AlphaSeek：面向多源金融数据的轨迹级自迭代因子挖掘框架
AlphaSeek: Trajectory-Level Self-Iterative Factor Mining Framework for Multi-Source Financial Data
AI总结：
针对现有量化因子挖掘方法的缺陷，提出AlphaSeek框架，整合自动化方向发现等技术，在CSI300上取得优异策略性能，且因子具跨市场迁移性。
AI中文摘要：
随着大语言模型的快速发展，大语言模型驱动的量化因子挖掘已成为愈发活跃的研究领域。然而，现有方法仍存在方向设计主观、最新多源信息整合有限、语义漂移、因子冗余，以及缺乏从因子发现到投资组合回测的端到端反馈循环等问题。为解决这些局限，我们提出AlphaSeek——一种面向量化投资的端到端因子挖掘框架，整合了自动化方向发现、轨迹级因子演化挖掘与自迭代投资组合优化。AlphaSeek首先收集并总结多源金融信息，以识别有前景的挖掘方向；随后通过将优化单元从单一因子表达式扩展至涵盖假设生成、因子构建、验证、回测与反馈的完整研究轨迹，开展轨迹级因子挖掘；基于此设计，我们引入演化算子——并行方向扩展、变异与交叉，以提升搜索多样性、精炼质量与因子鲁棒性；最后，AlphaSeek构建自迭代因子投资组合，允许新发现的因子在感知冗余的约束下与现有最先进（SOTA）因子库交互。在CSI300上的实验显示，AlphaSeek在CSI300上实现了最强的策略级整体性能，其年化收益率（ARR）为8.28%，信息比率（IR）为1.29，最大回撤（MDD）为6.28%，同时在因子预测指标上具有竞争力，其信息系数（IC）为0.0454；且在CSI300上挖掘的因子在零样本设置下，于CSI500上也实现了优于其他模型的强时间序列收益表现，表明其具有良好的跨市场迁移能力。
英文摘要：
With the rapid rise of large language models, LLM-driven quantitative factor mining has become an increasingly active research area. However, existing methods still suffer from subjective direction design, limited integration of up-to-date multi-source information, semantic drift, factor redundancy, and the absence of an end-to-end feedback loop from factor discovery to portfolio backtesting. To address these limitations, we propose AlphaSeek, an end-to-end factor mining framework for quantitative investment that integrates automated direction discovery, trajectory-level factor evolution mining and self-iterative portfolio optimization. AlphaSeek first collects and summarizes multi-source financial information to identify promising mining directions. It then performs trajectory-level factor mining by extending the optimization unit from a single factor expression to a complete research trajectory covering hypothesis generation, factor construction, validation, backtesting, and feedback. Based on this design, we introduce evolution operators - parallel direction expansion, mutation and crossover - to improve search diversity, refinement quality and factor robustness. Finally, AlphaSeek constructs a self-iterative factor portfolio, allowing newly discovered factors to interact with an existing state-of-the-art(SOTA) factor library under redundancy-aware constraints. Experiments on CSI300 show that AlphaSeek achieves the strongest overall strategy-level performance on CSI300 with ARR of 8.28%, IR of 1.29 and MDD of 6.28%, while remaining competitive on factor predictive metrics with IC of 0.0454, while factors mined on CSI300 also achieve strong time-series return performance on CSI500 than other models, suggesting promising cross-market transferability under a zero-shot setting.
