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Create and export various charts (line, bar, pie, candlestick, heatmap), dashboards, and HTML reports with real-time data updates and automatic refreshing.
# Data Visualization - 数据可视化仪表盘
> 图表生成/仪表盘制作/实时数据看板/自动报告
> 最后更新：2026-04-13
---
## 功能概述
- 📊 **图表生成**：折线/柱状/饼图/热力图
- 📈 **K线图表**：股票/期货专用图表
- 🖥️ **仪表盘**：多图表组合看板
- 🔄 **实时更新**：数据自动刷新
- 📝 **报告导出**：PDF/HTML/PNG
---
## 图表生成
### 1. Matplotlib基础图表
```python
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg') # 非交互式后端
# 设置中文
plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial']
plt.rcParams['axes.unicode_minus'] = False
def plot_line(data, title, xlabel, ylabel, output='chart.png'):
"""折线图"""
plt.figure(figsize=(12, 6))
plt.plot(data['x'], data['y'])
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
plt.savefig(output, dpi=100, bbox_inches='tight')
plt.close()
return output
def plot_bar(categories, values, title, output='bar.png'):
"""柱状图"""
plt.figure(figsize=(12, 6))
plt.bar(categories, values)
plt.title(title)
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig(output, dpi=100)
plt.close()
return output
def plot_pie(sizes, labels, title, output='pie.png'):
"""饼图"""
plt.figure(figsize=(8, 8))
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title(title)
plt.savefig(output)
plt.close()
return output
```
### 2. K线图表
```python
import mplfinance as mpf
def plot_candlestick(data, title='K线图', output='candle.png'):
"""K线图"""
# data需要包含: Date, Open, High, Low, Close, Volume
mpf.plot(
data.set_index('Date'),
type='candle',
title=title,
style='charles',
savefig=output
)
return output
def plot_volume(data, output='volume.png'):
"""成交量图"""
mpf.plot(
data.set_index('Date'),
type='line',
volume=True,
savefig=output
)
return output
```
### 3. 热力图
```python
import seaborn as sns
import numpy as np
def plot_heatmap(data, title, output='heatmap.png'):
"""热力图"""
plt.figure(figsize=(12, 8))
sns.heatmap(data, annot=True, fmt='.2f', cmap='YlOrRd')
plt.title(title)
plt.tight_layout()
plt.savefig(output)
plt.close()
return output
def correlation_matrix(df, output='corr.png'):
"""相关系数矩阵"""
plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Matrix')
plt.savefig(output)
plt.close()
return output
```
---
## 仪表盘制作
### 1. 多图表组合
```python
from matplotlib.gridspec import GridSpec
def create_dashboard(data, output='dashboard.png'):
"""创建仪表盘"""
fig = plt.figure(figsize=(16, 10))
gs = GridSpec(3, 2, figure=fig)
# 图1：K线
ax1 = fig.add_subplot(gs[0, :])
mpf.plot(data['kline'], type='candle', ax=ax1, show=False)
# 图2：成交量
ax2 = fig.add_subplot(gs[1, 0])
ax2.bar(data['volume']['date'], data['volume']['vol'])
ax2.set_title('Volume')
# 图3：资金流
ax3 = fig.add_subplot(gs[1, 1])
ax3.plot(data['money_flow'])
ax3.set_title('Money Flow')
# 图4：收益曲线
ax4 = fig.add_subplot(gs[2, :])
ax4.plot(data['equity'])
ax4.set_title('Equity Curve')
plt.tight_layout()
plt.savefig(output, dpi=100)
plt.close()
return output
```
### 2. 实时看板
```python
import time
class LiveDashboard:
"""实时看板"""
def __init__(self, refresh_interval=60):
self.refresh_interval = refresh_interval
self.data_sources = {}
def add_source(self, name, func):
"""添加数据源"""
self.data_sources[name] = func
def update(self):
"""更新所有数据"""
results = {}
for name, func in self.data_sources.items():
try:
