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a-share-trade-classification

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A股交易分类/Lee-Ready算法。当用户说"交易分类"、"Lee-Ready"、"买卖分类"、"主动买卖"、"trade classification"、"BVC"时触发。基于 cn-stock-data 获取数据，对成交进行买卖方向分类。支持 formal/brief 两种输出风格。

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交易分类/Lee-Ready算法助手

数据获取

通过 cn-stock-data skill 获取数据：

  • 逐笔成交: 成交价格/数量/时间
  • 盘口数据: 成交时的买卖报价
  • BSFlag: A股自带的买卖标志(如有)

分析工作流

Step 1: 分类算法

  • Quote Rule：成交价>中间价→买入，<中间价→卖出
  • Tick Rule：成交价>上一笔→买入(uptick)，<上一笔→卖出
  • Lee-Ready：先用Quote Rule，平局时用Tick Rule
  • BVC(Bulk Volume Classification)：基于价格变动的批量分类

Step 2: A股数据处理

  • 深交所逐笔数据自带BSFlag，可直接使用
  • 上交所需要用Lee-Ready算法推断
  • 集合竞价成交的分类：通常标记为中性
  • 大宗交易的分类：单独处理

Step 3: 分类质量评估

  • 准确率：与真实BSFlag对比(深交所可验证)
  • Lee-Ready在A股的准确率约85-90%
  • Tick Rule在A股的准确率约75-80%
  • BVC方法准确率较低但计算简单

Step 4: 分类结果应用

  • 净买入量 = 主动买入量 - 主动卖出量
  • 订单流不平衡(OFI)：预测短期价格方向
  • VPIN计算：基于买卖分类的波动率预测
  • 大单方向分析：大单的买卖方向统计

Step 5: 输出报告

输出格式

formal 风格（研报级）

# [标的] 交易分类分析报告

## 一、分类结果
| 时段 | 主动买(万股) | 主动卖(万股) | 净买入 |
|------|------------|------------|--------|

## 二、分类方法
[使用的算法与准确率]

## 三、订单流分析
[OFI、净买入趋势]

## 四、信号解读

brief 风格（快速分析）

## [标的] 交易分类速览
- 主动买入 58%，主动卖出 42%
- 净买入 +1,200万股
- 大单(>10万)净买入 +350万股
- 信号：资金偏多头

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