A股算法执行/智能拆单策略。当用户说"算法执行"、"智能拆单"、"execution algo"、"算法交易"、"拆单策略"、"大单拆分"时触发。基于 cn-stock-data 获取数据,设计算法执行方案。支持 formal/brief 两种输出风格。
Scanned 9/7/2026
Install to Claude Code
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---
name: a-share-execution-algo
description: A股算法执行/智能拆单策略。当用户说"算法执行"、"智能拆单"、"execution algo"、"算法交易"、"拆单策略"、"大单拆分"时触发。基于 cn-stock-data 获取数据,设计算法执行方案。支持 formal/brief 两种输出风格。
---
# 算法执行/智能拆单助手
## 数据获取
通过 cn-stock-data skill 获取数据:
- **分钟K线**: 日内成交量分布
- **实时行情**: 盘口深度与价格
- **历史成交**: 日均成交量/波动率
## 分析工作流
### Step 1: 算法选择
- TWAP:时间均匀拆分,适合流动性好的标的
- VWAP:按成交量分布拆分,机构最常用基准
- IS(Implementation Shortfall):最小化执行缺口
- POV(Percentage of Volume):按参与率执行
### Step 2: 参数配置
- 执行窗口:开始时间→结束时间
- 参与率上限:通常5-20%,避免过大市场冲击
- 价格保护:设置限价,超出范围暂停执行
- 紧急度:aggressive/neutral/passive
### Step 3: 实时调整
- 价格偏离监控:实际均价vs基准的偏差
- 成交量偏离:实际成交量vs预测的偏差
- 市场状态变化:波动率突增时降低执行速度
- 剩余量管理:尾盘加速完成剩余订单
### Step 4: 执行评估
- 执行均价 vs VWAP/TWAP/到达价
- 市场冲击成本估算
- 时机选择成本:延迟执行的机会成本
- 总执行成本 = 冲击 + 时机 + 佣金
### Step 5: 输出报告
## 输出格式
### formal 风格(研报级)
```
# [标的] 算法执行方案
## 一、方案配置
| 参数 | 设置 |
|------|------|
| 算法 | VWAP |
| 总量 | 50万股 |
## 二、执行计划
[分时段拆单明细]
## 三、风控参数
[价格保护、参与率]
## 四、预期成本
```
### brief 风格(快速分析)
```
## [标的] 执行方案速览
- VWAP算法,50万股,执行窗口9:30-14:30
- 参与率上限15%,预期冲击0.08%
- 价格保护:±1%
- 预期总成本:0.15%
```
参考 `references/execution-algo-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
```python
# 调用 skill
result = run_skill({
"param1": "value1",
"param2": "value2"
})
```
### 示例 2: 命令行使用
```bash
python scripts/run_skill.py --input data.json
```
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