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English(EN) GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data

GoAnt 框架增强了市场数据中的 Alpha 因子发现

研究人员开发了 GoAnt,一个新颖的多智能体搜索框架,旨在发现市场微观结构数据中的 Alpha 因子。该系统采用探索者(Explorer)、利用(Exploiter)和连接者(Connector)智能体,以及共享的自适应心智图(Mental Map)和 Queen 调度器,以提高交易信号的鲁棒性和多样性。GoAnt 在真实的 A 股微观结构数据上表现出显著的改进,与现有的基线方法相比,实现了更高的质量加权收益和更好的样本外表现。 AI

影响 该框架通过改进金融数据中预测信号的发现,可能带来更鲁棒和多样化的交易策略。

排序理由 该集群描述了一篇研究论文,详细介绍了一种用于金融市场 Alpha 因子发现的新框架。

在 Hugging Face Daily Papers 阅读 →

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GoAnt 框架增强了市场数据中的 Alpha 因子发现

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该集群描述了一篇研究论文,详细介绍了一种用于金融市场 Alpha 因子发现的新框架。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Stella Zhao, Tommy Sha ·

    GoAnt: 用于市场微观结构数据中 Alpha 因子发现的质量-多样性多智能体搜索

    arXiv:2609.08719v1 Announce Type: new Abstract: Automated alpha factor discovery searches symbolic trading signals from price-volume panels and order-book data under a fixed evaluation budget. Existing single- and multi-agent program-search systems can overfit predictive proxies …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    GoAnt: 用于市场微观结构数据中 Alpha 因子发现的质量-多样性多智能体搜索

    Automated alpha factor discovery searches symbolic trading signals from price-volume panels and order-book data under a fixed evaluation budget. Existing single- and multi-agent program-search systems can overfit predictive proxies that fail after execution costs and repeatedly e…