Abstract
We introduce RegimeNAS, a novel differentiable architecture search framework specifically designed to enhance cryptocurrency trading performance by explicitly integrating market regime awareness. Addressing the limitations of static deep learning models in highly dynamic financial environments, RegimeNAS features three core innovations: (1) a theoretically grounded Bayesian search space optimizing architectures with provable convergence properties; (2) specialized, dynamically activated neural modules (Volatility, Trend, and Range blocks) tailored for distinct market conditions; and (3) a multi-objective loss function incorporating market-specific penalties (e.g., volatility matching, transition smoothness) alongside mathematically enforced Lipschitz stability constraints. Regime identification leverages multi-head attention across multiple timeframes for improved accuracy and uncertainty estimation. Rigorous empirical evaluation on extensive real-world cryptocurrency data demonstrates that RegimeNAS significantly outperforms state-of-the-art benchmarks, achieving an 80.3% Mean Absolute Error reduction compared to the best traditional recurrent baseline and converging substantially faster (9 vs. 50+ epochs). Ablation studies and regime-specific analysis confirm the critical contribution of each component, particularly the regime-aware adaptation mechanism. This work underscores the imperative of embedding domain-specific knowledge, such as market regimes, directly within the NAS process to develop robust and adaptive models for challenging financial applications.
Abstract (translated)
我们介绍了一种新颖的可微架构搜索框架RegimeNAS，该框架专门设计用于通过明确地整合市场制度意识来提升加密货币交易的表现。针对静态深度学习模型在高度动态金融环境中的局限性，RegimeNAS具备三大核心创新：(1) 基于贝叶斯理论并具有证明收敛特性的优化架构搜索空间；(2) 专为不同市场条件定制的、可动态激活的神经模块（波动率、趋势和区间块）；以及 (3) 结合了市场特定惩罚（如波动匹配、转换平滑度等）及数学强制性Lipschitz稳定性约束的多目标损失函数。制度识别利用跨多个时间框架的多头注意力机制，以提高准确性和不确定性估计。 在广泛的现实世界加密货币数据上的严格实证评估表明，RegimeNAS显著优于最先进的基准测试，在均方绝对误差方面相比最好的传统循环基线模型减少了80.3%，并且收敛速度明显更快（9个周期对比50多个周期）。消融研究和制度特定分析证实了每个组件对整体性能的关键贡献，尤其是制度感知适应机制。这项工作强调了在NAS过程中直接嵌入领域特定知识（如市场制度）的重要性，以开发出适用于复杂金融应用的稳健且自适应模型。
URL
https://arxiv.org/abs/2508.11338
