dragon

.claude/settings.local.json

@@ -43,7 +43,11 @@
       "Bash(PYTHONPATH=. /c/Users/erwin/AppData/Local/Programs/Python/Python314/python.exe:*)",
       "Bash(git add:*)",
       "Bash(git check-ignore:*)",
-      "Bash(git rm:*)"
+      "Bash(git rm:*)",
+      "Bash(git commit:*)",
+      "Bash(git push:*)",
+      "Bash(../.venv/Scripts/python.exe auto_report_long_only_t1.py)",
+      "Bash(../../.venv/Scripts/python.exe auto_report_long_only_t1.py)"
     ]
   }
 }

a50-futures/A50_futures_30min_20260305.csv

@@ -1,321 +0,0 @@
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-2026-03-05 10:00:00.308994,14627.67,14669.0,14580.05,14618.35,190373,0.006259198123273135
-2026-03-05 10:30:00.308994,14768.92,14812.96,14731.19,14769.0,161929,0.010305540638991362
-2026-03-05 11:00:00.308994,14780.94,14803.15,14752.2,14769.8,356677,5.416751303410905e-05
-2026-03-05 13:00:00.308994,14844.2,14889.18,14805.53,14850.07,173861,0.0054347384527888565
-2026-03-05 13:30:00.308994,14829.43,14835.71,14804.46,14813.88,140461,-0.0024370255493745274
-2026-03-05 14:00:00.308994,14851.81,14861.88,14820.84,14854.15,230513,0.0027183965308210656
-2026-03-05 14:30:00.308994,14831.57,14884.29,14806.05,14841.66,498872,-0.0008408424581682894

a50-futures/fetch_a50_data.py

@@ -1,347 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-A50期货 - 30分钟K线数据获取
-标的：富时中国A50指数期货 (CN0Y) / 华夏A50ETF (159601) 作为替代
-数据周期：近40天
-
-注意：由于网络限制，部分数据源可能无法访问
-"""
-
-import pandas as pd
-import numpy as np
-import akshare as ak
-import requests
-import json
-from datetime import datetime, timedelta
-import warnings
-warnings.filterwarnings('ignore')
-
-
-class A50FuturesDataFetcher:
-    """A50期货数据获取类"""
-    
-    def __init__(self):
-        self.symbol = "CN0Y"  # A50期货主力合约代码
-        print(f"A50期货数据获取器初始化")
-        print(f"标的代码: {self.symbol}")
-    
-    def fetch_30min_data(self, days=40):
-        """
-        获取A50相关指数30分钟K线数据
-        
-        Parameters:
-            days: 获取近N天的数据（默认40天）
-        
-        Returns:
-            DataFrame with columns: datetime, open, high, low, close, volume
-        """
-        end_date = datetime.now()
-        start_date = end_date - timedelta(days=days+5)
-        
-        print(f"\n{'='*60}")
-        print(f"获取A50相关数据 (近{days}天)")
-        print(f"时间范围: {start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}")
-        print(f"{'='*60}")
-        
-        # 尝试多个数据源
-        df = None
-        
-        # 方法1: 新浪财经A50期货
-        df = self._fetch_sina_a50_futures(start_date, end_date)
-        if df is not None and len(df) > 0:
-            print(f"\n✅ 使用新浪财经A50期货数据")
-            return df
-        
-        # 方法2: 使用A50 ETF作为替代
-        df = self._fetch_a50_etf(start_date, end_date)
-        if df is not None and len(df) > 0:
-            print(f"\n✅ 使用A50 ETF数据作为替代")
-            return df
-        
-        # 方法3: 使用上证50指数
-        df = self._fetch_sz50_index(start_date, end_date)
-        if df is not None and len(df) > 0:
-            print(f"\n✅ 使用上证50指数作为替代")
-            return df
-        
-        # 方法4: 生成模拟数据（用于测试）
-        print("\n⚠️ 无法获取实时数据，生成模拟数据用于测试...")
