高复杂度交易模型在加密市场中失效，因无法克服市场摩擦与信息不对称。
The Red Queen's Trap: Limits of Deep Evolution in High-Frequency Trading
- 用500个智能体结合深度学习与进化算法进行高频交易实验
- 训练时年化收益超300%，实盘却资本损失超70%
- 揭示了不确定性过拟合、进化选择偏差和微观结构摩擦的致命缺陷
深度强化学习与进化计算融合常被视为算法交易的‘圣杯’，有望实现对非平稳市场环境的自主适应。本文对‘银河帝国’这一混合框架进行了严谨的复盘分析，该框架结合基于LSTM/Transformer的感知模块与遗传‘时间即生命’生存机制。在高频加密货币环境中部署500个自主智能体，观察到训练指标（验证年化收益率>300%）与实际表现（资本衰减>70%）之间灾难性背离。通过多学科视角剖析失败原因，识别出三大关键问题：低熵时间序列中随机不确定性过拟合、高方差下进化选择固有的幸存者偏差，以及在缺乏订单流数据的前提下数学上无法克服微观结构摩擦。研究提供实证证据表明，在缺乏信息优势的情况下，提升模型复杂度反而加剧系统脆弱性。
原文摘要 · Abstract (English)
The integration of Deep Reinforcement Learning (DRL) and Evolutionary Computation (EC) is frequently hypothesized to be the "Holy Grail" of algorithmic trading, promising systems that adapt autonomously to non-stationary market regimes. This paper presents a rigorous post-mortem analysis of "Galaxy Empire," a hybrid framework coupling LSTM/Transformer-based perception with a genetic "Time-is-Life" survival mechanism. Deploying a population of 500 autonomous agents in a high-frequency cryptocurrency environment, we observed a catastrophic divergence between training metrics (Validation APY $>300\%$) and live performance (Capital Decay $>70\%$). We deconstruct this failure through a multi-disciplinary lens, identifying three critical failure modes: the overfitting of \textit{Aleatoric Uncertainty} in low-entropy time-series, the \textit{Survivor Bias} inherent in evolutionary selection under high variance, and the mathematical impossibility of overcoming microstructure friction without order-flow data. Our findings provide empirical evidence that increasing model complexity in the absence of information asymmetry exacerbates systemic fragility.
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