369 lines
11 KiB
Markdown
369 lines
11 KiB
Markdown
---
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name: chart-drawing
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description: 技術分析圖表繪製知識庫。用 Python matplotlib 繪製各種技術型態圖，每種型態分開畫，輸出 PNG 圖片。
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---
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# 技術分析圖表繪製
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## 環境需求
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```bash
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pip install yfinance matplotlib mplfinance pandas numpy
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```
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## ⚠️ 繪圖必讀規則（每次畫圖前必須遵守）
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**以下 5 條規則缺一不可，否則圖片會壞掉或看不到：**
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### 規則 1：必須在最開頭設定 Agg backend（無 GUI 環境）
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```python
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import matplotlib
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matplotlib.use('Agg') # 必須在 import pyplot 之前！
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import matplotlib.pyplot as plt
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```
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### 規則 2：必須設定中文字體（否則中文標題變方框）
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```python
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import matplotlib.pyplot as plt
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# macOS 中文字體設定
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'PingFang TC', 'STHeiti']
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plt.rcParams['axes.unicode_minus'] = False # 負號正常顯示
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```
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### 規則 3：禁止使用 plt.show()（會卡住或報錯）
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```python
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# ❌ 錯誤
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plt.show()
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# ✅ 正確 — 只用 savefig
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plt.savefig('docs/fin/charts/NVDA-kline.png', dpi=150, bbox_inches='tight')
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plt.close('all') # 必須關閉，釋放記憶體
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```
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### 規則 4：每張圖結尾必須 plt.close('all')
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```python
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plt.savefig(output_path, dpi=150, bbox_inches='tight')
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plt.close('all') # 不加這行，下一張圖會疊在上面
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```
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### 規則 5：繪圖前必須建立目錄
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```python
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import os
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os.makedirs('docs/fin/charts', exist_ok=True)
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```
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## 完整繪圖模板（通用前置碼）
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**每次繪圖都必須以這段開頭：**
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```python
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import yfinance as yf
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import pandas as pd
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import numpy as np
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import os
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# 中文字體
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'PingFang TC', 'STHeiti']
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plt.rcParams['axes.unicode_minus'] = False
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# 建立輸出目錄
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os.makedirs('docs/fin/charts', exist_ok=True)
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# 下載數據（美股）
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ticker = "NVDA"
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df = yf.download(ticker, period="6mo", interval="1d")
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close = df['Close'].squeeze()
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dates = df.index
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```
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## 數據取得
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```python
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# 美股
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df = yf.download("NVDA", period="1y", interval="1d")
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# 台股（代號加 .TW）
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df = yf.download("2330.TW", period="1y", interval="1d")
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# 注意：yfinance 回傳的 DataFrame 可能是 MultiIndex
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# 取單一欄位時用 .squeeze() 確保是 Series
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close = df['Close'].squeeze()
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```
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## 圖表類型與範本
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### 1. K 線圖 + 均線（基礎圖）
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```python
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import matplotlib
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matplotlib.use('Agg')
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import mplfinance as mpf
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import yfinance as yf
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import os
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os.makedirs('docs/fin/charts', exist_ok=True)
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ticker = "NVDA"
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df = yf.download(ticker, period="6mo", interval="1d")
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# mplfinance 的 savefig 要用 dict 格式
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save_config = dict(fname=f'docs/fin/charts/{ticker}-kline.png', dpi=150, bbox_inches='tight')
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mpf.plot(df, type='candle', style='charles',
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mav=(20, 50, 200),
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volume=True,
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title=f'{ticker} K線圖 + 均線',
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figsize=(14, 8),
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savefig=save_config)
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# mplfinance 會自動 close
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print(f"✅ 圖表已儲存: docs/fin/charts/{ticker}-kline.png")
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```
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### 2. 支撐壓力圖
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```python
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import yfinance as yf
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import os
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'PingFang TC', 'STHeiti']
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plt.rcParams['axes.unicode_minus'] = False
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os.makedirs('docs/fin/charts', exist_ok=True)
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ticker = "NVDA"
