A股动态对冲/Delta中性策略。当用户说"动态对冲"、"Delta对冲"、"Delta中性"、"dynamic hedging"、"对冲调整"、"对冲比例"时触发。基于 cn-stock-data 获取数据,设计动态对冲方案。支持 formal/brief 两种输出风格。
Scanned 9/7/2026
Install to Claude Code
npx -y skills add aifinlab/FinClaw --skill a-share-dynamic-hedging --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: a-share-dynamic-hedging
description: A股动态对冲/Delta中性策略。当用户说"动态对冲"、"Delta对冲"、"Delta中性"、"dynamic hedging"、"对冲调整"、"对冲比例"时触发。基于 cn-stock-data 获取数据,设计动态对冲方案。支持 formal/brief 两种输出风格。
---
# 动态对冲/Delta中性策略助手
## 数据获取
通过 cn-stock-data skill 获取数据:
- **持仓数据**: 股票/期权/期货持仓
- **Greeks数据**: 组合Delta/Gamma
- **实时行情**: 标的价格与波动率
## 分析工作流
### Step 1: 对冲需求分析
- 组合Delta:当前方向性风险敞口
- 对冲目标:完全中性/部分对冲/尾部保护
- 对冲工具:股指期货/ETF/期权
- 对冲比例:Beta调整后的最优对冲比
### Step 2: 对冲比例计算
- 最小方差对冲比 h* = ρ × σ_s/σ_f
- Beta对冲:h = β × 组合市值/期货合约价值
- 期权Delta对冲:h = 组合Delta/期权Delta
- 跨品种对冲:用相关资产近似对冲
### Step 3: 动态调整策略
- 固定时间调整:每日/每周重新计算对冲比
- 阈值触发调整:Delta偏离超过阈值时调整
- Gamma scalping:利用Gamma正值做高频对冲
- 成本约束:对冲调整频率vs交易成本的权衡
### Step 4: 对冲效果评估
- 对冲有效性:组合波动率降低比例
- 对冲成本:期货贴水/期权权利金/交易费用
- 基差风险:现货与对冲工具的不完美相关
- 对冲后的残余风险分析
### Step 5: 输出报告
## 输出格式
### formal 风格(研报级)
```
# 动态对冲方案报告
## 一、对冲需求
| 指标 | 数值 |
|------|------|
| 组合Delta | +0.85 |
## 二、对冲方案
[工具选择、对冲比例]
## 三、调整规则
[触发条件、调整频率]
## 四、成本与效果
```
### brief 风格(快速分析)
```
## 动态对冲速览
- 组合Delta +0.85,需对冲
- 方案:卖出IF期货3手
- 对冲后Delta降至+0.05
- 年化对冲成本约2%(期货贴水)
```
参考 `references/dynamic-hedging-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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