Menú
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。
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
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.
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
| 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