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df = yf.download(ticker, period="6mo", interval="1d")
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close = df['Close'].squeeze()
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fig, ax = plt.subplots(figsize=(14, 8))
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ax.plot(df.index, close, 'b-', linewidth=1.5, label='收盤價')
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# 標註支撐壓力（由 technical-analyst 提供具體數值）
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support = 120 # 替換為實際值
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resistance = 150 # 替換為實際值
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ax.axhline(y=support, color='green', linestyle='--', linewidth=2, label=f'支撐 ${support}')
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ax.axhline(y=resistance, color='red', linestyle='--', linewidth=2, label=f'壓力 ${resistance}')
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ax.set_title(f'{ticker} 支撐壓力圖', fontsize=16)
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ax.set_ylabel('價格 (USD)', fontsize=12)
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ax.legend(fontsize=12)
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ax.grid(True, alpha=0.3)
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output_path = f'docs/fin/charts/{ticker}-support-resistance.png'
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plt.savefig(output_path, dpi=150, bbox_inches='tight')
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plt.close('all')
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print(f"✅ 圖表已儲存: {output_path}")
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```
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### 3. RSI 圖
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```python
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import yfinance as yf
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import os
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'PingFang TC', 'STHeiti']
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plt.rcParams['axes.unicode_minus'] = False
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os.makedirs('docs/fin/charts', exist_ok=True)
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ticker = "NVDA"
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df = yf.download(ticker, period="6mo", interval="1d")
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close = df['Close'].squeeze()
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delta = close.diff()
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gain = delta.where(delta > 0, 0).rolling(14).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(14).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10), height_ratios=[3, 1])
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ax1.plot(df.index, close, 'b-', linewidth=1.5)
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ax1.set_title(f'{ticker} 股價', fontsize=14)
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ax1.set_ylabel('價格 (USD)', fontsize=12)
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ax1.grid(True, alpha=0.3)
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ax2.plot(df.index, rsi, 'purple', linewidth=1.5)
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ax2.axhline(y=70, color='red', linestyle='--', alpha=0.7, label='超買 70')
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ax2.axhline(y=30, color='green', linestyle='--', alpha=0.7, label='超賣 30')
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ax2.fill_between(df.index, 70, 100, alpha=0.1, color='red')
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ax2.fill_between(df.index, 0, 30, alpha=0.1, color='green')
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ax2.set_title('RSI(14)', fontsize=14)
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ax2.set_ylim(0, 100)
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ax2.legend(fontsize=10)
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ax2.grid(True, alpha=0.3)
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output_path = f'docs/fin/charts/{ticker}-rsi.png'
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plt.tight_layout()
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plt.savefig(output_path, dpi=150, bbox_inches='tight')
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plt.close('all')
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print(f"✅ 圖表已儲存: {output_path}")
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```
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### 4. MACD 圖
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```python
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import yfinance as yf
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import os
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'PingFang TC', 'STHeiti']
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plt.rcParams['axes.unicode_minus'] = False
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os.makedirs('docs/fin/charts', exist_ok=True)
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ticker = "NVDA"
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df = yf.download(ticker, period="6mo", interval="1d")
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close = df['Close'].squeeze()
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ema12 = close.ewm(span=12).mean()
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ema26 = close.ewm(span=26).mean()
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macd_line = ema12 - ema26
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signal = macd_line.ewm(span=9).mean()
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histogram = macd_line - signal
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10), height_ratios=[3, 1])
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ax1.plot(df.index, close, 'b-', linewidth=1.5)
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ax1.set_title(f'{ticker} 股價', fontsize=14)
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ax1.set_ylabel('價格 (USD)', fontsize=12)
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ax1.grid(True, alpha=0.3)
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ax2.plot(df.index, macd_line, 'b-', label='MACD', linewidth=1.5)
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ax2.plot(df.index, signal, 'r-', label='Signal', linewidth=1.5)
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colors = ['green' if v >= 0 else 'red' for v in histogram]
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ax2.bar(df.index, histogram, color=colors, alpha=0.5, label='Histogram')
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ax2.set_title('MACD (12, 26, 9)', fontsize=14)
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ax2.legend(fontsize=10)
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ax2.grid(True, alpha=0.3)
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output_path = f'docs/fin/charts/{ticker}-macd.png'
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plt.tight_layout()
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plt.savefig(output_path, dpi=150, bbox_inches='tight')
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plt.close('all')
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print(f"✅ 圖表已儲存: {output_path}")
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```
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### 5. 布林通道圖
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```python
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import yfinance as yf
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import os
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'PingFang TC', 'STHeiti']