-        df = self._generate_sample_data(days)
-        return df
-    
-    def _fetch_sina_a50_futures(self, start_date, end_date):
-        """从新浪财经获取A50期货30分钟数据"""
-        try:
-            print("\n[数据源0] 新浪财经A50期货...")
-            
-            import requests
-            import json
-            
-            # 新浪财经A50期货接口
-            url = "https://stock.finance.sina.com.cn/futures/api/jsonp.php/var_a50=/GlobalService.getMink?symbol=CN0Y&scale=30"
-            
-            headers = {
-                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
-                'Referer': 'https://finance.sina.com.cn/'
-            }
-            
-            response = requests.get(url, headers=headers, timeout=15)
-            
-            # 解析JSONP响应
-            json_start = response.text.find('[')
-            json_end = response.text.rfind(']') + 1
-            json_str = response.text[json_start:json_end]
-            
-            data_dict = json.loads(json_str)
-            
-            if data_dict and isinstance(data_dict, list):
-                data_list = []
-                for item in data_dict:
-                    try:
-                        data_list.append({
-                            'datetime': item.get('day'),
-                            'open': float(item.get('open', 0)),
-                            'high': float(item.get('high', 0)),
-                            'low': float(item.get('low', 0)),
-                            'close': float(item.get('close', 0)),
-                            'volume': float(item.get('volume', 0))
-                        })
-                    except Exception:
-                        continue
-                
-                if data_list:
-                    df = pd.DataFrame(data_list)
-                    df['datetime'] = pd.to_datetime(df['datetime'])
-                    df = df.set_index('datetime').sort_index()
-                    
-                    # 筛选时间范围
-                    df = df[(df.index >= start_date) & (df.index <= end_date)]
-                    
-                    if len(df) > 10:
-                        print(f"   ✅ 获取成功: {len(df)}条")
-                        return df
-            
-            print(f"   数据不足")
-                
-        except Exception as e:
-            print(f"   获取失败: {e}")
-        
-        return None
-    
-    def _fetch_a50_etf(self, start_date, end_date):
-        """获取华夏A50ETF数据作为A50期货的替代"""
-        try:
-            print("\n[数据源1] 获取华夏A50ETF(159601)数据...")
-            
-            # 使用akshare获取ETF分钟数据
-            df = ak.fund_etf_hist_min_em(symbol="159601", period="30",
-                                          start_date=start_date.strftime('%Y%m%d%H%M%S'),
-                                          end_date=end_date.strftime('%Y%m%d%H%M%S'))
-            
-            if df is not None and not df.empty and len(df) > 10:
-                # 标准化列名
-                df = df.rename(columns={
-                    '时间': 'datetime',
-                    '开盘': 'open',
-                    '收盘': 'close',
-                    '最高': 'high',
-                    '最低': 'low',
-                    '成交量': 'volume'
-                })
-                
-                df['datetime'] = pd.to_datetime(df['datetime'])
-                df = df.set_index('datetime').sort_index()
-                
-                print(f"   ✅ 获取成功: {len(df)}条")
-                return df
-            else:
-                print(f"   数据不足")
-                
-        except Exception as e:
-            print(f"   获取失败: {e}")
-        
-        return None
-    
-    def _fetch_sz50_index(self, start_date, end_date):
-        """获取上证50指数作为替代"""
-        try:
-            print("\n[数据源2] 获取上证50指数(000016)数据...")
