From 861e590441b97bdc023e72ae42755fd170200d34 Mon Sep 17 00:00:00 2001
From: aszerW <aszer27937@gmail.com>
Date: Fri, 8 May 2026 22:25:22 +0800
Subject: [PATCH] =?UTF-8?q?feat(report):=20=E7=AD=96=E7=95=A5KPI=E6=95=B0?=
 =?UTF-8?q?=E6=8D=AE=E7=BB=9F=E4=B8=80=E6=9D=A5=E6=BA=90=EF=BC=8C=E9=81=BF?=
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修改内容：
1. strategies/rotation/report.py
   - 新增保存策略KPI到JSON文件的功能
   - 保存指标：累计收益、年化收益、夏普比率、最大回撤、Calmar比率、日胜率
   - 同时保存基准指标和回测区间信息
   - 输出路径: results/report_metrics.json

2. visualization/report_generator/generate_report.py
   - 加载策略KPI JSON文件（优先）
   - 直接使用轮动策略输出的指标，不再重复计算
   - 保留备用计算逻辑（JSON不存在时）
   - 确保HTML报告与轮动策略结果完全一致

效果：
- KPI指标统一从轮动策略回测结果获取
- 避免重复计算导致的数据不一致
- 数据来源清晰可追溯
- HTML报告指标与终端输出完全一致
---
 strategies/rotation/report.py                 | 31 ++++++++++
 .../report_generator/generate_report.py       | 57 ++++++++++++++++++-
 2 files changed, 87 insertions(+), 1 deletion(-)

diff --git a/strategies/rotation/report.py b/strategies/rotation/report.py
index ab9e245..a66cd24 100644
--- a/strategies/rotation/report.py
+++ b/strategies/rotation/report.py
@@ -133,6 +133,37 @@ def generate_performance_report(
         etf_nav_data_raw=etf_nav_data_raw,
     )
 
+    # 保存整体策略KPI到JSON文件
+    import json
+    metrics_dict = {
+        "策略": {
+            "累计收益": float(s_total_return),
+            "年化收益(自然日)": float(s_cagr_nat),
+            "年化收益(交易日)": float(s_cagr_trd),
+            "夏普比率": float(s_sharpe),
+            "最大回撤": float(s_max_dd),
+            "Calmar比率": float(s_calmar),
+            "日胜率": float(s_win_rate),
+            "回测区间": {
+                "开始": strategy_nav.index.min().strftime("%Y-%m-%d"),
+                "结束": strategy_nav.index.max().strftime("%Y-%m-%d"),
+                "交易天数": len(strategy_nav)
+            }
+        },
+        "基准": {
+            "累计收益": float(b_total_return),
+            "年化收益(自然日)": float(b_cagr_nat),
+            "夏普比率": float(b_sharpe),
+            "最大回撤": float(b_max_dd),
+            "名称": benchmark_name
+        }
+    }
+    
+    metrics_path = f"{save_path}_metrics.json"
+    with open(metrics_path, 'w', encoding='utf-8') as f:
+        json.dump(metrics_dict, f, indent=2, ensure_ascii=False)
+    print(f"策略指标已保存: {metrics_path}")
+
     # 返回指标字典
     return {
         "累计收益": s_total_return,
diff --git a/visualization/report_generator/generate_report.py b/visualization/report_generator/generate_report.py
index 4903204..0e7226c 100644
--- a/visualization/report_generator/generate_report.py
+++ b/visualization/report_generator/generate_report.py
@@ -15,6 +15,7 @@ from datetime import datetime
 import argparse
 import os
 import sys
+import json
 
 
 class ReportGenerator:
@@ -24,6 +25,7 @@ class ReportGenerator:
         self.results_dir = results_dir
         self.summary_df = None
         self.trades_df = None
+        self.metrics = None  # 从JSON加载的策略KPI
         
     def load_data(self):
         """加载回测数据"""
@@ -48,10 +50,63 @@ class ReportGenerator:
         self.trades_df['进场日期'] = pd.to_datetime(self.trades_df['进场日期'])
         self.trades_df['出场日期'] = pd.to_datetime(self.trades_df['出场日期'])
         
+        # 加载策略KPI JSON文件（如果存在）
+        metrics_path = os.path.join(self.results_dir, 'report_metrics.json')
+        if os.path.exists(metrics_path):
+            with open(metrics_path, 'r', encoding='utf-8') as f:
+                self.metrics = json.load(f)
+            print(f"✅ 加载策略指标: {metrics_path}")
+        else:
+            print(f"⚠️ 未找到策略指标文件: {metrics_path}")
+        
         print(f"✅ 数据加载成功: {len(self.trades_df)} 条交易记录")
         
     def calculate_kpis(self, trades_filtered=None):
-        """计算关键指标"""
+        """计算关键指标 - 优先使用轮动策略输出的指标"""
+        
+        # 如果有从JSON加载的策略KPI，直接使用
+        if self.metrics is not None and '策略' in self.metrics:
+            strategy_metrics = self.metrics['策略']
+            print("✅ 使用轮动策略输出的KPI指标")
+            
+            # 计算调仓次数（需要从trades数据获取）
+            df = trades_filtered if trades_filtered is not None else self.trades_df
+            total_trades = len(df)
+            
+            # 最佳品种（从summary获取）
+            if self.summary_df is not None:
+                symbol_col = self.summary_df['累计收益']
+                best_symbol = self.summary_df.loc[symbol_col.idxmax(), '品种代码']
+            else:
+                best_symbol = 'N/A'
+            
+            # 平均持仓天数
+            avg_holding_days = df['持仓天数'].mean() if len(df) > 0 else 0
+            
+            # 盈亏次数（基于trades数据）
+            # 转换持仓收益为数值
+            if df['持仓收益'].dtype == 'object':
+                returns_num = df['持仓收益'].str.rstrip('%').astype(float)
+            else:
+                returns_num = df['持仓收益']
+            win_count = (returns_num > 0).sum()
+            loss_count = len(df) - win_count
+            
+            return {
+                'total_return': f"{strategy_metrics['累计收益'] * 100:.2f}",
+                'annual_return': f"{strategy_metrics['年化收益(自然日)'] * 100:.2f}",
+                'win_rate': f"{strategy_metrics['日胜率'] * 100:.2f}",
+                'max_drawdown': f"{strategy_metrics['最大回撤'] * 100:.2f}",
+                'sharpe_ratio': f"{strategy_metrics['夏普比率']:.2f}",
+                'total_trades': str(total_trades),
+                'best_symbol': best_symbol,
+                'avg_holding_days': f"{avg_holding_days:.1f}",
+                'win_count': int(win_count),
+                'loss_count': int(loss_count)
+            }
+        
+        # 否则重新计算（备用方案）
+        print("⚠️ 未找到策略KPI，重新计算...")
         df = trades_filtered if trades_filtered is not None else self.trades_df
         
         # 使用净值计算真实收益