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plt.rcParams['axes.unicode_minus'] = False
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os.makedirs('docs/fin/charts', exist_ok=True)
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ticker = "NVDA"
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df = yf.download(ticker, period="6mo", interval="1d")
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close = df['Close'].squeeze()
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sma20 = close.rolling(20).mean()
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std20 = close.rolling(20).std()
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upper = sma20 + 2 * std20
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lower = sma20 - 2 * std20
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fig, ax = plt.subplots(figsize=(14, 8))
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ax.plot(df.index, close, 'b-', linewidth=1.5, label='收盤價')
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ax.plot(df.index, sma20, 'orange', linewidth=1, label='SMA(20)')
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ax.plot(df.index, upper, 'red', linewidth=0.8, linestyle='--', label='上軌')
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ax.plot(df.index, lower, 'green', linewidth=0.8, linestyle='--', label='下軌')
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ax.fill_between(df.index, upper, lower, alpha=0.1, color='gray')
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ax.set_title(f'{ticker} 布林通道 (20, 2)', fontsize=16)
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ax.set_ylabel('價格 (USD)', fontsize=12)
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ax.legend(fontsize=12)
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ax.grid(True, alpha=0.3)
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output_path = f'docs/fin/charts/{ticker}-bollinger.png'
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plt.tight_layout()
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plt.savefig(output_path, dpi=150, bbox_inches='tight')
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plt.close('all')
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print(f"✅ 圖表已儲存: {output_path}")
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```
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## 型態辨識圖（手動標註）
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當 technical-analyst 識別出型態時，用以下模板繪製：
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### 頭肩頂/底、雙頂/底、三角收斂等
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```python
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import yfinance as yf
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import numpy as np
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import os
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'Heiti TC', 'PingFang TC', 'STHeiti']
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plt.rcParams['axes.unicode_minus'] = False
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os.makedirs('docs/fin/charts', exist_ok=True)
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ticker = "NVDA"
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pattern_name = "double-bottom" # 替換為實際型態名
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pattern_label = "雙底" # 替換為中文名
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df = yf.download(ticker, period="6mo", interval="1d")
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close = df['Close'].squeeze()
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fig, ax = plt.subplots(figsize=(14, 8))
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ax.plot(df.index, close, 'b-', linewidth=1.5)
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# 標註型態關鍵點（由 technical-analyst 提供具體座標）
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# 範例：雙底
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# bottom1_date = df.index[50]
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# bottom2_date = df.index[80]
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# bottom1_price = close.iloc[50]
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# bottom2_price = close.iloc[80]
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# neckline = 150
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#
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# ax.scatter([bottom1_date, bottom2_date],
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# [bottom1_price, bottom2_price],
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# color='green', s=150, zorder=5, marker='^', label=f'{pattern_label}底部')
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# ax.axhline(y=neckline, color='orange', linestyle='--', linewidth=2, label=f'頸線 ${neckline}')
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ax.set_title(f'{ticker} 型態辨識 — {pattern_label}', fontsize=16)
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ax.set_ylabel('價格 (USD)', fontsize=12)
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ax.legend(fontsize=12)
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ax.grid(True, alpha=0.3)
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output_path = f'docs/fin/charts/{ticker}-pattern-{pattern_name}.png'
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plt.tight_layout()
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plt.savefig(output_path, dpi=150, bbox_inches='tight')
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plt.close('all')
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print(f"✅ 圖表已儲存: {output_path}")
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```
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## 圖表命名規則
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```
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docs/fin/charts/
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├── [TICKER]-kline.png # K 線 + 均線
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├── [TICKER]-support-resistance.png # 支撐壓力
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├── [TICKER]-rsi.png # RSI
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├── [TICKER]-macd.png # MACD
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├── [TICKER]-bollinger.png # 布林通道
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├── [TICKER]-pattern-[型態名].png # 型態辨識
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└── [TICKER]-volume.png # 量能分析
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```
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## 注意事項（必讀 Checklist）
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每次繪圖前，確認以下 checklist 全部打勾：
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- [ ] `matplotlib.use('Agg')` 在最開頭（import pyplot 之前）
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- [ ] `plt.rcParams['font.sans-serif']` 已設定中文字體
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- [ ] `plt.rcParams['axes.unicode_minus'] = False`
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- [ ] `os.makedirs('docs/fin/charts', exist_ok=True)`
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- [ ] 使用 `df['Close'].squeeze()` 取得 Series（避免 MultiIndex 問題）
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- [ ] `plt.savefig(path, dpi=150, bbox_inches='tight')` 而非 `plt.show()`
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- [ ] `plt.close('all')` 在 savefig 之後
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- [ ] `print(f"✅ 圖表已儲存: {output_path}")` 確認輸出
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- [ ] 台股代號用數字（如 `2330-kline.png`）
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- [ ] 每種型態**獨立一張圖**，不要混在一起 |