arXiv：cs.AI· Chaoqun Yang, Qian Wang, Fengbin Zhu, Xinyu Lin, Bingsheng He, Roger Zimmermann, Tat-Seng Chua·· 5 小时前AI 评分39
TradeGrad：用文本梯度优化交易策略
Trading Strategy Optimization via Textual Gradient
AI 导读
研究者提出 TradeGrad，一个经验引导的文本梯度框架，用于稳健的交易策略优化，通过积累的优化经验估计文本梯度并采用多尺度修正，同时引入 Cross-Period Robust Objective（CPRO）强调不利历史时期的表现以提升时间稳健性。
正文
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 this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.03128 [cs.AI] |
| (or arXiv:2610.03128v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03128 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chaoqun Yang [view email]
[v1]
Fri, 2 Oct 2026 10:48:35 UTC (971 KB)
来源：arXiv：cs.AI · arxiv.org