Machine Learning Models for Multi-Horizon Classification of Bitcoin Future Price Movements

This study presents a comprehensive framework for multi-horizon classification of Bitcoin futures price movements using machine learning. Five models of Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost were systematically evaluated across three timeframes (4h, 12h, 1d) and four classification schemes: binary (up/down) and multi-class (up/down/stable) with thresholds of 0.5%, 1.0%, and 1.5%, totaling 60 distinct experimental configurations. Rather than pursuing a strong predictive performance, this study prioritizes a rigorous comparative analysis across multiple dimensions to identify which combinations of model, timeframe, and classification scheme are most effective for Bitcoin futures. The results demonstrate that binary classification achieves the best predictive performance, with shorter timeframes yielding significantly better results, confirming the effectiveness of technical indicators in capturing the rapid price dynamics of Bitcoin futures. Notably, CatBoost achieved the highest F1-score for binary classification, while Random Forest proved the most robust model across diverse configurations. Feature importance analysis revealed that momentum-based indicators are the dominant predictors of price direction, while volatility features play a critical role in distinguishing sideways movements from directional ones in multi-class settings. Furthermore, the study demonstrates that narrower classification thresholds (0.5%) introduce noisier class boundaries and degrade performance, whereas wider thresholds (1.0%-1.5%) yield more stable results. These findings provide actionable guidelines for algorithmic trading in Bitcoin futures and establish a reproducible benchmark for future research.