GoAnt is a quality‑diversity multi‑agent search framework designed for discovering alpha factors in market microstructure data. It employs non‑communicating Explorer, Exploiter, and Connector workers that share an adaptive Mental Map and a Queen dispatcher to allocate evaluation budgets efficiently. On real A‑share data from 2023‑2026, GoAnt achieves significantly higher quality‑weighted yields than baseline methods and maintains strong out‑of‑sample performance.
By Stella Zhao, Tommy Sha
arXiv:2606. 29194v1 Announce Type: new Abstract: Automated alpha mining holds the scoring function fixed and varies the search algorithm over it.
By Yuqi Li, Siyuan Liu, Bingjun Liu
Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery. However, existing LLM-based systems often delegate both factor construction and search decisions to the agent itself, without an explicit exploration space or a principled mechanism for navigating that space.
arXiv:2607. 26642v1 Announce Type: new Abstract: Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery.
By Jingyang Yi, Jian Yang, Yifei Jin, Yuqi Li, Jian Li
arXiv:2508. 00554v5 Announce Type: replace-cross Abstract: In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy and non-stationary market information.
By Li Zhao, Rui Sun, Zuoyou Jiang, Bo Yang, Yuxiao Bai, Mengting Chen, Jing Li, Zuo Bai
AlphaDiverse is a framework that enhances large language model–based multi‑agent systems for alpha factor mining by addressing cost, availability, and confidentiality constraints. It generates diverse research paths through varied environments and post‑training local agents, then fine‑tunes these agents with supervised learning and optimizes them jointly using a GRPO method that balances predictive quality and diversity. The approach limits research feedback to inner‑period data and evaluates a frozen model on outer‑period data to avoid test‑set tuning, demonstrating competitive prediction and broader exploration across four Chinese stock universes.
By Qingzhuo Wang, Zikun Wei, Zhihua Wei, Wen Shen