Applying reinforcement learning in Bitcoin trading to select technical strategies based on Deep Q-Network

This paper proposes a Deep Q-Network-based reinforcement learning framework to optimize the selection of technical trading strategies for Bitcoin. Instead of directly predicting prices, the agent chooses among five predefined strategies—RSI, SMA Crossover, Bollinger Bands, Momentum-20d, and VWAP Reversion—allowing actions to remain interpretable while adapting dynamically to market conditions. Market states are constructed from technical indicators, normalized, and reduced in dimensionality using Principal Component Analysis to ensure robustness and efficiency. Empirical tests on Bitcoin data from 2022 to mid-2025 show remarkable performance: starting from $1,000,000, the agent achieved more than 120-fold growth in Net Asset Value, significantly outperforming Buy-and-Hold and single-strategy benchmarks. The model flexibly emphasized momentum during uptrends, limited mean-reversion during breakouts, and managed drawdowns when temporarily misaligned. Out-of-sample results further confirmed generalization. Overall, the framework demonstrates a practical, explainable, and scalable approach to algorithmic trading in digital asset markets.