Risk-Aware Crypto Price Prediction Using DQN with Volatility-Adjusted Rewards Across Multi-Period State Representations

Forecasting Bitcoin prices remains a complex task due to the asset’s inherent and significant volatility. Traditional reinforcement learning (RL) models often rely on a single observation from the time series, potentially missing out on short-term patterns that could enhance prediction performance. This study presents a Deep Q-Network (DQN) model that utilizes a multi-step state representation, incorporating consecutive historical timesteps to reflect recent market behavior more accurately. By doing so, the model can more effectively identify short-term trends under volatile conditions. Additionally, we propose a novel reward mechanism that adjusts for volatility by penalizing large prediction errors more heavily during periods of high market volatility, thereby encouraging more risk-aware forecasting behavior. We validate the effectiveness of our approach through extensive experiments on Bitcoin data across minutely, hourly, and daily timeframes. The proposed model achieves notable results, including a Mean Absolute Percentage Error (MAPE) of 10.12%, Root Mean Squared Error (RMSE) of 815.33, and Value-at-Risk (VaR) of 0.04. These outcomes demonstrate the advantages of integrating short-term temporal features and volatility sensitivity into RL frameworks for more reliable cryptocurrency price prediction.