{"ID":2832702,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15732","arxiv_id":"2512.15732","title":"The Red Queen's Trap: Limits of Deep Evolution in High-Frequency Trading","abstract":"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 $\u003e300\\%$) and live performance (Capital Decay $\u003e70\\%$). 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.","short_abstract":"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 fr...","url_abs":"https://arxiv.org/abs/2512.15732","url_pdf":"https://arxiv.org/pdf/2512.15732v1","authors":"[\"Yijia Chen\"]","published":"2025-12-05T19:30:26Z","proceeding":"q-fin.TR","tasks":"[\"q-fin.TR\",\"cs.LG\",\"cs.NE\",\"q-fin.CP\"]","methods":"[\"Reinforcement Learning\",\"Transformer\"]","has_code":false}