refactor(archive): move unused modules to archive/
Archive legacy framework and utility modules that are no longer referenced by the active core (datasource/ and rotation/): - framework/ -> archive/framework/ - framework_v2/ -> archive/framework_v2/ - strategies/ -> archive/strategies/ - config/ -> archive/config/ - visualization/ -> archive/visualization/ - scripts/ -> archive/scripts/ - tests/ -> archive/tests/ - run_rotation.py, run_us_rotation.py -> archive/single_files/ - compare_*.py, test_api_dates.py -> archive/single_files/
This commit is contained in:
144
archive/visualization/report_generator/README.md
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archive/visualization/report_generator/README.md
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# ETF轮动策略报告生成器
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生成精美的 HTML 策略报告，展示回测结果和关键指标。
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## 功能特性
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- ✅ **策略 KPI** - 累计收益、年化收益、胜率、夏普比率等
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- ✅ **净值曲线** - 交互式折线图，支持缩放和悬停
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- ✅ **月度收益** - 柱状图展示每月收益分布
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- ✅ **盈亏分布** - 饼图展示盈利/亏损比例
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- ✅ **品种排行** - 横向条形图展示各品种表现
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- ✅ **调仓记录** - 可按日期和品种筛选的交易明细表格
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- ✅ **现代化 UI** - 渐变色头部、卡片布局、响应式设计
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- ✅ **打印友好** - 支持直接打印为 PDF
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## 使用方法
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### 基础用法
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```bash
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# 生成完整报告
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python visualization/report_generator/generate_report.py
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# 指定时间区间
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python visualization/report_generator/generate_report.py --start 2024-01-01 --end 2024-12-31
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# 指定输出目录
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python visualization/report_generator/generate_report.py --output my_reports
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```
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### Python API 调用
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```python
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from visualization.report_generator.generate_report import ReportGenerator
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# 创建生成器
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generator = ReportGenerator(results_dir='results')
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# 生成报告
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output_file = generator.generate(
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start_date='2024-01-01',
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end_date='2024-12-31',
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output_dir='reports'
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)
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print(f"报告已生成: {output_file}")
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```
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### 定时生成（可选）
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```bash
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# 添加到 crontab，每天生成一次
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0 9 * * * cd /path/to/etf && python visualization/report_generator/generate_report.py
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```
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## 依赖
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```bash
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pip install pandas numpy jinja2
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```
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## 文件结构
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```
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visualization/report_generator/
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├── template.html # HTML 模板
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├── generate_report.py # 报告生成脚本
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└── README.md # 说明文档
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```
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## 输出示例
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生成的报告包含：
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1. **头部区域** - 报告标题和数据区间
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2. **KPI 卡片** - 8 个关键指标（收益、胜率、夏普比等）
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3. **净值曲线** - 带渐变填充的折线图
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4. **月度收益** - 红绿柱状图
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5. **盈亏分布** - 环形饼图
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6. **品种排行** - 横向条形图
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7. **调仓表格** - 支持筛选和打印
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## 自定义
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### 修改配色方案
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编辑 `template.html` 中的 CSS 变量：
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```css
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:root {
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--primary-color: #1890ff;
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--success-color: #52c41a;
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--danger-color: #ff4d4f;
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}
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```
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### 添加新指标
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在 `generate_report.py` 的 `calculate_kpis()` 方法中添加：
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```python
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def calculate_kpis(self, trades_filtered):
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# ... 现有代码 ...
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# 添加新指标
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new_metric = ...
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return {
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'total_return': ...,
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'new_metric': new_metric, # 新增
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...
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}
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```
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然后在模板中使用：
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```html
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<div class="kpi-value">{{ new_metric }}</div>
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```
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## 技术栈
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- **模板引擎**: Jinja2
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- **图表库**: ECharts 5.4
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- **样式框架**: Bootstrap 5.3
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- **图标**: Bootstrap Icons
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## 注意事项
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1. 确保 `results/report_summary.csv` 和 `results/report_trades.csv` 存在
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2. 数据格式需符合预期（参考现有 CSV 文件）
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3. 生成的 HTML 文件可离线查看（ECharts 使用 CDN）
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4. 打印时筛选栏会自动隐藏
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## 示例输出
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```
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🚀 开始生成策略报告...
