The Red Queen's Trap: Limits of Deep Evolution in High-Frequency Trading
This work highlights critical limitations in applying complex AI methods to high-frequency trading, showing they can fail catastrophically without addressing information asymmetry, which is important for researchers and practitioners in algorithmic finance.
The paper analyzed a hybrid deep reinforcement learning and evolutionary computation framework for high-frequency cryptocurrency trading, finding that it achieved over 300% APY in validation but led to over 70% capital decay in live deployment due to issues like overfitting and survivor bias.
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.