A股日内模式/分时走势量化分析

SkillCommerce & finance

Quantitative analysis of A-share intraday patterns and time-share price movements. Triggered when the user says "日内模式", "intraday", "分时", "盘中走势", "几点涨", or "尾盘规律". Quantitatively analyzes A-share intraday trading patterns. Supports formal and brief styles.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the A股日内模式/分时走势量化分析 skill

What this skill tells your AI

The instructions your AI receives, as published by aifinlab/finclaw in skills/a-share-intraday-pattern/SKILL.md and read by ahel’s review.

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: 获取分钟级K线

Step 2: 日内收益率分布

各时段(开盘/早盘/午盘/尾盘)的平均涨跌幅

Step 3: 日内量能分布

各时段成交量占比和变化规律

Step 4: 日内模式识别

  • U型成交量(开盘尾盘放量,盘中缩量)
  • 尾盘效应(最后30分钟异常)
  • 午后效应

Step 5: 输出

维度formalbrief
时段分析各时段详细统计关键时段
量能分布分时量能图U型特征
规律总结历史统计验证今日模式
默认风格:brief。

关键规则

  1. A股开盘30分钟和收盘30分钟波动最大
  2. 集合竞价(9:15-9:25)反映隔夜消息消化
  3. 午后1:00-1:30常有政策/消息发布影响
  4. 尾盘集合竞价(14:57-15:00)可能被操纵
  5. 日内模式在不同市场状态下会变化

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

Signals

GitHub stars
241
Forks
38
Last commit
May 2026
Advanced
Catalog kind
skill
Gateway key
a-share-intraday-pattern
Source
github.com/aifinlab/finclaw