القائمة
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
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Create beautiful visual art in .png and .pdf 文檔 using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
| 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