smithery.ai

quant-ml-purged-cv-integration

將 Purged CV 整合到 ML 訓練流程的標準模式

First seen Mar 26, 2026

Installation

$ npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Package contents

Files included with this skill beyond the listing page.

skill md SKILL.md 1,120 B
docs SUMMARY.md 92 B

History

Mar 26, 2026 First seen on skills.sh
Sep 9, 2026 First recorded snapshot · 1 installs

SKILL.md

Purged CV 整合模式 (ML Training Integration)

適用場景

當在金融時間序列上訓練 ML 模型時，必須使用 Purged CV 而非標準 K-Fold，以防止數據洩露。

核心原則

⚠️ 禁止在金融序列上使用 sklearn.model_selection.KFold。
標準 K-Fold 會導致訓練集和測試集在時間上重疊，造成過擬合幻覺。

標準整合模式

[!TIP]
已提取代碼至：[quant-ml-purged-cv-integrationexamples1.py](examples/quant-ml-purged-cv-integrationexamples1.py)

關鍵參數

參數	說明	建議值
`purge_window`	測試集之前剔除的樣本數	標籤前視長度 (如 5 天)
`embargo_window`	測試集之後剔除的樣本數	序列相關性半衰期
`ntestsplits`	測試集組數	1-2 (增加路徑多樣性)

進化來源

Phase 3: 實作 CombinatorialPurgedKFold 並整合至 MLP
Auditor 審計: 確認 Purge/Embargo 機制正確隔離 Train/Val

npx skills add https://smithery.ai