发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出TradeGrad框架，利用经验引导的文本梯度和跨期稳健目标优化交易策略，在多个市场实现最佳表现，显著提升收益和鲁棒性。
AI 中文摘要
量化交易策略设计旨在从历史数据中发现在未来市场中仍然有效的交易程序，这可以被视为一个黑箱程序优化问题。基于大语言模型的文本梯度提供了一种有前景的方法，通过为迭代策略优化提供明确的优化方向。然而，直接应用文本梯度面临两个挑战：（1）优化是短视的，未能充分利用先前评估的经验；（2）聚合的回测反馈忽视了时间鲁棒性，可能偏好仅在特定市场时期表现良好的策略。为应对这些挑战，我们提出了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。TradeGrad利用累积的优化经验来估计文本梯度，并采用多尺度修订进行策略探索和细化。它进一步引入了跨期稳健目标（CPRO），该目标强调在不利历史时期的表现，以促进时间鲁棒性。在中国A股和美国股票市场的横截面和时间序列策略设计实验表明，TradeGrad在所有四种设置中均实现了最佳的样本内和样本外表现。值得注意的是，其中国横截面策略实现了27.99%的年化收益率、12.19%的最大回撤和1.63的夏普比率，比沪深300基准高出约68%。进一步的分析验证了所提出的组件，并显示了在整个优化过程中样本内和样本外表现的一致改进。代码可在以下网址获取：此https URL。
英文摘要
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.