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✅ 数据加载成功: 1233 条交易记录
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📊 筛选后数据: 1233 条记录
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✅ 报告已生成: reports/strategy_report_20260508_210000.html
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📁 文件大小: 125.3 KB
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🌐 在浏览器中打开: file:///Users/aszer/Documents/vscode/etf/reports/strategy_report_20260508_210000.html
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```
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7
archive/visualization/report_generator/__init__.py
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archive/visualization/report_generator/__init__.py
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"""
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ETF轮动策略报告生成器
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"""
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from .generate_report import ReportGenerator
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__all__ = ['ReportGenerator']
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420
archive/visualization/report_generator/generate_report.py
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archive/visualization/report_generator/generate_report.py
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"""
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ETF轮动策略报告生成器
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=======================
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从回测数据生成精美的 HTML 策略报告
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使用方法:
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python generate_report.py
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python generate_report.py --start 2024-01-01 --end 2024-12-31
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"""
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import pandas as pd
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import numpy as np
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from jinja2 import Template
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from datetime import datetime
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import argparse
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import os
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import sys
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import json
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class ReportGenerator:
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"""策略报告生成器"""
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def __init__(self, results_dir='results'):
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self.results_dir = results_dir
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self.summary_df = None
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self.trades_df = None
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self.metrics = None # 从JSON加载的策略KPI
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self.nav_df = None # 从CSV加载的净值曲线
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def load_data(self):
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"""加载回测数据"""
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# 加载汇总数据
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summary_path = os.path.join(self.results_dir, 'report_summary.csv')
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if not os.path.exists(summary_path):
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raise FileNotFoundError(f"找不到汇总数据文件: {summary_path}")
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self.summary_df = pd.read_csv(summary_path)
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# 转换百分比
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for col in ['胜率', '平均收益', '累计收益', '最大单次收益', '最大单次亏损']:
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if col in self.summary_df.columns:
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self.summary_df[col] = self.summary_df[col].str.rstrip('%').astype(float)
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# 加载交易记录
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trades_path = os.path.join(self.results_dir, 'report_trades.csv')
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if not os.path.exists(trades_path):
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raise FileNotFoundError(f"找不到交易记录文件: {trades_path}")
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self.trades_df = pd.read_csv(trades_path)
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self.trades_df['进场日期'] = pd.to_datetime(self.trades_df['进场日期'])
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self.trades_df['出场日期'] = pd.to_datetime(self.trades_df['出场日期'])
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# 加载策略KPI JSON文件（如果存在）
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metrics_path = os.path.join(self.results_dir, 'report_metrics.json')
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if os.path.exists(metrics_path):
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with open(metrics_path, 'r', encoding='utf-8') as f:
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self.metrics = json.load(f)
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print(f"✅ 加载策略指标: {metrics_path}")
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else:
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print(f"⚠️ 未找到策略指标文件: {metrics_path}")
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# 加载净值曲线CSV文件（如果存在）
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nav_path = os.path.join(self.results_dir, 'report_nav.csv')
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if os.path.exists(nav_path):
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self.nav_df = pd.read_csv(nav_path)
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self.nav_df['日期'] = pd.to_datetime(self.nav_df['日期'])
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print(f"✅ 加载净值曲线: {nav_path} ({len(self.nav_df)} 条记录)")
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else:
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print(f"⚠️ 未找到净值曲线文件: {nav_path}")
