The Red Queen's Trap: Limits of Deep Evolution in High-Frequency Trading
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
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 textitAleatoric Uncertainty in low-entropy time-series, the textitSurvivor 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 21 references that are in the map. First posted on arXiv 5 Dec 2025.
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- 2017Attention Is All You Needcited by 60,361
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