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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。
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Showing SKILL.md
| name | a-share-lead-lag |
| description | 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。 |
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]
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）
检验A是否Granger因果引起B（或反向）
构建多标的间的领先-滞后关系网络
| 维度 | formal | brief |
|---|---|---|
| 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。 |
# 调用 skill
result = run_skill({
"param1": "value1",
"param2": "value2"
})
python scripts/run_skill.py --input data.json
A股逆向选择/信息不对称分析。当用户说"逆向选择"、"adverse selection"、"信息不对称"、"知情交易者"、"逆向选择成本"、"信息劣势"时触发。基于 cn-stock-data 获取数据，度量逆向选择与信息不对称程度。支持 formal/brief 两种输出风格。
A股量价异常检测/异动监控。当用户说"异常检测"、"异动"、"anomaly"、"量价异常"、"异常波动"、"XX有异动"时触发。量化检测股价和成交量异常。支持formal和brief风格。
A股自相关/序列相关性/收益率自相关结构分析。当用户说"自相关"、"autocorrelation"、"序列相关"、"收益率预测性"、"动量还是反转"、"自相关系数"、"ACF"、"PACF"、"Ljung-Box"、"收益率是否可预测"、"随机游走检验"时触发。MUST USE when user asks about return autocorrelation, serial correlation tests, or whether a stock's returns are predictable. 量化分析收益率的自相关结构（ACF/PACF、Ljung-Box检验、随机游走检验）。支持formal和brief风格。
Based on SOC occupation classification