The Red Queen’s Trap: Limits of Deep Evolution in High-Frequency Trading

ArXiv ID: 2512.15732 “View on arXiv”

Authors: Yijia Chen

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 $>300%$) 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.

Keywords: Deep Reinforcement Learning, Evolutionary Computation, LSTM, overfitting, microstructure friction, Cryptocurrency (High-Frequency)

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced mathematical formulations including POMDPs, attention mechanisms, and information theory, while presenting rigorous empirical evidence from a high-fidelity live simulation with detailed failure analysis and specific performance metrics.
  flowchart TD
    A["Research Goal<br>Investigate DRL + EC<br>&quot;Holy Grail&quot; Hypothesis"] --> B["Methodology<br>Hybrid LSTM/Transformer +<br>Genetic Survival (Galaxy Empire)<br>500 Agents (Crypto HF)"]
    B --> C["Data & Inputs<br>Cryptocurrency Market Data<br>(Low Entropy, Microstructure Noise)"]
    C --> D["Computational Process<br>Validation: Training & Simulation<br>Observed: $>300\%$ Validation APY"]
    D --> E["Real-World Outcome<br>Live Deployment: Capital Decay $>70\%<br>Training/Live Divergence"]
    E --> F["Key Findings / Failure Modes<br>1. Overfitting Aleatoric Uncertainty<br>2. Survivor Bias in High Variance<br>3. Microstructure Friction (No Order Flow)"]
    F --> G["Conclusion<br>Complexity w/o Info Asymmetry<br>Increases Fragility"]