Hybrid machine learning and stochastic volatility models with blockchain data for high-frequency cryptocurrency trading

High-frequency cryptocurrency markets, such as Bitcoin, exhibit extreme volatility, posing significant challenges for traditional stochastic volatility models like the Heston framework, which often struggle to capture nonlinear patterns and sudden price jumps. This study proposes a hybrid framework that integrates the Heston stochastic volatility model with Long Short-Term Memory (LSTM) neural networks, leveraging real-time blockchain data feeds, including transaction counts, to enhance volatility forecasting. Using 1-minute Bitcoin data from January to March 2025, the hybrid model demonstrates superior forecasting accuracy, reducing the Mean Squared Error by 43% compared to the Heston model and by 20% compared to the standalone LSTM. In a high-frequency trading simulation over March 2025, the hybrid model achieves a cumulative return of 18.5%, a Sharpe ratio of 2.1, and a maximum drawdown of 4.2%, outperforming Heston (10.2%, 1.3, 6.8%) and LSTM (14.8%, 1.7, 5.5%). These findings highlight the model’s potential for algorithmic traders seeking robust volatility predictions and improved risk-adjusted returns in crypto markets. Additionally, the hybrid model’s interpretable stochastic base supports regulatory transparency, addressing compliance needs in a rapidly evolving financial landscape.