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a-share-var-analysis

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A股VaR风险价值/条件VaR分析。当用户说"VaR"、"风险价值"、"value at risk"、"CVaR"、"ES"、"预期损失"、"最大可能亏多少"时触发。量化计算组合VaR和CVaR。支持formal和brief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-var-analysis/SKILL.md

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A股VaR风险价值/条件VaR分析

数据源

SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts"
python "$SCRIPTS/cn_stock_data.py" kline --code [CODE] --freq daily --start [日期]
python "$SCRIPTS/cn_stock_data.py" quote --code [CODE]
python "$SCRIPTS/cn_stock_data.py" finance --code [CODE]

Workflow

Step 1: 获取收益率序列

Step 2: 计算VaR

  • 历史模拟法：收益率排序取分位数
  • 参数法：假设正态分布，VaR = μ - z_α × σ
  • Monte Carlo模拟：模拟10000条路径

Step 3: 计算CVaR(ES)

CVaR = E[Loss | Loss > VaR]，尾部平均损失

Step 4: 压力测试

用历史极端情景（2008/2015/2020）测算极端VaR

Step 5: 输出

维度formalbrief
VaR多方法对比+置信度95%VaR
CVaR尾部风险分析CVaR值
压力测试历史情景分析无
默认风格：brief。

关键规则

  1. VaR 只回答正常情况下的最大损失——尾部风险需用CVaR
  2. 历史模拟法最直观但依赖历史数据充分性
  3. 参数法假设正态分布——A股收益率尖峰肥尾，会低估风险
  4. 持有期不同VaR差异巨大——日VaR × √T ≈ T日VaR（近似）
  5. 回测VaR：实际突破次数应接近理论水平

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