp. 49–52). IEEE. https://doi.org/10.1109/CCSSE.2018.8724810
19. Khaidem, L., Saha, S., & Dey, S. (2016). Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003.
20. Khaniki, M., & Manthouri, M. (2024). Enhancing price prediction in cryptocurrency using transformer neural network and technical indicators. arXiv preprint arXiv:2403.03606.
21. Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random forest. In Information Computing and Applications: Third International Conference, ICICA 2012 (pp. 246–252). Springer.
22. Mansilha, M., & Simoes, M. (2024). Bitcoin price prediction using Monte Carlo simulation.
23. Mathur, M., Mhadalekar, S., Mhatre, S., & Mane, V. (2021). Algorithmic trading bot. In ITM Web of Conferences, 40, 03041. https://doi.org/10.1051/itmconf/20214003041
24. McNally, S., Roche, J., & Caton, S. (2018). Predicting the price of Bitcoin using machine learning. In 2018 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (pp. 339–343). IEEE. https://doi.org/10.1109/PDP2018.2018.00060
25. Mehrdoust, F. (2024). Forecasting Bitcoin price by a hybrid structure based on ARIMA, SVM and LSSVM models. SSRN. https://doi.org/10.2139/ssrn.4374994
26. Metin, S. (2025). Derin öğrenme yöntemleri ile Bitcoin fiyat analizi. Munzur Üniversitesi Sosyal Bilimler Dergisi, 93–111.
27. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf
28. Nas, S., & Ünal, A. (2023). Bitcoin fiyat değişimlerinin makine öğrenmesi yöntemi ile tahmin edilmesi. İşletme Araştırmaları Dergisi, 15 (4), 2597–2608. https://doi.org/10.20491/isarder.2023.1735
29. Orte, F., Mira, J., Sánchez, M., & Solana, P. (2023). A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction. Research in International Business and Finance, 64, 101829. https://doi.org/10.1016/j.ribaf.2022.101829
30. Park, S., & Yang, J.-S. (2024). Machine learning models based on bubble analysis for Bitcoin market crash prediction. Engineering Applications of Artificial Intelligence, 135, 107999. https://doi.org/10.1016/j.engappai.2023.107999
31. Salkar, T., Shinde, A., Tamhankar, N., & Bhagat, N. (2021). Algorithmic trading using technical indicators. In 2021 International Conference on Communication Information and Computing Technology (ICCICT) (pp. 1–6). IEEE. https://doi.org/10.1109/ICCICT50803.2021.9510135
32. Sinha, H. (2024). Predicting Bitcoin prices using machine learning techniques with historical data. International Journal of Creative Research Thoughts (IJCRT), 12 (8).
33. Sukma, N., & Namahoot, C. (2024). An algorithmic trading approach merging machine learning with multi-indicator strategies for optimal performance. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3516053
34. Tayib, H., & Abdulazeez, A. (2024). A review of Bitcoin price prediction based on deep learning algorithms. Journal of Soft Computing and Data Mining, 13 (2), 3582–3612.
35. Tripathi, B., & Sharma, R. (2023). Modeling Bitcoin prices using signal processing methods, Bayesian optimization, and deep neural networks. Computational Economics, 62 (4), 1919–1945. https://doi.org/10.1007/s10614-022-10325-8
36. Vijh, M., Chandola, D., Tikkiwal, V., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167, 599–602. https://doi.org/10.1016/j.procs.2020.03.326
37. Vo, A., & Yost-Bremm, C. (2020). A high-frequency algorithmic trading strategy for cryptocurrency. Journal of Computer Information Systems, 60 (6), 555–568. https://doi.org/10.1080/08874417.2018.1552090