Abstract
Abstract The evaluation of quantitative strategies relies on fragmented metrics creating dangerous “evaluation traps,” concealing catastrophic tail risk (the “Sortino Trap”) or masking pathological volatility (the “Calmar Trap”). This deficiency is especially consequential in Deep Reinforcement Learning (DRL), where agents relentlessly optimize given objective functions. This paper introduces a unified, five-principle framework, deriving two robust metrics: the Generalized Robust Trading Strategy Score (GRTSS), integrating path-dependency and survival via a hyperbolic penalty, and the Log-Transformed Market Adaptability Index (MAIL), addressing the “luck vs. skill” dilemma by decoupling decision consistency from outcome magnitude. Validation via Monte Carlo simulations, real-world backtests, sensitivity analysis, and a rigorous multi-asset comparative analysis (2020–2024) covering Stocks, Gold, Forex, and Crypto demonstrates the framework’s superiority over industry standards, including STARR, CVaR, Omega, Kappa, Ulcer Index, DSR, Gain-Loss, Rachev, and CDaR. While competitors favored Bitcoin despite a 76% drawdown, GRTSS correctly prioritized capital preservation, identifying Gold as the superior risk-adjusted asset. Furthermore, while traditional metrics computationally break down in pathological scenarios (e.g., returning − 4.82e + 14), our framework remains mathematically stable. Finally, we empirically implement a differentiable Soft-GRTSS surrogate objective. By training a Proximal Policy Optimization (PPO) agent in a non-stationary synthetic market with embedded Black Swan events, we demonstrate that the agent successfully learns survival-aware policies, avoiding catastrophic ruin while achieving positive equity growth. This confirms Soft-GRTSS as a tractable, non-exploding gradient signal for the gradient-based training of risk-aware AI trading systems.