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金融時間序列數據清洗的標準流程，整合 MAD 檢測與布朗橋插值
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Based on SOC occupation classification
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 | quant-data-cleaning-pipeline |
| description | 金融時間序列數據清洗的標準流程，整合 MAD 檢測與布朗橋插值 |
當處理金融 OHLCV 數據時，需要同時處理異常值和缺失值，且必須保持時間序列的統計特性。
⚠️ 禁止 使用
ffill().bfill()或interpolate(method='linear')處理金融數據。 這會人為降低波動率，導致 VaR 低估和風險模型失效。
[!TIP] 已提取代碼至：quant-data-cleaning-pipeline_examples_1.txt
[!TIP] 已提取代碼至：quant-data-cleaning-pipeline_examples_2.py
DataLoader 使用 ffill/bfill 掩蓋數據缺口