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生成適用於金融機器學習的高質量特徵。包含分數階差分 (Fractional Differentiation) 與無前視偏差的滾動標準化。
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| name | advanced-feature-engineering |
| description | 生成適用於金融機器學習的高質量特徵。包含分數階差分 (Fractional Differentiation) 與無前視偏差的滾動標準化。 |
| trigger | when_needed |
| language | zh-TW |
| adapted_from | skills/高級特徵工程/quant-feature-eng/SKILL.md |
| version | 1.0.0-antigravity |
| original_license | Unknown |
技能版本: v1.0 (Antigravity 適配版) 原始來源: local/quant-feature-eng 語言: 繁體中文
生成適用於金融機器學習的高質量特徵。此技能專注於解決金融數據的非平穩性問題，同時保留數據的長期記憶，並確保標準化過程不引入未來數據。
此技能適用於以下情況：
標準的一階差分（Returns）雖然能使數據平穩，但會抹去價格的歷史記憶。分數階差分保留了最多的相關性，同時達成平穩性。 權重計算公式（二項式展開）： $$w_k = -w_{k-1} \frac{d - k + 1}{k}$$ 目標是尋找最小的 $d$ 值（$0 < d < 1$），使得序列通過 ADF 檢驗 ($p < 0.05$)。
嚴禁使用全局 Z-Score ($x - \mu / \sigma$)，因為全局統計量包含未來信息。 正確做法是使用滾動窗口統計量，並進行 Shift 操作以確保 $t$ 時刻僅使用 $t-1$ 及之前的數據： $$Z_t = \frac{x_t - \mu_{t-1}}{\sigma_{t-1}}$$
分數階差分優化：
標準化與極端值處理：
參考實作範本：implementation_template.py
請參閱 examples/ 目錄下的範例代碼以獲取完整實現細節。
skills/_base/coding_style.md 編碼規範