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a-share-distribution-analysis

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A股收益率分布/统计特征分析。当用户说"收益率分布"、"distribution"、"正态检验"、"偏度"、"峰度"、"收益率统计"时触发。量化分析收益率分布特征。支持formal和brief风格。

QUICK START

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-distribution-analysis/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-distribution-analysis/. Do not write files or run scripts until I approve.

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

A股收益率分布/统计特征分析

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K线收益率序列

Step 2: 描述性统计

均值/中位数/标准差/偏度/峰度/最大值/最小值

Step 3: 正态性检验

  • Jarque-Bera检验
  • Shapiro-Wilk检验
  • QQ图分析

Step 4: 分布拟合

拟合t分布/GED分布/混合正态，比较拟合优度

Step 5: 输出

维度formalbrief
统计量完整描述性统计偏度/峰度
正态检验多种检验结果是否正态
分布拟合最佳拟合分布分布类型
默认风格：brief。

关键规则

  1. A股收益率不服从正态分布——尖峰肥尾特征显著
  2. 负偏度意味着下跌极端值更多——投资者面临左尾风险
  3. 峰度>3说明极端收益出现频率高于正态预期
  4. 分布假设影响VaR/期权定价等所有风险计算
  5. 不同市值股票的分布特征差异大——小盘更尖峰

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