[
    {
        "id": "osp-19537",
        "type": "article-journal",
        "title": "Trading Strategy Optimization via Textual Gradient",
        "author": [
            {
                "family": "Yang",
                "given": "Chaoqun"
            },
            {
                "family": "Wang",
                "given": "Qian"
            },
            {
                "family": "Zhu",
                "given": "Fengbin"
            },
            {
                "family": "Lin",
                "given": "Xinyu"
            },
            {
                "family": "He",
                "given": "Bingsheng"
            },
            {
                "family": "Zimmermann",
                "given": "Roger"
            },
            {
                "family": "Chua",
                "given": "Tat-Seng"
            }
        ],
        "URL": "https://omanscience.com/en/articles/trading-strategy-optimization-via-textual-gradient",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Quantitative trading strategy design aims to discover trading programs from historical data that remain effective in future markets, which can be viewed as a black-box program optimization problem. LLM-based textual gradients offer a promising approach by providing explicit optimization directions for iterative strategy refinement. However, directly applying textual gradients faces two challenges: (1) optimization is myopic, underutilizing experience from previous evaluations; and (2) aggregate backtest feedback overlooks temporal robustness, potentially favoring strategies that perform well only in specific market periods. To address these challenges, we propose TradeGrad, an experience-guided textual-gradient framework for robust trading strategy optimization. TradeGrad leverages accumulated optimization experience to estimate textual gradients and employs multi-scale revisions for both strategy exploration and refinement. It further introduces the Cross-Period Robust Objective (CPRO), which emphasizes performance in unfavorable historical periods to promote temporal robustness. Experiments on cross-sectional and time-series strategy design in Chinese A-share and U.S. equity markets show that TradeGrad achieves the best in-sample and out-of-sample performance across all four settings. Notably, its Chinese cross-sectional strategy achieves 27.99% annualized return, 12.19% maximum drawdown, and a Sharpe ratio of 1.63, approximately 68% higher than the CSI 300 benchmark. Further analyses validate the proposed components and show consistent improvements in both in-sample and out-of-sample performance throughout optimization. The code is available at https://github.com/transcend-0/TradeGrad."
    }
]