GoAnt：市场微观结构数据中用于Alpha因子发现的质量-多样性多智能体搜索
GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data
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中文总结 AI 辅助
GoAnt提出一种质量-多样性多智能体搜索框架，通过不通信的探索者、利用者和连接者及共享心智地图，在固定预算下发现非冗余Alpha因子，在A股数据上显著提升产出和样本外质量。
中文摘要 AI 辅助
自动Alpha因子发现在固定评估预算下，从价格-成交量面板和订单簿数据中搜索符号化交易信号。现有的单智能体和多智能体程序搜索系统可能过拟合预测代理，这些代理在执行成本后失效，并反复探索冗余的因子族，限制了执行鲁棒性和行为多样性。我们提出GoAnt，一个质量-多样性多智能体搜索框架，结合了不通信的探索者、利用者和连接者工作线程，以及一个共享的自适应心智地图和一个紧凑的皇后调度器。心智地图根据无泄漏执行概况组织候选因子，并为每个生态位保留一个精英，而皇后则根据显式搜索状态摘要重新分配评估预算。我们还定义了一个与地图无关的有效产出协议，该协议直接从每种方法的评估记录中计数高质量、相互非冗余的因子，为基于存档和无地图系统提供相同的衡量标准。在覆盖2023年至2026年的真实A股微观结构数据上，GoAnt在价格-成交量和订单簿设置中分别达到41.8和47.6的质量加权产出，在匹配预算下比最强基线分别提高57%和97%。其锁定种群在样本外保留0.64和0.67的样本内质量，而静态地图为0.61和0.63。
英文摘要
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 explore redundant factor families, limiting execution robustness and behavioral diversity. We introduce GoAnt, a quality-diversity multi-agent search framework that combines non-communicating Explorer, Exploiter and Connector workers with a shared adaptive Mental Map and a compact Queen dispatcher. The Mental Map organizes candidates by leakage-free execution profiles and retains one elite per niche, while the Queen reallocates the evaluation budget from explicit search-state summaries. We also define a map-independent effective-yield protocol that counts high-quality, mutually nonredundant factors directly from each method's evaluation records, giving archive-based and map-free systems the same ruler. On real A-share microstructure data spanning 2023--2026, GoAnt reaches quality-weighted yields of 41.8 and 47.6 in price-volume and order-book settings, improving the strongest baseline by 57% and 97% under matched budgets. Its locked populations retain 0.64 and 0.67 of in-sample quality out of sample, compared with 0.61 and 0.63 for a static map.
发表机构
- University of Minnesota（明尼苏达大学）
- Stony Brook University（石溪大学）
机构由 AI 辅助整理，请以论文原文为准。
