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a-share-autocorrelation

Business
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A股自相关/序列相关性/收益率自相关结构分析。当用户说"自相关"、"autocorrelation"、"序列相关"、"收益率预测性"、"动量还是反转"、"自相关系数"、"ACF"、"PACF"、"Ljung-Box"、"收益率是否可预测"、"随机游走检验"时触发。MUST USE when user asks about return autocorrelation, serial correlation tests, or whether a stock's returns are predictable. 量化分析收益率的自相关结构（ACF/PACF、Ljung-Box检验、随机游走检验）。支持formal和brief风格。

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A股自相关/序列相关性分析

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K线收益率序列

Step 2: 计算自相关函数(ACF)

lag 1-20的自相关系数

Step 3: 偏自相关函数(PACF)

Step 4: Ljung-Box检验

检验序列是否存在显著自相关

Step 5: 输出

维度formalbrief
ACF/PACF完整图表关键lag
检验LB统计量+p值有无自相关
含义动量/反转判断交易含义
默认风格：brief。

关键规则

  1. 正自相关=动量效应（涨了还会涨）
  2. 负自相关=反转效应（涨了会跌回）
  3. A股日频负自相关较明显（T+1导致的隔日反转）
  4. 周频/月频正自相关更显著（中期动量）
  5. 自相关结构是时间序列策略的基础

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