Trading Strategy Optimization via Textual Gradient
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-...
| Authors | Chaoqun Yang, Qian Wang, Fengbin Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 October 2026 |
| Last updated at source | 6 October 2026 |
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