QuantaAlpha：基于大语言模型的阿尔法因子挖掘进化框架

QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining

February 6, 2026

作者: Jun Han, Shuo Zhang, Wei Li, Zhi Yang, Yifan Dong, Tu Hu, Jialuo Yuan, Xiaomin Yu, Yumo Zhu, Fangqi Lou, Xin Guo, Zhaowei Liu, Tianyi Jiang, Ruichuan An, Jingping Liu, Biao Wu, Rongze Chen, Kunyi Wang, Yifan Wang, Sen Hu, Xinbing Kong, Liwen Zhang, Ronghao Chen, Huacan Wang

cs.AI

摘要

金融市场具有高噪声与非平稳特性，使得阿尔法因子挖掘对回测结果中的噪声及市场机制的突变高度敏感。虽然近期出现的智能体框架提升了阿尔法挖掘的自动化程度，但往往缺乏可控的多轮搜索机制和已验证经验的可复用性。针对这些挑战，我们提出QuantaAlpha框架——一种将每次端到端挖掘过程视为轨迹的进化式阿尔法挖掘系统，通过轨迹级变异与交叉操作优化因子。该框架能定位轨迹中的次优步骤进行针对性修正，并重组互补的高收益片段以实现有效模式复用，从而在多次挖掘迭代中实现结构化探索与优化。在因子生成过程中，QuantaAlpha确保假设、因子表达式与可执行代码之间的语义一致性，同时约束生成因子的复杂度和冗余度以缓解因子拥挤。基于沪深300指数的广泛实验表明，该框架相较强基线模型与现有智能体系统取得稳定收益。当采用GPT-5.2时，QuantaAlpha的信息系数达到0.1501，年化收益率达27.75%，最大回撤控制在7.98%。此外，在沪深300上挖掘的因子可有效迁移至中证500和标普500指数，四年累计超额收益分别达160%和137%，表明该框架在市场分布变化下具有强鲁棒性。

English

Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose QuantaAlpha, an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors through trajectory-level mutation and crossover operations. QuantaAlpha localizes suboptimal steps in each trajectory for targeted revision and recombines complementary high-reward segments to reuse effective patterns, enabling structured exploration and refinement across mining iterations. During factor generation, QuantaAlpha enforces semantic consistency across the hypothesis, factor expression, and executable code, while constraining the complexity and redundancy of the generated factor to mitigate crowding. Extensive experiments on the China Securities Index 300 (CSI 300) demonstrate consistent gains over strong baseline models and prior agentic systems. When utilizing GPT-5.2, QuantaAlpha achieves an Information Coefficient (IC) of 0.1501, with an Annualized Rate of Return (ARR) of 27.75% and a Maximum Drawdown (MDD) of 7.98%. Moreover, factors mined on CSI 300 transfer effectively to the China Securities Index 500 (CSI 500) and the Standard & Poor's 500 Index (S&P 500), delivering 160% and 137% cumulative excess return over four years, respectively, which indicates strong robustness of QuantaAlpha under market distribution shifts.