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a-share-calendar-spread

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A股日历价差/跨期策略。当用户说"日历价差"、"calendar spread"、"跨期"、"近远月价差"、"时间价差"、"跨期套利"时触发。基于 cn-stock-data 获取数据,分析期权/期货日历价差策略。支持 formal/brief 两种输出风格。

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

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日历价差/跨期策略助手

数据获取

通过 cn-stock-data skill 获取数据:

  • 期权数据: 不同到期月份的期权价格
  • 期货数据: 不同月份合约价格
  • 波动率数据: 各月份IV

分析工作流

Step 1: 价差分析

  • 期权日历价差 = 远月期权价格 - 近月期权价格
  • 期货跨期价差 = 远月期货 - 近月期货
  • 价差的历史分布与当前分位数
  • 价差的季节性模式

Step 2: 策略构建

  • 买入日历价差:买远月+卖近月(做多时间价值)
  • 卖出日历价差:卖远月+买近月(做空时间价值)
  • 对角价差:不同行权价+不同到期月
  • 条件选择:低IV环境买日历,高IV环境卖日历

Step 3: Greeks管理

  • Theta:近月Theta衰减快于远月(策略核心收益)
  • Vega:日历价差通常Vega为正(做多波动率)
  • Delta:保持Delta中性或小幅偏向
  • Gamma:近月到期前Gamma风险增大

Step 4: 到期管理

  • 近月到期前的展期决策
  • 展期时机:近月剩余价值<0.5%时展期
  • 展期成本:新近月的时间价值
  • A股特征:月度合约间隔,展期频率固定

Step 5: 输出报告

输出格式

formal 风格(研报级)

# 日历价差策略报告

## 一、价差分析
| 组合 | 近月 | 远月 | 价差 | 分位 |
|------|------|------|------|------|

## 二、策略方案
[具体合约、Greeks]

## 三、情景分析
[标的±5%/IV±5%盈亏]

## 四、展期计划

brief 风格(快速分析)

## 日历价差速览
- 近月ATM Call 0.15,远月 0.28
- 价差 0.13,历史P40
- 建议:买入日历价差(IV偏低)
- 最大亏损:净支出0.13

参考 references/calendar-spread-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