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。
Scanned 9/7/2026
Install to Claude Code
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---
name: a-share-calendar-spread
description: 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。
---
# 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
```python
# 调用 skill
result = run_skill({
"param1": "value1",
"param2": "value2"
})
```
### 示例 2: 命令行使用
```bash
python scripts/run_skill.py --input data.json
```
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