186 lines
5.0 KiB
Markdown
186 lines
5.0 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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## 數據取得
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```python
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import yfinance as yf
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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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```
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## 核心原則
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1. **每種型態分開畫** — 不要把所有東西混在一張圖上
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2. **圖片要清晰** — 至少 1200x800 像素，字體夠大
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3. **標註關鍵價位** — 支撐、壓力、進場點用不同顏色標示
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4. **存成 PNG** — 存到 `docs/fin/charts/` 目錄下
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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 mplfinance as mpf
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import yfinance as yf
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df = yf.download("NVDA", period="6mo", interval="1d")
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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='NVDA K線圖 + 均線',
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figsize=(14, 8),
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savefig='docs/fin/charts/NVDA-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.pyplot as plt
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import yfinance as yf
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df = yf.download("NVDA", period="6mo", interval="1d")
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close = df['Close'].values.flatten()
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dates = df.index
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fig, ax = plt.subplots(figsize=(14, 8))
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ax.plot(dates, close, 'b-', linewidth=1.5, label='收盤價')
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# 標註支撐壓力（需手動或演算法計算）
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support = 120 # 範例值
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resistance = 150
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ax.axhline(y=support, color='green', linestyle='--', label=f'支撐 ${support}')
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ax.axhline(y=resistance, color='red', linestyle='--', label=f'壓力 ${resistance}')
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ax.set_title('NVDA 支撐壓力圖', fontsize=16)
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ax.legend(fontsize=12)
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig('docs/fin/charts/NVDA-support-resistance.png', dpi=150)
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plt.show()
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```
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### 3. RSI 圖
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```python
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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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df = yf.download("NVDA", 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('NVDA 股價', fontsize=14)
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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()
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ax2.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig('docs/fin/charts/NVDA-rsi.png', dpi=150)
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plt.show()
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```
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### 4. MACD 圖
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```python
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import matplotlib.pyplot as plt
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import yfinance as yf
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df = yf.download("NVDA", 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('NVDA 股價', fontsize=14)
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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', fontsize=14)
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ax2.legend()
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ax2.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig('docs/fin/charts/NVDA-macd.png', dpi=150)
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plt.show()
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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.pyplot as plt
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import yfinance as yf
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df = yf.download("NVDA", 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('NVDA 布林通道', fontsize=16)
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ax.legend(fontsize=12)
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig('docs/fin/charts/NVDA-bollinger.png', dpi=150)
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plt.show()
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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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# 通用型態標註模板
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import yfinance as yf
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```
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