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Get Started Free →aifinlab/a-share-lead-lag
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。
Passed 11 of 16 test cases with the skill, and 7 of 16 without it.
and 6 more in the full report
6 fixed9 passed either way6 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which subcommand of cn_stock_data.py should be executed to fetch real-time or latest market snapshot quotes for target stocks before running lead-lag analysis?
| Checked | Without skill | With skill |
|---|---|---|
| The command uses the quote subcommand of cn_stock_data.py. | Missed | Passed |
The instructions your model reads when the skill loads.
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"
})
test cases passed, vs 7 / 16 without
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