aifinlab/finclaw/a-share-lead-lag

Claude Code and agent skill by aifinlab/finclaw

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A股领先滞后关系/板块传导分析。当用户说"领先滞后"、"lead lag"、"谁先涨"、"传导"、"板块传导"、"龙头带动"时触发。量化分析股票/板块间的领先滞后关系。支持formal和brief风格。

Install

npx vskill@latest install aifinlab/finclaw/a-share-lead-lag

SKILL.md

A股领先滞后关系/板块传导分析

数据源

bash
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: 获取多标的K线
Step 2: 交叉相关分析

计算标的A在t期收益率与标的B在t+k期收益率的相关性（k=-5到+5）

Step 3: Granger因果检验

检验A是否Granger因果引起B（或反向）

Step 4: 领先滞后图谱

构建多标的间的领先-滞后关系网络

Step 5: 输出
维度formalbrief
相关矩阵多lag完整矩阵最强领先关系
因果检验Granger检验结果领先/滞后天数
关系图谱完整网络图Top 3领先者

默认风格：brief。

关键规则

  1. 领先滞后关系可能不稳定——需滚动窗口验证
  2. 相关性≠因果——Granger检验也只是统计因果
  3. A股中上游→下游传导较明显（如铜→电线电缆）
  4. 大盘股往往领先小盘股1-2日
  5. 北向资金动向常领先A股1-3日

使用示例

示例 1: 基本使用
python
# 调用 skill
result = run_skill({
    "param1": "value1",
    "param2": "value2"
})
示例 2: 命令行使用
bash
python scripts/run_skill.py --input data.json

Security

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  • Model reviewPassed

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Source

Popularity

Repository stars
180
Repository forks
29
7-day trend score
26

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