Research on Optimization of Cryptocurrency Trading Strategies Based on Reinforcement Learning – combining traditional machine learning and deep reinforcement learning methods

The cryptocurrency market, with its 24/7 trading and high volatility, challenges traditional quantitative strategies in path dependency, high-frequency optimization, and risk control. A "prediction-decision" framework is proposed, integrating Gradient Boosting Regression Trees (GBRT) for short-term forecasting and deep reinforcement learning techniques including Rainbow Deep Q-Network (Rainbow DQN) and Soft Actor-Critic (SAC) algorithms for dynamic optimization. The framework combines the complementary strengths of GBRT's pattern recognition capabilities and deep reinforcement learning's adaptive decision-making mechanisms. A spatiotemporal experience replay mechanism tailored to cryptocurrency fat-tailed distributions boosts BTC/USDT annual returns by 37.2% and enhances TD3's drawdown control by 63% during the LUNA crisis. Empirical results show SAC achieves 152% excess returns (Sharpe 2.81) in ETH/USDT trading, while Rainbow DQN yields 287% returns in trend markets. A dynamic reward function reduces maximum drawdown from 42.7% to 19.3%, and the hybrid architecture curtails losses by 23.8% during the 2020 "March 12" crash. Curriculum learning accelerates TD3 convergence by 59% with 37% GPU memory reduction. This study establishes a verifiable algorithmic framework and advances high-frequency trading through synergistic ML/RL integration, offering a dynamic decision-making paradigm for evolving financial markets.