English(EN) FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment

FactorEngine框架利用LLM引导的代码生成挖掘金融市场信号

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了FactorEngine (FE)，一个用于在金融市场中发现预测信号的新框架。FE将因子视为可执行的、图灵完备的代码，将逻辑修订与参数优化分离，并采用LLM引导的搜索。它还通过多代理管道将非结构化金融报告整合到可执行的因子程序中，并使用经验知识库进行精炼。回测表明，FE在预测稳定性和投资组合影响方面显著优于基线方法。 AI

影响该框架可以通过自动化因子发现和提高可解释性来增强量化交易策略的效率和有效性。

排序理由详细介绍量化投资新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Qinhong Lin, Ruitao Feng, Yinglun Feng, Zhenxin Huang, Yukun Chen, Zhongliang Yang, Linna Zhou, Binjie Fei, Jiaqi Liu, Yu Li · 2026-09-16 04:00

FactorEngine：面向量化投资的程序级知识注入因子挖掘框架

arXiv:2603.16365v3 Announce Type: replace Abstract: We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement that mined factors be directly executable and auditable, and that the discovery pro…

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FactorEngine：面向量化投资的程序级知识注入因子挖掘框架

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