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AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agentic Search

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  • Weicheng Ye
  • Youran Sun
  • Xingyu Ren
  • Shunyao Yu
  • Chugang Yi
  • Haizhao Yang

Abstract

Language models can propose many plausible trading factors, but an autonomous research system must also allocate its evaluation budget, verify its own evidence, and preserve how each candidate was produced. We present AgonAlpha, an architecture that searches over frozen research artifacts---hypotheses, executable expressions, platform evidence, rationales, and review status---rather than formulas alone. To our knowledge, AgonAlpha is the first alpha-mining system to combine verified artifact search, a fresh-context adversarial reviewer with re-execution and veto authority, and pending-aware parallel budget allocation, together with a complete public evidence trail. Independent deployments on WorldQuant BRAIN produced SPECTACULAR-grade alphas across five users and six model backends, with Fitness reaching 9.50 and Sharpe reaching 3.48, while retaining prompt-to-expression provenance for every submission.

Suggested Citation

  • Weicheng Ye & Youran Sun & Xingyu Ren & Shunyao Yu & Chugang Yi & Haizhao Yang, 2026. "AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agentic Search," Papers 2608.11250, arXiv.org.
    • Handle: RePEc:arx:papers:2608.11250
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