Formulaic alpha factor mining is a critical yet challenging task in quantitative investment, characterized by a vast search space and the need for domain-informed, interpretable signals. However, finding novel signals becomes increasingly difficult as the library grows due to high redundancy. We propose FactorMiner, a lightweight and flexible self-evolving agent framework designed to navigate this complex landscape through continuous knowledge accumulation. FactorMiner combines a Modular Skill Architecture that encapsulates systematic financial evaluation into executable tools with a structured Experience Memory that distills historical mining trials into actionable insights (successful patterns and failure constraints). By instantiating the Ralph Loop paradigm -- retrieve, generate, evaluate, and distill -- FactorMiner iteratively uses memory priors to guide exploration, reducing redundant search while focusing on promising directions. Experiments on multiple datasets across different assets and Markets show that FactorMiner constructs a diverse library of high-quality factors with competitive performance, while maintaining low redundancy among factors as the library scales. Overall, FactorMiner provides a practical approach to scalable discovery of interpretable formulaic alpha factors under the "Correlation Red Sea" constraint.

翻译：公式化阿尔法因子挖掘是量化投资中一项关键且具有挑战性的任务，其特点是搜索空间巨大，且需要具备领域知识、可解释的信号。然而，随着因子库的增长，由于高度冗余性，发现新信号变得日益困难。我们提出了FactorMiner，一个轻量级、灵活的自演进智能体框架，旨在通过持续的知识积累来应对这一复杂局面。FactorMiner结合了模块化技能架构与结构化经验记忆：前者将系统化的金融评估封装为可执行工具，后者则将历史挖掘尝试提炼为可操作的见解（成功模式与失败约束）。通过实例化Ralph循环范式——检索、生成、评估与提炼——FactorMiner迭代地利用记忆先验来引导探索，从而减少冗余搜索，同时聚焦于有前景的方向。在跨不同资产与市场的多个数据集上的实验表明，FactorMiner能够构建一个具有竞争力表现的高质量因子多样化库，并在因子库扩展时保持因子间的低冗余度。总体而言，FactorMiner为在“相关性红海”约束下可扩展地发现可解释的公式化阿尔法因子提供了一种实用方法。