Deep Learning Models for Bitcoin Price Prediction

Abstract

Abstract — The volatile nature of cryptocurrency markets has spurred interest in predictive models to aid investment and trading strategies. This study extends previous works by incorporating all available technical indicators, leveraging the TA library, and evaluating multiple deep learning architectures for Bitcoin price prediction. Using daily OHLC data for Bitcoin (BTC) sourced from Yahoo Finance, we implement Transformer-based Multi-Head Attention with GRU and LSTM layers, along with standalone LSTM and GRU models. These architectures are compared in terms of predictive accuracy to identify the most effective approach for capturing market dynamics. Our results provide a comprehensive analysis of model performance, highlighting the potential of advanced neural network architectures and technical indicators for improving cryptocurrency price prediction.