-            
-            df = ak.index_zh_a_hist_min_em(symbol="000016", period="30",
-                                            start_date=start_date.strftime('%Y%m%d%H%M%S'),
-                                            end_date=end_date.strftime('%Y%m%d%H%M%S'))
-            
-            if df is not None and not df.empty and len(df) > 10:
-                df = df.rename(columns={
-                    '时间': 'datetime',
-                    '开盘': 'open',
-                    '收盘': 'close',
-                    '最高': 'high',
-                    '最低': 'low',
-                    '成交量': 'volume'
-                })
-                
-                df['datetime'] = pd.to_datetime(df['datetime'])
-                df = df.set_index('datetime').sort_index()
-                
-                print(f"   ✅ 获取成功: {len(df)}条")
-                return df
-            else:
-                print(f"   数据不足")
-                
-        except Exception as e:
-            print(f"   获取失败: {e}")
-        
-        return None
-    
-    def _generate_sample_data(self, days=40):
-        """生成A50风格的模拟数据（用于测试）"""
-        print("\n[模拟数据] 生成A50风格模拟数据...")
-        
-        # 生成时间序列（交易日9:30-11:30, 13:00-15:00）
-        dates = pd.date_range(end=datetime.now(), periods=days, freq='B')  # 工作日
-        all_times = []
-        
-        for date in dates:
-            # 上午 9:30-11:30 (4个30分钟)
-            for hour, minute in [(9, 30), (10, 0), (10, 30), (11, 0)]:
-                all_times.append(date.replace(hour=hour, minute=minute))
-            # 下午 13:00-15:00 (4个30分钟)
-            for hour, minute in [(13, 0), (13, 30), (14, 0), (14, 30)]:
-                all_times.append(date.replace(hour=hour, minute=minute))
-        
-        all_times = sorted([t for t in all_times if t <= datetime.now()])
-        n = len(all_times)
-        
-        # A50基准价约13500点
-        base_price = 13500
-        
-        # 生成随机价格序列（带趋势）
-        np.random.seed(42)
-        returns = np.random.normal(0.0002, 0.008, n)  # A50波动率
-        
-        # 添加一些趋势
-        trend = np.sin(np.linspace(0, 4*np.pi, n)) * 0.002
-        returns += trend
-        
-        # 计算价格
-        prices = base_price * np.exp(np.cumsum(returns))
-        
-        # 生成OHLC数据
-        data = []
-        for i, (time, price) in enumerate(zip(all_times, prices)):
-            volatility = price * 0.003  # 0.3%波动
-            open_price = price + np.random.normal(0, volatility * 0.3)
-            high_price = max(open_price, price) + np.random.uniform(0, volatility)
-            low_price = min(open_price, price) - np.random.uniform(0, volatility)
-            close_price = price
-            volume = np.random.randint(100000, 500000)
-            
-            data.append({
-                'datetime': time,
-                'open': round(open_price, 2),
-                'high': round(high_price, 2),
-                'low': round(low_price, 2),
-                'close': round(close_price, 2),
-                'volume': volume
-            })
-        
-        df = pd.DataFrame(data)
-        df = df.set_index('datetime').sort_index()
-        
-        print(f"   ✅ 生成模拟数据: {len(df)}条")
-        print(f"   ⚠️  注意：此为模拟数据，仅用于测试")
-        
-        return df
-    
-    def save_to_csv(self, df, filename=None):
-        """保存数据到CSV文件"""
-        if df is None or len(df) == 0:
-            print("❌ 无数据可保存")
-            return None
-        
-        if filename is None:
-            filename = f"A50_futures_30min_{datetime.now().strftime('%Y%m%d')}.csv"
-        
-        try:
-            df.to_csv(filename)
-            import os
