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a-share-dispersion-trade

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A股离散度交易/相关性策略。当用户说"离散度"、"dispersion"、"相关性交易"、"correlation trading"、"指数vs成分股波动率"、"离散度套利"时触发。基于 cn-stock-data 获取数据，分析指数与成分股波动率离散度。支持 formal/brief 两种输出风格。

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How to use this skill

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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/a-share-dispersion-trade/SKILL.md

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离散度交易/相关性策略助手

数据获取

通过 cn-stock-data skill 获取数据：

  • 指数期权: 指数IV数据
  • 成分股期权/波动率: 个股IV或HV
  • 指数权重: 成分股权重

分析工作流

Step 1: 离散度计算

  • 隐含相关性 = 指数IV² / 加权成分股IV²
  • 已实现相关性 = 成分股收益率相关系数均值
  • 离散度 = 成分股IV加权均值 - 指数IV
  • 相关性风险溢价 = 隐含相关性 - 已实现相关性

Step 2: 离散度交易构建

  • 做多离散度：买成分股期权+卖指数期权
  • 做空离散度：卖成分股期权+买指数期权
  • Vega中性：调整名义金额使组合Vega为零
  • Delta对冲：保持方向中性

Step 3: 相关性分析

  • 相关性的均值回归特征
  • 危机时相关性飙升(correlation breakdown)
  • 行业内vs行业间相关性差异
  • 相关性与市场状态的关系

Step 4: A股离散度特征

  • A股成分股期权有限，可用HV替代IV
  • 板块轮动导致离散度周期性变化
  • 牛市末期离散度通常扩大
  • 可用ETF期权近似成分股波动率

Step 5: 输出报告

输出格式

formal 风格（研报级）

# 离散度交易分析报告

## 一、离散度指标
| 指标 | 数值 | 分位数 |
|------|------|--------|

## 二、相关性分析
[隐含vs已实现相关性]

## 三、交易方案
[具体期权组合]

## 四、风险控制

brief 风格（快速分析）

## 离散度速览
- 隐含相关性 0.65 vs 已实现 0.55
- 相关性溢价 +10%，偏高
- 建议：做多离散度(卖指数期权+买成分股)
- 风险：市场恐慌时相关性飙升

参考 references/dispersion-trade-guide.md 获取详细方法论与 A股实证研究。

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