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a-share-option-pricing

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A股期权定价/BSM模型分析。当用户说"期权定价"、"BSM"、"Black-Scholes"、"期权估值"、"理论价格"、"定价模型"、"二叉树定价"时触发。基于 cn-stock-data 获取数据，进行期权定价与估值分析。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-option-pricing/SKILL.md

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期权定价/BSM模型分析助手

数据获取

通过 cn-stock-data skill 获取数据：

  • 期权数据: 市场价格/行权价/到期日
  • 标的行情: 现价/股息率
  • 利率数据: 无风险利率曲线

分析工作流

Step 1: BSM定价

  • 欧式期权BSM公式：C = SN(d1) - Ke^(-rT)*N(d2)
  • 输入参数：S(标的价)、K(行权价)、T(到期时间)、r(利率)、σ(波动率)
  • 股息调整：连续股息率q的修正
  • 理论价格 vs 市场价格的偏差分析

Step 2: 数值定价方法

  • 二叉树模型：美式期权定价
  • 蒙特卡洛模拟：路径依赖期权
  • 有限差分法：偏微分方程数值解
  • 各方法的精度与计算效率对比

Step 3: 定价偏差分析

  • 市场价格 vs 理论价格：溢价/折价
  • 偏差来源：流动性溢价/供需不平衡/模型误差
  • 套利机会识别：Put-Call Parity偏离
  • 波动率微笑对BSM定价的修正

Step 4: 高级定价模型

  • Heston随机波动率模型
  • SABR模型：波动率曲面拟合
  • 局部波动率模型：Dupire方程
  • 跳跃扩散模型：Merton Jump-Diffusion

Step 5: 输出报告

输出格式

formal 风格（研报级）

# [标的] 期权定价分析报告

## 一、BSM定价
| 合约 | 市场价 | 理论价 | 偏差 |
|------|--------|--------|------|

## 二、定价偏差
[偏差分析、套利机会]

## 三、高级模型
[Heston/SABR定价对比]

## 四、交易建议

brief 风格（快速分析）

## [标的] 定价速览
- BSM理论价 0.285，市场价 0.295
- 溢价 +3.5%，流动性溢价为主
- Put-Call Parity偏差 0.2%，无套利
- Heston定价 0.290，更接近市场

参考 references/option-pricing-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