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print(f"✅ 数据加载成功: {len(self.trades_df)} 条交易记录")
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def calculate_kpis(self, trades_filtered=None):
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"""计算关键指标 - 优先使用轮动策略输出的指标"""
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# 如果有从JSON加载的策略KPI，直接使用
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if self.metrics is not None and '策略' in self.metrics:
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strategy_metrics = self.metrics['策略']
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print("✅ 使用轮动策略输出的KPI指标")
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# 计算调仓次数（需要从trades数据获取）
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df = trades_filtered if trades_filtered is not None else self.trades_df
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total_trades = len(df)
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# 最佳品种（从summary获取）
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if self.summary_df is not None:
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symbol_col = self.summary_df['累计收益']
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best_symbol = self.summary_df.loc[symbol_col.idxmax(), '品种代码']
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else:
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best_symbol = 'N/A'
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# 平均持仓天数
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avg_holding_days = df['持仓天数'].mean() if len(df) > 0 else 0
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# 盈亏次数（基于trades数据）
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# 转换持仓收益为数值（统一处理百分号格式）
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if '持仓收益' in df.columns:
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# 使用通用转换方法
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returns_series = df['持仓收益'].apply(
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lambda x: float(str(x).rstrip('%')) if pd.notna(x) else 0.0
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)
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win_count = (returns_series > 0).sum()
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loss_count = (returns_series < 0).sum()
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else:
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win_count = 0
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loss_count = 0
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return {
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'total_return': f"{strategy_metrics['累计收益'] * 100:.2f}",
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'annual_return': f"{strategy_metrics['年化收益(自然日)'] * 100:.2f}",
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'win_rate': f"{strategy_metrics['日胜率'] * 100:.2f}",
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'max_drawdown': f"{strategy_metrics['最大回撤'] * 100:.2f}",
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'sharpe_ratio': f"{strategy_metrics['夏普比率']:.2f}",
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'best_symbol': best_symbol,
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'avg_holding_days': f"{avg_holding_days:.1f}",
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'win_count': int(win_count),
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'loss_count': int(loss_count)
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}
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# 否则重新计算（备用方案）
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print("⚠️ 未找到策略KPI，重新计算...")
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df = trades_filtered if trades_filtered is not None else self.trades_df
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# 使用净值计算真实收益
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# 按日期分组计算每日组合净值
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daily_nav = df.groupby('出场日期').apply(
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lambda x: (x['出场净值'].astype(float) * x['仓位占比'].str.rstrip('%').astype(float) / 100).sum(),
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include_groups=False
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).reset_index()
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daily_nav.columns = ['date', 'nav']
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daily_nav = daily_nav.sort_values('date')
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# 总收益 = (最终净值 - 初始净值) / 初始净值 * 100
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initial_nav = daily_nav['nav'].iloc[0]
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final_nav = daily_nav['nav'].iloc[-1]
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total_return = (final_nav - initial_nav) / initial_nav * 100
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# 年化收益
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days = (daily_nav['date'].iloc[-1] - daily_nav['date'].iloc[0]).days
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if days > 0:
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annual_return = total_return / (days / 365.0)
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else:
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annual_return = 0
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# 胜率 - 使用净值变化
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daily_nav['nav_change'] = daily_nav['nav'].pct_change()
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win_count = (daily_nav['nav_change'] > 0).sum()
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total_count = len(daily_nav) - 1 # 减去第一天
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win_rate = (win_count / total_count * 100) if total_count > 0 else 0
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loss_count = total_count - win_count
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# 夏普比率
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if daily_nav['nav_change'].std() > 0:
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sharpe_ratio = daily_nav['nav_change'].mean() / daily_nav['nav_change'].std() * np.sqrt(252)
