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a-share-sector-spread

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A股板块价差/行业估值差分析。当用户说"板块价差"、"sector spread"、"行业估值差"、"板块分化"、"分化有多大"时触发。量化分析板块间价差和估值差异。支持formal和brief风格。

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How to use this skill

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  3. Review the proposed files and risks before you approve installation.
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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/a-share-sector-spread/SKILL.md

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A股板块价差/行业估值差分析

数据源

SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts"
python "$SCRIPTS/cn_stock_data.py" kline --code [CODE] --freq daily --start [日期]
python "$SCRIPTS/cn_stock_data.py" quote --code [CODE]
python "$SCRIPTS/cn_stock_data.py" finance --code [CODE]

Workflow

Step 1: 获取各行业估值和K线数据

Step 2: 计算板块价差

  • 行业间估值差（PE/PB差值）
  • 行业间涨跌幅差
  • 历史价差分位数

Step 3: 分化程度分析

  • 行业收益率离散度（标准差）
  • 行业估值离散度
  • 与历史分化程度对比

Step 4: 均值回归信号

过度分化→可能反转

Step 5: 输出

维度formalbrief
价差完整行业价差矩阵最大价差
分化度离散度时序当前分化水平
回归信号历史分位分析是否过度分化
默认风格：brief。

关键规则

  1. 行业估值差扩大到极端时倾向回归
  2. 分化程度与市场结构化行情正相关
  3. 过度分化=板块轮动机会
  4. A股行业PE差异巨大——需用PB或PS辅助对比
  5. 新兴行业vs传统行业的估值差有趋势性成分

使用示例

示例 1: 基本使用

# 调用 skill
result = run_skill({
    "param1": "value1",
    "param2": "value2"
})

示例 2: 命令行使用

python scripts/run_skill.py --input data.json