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a-share-vwap-strategy

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A股VWAP算法/成交量加权策略。当用户说"VWAP"、"成交量加权"、"VWAP策略"、"VWAP突破"、"VWAP算法"、"量价加权"时触发。基于 cn-stock-data 获取数据,计算VWAP、分析偏离度、构建VWAP交易策略。支持 formal/brief 两种输出风格。

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How to use this skill

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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/a-share-vwap-strategy/SKILL.md

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VWAP算法/成交量加权策略助手

数据获取

通过 cn-stock-data skill 获取数据:

  • 分钟K线: 1分钟线含成交量
  • 实时行情: 最新价格与累计成交量
  • 历史日线: 用于计算历史VWAP分布

分析工作流

Step 1: VWAP计算与分解

  • 日内VWAP = Σ(Price_i × Volume_i) / Σ Volume_i
  • 滚动VWAP:N日滚动成交量加权均价
  • 分时段VWAP:上午/下午分别计算
  • 锚定VWAP:从特定事件点开始计算

Step 2: VWAP偏离度分析

  • 偏离度 = (Price - VWAP) / VWAP × 100%
  • 偏离度的历史分布与分位数
  • 偏离度均值回归特征:超过±1%后回归概率
  • 机构大单通常以VWAP为执行基准

Step 3: VWAP交易信号

  • 突破信号:价格从下方突破VWAP,放量确认
  • 支撑/压力:VWAP作为日内动态支撑压力位
  • 多空分界:价格在VWAP上方偏多,下方偏空
  • 结合成交量分布确认VWAP有效性

Step 4: VWAP执行算法

  • 目标:使执行均价尽量接近VWAP
  • 成交量预测:基于历史日内成交量分布曲线
  • 动态调整:实际成交量偏离预测时的修正
  • A股特征:开盘和尾盘成交量占比高

Step 5: 输出报告

输出格式

formal 风格(研报级)

# [标的] VWAP策略分析报告

## 一、VWAP计算
| 类型 | VWAP | 当前价 | 偏离度 |
|------|------|--------|--------|

## 二、偏离度分析
[历史分布、回归概率]

## 三、交易信号
[突破/支撑信号状态]

## 四、执行建议
[成交量预测、拆单方案]

brief 风格(快速分析)

## [标的] VWAP速览
- 日内VWAP 25.32,当前价 25.48 (+0.63%)
- 价格在VWAP上方,偏多
- 偏离度P72,尚未到极值
- 建议:VWAP附近可加仓

参考 references/vwap-strategy-guide.md 获取详细方法论与 A股实证研究。

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