Bitcoin Trend Reversal Prediction with Tree-Based Ensemble Machine Learning

Year 2024, , 13 - 22, 27.03.2024

Sergül Ürgenç , Barış Aşıkgil

https://doi.org/10.34110/forecasting.1390292

Abstract

In recent years, Bitcoin (BTC) has become the most popular digital asset in the cryptocurrency market. Its prices are highly volatile due to rapidly increasing investor interest, making it difficult to predict price movements. The aim of this study is to predict trend reversals in BTC price movements by using tree-based ensemble machine learning techniques and compare the success rates of these techniques. For this purpose, the study focuses on points where the trend changes. The ‘buy’, ‘sell’, and ‘hold’ classes are balanced through under-sampling. Extreme Gradient Boosting (XGB), Random Forest (RF) and Random Trees (RT) models are developed. The results are evaluated by using precision, recall, specificity, F1 score and accuracy metrics. The study concludes that the XGB model exhibits higher success compared to other models.

Keywords

Bitcoin trend prediction, Classification, Cryptocurrency price analysis, Machine learning, Tree-based algorithms

References

• [1] S. Ürgenç, Predicting Bitcoin Trends Reversals With Machine Learning Methods (Makine Öğrenmesi Yöntemleri ile Bitcoin Trend Dönüşlerinin Tahmin Edilmesi), (2023). Master Thesis, Mimar Sinan Fine Arts University, Istanbul.
• [2] N.T. İnce, Predicting The Bitcoin Trend Using Technical Indicators For Deep Learning Algorithmic Features, (2019). Master Thesis, Boğaziçi University, Istanbul.
• [3] Z. Qiang, J. Shen, Bitcoin High-Frequency Trend Prediction with Convolutional and Recurrent Neural Networks, Comput. Sci. (2021).
• [4] S. Cavalli, M. Amoretti, CNN-based multivariate data analysis for bitcoin trend prediction, Appl. Soft Comput. 101 (2021) 107065. doi: 10.1016/J.ASOC.2020.107065.
• [5] S. Alonso-Monsalve, A.L. Suárez-Cetrulo, A. Cervantes, D. Quintana, Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators, Expert Syst. Appl. 149 (2020) 113250. doi: 10.1016/J.ESWA.2020.113250.
• [6] I.E. Livieris, E. Pintelas, S. Stavroyiannis, P. Pintelas, Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series, Algorithms 2020, Vol. 13, Page 121. 13 (2020) 121. doi:10.3390/A13050121.
• [7] G. Cohen, Forecasting Bitcoin Trends Using Algorithmic Learning Systems, Entropy 2020, Vol. 22, Page 838. 22 (2020) 838. doi:10.3390/E22080838.
• [8] E. Akyildirim, A. Goncu, A. Sensoy, Prediction of cryptocurrency returns using machine learning, Ann. Oper. Res. 297 (2021) 3–36. doi:10.1007/S10479-020-03575-Y/TABLES/18.
• [9] M.A. Atçeken, Trading Strategy Based Classification On Cryptocurrency Price Prediction, (2021). Master Thesis, TED University, Ankara.
• [10] F. Valencia, A. Gómez-Espinosa, B. Valdés-Aguirre, Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning, Entropy 2019, Vol. 21, Page 589. 21 (2019) 589. doi:10.3390/E21060589.
• [11] S. Ji, J. Kim, H. Im, A Comparative Study of Bitcoin Price Prediction Using Deep Learning, Math. 2019, Vol. 7, Page 898. 7 (2019) 898. doi:10.3390/MATH7100898.
• [12] D.H. Kwon, J.B. Kim, J.S. Heo, C.M. Kim, Y.H. Han, Time Series Classification of Cryptocurrency Price Trend Based on a Recurrent LSTM Neural Network, J. Inf. Process. Syst. 15 (2019) 694–706. doi:10.3745/JIPS.03.0120.
• [13] T. Shintate, L. Pichl, Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning, J. Risk Financ. Manag. 2019, Vol. 12, Page 17. 12 (2019) 17. doi:10.3390/JRFM12010017.
• [14] T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 13-17-August-2016 (2016) 785–794. doi:10.1145/2939672.2939785.
• [15] L. Breiman, Random forests, Mach. Learn. 45 (2001) 5–32. doi:10.1023/A:1010933404324/METRICS.
• [16] C.R. Sekhar, Minal, E. Madhu, Mode Choice Analysis Using Random Forrest Decision Trees, Transp. Res. Procedia. 17 (2016) 644–652. doi: 10.1016/J.TRPRO.2016.11.119.
• [17] T.K. Ho, Random decision forests, Proc. Int. Conf. Doc. Anal. Recognition, ICDAR. 1 (1995) 278–282. doi:10.1109/ICDAR.1995.598994.
• [18] N. Boodhun, · Manoj Jayabalan, Risk prediction in life insurance industry using supervised learning algorithms, Complex Intell. Syst. 2018 42. 4 (2018) 145–154. doi:10.1007/S40747-018-0072-1.
• [19] J. Fan, C. Zhang, J. Zhang, Generalized Likelihood Ratio Statistics and Wilks Phenomenon. 29 (2001) 153–193. doi:10.1214/AOS/996986505.
• [20] R. A., Fisher, The Conditions Under Which χ2 Measures the Discrepancey Between Observation and Hypothesis, Journal of the Royal Statistical Society (1924) 442-450.
• [21] K. Pearson, LIII. On lines and planes of closest fit to systems of points in space, London, Edinburgh, Dublin Philos. Mag. J. Sci. 2 (1901) 559–572. doi:10.1080/14786440109462720.
• [22] H. Abdi, L.J. Williams, Principal component analysis, Wiley Interdiscip. Rev. Comput. Stat. 2 (2010) 433–459. doi:10.1002/WICS.101.
• [23] Binance - Bitcoin, Ethereum ve Altcoin’ler için Kripto Para Borsası, (n.d.). https://www.binance.com/tr (accessed October 12, 2023).