发表机构
School of Computing and Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对金融市场难以发现交易信号问题，介绍内存驱动的XAlpha人工智能量化研究员，它通过多源研究内存系统，实现从假设到代码的闭环阿尔法发现过程，实验表明其比基线有更强的阿尔法发现性能。
AI 中文摘要
金融市场嘈杂、非平稳且高维，难以发现预测性和稳健的交易信号。阿尔法发现已从人工因子设计发展到机器学习、进化搜索和基于大语言模型的框架，提高了因子生成、搜索和评估的效率。然而，现有方法大多仍只是自动化孤立步骤，而非作为能吸收外部知识、闭合假设到代码验证循环并从积累的发现反馈中学习的端到端量化研究员。为填补这一空白，我们引入了XAlpha，一个用于持续从假设到代码的阿尔法发现的内存驱动人工智能量化研究员。XAlpha维护一个多源研究内存系统，整合基于报告的金融知识与前代和研究周期的发现反馈。在这个内存系统的引导下，一个宏观大脑规划研究主题并选择合适的原型；一个微观大脑将规划的假设池转化为可执行的因子代码，并事前验证假设想法、代码逻辑和金融合理性之间的三向对齐；一个交叉大脑将实证结果整合为生成级反馈、周期级总结和原型级研究线索以供未来探索。通过这种方式，XAlpha将阿尔法挖掘从孤立的因子生成转变为一个不断读取、假设、实施、验证、反思和进化的闭环研究过程。对沪深300指数的实验表明，XAlpha比代表性基线具有更强的整体阿尔法发现性能。
英文摘要
Financial markets are noisy, non-stationary, and high-dimensional, making it difficult to discover predictive and robust trading signals. Alpha discovery has evolved from manual factor design to machine learning, evolutionary search, and recent LLM-based frameworks, improving the efficiency of factor generation, search, and evaluation. However, existing methods still mostly automate isolated steps, rather than functioning as end-to-end quant researchers that can absorb external knowledge, close the hypothesis-to-code validation loop, and learn from accumulated discovery feedback. To fill this gap, we introduce XAlpha, a memory-driven AI Quant Researcher for continuous hypothesis-to-code alpha discovery. XAlpha maintains a multi-source research memory system that integrates report-grounded financial knowledge with discovery feedback from prior generations and research cycles. Guided by this memory system, a Macro Brain plans research themes and selects suitable Archetypes; a Micro Brain transforms the planned hypothesis pool into executable factor code and verifies ex-ante tri-alignment among the hypothesis idea, code logic, and financial plausibility; and a Cross Brain consolidates empirical outcomes into generation-level feedback, cycle-level summaries, and archetype-level research cues for future exploration. In this way, XAlpha turns alpha mining from isolated factor generation into a closed-loop research process that continuously reads, hypothesizes, implements, validates, reflects, and evolves. Experiments on CSI300 show that XAlpha achieves stronger overall alpha discovery performance than representative baselines.
