Bitcoin Trend Prediction with Attention-Based Deep Learning Models and Technical Indicators

This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in normalizing these indicators to accurately reflect market trends and reversals. Utilizing historical OHLCV data along with four key technical indicators (SMA, EMA, TEMA, and MACD), the models classify trends into uptrend, downtrend, and neutral categories. Experimental results demonstrate that the inclusion of technical indicators, particularly MACD, significantly improves prediction accuracy. Furthermore, the Attention-GRU model offers computational efficiency suitable for real-time applications, while the Attention-LSTM model excels in capturing long-term dependencies. These findings contribute valuable insights for financial forecasting, providing practical tools for cryptocurrency traders and investors.