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else:
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sharpe_ratio = 0
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# 最大回撤
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running_max = daily_nav['nav'].cummax()
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drawdown = (daily_nav['nav'] - running_max) / running_max * 100
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max_drawdown = drawdown.min()
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# 调仓次数
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total_trades = len(df)
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# 最佳品种 - 从 summary 获取
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if self.summary_df is not None:
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symbol_col = self.summary_df['累计收益']
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if symbol_col.dtype == 'object':
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symbol_col_num = symbol_col.str.rstrip('%').astype(float)
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else:
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symbol_col_num = symbol_col
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best_symbol = self.summary_df.loc[symbol_col_num.idxmax(), '品种代码']
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else:
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best_symbol = 'N/A'
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# 平均持仓天数
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avg_holding_days = df['持仓天数'].mean() if len(df) > 0 else 0
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return {
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'total_return': f"{total_return:.2f}",
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'annual_return': f"{annual_return:.2f}",
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'win_rate': f"{win_rate:.2f}",
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'max_drawdown': f"{max_drawdown:.2f}",
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'sharpe_ratio': f"{sharpe_ratio:.2f}",
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'total_trades': str(total_trades),
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'best_symbol': best_symbol,
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'avg_holding_days': f"{avg_holding_days:.1f}",
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'win_count': int(win_count),
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'loss_count': int(loss_count)
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}
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def prepare_chart_data(self, trades_filtered=None):
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"""准备图表数据 - 优先使用轮动策略输出的净值曲线"""
|
||||
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||||
# 如果有从CSV加载的净值曲线，直接使用
|
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if self.nav_df is not None:
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print("✅ 使用轮动策略输出的净值曲线")
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# 净值曲线数据 - 直接读取
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nav_dates = self.nav_df['日期'].dt.strftime('%Y-%m-%d').tolist()
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nav_values = self.nav_df['策略净值'].round(4).tolist()
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benchmark_values = self.nav_df['基准净值'].round(4).tolist()
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||||
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# 月度收益数据 - 从净值计算
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self.nav_df['年月'] = self.nav_df['日期'].dt.to_period('M')
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monthly_nav = self.nav_df.groupby('年月').agg({
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'策略净值': 'last'
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}).reset_index()
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||||
monthly_nav.columns = ['年月', 'nav']
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monthly_nav = monthly_nav.sort_values('年月')
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monthly_nav['nav_change'] = monthly_nav['nav'].pct_change() * 100
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||||
monthly_nav['nav_change'] = monthly_nav['nav_change'].fillna(0)
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||||
monthly_nav['年月_str'] = monthly_nav['年月'].astype(str)
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||||
monthly_dates = monthly_nav['年月_str'].tolist()
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||||
monthly_values = monthly_nav['nav_change'].round(2).tolist()
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||||
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||||
# 盈亏分布 - 从trades数据计算
|
||||
df = trades_filtered if trades_filtered is not None else self.trades_df
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||||
df = df.copy()
|
||||
# 使用通用转换方法处理持仓收益
|
||||
df['持仓收益_num'] = df['持仓收益'].apply(
|
||||
lambda x: float(str(x).rstrip('%')) if pd.notna(x) else 0.0
|
||||
)
|
||||
|
||||
positive_returns = df[df['持仓收益_num'] > 0]['持仓收益_num'].tolist()
|
||||
negative_returns = df[df['持仓收益_num'] <= 0]['持仓收益_num'].tolist()
|
||||
|
||||
# 品种收益排行 - 使用累计收益列
|
||||
symbol_returns = self.summary_df.set_index('品种代码')['累计收益']
|
||||
symbol_returns = symbol_returns.sort_values()
|
||||
symbol_names = []
|
||||
symbol_returns_list = []
|
||||
|
||||
for code, ret in symbol_returns.items():
|
||||
name = self.summary_df[self.summary_df['品种代码'] == code]
|
||||
if len(name) > 0:
|
||||
symbol_names.append(name.iloc[0]['品种名称'])
|
||||
else:
|
||||
symbol_names.append(code)
|
||||
symbol_returns_list.append(ret)
|
||||
|
||||
return {
|
||||
'nav_dates': nav_dates,
|
||||
'nav_values': nav_values,
|
||||
'benchmark_values': benchmark_values,
|
||||
'monthly_dates': monthly_dates,
|
||||
'monthly_values': monthly_values,
|
||||
'positive_returns': positive_returns,
|
||||
'negative_returns': negative_returns,
|
||||
'symbol_names': symbol_names,
|
||||
'symbol_returns': symbol_returns_list,
|
||||
}
|
||||
|
||||
# 否则重新计算（备用方案）
|
||||
print("⚠️ 未找到净值曲线，重新计算...")
