A股Alpha衰减分析
SkillMonitoring & opsA-share Alpha decay / factor crowding analysis. Triggered when the user says "Alpha衰减", "alpha decay", "因子拥挤", "策略容量", "因子失效", "XX因子还有效吗", "crowding", or "策略拥挤". Analyzes the decay trend of a factor's/strategy's Alpha over time, factor crowding metrics, and strategy capacity limits. Supports a resea
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Alpha衰减分析 skill
What this skill tells your AI
The instructions your AI receives, as published by aifinlab/finclaw in skills/a-share-alpha-decay/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 "$SCRIPTS/cn_stock_data.py" finance --code [CODE]
python "$SCRIPTS/cn_stock_data.py" quote --code [CODE1],[CODE2],...
Workflow
Step 1: 选择因子/策略
确定要分析 Alpha 衰减的目标：特定因子（如低 PE）或策略（如动量）
Step 2: 分时段 IC/收益分析
- 将历史数据分为多个子时段（如每年/每半年）
- 分别计算各时段的 IC、因子收益率、多空组合收益
- 观察 Alpha 随时间的变化趋势
Step 3: 滚动窗口衰减曲线
- 使用滚动窗口（如 252 日）计算 IC/IR
- 绘制 IC 的时间序列，观察趋势性下降
- 计算 IC 的结构性断点（Chow test）
Step 4: 拥挤度指标
- 估值收敛度：因子多头组 vs 空头组的估值差收窄
- 换手率集中度：因子多头组的换手率异常升高
- 相关性上升：多头组内股票相关性增加
- 策略容量：以冲击成本估计最大可容纳资金量
Step 5: 输出
| 维度 | formal | brief |
|---|---|---|
| 衰减分析 | 完整时序+断点检验 | 当前 IC vs 历史均值 |
| 拥挤度 | 多维指标矩阵 | 拥挤/正常/低估 |
| 容量 | 详细估算 | 大/中/小 |
默认风格：brief。
关键规则
- Alpha 衰减是正常现象——被更多人发现的因子会被套利掉
- 区分周期性衰减（市场风格切换）和结构性衰减（因子失效）
- A 股因子生命周期通常 3-5 年，短于美股
- 小盘股因子容量小，衰减更快
Signals
- GitHub stars
- 241
- Forks
- 38
- Last commit
- May 2026
Advanced
- Catalog kind
- skill
- Gateway key
a-share-alpha-decay- Source
- github.com/aifinlab/finclaw