-            print(f"\n💾 数据已保存: {filename}")
-            print(f"   数据条数: {len(df)}")
-            print(f"   文件大小: {round(os.path.getsize(filename)/1024, 2)} KB")
-            return filename
-        except Exception as e:
-            print(f"❌ 保存失败: {e}")
-            return None
-    
-    def show_statistics(self, df):
-        """显示数据统计信息"""
-        if df is None or len(df) == 0:
-            return
-        
-        print(f"\n{'='*60}")
-        print(f"数据统计信息")
-        print(f"{'='*60}")
-        
-        print(f"\n【基本信息】")
-        print(f"  数据条数: {len(df)}")
-        print(f"  时间区间: {df.index[0]} 至 {df.index[-1]}")
-        unique_dates = set([d.date() for d in pd.to_datetime(df.index)])
-        print(f"  交易天数: {len(unique_dates)}天")
-        
-        print(f"\n【价格统计】")
-        print(f"  最高价: {df['high'].max():.2f}")
-        print(f"  最低价: {df['low'].min():.2f}")
-        print(f"  最新价: {df['close'].iloc[-1]:.2f}")
-        
-        # 计算涨跌幅
-        price_change = (df['close'].iloc[-1] - df['close'].iloc[0]) / df['close'].iloc[0] * 100
-        print(f"  区间涨跌: {price_change:+.2f}%")
-        
-        print(f"\n【成交量统计】")
-        print(f"  总成交量: {df['volume'].sum():,.0f}")
-        print(f"  日均成交: {df['volume'].mean():,.0f}")
-        
-        print(f"\n【波动率统计】")
-        df['returns'] = df['close'].pct_change()
-        volatility = df['returns'].std() * np.sqrt(48) * 100  # 年化波动率
-        print(f"  年化波动率: {volatility:.2f}%")
-
-
-def main():
-    """主程序"""
-    print("="*70)
-    print("A50期货30分钟K线数据获取工具")
-    print("="*70)
-    print(f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    
-    # 创建数据获取器
-    fetcher = A50FuturesDataFetcher()
-    
-    # 获取近40天数据
-    df = fetcher.fetch_30min_data(days=40)
-    
-    if df is not None and len(df) > 0:
-        # 显示统计信息
-        fetcher.show_statistics(df)
-        
-        # 保存到CSV
-        fetcher.save_to_csv(df)
-        
-        # 显示最新10条数据
-        print(f"\n{'='*60}")
-        print(f"最新10条数据")
-        print(f"{'='*60}")
-        print(df.tail(10)[['open', 'high', 'low', 'close', 'volume']].to_string())
-        
-        print(f"\n✅ 数据获取完成!")
-    else:
-        print(f"\n❌ 数据获取失败")
-    
-    print("="*70)
-
-
-if __name__ == "__main__":
-    import os
-    main()

a50-futures/fetch_a50_v2.py

@@ -1,274 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-A50期货 - 30分钟K线数据获取（多方案尝试）
-标的：富时中国A50指数期货 (CN0Y, XIN9)
-"""
-
-import pandas as pd
-import numpy as np
-import akshare as ak
-import requests
-import json
-import re
-from datetime import datetime, timedelta
-import warnings
-warnings.filterwarnings('ignore')
-
-
-class A50FuturesFetcher:
-    """A50期货数据获取器 - 多方案"""
-    
-    def __init__(self):
-        print("="*70)
-        print("A50期货30分钟K线数据获取工具")
-        print("="*70)
-    
-    def fetch_data(self, days=40):
-        """获取数据 - 尝试多种方案"""
-        end_date = datetime.now()
-        start_date = end_date - timedelta(days=days+5)
-        
-        print(f"\n目标: 获取近{days}天30分钟K线")
-        print(f"时间范围: {start_date.strftime('%Y-%m-%d')} ~ {end_date.strftime('%Y-%m-%d')}")
-        
-        # 方案1: AKShare期货分钟数据
-        print("\n" + "-"*70)
-        print("[方案1] AKShare期货分钟数据...")
-        df = self._try_akshare_futures(start_date, end_date)
-        if df is not None: return df
-        
-        # 方案2: AKShare股指期货
-        print("\n" + "-"*70)
-        print("[方案2] AKShare股指期货(CN0Y)...")
-        df = self._try_akshare_cn0y(start_date, end_date)
-        if df is not None: return df
-        
-        # 方案3: 新浪财经期货
-        print("\n" + "-"*70)
-        print("[方案3] 新浪财经期货接口...")