|
||||
df = trades_filtered if trades_filtered is not None else self.trades_df
|
||||
|
||||
# 转换持仓收益为数值（统一处理百分号格式）
|
||||
df = df.copy()
|
||||
df['持仓收益_num'] = df['持仓收益'].apply(
|
||||
lambda x: float(str(x).rstrip('%')) if pd.notna(x) else 0.0
|
||||
)
|
||||
|
||||
df_sorted = df.sort_values('出场日期')
|
||||
|
||||
# 净值曲线数据 - 使用出场净值（考虑仓位加权）
|
||||
# 按日期分组，计算每日的加权平均净值
|
||||
daily_nav = df_sorted.groupby('出场日期').apply(
|
||||
lambda x: (x['出场净值'].astype(float) * x['仓位占比'].str.rstrip('%').astype(float) / 100).sum(),
|
||||
include_groups=False
|
||||
).reset_index()
|
||||
daily_nav.columns = ['date', 'nav']
|
||||
daily_nav = daily_nav.sort_values('date')
|
||||
|
||||
nav_values = daily_nav['nav'].round(4).tolist()
|
||||
nav_dates = daily_nav['date'].dt.strftime('%Y-%m-%d').tolist()
|
||||
benchmark_values = [] # 备用方案无基准数据
|
||||
|
||||
# 月度收益数据 - 使用净值变化计算
|
||||
df_copy = df_sorted.copy()
|
||||
df_copy['年月'] = df_copy['出场日期'].dt.to_period('M')
|
||||
monthly_nav = df_copy.groupby('年月').apply(
|
||||
lambda x: (x['出场净值'].astype(float) * x['仓位占比'].str.rstrip('%').astype(float) / 100).sum(),
|
||||
include_groups=False
|
||||
).reset_index()
|
||||
monthly_nav.columns = ['年月', 'nav']
|
||||
monthly_nav = monthly_nav.sort_values('年月')
|
||||
monthly_nav['nav_change'] = monthly_nav['nav'].pct_change() * 100
|
||||
monthly_nav['nav_change'] = monthly_nav['nav_change'].fillna(0)
|
||||
|
||||
monthly_nav['年月_str'] = monthly_nav['年月'].astype(str)
|
||||
monthly_dates = monthly_nav['年月_str'].tolist()
|
||||
monthly_values = monthly_nav['nav_change'].round(2).tolist()
|
||||
|
||||
# 品种收益排行 - 使用累计收益列
|
||||
symbol_returns = self.summary_df.set_index('品种代码')['累计收益']
|
||||
if symbol_returns.dtype == 'object':
|
||||
symbol_returns = symbol_returns.str.rstrip('%').astype(float)
|
||||
symbol_returns = symbol_returns.sort_values()
|
||||
symbol_names = []
|
||||
symbol_returns_list = []
|
||||
|
||||
for code, ret in symbol_returns.items():
|
||||
name = self.summary_df[self.summary_df['品种代码'] == code]
|
||||
if len(name) > 0:
|
||||
symbol_names.append(name.iloc[0]['品种名称'])
|
||||
else:
|
||||
symbol_names.append(code)
|
||||
symbol_returns_list.append(round(ret, 2))
|
||||
|
||||
# 唯一品种列表
|
||||
symbols = df['品种代码'].unique().tolist()
|
||||
|
||||
return {
|
||||
'nav_dates': nav_dates,
|
||||
'nav_values': [round(v, 2) for v in nav_values],
|
||||
'monthly_dates': monthly_dates,
|
||||
'monthly_values': monthly_values,
|
||||
'symbol_names': symbol_names,
|
||||
'symbol_returns': symbol_returns_list,
|
||||
'symbols': symbols
|
||||
}
|
||||
|
||||
def generate(self, start_date=None, end_date=None, output_dir='reports'):
|
||||
"""生成报告"""
|
||||
print("🚀 开始生成策略报告...")
