Machine Learning-Based Forecasting of Bitcoin Price Movements

Abstract

In the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks, Gradient Boosting, LightGBM (LGBM) Regressor, and Random Forest algorithms to unravel the complexities of price movements. We perform a comprehensive analysis, and observe patterns and dependencies within historical data at hour-long intervals in the last 30 and 45 days, by using a holdout technique with 80% of the data used for training and 20% used for testing. We evaluate the models using four standard regression metrics. The training data incorporates a diverse range of features capturing hourly trends, day-of-the-week variations, and the correlation between opening and closing prices. Our study delves into the ability for forecasting Bitcoin price movements using ensemble algorithms and LSTM. The results show best performance for the LSTM models, especially when trained on longer training intervals. Namely, our LSTM model obtains R2 of 0.98 when trained on 30 days and 0.99 when trained on 45 days. In comparison, the ensemble methods show volatility and lower predictive ability.

Keywords

• cryptocurrency
• bitcoin
• machine learning
• long short-term memory
• random forest
• gradient boosting
• light gradient boosting