The GT-Score: A Robust Objective Function for Reducing Overfitting in Data-Driven Trading Strategies

TL;DR

The paper proposes the GT-Score, a new composite objective function designed to reduce overfitting in data-driven trading strategies, leading to more reliable and robust financial models.

Contribution

It introduces the GT-Score, an innovative objective function that incorporates performance, significance, consistency, and risk to enhance strategy robustness against overfitting.

Findings

GT-Score improves generalization ratio by 98% in validation.

Statistical tests show significant differences favoring GT-Score.

Embedding anti-overfitting structures enhances backtest reliability.

Abstract

Overfitting remains a critical challenge in data-driven financial modeling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency, and downside risk to guide optimization toward more robust trading strategies. This approach directly addresses critical pitfalls in quantitative strategy development, specifically data snooping during optimization and the unreliability of statistical inference under non-normal return distributions. Using historical stock data for 50 S&P 500 companies spanning 2010-2024, we conduct an empirical evaluation that includes walk-forward validation with nine sequential time splits and a Monte Carlo study with 15 random seeds across three trading strategies. In walk-forward validation, GT-Score improves the generalization ratio (validation return divided by training return) by 98% relative to baseline objective functions. Paired statistical tests on Monte Carlo out-of-sample returns indicate statistically detectable differences between objective functions (p < 0.01 for comparisons with Sortino and Simple), with small effect sizes. These results suggest that embedding an anti-overfitting structure into the objective can improve the reliability of backtests in quantitative research. Reproducible code and processed result files are provided as supplementary materials.