A股板块价差/行业估值差分析
SkillDev toolsThis skill gives your AI the ability to analyze price and valuation gaps between A-share industry sectors. When you ask about sector spread or divergence, it quantifies how large the differences between sectors are. Results can be delivered in formal or brief style.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding it, ask your AI how large the sector spread or valuation gap is between A-share industries, for example how much sectors have diverged. You can specify whether you want the comparison in formal or brief style.
Then ask your AI: use the A股板块价差/行业估值差分析 skill
What your AI can do with it
- Quantify price spreads between A-share sectors
- Measure valuation differences across industries
- Show how large the divergence between sectors is
- Run an analysis when you mention sector spread, sector divergence, or valuation gaps
- Deliver results in formal or brief style
What this skill tells your AI
The instructions your AI receives, as published by aifinlab/finclaw in skills/a-share-sector-spread/SKILL.md and read by ahel’s review.
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: 获取各行业估值和K线数据
Step 2: 计算板块价差
- 行业间估值差（PE/PB差值）
- 行业间涨跌幅差
- 历史价差分位数
Step 3: 分化程度分析
- 行业收益率离散度（标准差）
- 行业估值离散度
- 与历史分化程度对比
Step 4: 均值回归信号
过度分化→可能反转
Step 5: 输出
| 维度 | formal | brief |
|---|---|---|
| 价差 | 完整行业价差矩阵 | 最大价差 |
| 分化度 | 离散度时序 | 当前分化水平 |
| 回归信号 | 历史分位分析 | 是否过度分化 |
| 默认风格：brief。 |
关键规则
- 行业估值差扩大到极端时倾向回归
- 分化程度与市场结构化行情正相关
- 过度分化=板块轮动机会
- A股行业PE差异巨大——需用PB或PS辅助对比
- 新兴行业vs传统行业的估值差有趋势性成分
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
Signals
- GitHub stars
- 241
- Forks
- 38
- Last commit
- May 2026
Advanced
- Catalog kind
- skill
- Gateway key
a-share-sector-spread- Source
- github.com/aifinlab/finclaw