|
||||
|
||||
# 加载数据
|
||||
self.load_data()
|
||||
|
||||
# 筛选数据
|
||||
if start_date:
|
||||
start_date = pd.to_datetime(start_date)
|
||||
if end_date:
|
||||
end_date = pd.to_datetime(end_date)
|
||||
|
||||
trades_filtered = self.trades_df.copy()
|
||||
if start_date:
|
||||
trades_filtered = trades_filtered[trades_filtered['出场日期'] >= start_date]
|
||||
if end_date:
|
||||
trades_filtered = trades_filtered[trades_filtered['出场日期'] <= end_date]
|
||||
|
||||
print(f"📊 筛选后数据: {len(trades_filtered)} 条记录")
|
||||
|
||||
# 计算指标
|
||||
kpis = self.calculate_kpis(trades_filtered)
|
||||
chart_data = self.prepare_chart_data(trades_filtered)
|
||||
|
||||
# 准备交易记录 - 按出场日期倒序排列（最新在前）
|
||||
trades_display = trades_filtered.sort_values('出场日期', ascending=False).copy()
|
||||
trades_display['进场日期'] = trades_display['进场日期'].dt.strftime('%Y-%m-%d')
|
||||
trades_display['出场日期'] = trades_display['出场日期'].dt.strftime('%Y-%m-%d')
|
||||
trades_list = trades_display.to_dict('records')
|
||||
|
||||
# 分页参数
|
||||
page_size = 50 # 每页显示50条记录
|
||||
total_trades = len(trades_list)
|
||||
total_pages = (total_trades // page_size) + (1 if total_trades % page_size > 0 else 0)
|
||||
|
||||
# 读取模板
|
||||
template_path = os.path.join(os.path.dirname(__file__), 'template.html')
|
||||
with open(template_path, 'r', encoding='utf-8') as f:
|
||||
template = Template(f.read())
|
||||
|
||||
# 渲染模板
|
||||
html = template.render(
|
||||
report_date=datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||||
start_date=start_date.strftime('%Y-%m-%d') if start_date else trades_filtered['出场日期'].min().strftime('%Y-%m-%d'),
|
||||
end_date=end_date.strftime('%Y-%m-%d') if end_date else trades_filtered['出场日期'].max().strftime('%Y-%m-%d'),
|
||||
trades=trades_list,
|
||||
page_size=page_size,
|
||||
total_trades=total_trades,
|
||||
total_pages=total_pages,
|
||||
**kpis,
|
||||
**chart_data
|
||||
)
|
||||
|
||||
# 创建输出目录
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
# 保存报告（固定文件名）
|
||||
output_file = os.path.join(output_dir, 'strategy_report.html')
|
||||
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
f.write(html)
|
||||
|
||||
print(f"✅ 报告已生成: {output_file}")
|
||||
print(f"📁 文件大小: {os.path.getsize(output_file) / 1024:.1f} KB")
|
||||
print(f"🌐 在浏览器中打开: file://{os.path.abspath(output_file)}")
|
||||
|
||||
return output_file
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
parser = argparse.ArgumentParser(description='生成ETF轮动策略报告')
|
||||
parser.add_argument('--start', type=str, help='开始日期 (YYYY-MM-DD)')
|
||||
parser.add_argument('--end', type=str, help='结束日期 (YYYY-MM-DD)')
|
||||
parser.add_argument('--output', type=str, default='reports', help='输出目录')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
try:
|
||||
generator = ReportGenerator()
|
||||
generator.generate(
|
||||
start_date=args.start,
|
||||
end_date=args.end,
|
||||
output_dir=args.output
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"❌ 生成失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user