A股质量因子/盈利质量量化分析
SkillDev toolsQuantitative analysis of A-share quality factors / earnings quality. Triggered when the user says "质量因子", "quality factor", "盈利质量", "高质量", "ROE质量", or "应计". Quantitatively constructs and analyzes quality factors. Supports formal and brief styles.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the A股质量因子/盈利质量量化分析 skill
What this skill tells your AI
The instructions your AI receives, as published by aifinlab/finclaw in skills/a-share-quality-factor/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: 获取财务数据
Step 2: 构建质量因子
- 盈利能力: ROE/ROIC/毛利率
- 盈利稳定性: ROE标准差(过去5年)
- 应计质量: (净利润-经营现金流)/总资产
- 资产负债质量: 负债率/流动比率
Step 3: 因子检验
IC/IR分析、分组回测
Step 4: 输出
| 维度 | formal | brief |
|---|---|---|
| 因子值 | 多维度质量评分 | 综合质量分 |
| 因子效果 | IC/IR+分组收益 | 因子有效性 |
| 默认风格:brief。 |
关键规则
- 质量因子在熊市表现更突出(防御性)
- 低应计比例=高盈利质量(现金利润占比高)
- ROE高但自由现金流差→质量存疑
- 质量因子与价值因子结合效果更好
- A股财务造假风险需特别关注应计异常
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-quality-factor- Source
- github.com/aifinlab/finclaw