Bitcoin Trend Reversal Prediction with Tree-Based Ensemble Machine Learning

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

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