-        df = self._try_sina_futures(days)
-        if df is not None: return df
-        
-        # 方案4: 东方财富期货
-        print("\n" + "-"*70)
-        print("[方案4] 东方财富期货接口...")
-        df = self._try_eastmoney_futures()
-        if df is not None: return df
-        
-        # 方案5: 使用相关ETF作为替代
-        print("\n" + "-"*70)
-        print("[方案5] A50相关ETF数据...")
-        df = self._try_a50_etf(start_date, end_date)
-        if df is not None: return df
-        
-        # 方案6: 上证50指数作为替代
-        print("\n" + "-"*70)
-        print("[方案6] 上证50指数(000016)作为替代...")
-        df = self._try_sz50_index(start_date, end_date)
-        if df is not None: return df
-        
-        print("\n❌ 所有方案均失败")
-        return None
-    
-    def _try_akshare_futures(self, start_date, end_date):
-        """方案1: AKShare期货分钟数据"""
-        try:
-            # 尝试获取富时A50期货分钟数据
-            print("   尝试: futures_zh_minute_sina...")
-            
-            # 富时中国A50期货代码
-            symbol = "CN0Y"
-            df = ak.futures_zh_minute_sina(symbol=symbol, period="30")
-            
-            if df is not None and len(df) > 0:
-                df['datetime'] = pd.to_datetime(df['datetime'])
-                df = df.set_index('datetime').sort_index()
-                df = df[(df.index >= start_date) & (df.index <= end_date)]
-                
-                if len(df) > 10:
-                    print(f"   ✅ 成功: {len(df)}条")
-                    return df
-                    
-        except Exception as e:
-            print(f"   失败: {str(e)[:80]}")
-        return None
-    
-    def _try_akshare_cn0y(self, start_date, end_date):
-        """方案2: AKShare股指期货数据"""
-        try:
-            print("   尝试: futures_zh_realtime...")
-            
-            # 获取股指期货实时数据（包含历史）
-            df = ak.futures_zh_realtime(symbol="富时A50")
-            
-            if df is not None and len(df) > 0:
-                print(f"   获取到实时数据: {len(df)}条")
-                # 注意：这是实时报价，不是K线
-                return None
-                
-        except Exception as e:
-            print(f"   失败: {str(e)[:80]}")
-        return None
-    
-    def _try_sina_futures(self, days):
-        """方案3: 新浪财经期货K线"""
-        try:
-            print("   尝试: 新浪财经期货K线接口...")
-            
-            # 新浪财经A50连续合约
-            url = "https://stock.finance.sina.com.cn/futures/api/jsonp.php/var_CN0Y=/InnerFuturesNewService.getMinLine?symbol=CN0Y"
-            
-            headers = {
-                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
-                'Referer': 'https://finance.sina.com.cn/'
-            }
-            
-            response = requests.get(url, headers=headers, timeout=10)
-            
-            # 解析JSONP
-            text = response.text
-            json_start = text.find('[')
-            json_end = text.rfind(']') + 1
-            
-            if json_start > 0 and json_end > json_start:
-                json_str = text[json_start:json_end]
-                data = json.loads(json_str)
-                
-                if data and len(data) > 0:
-                    df_list = []
-                    for item in data:
-                        df_list.append({
-                            'datetime': item.get('time'),
-                            'open': float(item.get('open', 0)),
-                            'high': float(item.get('high', 0)),
-                            'low': float(item.get('low', 0)),
-                            'close': float(item.get('close', 0)),
-                            'volume': float(item.get('volume', 0))
-                        })
-                    
-                    df = pd.DataFrame(df_list)
-                    df['datetime'] = pd.to_datetime(df['datetime'])
-                    df = df.set_index('datetime').sort_index()
-                    
-                    # 只保留近N天
-                    start = datetime.now() - timedelta(days=days)
-                    df = df[df.index >= start]
-                    
-                    if len(df) > 10:
-                        print(f"   ✅ 成功: {len(df)}条")
-                        return df
-                        
-        except Exception as e:
-            print(f"   失败: {str(e)[:80]}")
-        return None
-    
-    def _try_eastmoney_futures(self):
-        """方案4: 东方财富期货数据"""
-        try:
-            print("   尝试: 东方财富期货接口...")
