Adversarial-Robust Deep Reinforcement Learning for High-Frequency Cryptocurrency Trading with Explainable AI Framework

High-frequency trading (HFT) in cryptocurrency markets has increasingly adopted deep reinforcement learning (DRL) algorithms to capitalize on microsecond-level price move- ments and market inefficiencies. However, the susceptibility of DRL models to adversarial attacks poses significant security risks, potentially leading to substantial financial losses and market manipulation. This paper introduces a novel adversarial- robust DRL framework specifically designed for high-frequency cryptocurrency trading, integrated with explainable AI (XAI) mechanisms to ensure regulatory compliance and transparency. We propose a multi-scale adversarial training methodology that addresses unique cryptocurrency market microstructure char- acteristics, including 24/7 trading, extreme volatility, and frag- mented liquidity across exchanges. Our framework incorporates five distinct attack vectors—FGSM, PGD, C&W, order book manipulation, and latency-based attacks—to comprehensively evaluate robustness. Defense mechanisms include adversarial training, defensive distillation, and a novel dynamic adapta- tion strategy that adjusts defenses based on real-time market conditions. The explainability component integrates SHAP for global feature importance and LIME for local decision in- terpretation, maintaining sub-10ms latency to meet HFT re- quirements. Experimental results on Bitcoin, Ethereum, and major altcoin datasets demonstrate that our adversarial-robust framework achieves 94.3% of baseline trading performance while successfully defending against 89.7% of adversarial attacks. The explainability framework provides transparent insights into trading decisions with 8.3ms average latency. This research con- tributes the first comprehensive adversarial defense framework for cryptocurrency HFT, advancing the state-of-the-art in secure and interpretable algorithmic trading systems.