PROMOTE 协议：为自适应金融研究智能体设计 anytime-valid 晋升检验
Can We Stop the Backtest Yet? Anytime-Valid Promotion Tests for Adaptive Financial Research Agents
arXiv 上这篇讲怎么防止交易智能体海量试规则时假阳性爆炸，实验里未调整检验 94.5% 假晋升，PROMOTE 压到 0，做量化的人可以看看。
论文针对金融研究智能体在数据依赖时刻生成和监控交易规则导致的检验失效问题，提出 PROMOTE 协议：给每个候选规则在诞生时分配可预测的 testing wealth，并只从后续收益累积 cost-aware e-process。在基于公开 ETF 和行业数据的回放实验中，重复未调整检验在 94.5% 的 3,000 条 null 路径上出现假晋升，PROMOTE 为 0.0%；在 15bp 扰动下达到 70.1% 目标功效，中位 crossing session 为 2,327。另一个让智能体自选候选和出生时间的实验中，3,000 个程序的假族跨越率为 1.77%。
Can We Stop the Backtest Yet? Anytime-Valid Promotion Tests for Adaptive Financial Research Agents
Financial research agents can generate and monitor trading rules faster than a human can document the resulting selection process. Candidates appear at data-dependent times, weak rules are abandoned, and promising ones are watched until they cross a reporting threshold. We introduce PROMOTE, a protocol that assigns every candidate predictable testing wealth at birth and accumulates a cost-aware e-process only from subsequent returns. If lifetime allocations sum to at most one, a weighted Ville argument bounds the probability of ever promoting a rule whose declared bounded net score has always had nonpositive conditional mean. The result permits dependent candidates, adaptive births, and indefinite monitoring. We audit the implementation in two settings. A retrospective pseudo-out-of-sample replay contains 24 panel--rule hypotheses, 3,000 shared-sign null paths, injected alternatives, and public ETF and industry data. Repeated unadjusted tests produce a family crossing in 94.5% of null paths, versus 0.0% for PROMOTE; at a 15-bp aligned ETF-component perturbation, PROMOTE has 70.1% target power and median crossing session 2,327. A separate exact mean-zero experiment lets an agent select candidate identities and birth times from past scores; the funded gate has 1.77% false-family crossings across 3,000 programs. The market replay produces zero promotions after costs. The certificate controls historical false promotion under the declared null; it is not a certificate that an earlier edge remains profitable at deployment. AI-use disclosure. Generative AI tools, including OpenAI Codex, assisted manuscript preparation. For this preprint version, Codex assisted with LaTeX editing, author and venue metadata, compilation, and layout checks.