GoAnt通过多智能体搜索，在有限预算下发现高质量、多样化的交易因子。
GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data
- 采用探索者、利用者和连接者协同工作，结合动态记忆地图与女王调度器。
- 在真实A股数据上，因子质量加权收益提升57%至97%，过拟合率更低。
- 适合量化交易研究者，尤其关注因子多样性与执行鲁棒性的场景。
自动化阿尔法因子发现需从价格-成交量面板和订单簿数据中搜索符号化交易信号，并受固定评估预算限制。现有单/多智能体程序搜索系统易过拟合预测代理，导致执行成本后失效，且反复探索冗余因子家族，影响执行鲁棒性与行为多样性。本文提出GoAnt，一种质量-多样性多智能体搜索框架，包含不通信的探索者、利用者与连接者，结合共享自适应心智地图与紧凑女王调度器。心智地图按无泄漏执行特征组织候选因子，每类保留一个精英；女王则根据显式搜索状态摘要重新分配评估预算。我们还定义了一种与地图无关的有效收益率协议，直接从各方法评估记录中统计高质量、互不冗余因子，使基于归档与无地图系统拥有相同衡量标准。在2023–2026年真实A股微观结构数据上，GoAnt在价格-成交量与订单簿设置下的质量加权收益分别达41.8与47.6，优于最强基线57%与97%，匹配预算条件下。其锁定种群在样本外保留0.64与0.67的样本内质量，优于静态地图的0.61与0.63。
原文摘要 · Abstract (English)
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.
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