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Technical Pattern Recognition

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A股技术形态识别:均线多头/空头排列、MACD金叉死叉、放量突破N日新高、缩量回踩均线,输出信号表 | A-share technical pattern recognition: MA bullish/bearish alignment, MACD golden/death cross, volume-breakout to N-day high, low-volume pullback to MA, outputs a signal table | A株テクニカルパターン認識:移動平均線のパーフェクトオーダー/逆オーダー、MACDゴールデン/デッドクロス、出来高を伴うN日高値ブレイク、出来高減少の押し目、シグナル表を出力

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  • Added September 2, 2026
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npx -y skills add Serennity007/awesome-stock-quant-skills --skill technical-pattern-recognition --agent claude-code

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SKILL.md
---
name: technical-pattern-recognition
description: A股技术形态识别:均线多头/空头排列、MACD金叉死叉、放量突破N日新高、缩量回踩均线,输出信号表 | A-share technical pattern recognition: MA bullish/bearish alignment, MACD golden/death cross, volume-breakout to N-day high, low-volume pullback to MA, outputs a signal table | A株テクニカルパターン認識:移動平均線のパーフェクトオーダー/逆オーダー、MACDゴールデン/デッドクロス、出来高を伴うN日高値ブレイク、出来高減少の押し目、シグナル表を出力
author: Serennity007
license: MIT
---

# A股技术形态识别(technical-pattern-recognition)

基于 akshare 新浪日线数据,对 A 股个股做常见技术形态的**机械式识别**,输出结构化信号表,供 AI 进一步解读。本技能只做形态识别,不做收益预测。

## 识别的形态

| 信号 | 含义 | 判定逻辑(默认参数) |
|---|---|---|
| `MA_BULLISH` | 均线多头排列 | MA5 > MA10 > MA20 > MA60 |
| `MA_BEARISH` | 均线空头排列 | MA5 < MA10 < MA20 < MA60 |
| `MACD_GOLDEN` | MACD 金叉 | DIF 上穿 DEA(近 3 日内发生) |
| `MACD_DEATH` | MACD 死叉 | DIF 下穿 DEA(近 3 日内发生) |
| `VOL_BREAKOUT` | 放量突破 N 日新高 | 收盘价创 N 日(默认 60)新高,且成交量 ≥ 20 日均量 × 1.5 |
| `PULLBACK_MA` | 缩量回踩均线 | 股价回踩至 MA20 ±2% 区间,且当日成交量 ≤ 20 日均量 × 0.8,且大方向为多头排列 |

详细说明见 `references/patterns.md`。

## 使用方法

```bash
pip install akshare pandas

# 单只股票识别(默认取近 250 个交易日)
python scripts/patterns.py 600519

# 多只股票 + 自定义突破窗口与输出
python scripts/patterns.py 600519,000858 --days 250 --window 60 --out signals.csv
```

## 分析流程(配合 AI 使用)

1. 运行 `patterns.py` 得到每只股票当前命中的信号与关键指标(均线、MACD、量比、距新高幅度)。
2. AI 结合信号组合做解读(例如 `MA_BULLISH + PULLBACK_MA` 是趋势中的回调买点候选;`VOL_BREAKOUT` 后需观察是否假突破)。
3. 形态信号是**滞后指标**,必须与基本面、市场环境结合,不可单独作为交易依据。

## 免责声明

本技能仅供学习研究,不构成投资建议。技术形态基于历史价格统计,不代表未来走势;任何信号都可能失效或出现假突破。

Files in this skill

  • README.en.md1.2 KB
  • README.ja.md1.5 KB
  • README.md1.1 KB
  • SKILL.md2.4 KB
  • docs/demo.en.md2.3 KB
  • docs/demo.ja.md2.5 KB
  • docs/demo.md2.1 KB
  • references/patterns.md2.4 KB
  • scripts/patterns.py5.1 KB

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