Developing Robust Crypto-Currency Trading Strategies: A Fusion of Machine Learning and Hidden Markov Modelling for Enhanced Performance

This study addresses the intricate challenges of predicting Bitcoin (BTC) prices and devising effective trading strategies by employing advanced models. Cryptocurrency markets, notably Bitcoin, exhibit dynamic behaviors that challenge traditional forecasting methods. This research introduces a comprehensive approach, combining Deep Learning models—specifically LSTM, Bi-LSTM, ARIMA, and Facebook Prophet—with Hidden Markov Models (HMMs). The proposed models demonstrate exceptional accuracy through rigorous evaluation, with the best achieving a noteworthy R2 Score of 98%. The integration of HMMs enhances the forecasting capabilities and facilitates the development of a robust trading strategy. This research contributes valuable insights into the fusion of deep learning and probabilistic modeling for improved BTC price prediction and trading decisions. The study’s outcomes underscore the potential of advanced techniques in optimizing decision-making processes within the volatile cryptocurrency landscape.