a-share-volume-price

Business

A股量价关系量化分析。当用户说"量价"、"volume price"、"缩量"、"放量"、"量价背离"、"天量天价"、"地量地价"、"换手率分析"、"成交量异常"时触发。基于 cn-stock-data 获取K线数据，量化分析量价关系模式，识别异常量价信号。支持研报风格（formal）和快速分析风格（brief）。

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A股量价关系量化分析

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]

量化计算：

QSCRIPTS="$SKILLS_ROOT/a-share-volume-price/scripts"
python "$QSCRIPTS/volume_price_analyzer.py" --data kline.json --window 20

Workflow

Step 1: 获取K线数据

获取包含 OHLCV（开高低收量）的K线数据。

Step 2: 计算量价指标

VWAP：成交量加权平均价
OBV：能量潮，累计量（涨加跌减）
VR（量比）：今日成交量 / 过去N日平均成交量
换手率 Z-score：当前换手率相对历史的标准化值

Step 3: 识别量价模式

8 种经典量价关系：量增价升/量增价跌/量缩价升/量缩价跌/量增价平/量缩价平/量平价升/量平价跌

Step 4: 异常检测

成交量 > 均值 + 2σ → 放量异常
成交量 < 均值 - 1.5σ → 极度缩量
量价背离：价格新高但成交量未创新高

Step 5: 输出

维度	formal	brief
指标	全部量价指标	VR+OBV方向
模式	近期模式序列分析	当日模式
异常	历史异常回测	有/无异常

默认风格：brief。

关键规则

量在价先——成交量变化往往领先于价格变化
A 股集合竞价量有特殊含义（开盘前15分钟）
涨跌停板的成交量需特殊处理（封板量 vs 开板量）
换手率比成交量更具可比性（剔除了流通盘大小差异）
量价背离是趋势衰竭的重要信号

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