results[name] = func()
except Exception as e:
print(f"Failed to update {name}: {e}")
return results
def run(self, duration=3600):
"""运行看板"""
start = time.time()
while time.time() - start < duration:
data = self.update()
self.render(data)
time.sleep(self.refresh_interval)
```
---
## HTML看板
### 1. Plotly交互图
```python
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
def create_html_dashboard(data):
"""创建HTML仪表盘"""
fig = make_subplots(
rows=2, cols=2,
subplot_titles=('K线', '成交量', '资金流', '收益曲线')
)
# K线
fig.add_trace(
go.Candlestick(x=data['date'], open=data['open'],
high=data['high'], low=data['low'], close=data['close']),
row=1, col=1
)
# 成交量
fig.add_trace(
go.Bar(x=data['date'], y=data['volume']),
row=1, col=2
)
# 收益曲线
fig.add_trace(
go.Scatter(x=data['date'], y=data['equity'], name='Equity'),
row=2, col=1
)
fig.update_layout(height=800, showlegend=True)
html = fig.to_html(full_html=False)
return html
```
### 2. 数据表格
```python
def create_data_table(data, columns, title='数据表'):
"""创建数据表格HTML"""
html = f'''
<h3>{title}</h3>
<table style="width:100%; border-collapse: collapse;">
<tr style="background:#f0f0f0;">
{"".join([f"<th>{c}</th>" for c in columns])}
</tr>
'''
for row in data:
html += '<tr>' + "".join([f"<td>{v}</td>" for v in row]) + '</tr>'
html += '</table>'
return html
```
---
## 核心命令
### 1. 生成图表
```
命令：生成图表 [类型] [数据]
示例：
- 生成图表 折线图 [1,2,3,4,5]
- 生成图表 柱状图 苹果:100,香蕉:80
```
### 2. 生成K线
```
命令：生成K线 [股票代码]
示例：
- 生成K线 600519
```
### 3. 生成报告
```
命令：生成报告 [报告名称]
示例：
- 生成报告 每日看板
- 生成报告 股票分析
```
---
## 应用场景
### 1. 股票分析报告
```python
def stock_report(code):
"""生成股票报告"""
# 获取数据
data = get_stock_data(code)
# 生成图表
chart1 = plot_candlestick(data['kline'], f'{code} K线')
chart2 = plot_bar(data['volume']['date'], data['volume']['vol'], '成交量')
# 生成HTML报告
html = f'''
<html>
<head><title>{code} 分析报告</title></head>
<body>
<h1>{code} 分析报告</h1>
<img src="{chart1}">
<img src="{chart2}">
</body>
</html>
'''
with open(f'{code}_report.html', 'w') as f:
f.write(html)
return f'{code}_report.html'
```
### 2. 策略回测报告
```python
def backtest_report(results):
"""生成回测报告"""
# 收益曲线
equity_chart = plot_line({
'x': results['dates'],
'y': results['equity']
}, '收益曲线', '日期', '资金')
# 统计
stats = calculate_stats(results)
html = f'''
<html>
<body>
<h1>策略回测报告</h1>
<img src="{equity_chart}">
<h2>统计数据</h2>
<ul>
<li>总收益率: {stats['total_return']:.2f}%</li>
<li>年化收益: {stats['annual_return']:.2f}%</li>
<li>夏普比率: {stats['sharpe']:.2f}</li>
<li>最大回撤: {stats['max_drawdown']:.2f}%</li>
</ul>
</body>
</html>
'''
return html
```
---
## 图表类型选择
| 场景 | 推荐图表 |
|------|---------|
| 趋势变化 | 折线图 |
| 对比分析 | 柱状图 |
| 占比分布 | 饼图/环形图 |
| 相关性 | 热力图/散点图 |
| K线走势 | 蜡烛图 |
| 分布情况 | 直方图/箱线图 |
| 地理数据 | 地图热力图 |
---
## 注意事项
1. **数据量**：大数据集要采样
2. **中文显示**：配置中文字体
3. **交互性**：需要交互用Plotly
4. **导出格式**：PNG清晰/HTML交互
## Code Implementation
Python实现: chart_generator.py
`python
from chart_generator import ChartGenerator, quick_chart
# 快速图表
chart = quick_chart(data, 'line', 'date', ['sales', 'profit'])
chart.save('chart.png')
# 或使用完整API
gen = ChartGenerator()
gen.line_chart(data, 'date', 'sales', title='Sales')
gen.bar_chart(data, 'category', 'value')
gen.pie_chart(data, 'name', 'value')
gen.scatter_chart(data, 'x', 'y', size='size', color='category')
`
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