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a-share-stock-clustering

Business
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A股股票聚类/相似股票发现。当用户说"聚类"、"clustering"、"相似股票"、"类似的股票"、"同类股票"、"走势相似"时触发。量化聚类分析股票相似性。支持formal和brief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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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/a-share-stock-clustering/SKILL.md

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A股股票聚类/相似股票发现

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: 获取多股数据

Step 2: 特征构建

  • 收益率特征：日/周收益率序列
  • 基本面特征：PE/PB/ROE/营收增速等
  • 技术特征：波动率/Beta/动量等

Step 3: 聚类分析

  • K-Means聚类（需指定K）
  • 层次聚类（树状图可视化）
  • DBSCAN（自动发现簇数）

Step 4: 簇特征分析

各簇的共同特征（行业/风格/基本面）

Step 5: 输出

维度formalbrief
聚类结果完整分簇列表目标股所在簇
簇特征各簇详细画像同类股票
应用配对交易候选Top 5相似股
默认风格：brief。

关键规则

  1. 特征标准化是聚类的前提——不同量纲需归一化
  2. K-Means对K值敏感——可用肘部法则或轮廓系数选K
  3. 收益率相似不等于基本面相似——需分维度聚类
  4. 聚类结果可用于配对交易候选筛选
  5. 行业分类≠统计聚类——后者可能发现隐含关联

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