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a-share-risk-budget

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A股风险预算/风险平价策略。当用户说"风险预算"、"risk budget"、"风险平价"、"risk parity"、"风险贡献"、"等风险配置"时触发。基于 cn-stock-data 获取数据,构建风险预算与风险平价组合。支持 formal/brief 两种输出风格。

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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I want to install this Agent Skill for this project in Codex.

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

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/a-share-risk-budget/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

风险预算/风险平价策略助手

数据获取

通过 cn-stock-data skill 获取数据:

  • 持仓数据: 个股/资产权重
  • 协方差矩阵: 资产间的风险结构
  • 风险预算: 各资产的目标风险贡献

分析工作流

Step 1: 风险贡献分解

  • 边际风险贡献 MCTR_i = ∂σ_p/∂w_i
  • 风险贡献 RC_i = w_i × MCTR_i
  • 风险贡献占比 = RC_i / σ_p
  • 验证:Σ RC_i = σ_p (Euler分解)

Step 2: 风险预算设定

  • 等风险预算:每个资产风险贡献相等
  • 自定义预算:按投资观点分配风险
  • 行业风险预算:控制行业层面的风险贡献
  • 因子风险预算:控制因子层面的风险暴露

Step 3: 最优权重求解

  • 目标:使实际风险贡献匹配预算
  • 优化算法:序列二次规划(SQP)/牛顿法
  • 约束:权重非负/权重之和=1/个股上限
  • 迭代求解:直到风险贡献偏差<阈值

Step 4: 风险平价实施

  • 再平衡频率:月度/季度
  • 杠杆选择:风险平价通常需要杠杆提升收益
  • 交易成本:再平衡的换手成本
  • A股实施:无杠杆版本的风险平价

Step 5: 输出报告

输出格式

formal 风格(研报级)

# 风险预算分析报告

## 一、风险贡献
| 资产 | 权重 | RC | RC占比 | 预算 |
|------|------|-----|--------|------|

## 二、最优权重
[风险预算下的最优配置]

## 三、组合特征
[预期收益/波动率/Sharpe]

## 四、再平衡建议

brief 风格(快速分析)

## 风险预算速览
- 当前:前3大持仓贡献70%风险
- 目标:等风险贡献(每只10%)
- 调整:减持高波动标的,增持低波动
- 预期Sharpe提升 +0.2

参考 references/risk-budget-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