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a-share-index-enhance

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A股指数增强策略/超额收益分析。当用户说"指数增强"、"index enhance"、"超额收益"、"跑赢指数"、"增强策略"、"alpha"、"怎么跑赢沪深300"时触发。基于 cn-stock-data 获取指数成分股数据，量化构建指数增强组合。支持研报风格（formal）和快速分析风格（brief）。

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A股指数增强策略

数据源

SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts"
python "$SCRIPTS/cn_stock_data.py" kline --code [INDEX_CODE] --freq daily --start [日期]
python "$SCRIPTS/cn_stock_data.py" finance --code [CODE1],[CODE2],...
python "$SCRIPTS/cn_stock_data.py" quote --code [CODE1],[CODE2],...

Workflow

Step 1: 确定基准指数

沪深300(SH000300) / 中证500(SH000905) / 中证1000(SH000852)

Step 2: 获取成分股数据

获取指数成分股列表、权重、K线数据、财务指标。

Step 3: 构建增强因子

  • 价值因子：EP、BP、DP（高于基准均值的股票超配）
  • 质量因子：ROE、毛利率、现金流稳定性
  • 动量因子：过去20日收益率（去除最近5日）
  • 低波因子：过去60日波动率（低波超配）

Step 4: 偏离度控制

  • 行业偏离 < ±3%（相对基准权重）
  • 个股偏离 < ±1%
  • 风格因子暴露中性化
  • 换手率约束：月度换手 < 30%

Step 5: 输出

维度formalbrief
因子构成多因子权重 + IC/IR主要alpha来源
组合构建完整超配/低配名单Top 10 超配
跟踪误差TE 目标 + 信息比率预期超额

默认风格：brief。

关键规则

  1. 指数增强的核心是控制跟踪误差（TE < 5%年化）
  2. 行业中性是底线——避免行业偏离贡献过多超额
  3. A 股特殊：ST/涨跌停/停牌股需特殊处理
  4. 交易成本显著影响超额——换手率控制很重要
  5. 成分股调整日（6月/12月）需注意调仓冲击

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