a-share-factor-timing

Business

A股因子择时/风格轮动量化/大小盘价值成长风格切换。当用户说"因子择时"、"factor timing"、"风格轮动"、"什么风格在涨"、"大盘还是小盘"、"价值还是成长"、"风格切换"、"因子轮动"、"大盘小盘占优"、"价值成长占优"、"风格择时"时触发。MUST USE when user asks about factor timing, style rotation between value/growth or large/small cap, or which investment style is currently outperforming. 基于 cn-stock-data 获取数据，量化分析因子/风格的轮动节奏。支持研报风格（formal）和快速分析风格（brief）。

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A股因子择时/风格轮动量化

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: 获取风格指数

获取大盘/小盘、价值/成长、高波/低波等风格指数K线。

Step 2: 计算因子收益

各风格因子的多空收益（做多因子值高的、做空因子值低的）
滚动IC（因子预测力变化）

Step 3: 因子动量分析

近期强势因子（过去20日因子收益排名）
因子动量：近5日因子收益 vs 近60日均值
因子拥挤度：因子估值扩散度

Step 4: 择时信号

宏观信号：利率/信用利差/PMI → 因子偏好
技术信号：因子价差的均值回归
情绪信号：因子拥挤度过高时反转

Step 5: 输出

维度	formal	brief
因子表现	各因子收益+IC	当前强势因子
轮动信号	多维度择时评分	推荐风格
历史规律	因子轮动周期分析	无

默认风格：brief。

关键规则

因子择时难度极高——多数学术研究表明因子择时不如长期持有
宏观驱动的风格轮动相对可预测（利率→价值/成长切换）
因子拥挤度是最有效的反转信号之一
A 股风格轮动比海外更剧烈——大小盘轮动尤为明显
保持因子分散化比精准择时更重要

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