factor-orthogonalize

Use when an agent needs to neutralize or orthogonalize a quantitative factor against industry, size, style exposures, or an existing factor library before evaluating, combining, or accepting the signal.

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Repositorio: quantskills/skill-factor-orthogonalize
Última actividad en el origen: 16 de julio de 2026 a las 09:37
Idioma detectado de SKILL.md: chino
Estrellas: 32
Forks: 1

Opciones de instalación

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npx skills add https://github.com/quantskills/skill-factor-orthogonalize --skill factor-orthogonalize

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SKILL.md

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name	factor-orthogonalize
description	Use when an agent needs to neutralize or orthogonalize a quantitative factor against industry, size, style exposures, or an existing factor library before evaluating, combining, or accepting the signal.
quantSkills	{"project_type":"skill","category":"factor","tags":["factor-orthogonalization","neutralization","style-exposure","residual-factor","factor-correlation","daily-cross-sectional-regression","industry-neutral","size-neutral","pandadata"],"platforms":["claude-code","codex","openclaw","cursor"],"status":"stable","validation_level":"production","maintainer_type":"community","summary_zh":"逐日截面 OLS 正交化：剥离行业(L1 one-hot) + 市值(log_dollar_vol) + 风格(beta/volatility) + 旧因子暴露，输出残差因子与暴露清零诊断报告。已对接 Pandadata 获取行业分类(sector_code_name)和风格控制变量。","summary_en":"Daily cross-sectional OLS orthogonalization against industry one-hot dummies, size (log dollar volume), style (beta, volatility), and legacy factor exposures. Outputs residual signal with exposure-zeroing diagnostics. Integrated with Pandadata for sector classification and style controls.","license":"GPL-3.0","repository":"https://github.com/quantskills/skill-factor-orthogonalize"}

{
  "version": 1,
  "task": {
    "placeholder": "请说明或上传原始 [date × symbol] 因子信号、数据路径及期望输出位置",
    "required": true
  },
  "fields": [
    {
      "key": "factor",
      "label": "原始因子或信号",
      "type": "text",
      "placeholder": "因子名、列名或信号文件路径"
    },
    {
      "key": "universe",
      "label": "股票池",
      "type": "select",
      "default": "000300.SH",
      "options": [
        { "value": "000300.SH", "label": "沪深300" },
        { "value": "000905.SH", "label": "中证500" },
        { "value": "399006.SZ", "label": "创业板指" },
        { "value": "000852.SH", "label": "中证1000" }
      ]
    },
    {
      "key": "controls",
      "label": "需剥离的暴露",
      "type": "textarea",
      "default": "行业、市值、beta、波动率",
      "placeholder": "如：行业、市值、beta、波动率、流动性、已有因子"
    }
  ],
  "prompt_template": "{{#task}}任务与材料：\n{{task}}\n\n{{/task}}{{#attachments}}用户上传的材料（已放入工作区）：\n{{attachments}}\n\n{{/attachments}}请对原始因子/信号{{#factor}}（{{factor}}）{{/factor}}在股票池 {{universe}} 内做逐日截面正交化；若表单未指明因子，以任务材料/附件给出的信号为准。剥离以下暴露：{{controls}}；仅使用当时可得控制变量，输出标准化残差因子，并比较正交前后的暴露、IC、Sharpe、换手率与覆盖率，输出中文报告。"
}