-            
-            # 东方财富A50期货
-            # 使用股指期货分钟数据接口
-            df = ak.futures_zh_minute_sina(symbol="XIN9", period="30")
-            
-            if df is not None and len(df) > 0:
-                df['datetime'] = pd.to_datetime(df['datetime'])
-                df = df.set_index('datetime').sort_index()
-                
-                if len(df) > 10:
-                    print(f"   ✅ 成功: {len(df)}条")
-                    return df
-                    
-        except Exception as e:
-            print(f"   失败: {str(e)[:80]}")
-        return None
-    
-    def _try_a50_etf(self, start_date, end_date):
-        """方案5: A50 ETF数据"""
-        try:
-            print("   尝试: 华夏A50ETF(159601)...")
-            
-            df = ak.fund_etf_hist_min_em(symbol="159601", period="30",
-                                          start_date=start_date.strftime('%Y%m%d%H%M%S'),
-                                          end_date=end_date.strftime('%Y%m%d%H%M%S'))
-            
-            if df is not None and len(df) > 10:
-                df = df.rename(columns={
-                    '时间': 'datetime', '开盘': 'open', '收盘': 'close',
-                    '最高': 'high', '最低': 'low', '成交量': 'volume'
-                })
-                df['datetime'] = pd.to_datetime(df['datetime'])
-                df = df.set_index('datetime').sort_index()
-                
-                print(f"   ✅ 成功: {len(df)}条 (ETF替代数据)")
-                return df
-                
-        except Exception as e:
-            print(f"   失败: {str(e)[:80]}")
-        return None
-    
-    def _try_sz50_index(self, start_date, end_date):
-        """方案6: 上证50指数"""
-        try:
-            print("   尝试: 上证50指数(000016)...")
-            
-            df = ak.index_zh_a_hist_min_em(symbol="000016", period="30",
-                                            start_date=start_date.strftime('%Y%m%d%H%M%S'),
-                                            end_date=end_date.strftime('%Y%m%d%H%M%S'))
-            
-            if df is not None and len(df) > 10:
-                df = df.rename(columns={
-                    '时间': 'datetime', '开盘': 'open', '收盘': 'close',
-                    '最高': 'high', '最低': 'low', '成交量': 'volume'
-                })
-                df['datetime'] = pd.to_datetime(df['datetime'])
-                df = df.set_index('datetime').sort_index()
-                
-                print(f"   ✅ 成功: {len(df)}条 (上证50替代数据)")
-                return df
-                
-        except Exception as e:
-            print(f"   失败: {str(e)[:80]}")
-        return None
-    
-    def save_and_show(self, df, filename=None):
-        """保存并显示数据"""
-        if df is None or len(df) == 0:
-            print("\n❌ 无数据")
-            return
-        
-        # 保存
-        if filename is None:
-            filename = f"A50_30min_{datetime.now().strftime('%Y%m%d_%H%M')}.csv"
-        
-        df.to_csv(filename)
-        print(f"\n💾 已保存: {filename}")
-        
-        # 统计
-        print(f"\n📊 数据统计:")
-        print(f"   数据条数: {len(df)}")
-        print(f"   时间区间: {df.index[0]} ~ {df.index[-1]}")
-        print(f"   价格区间: {df['low'].min():.2f} ~ {df['high'].max():.2f}")
-        print(f"   最新价格: {df['close'].iloc[-1]:.2f}")
-        
-        # 显示最新10条
-        print(f"\n📋 最新10条数据:")
-        print(df.tail(10)[['open', 'high', 'low', 'close', 'volume']].to_string())
-
-
-def main():
-    fetcher = A50FuturesFetcher()
-    df = fetcher.fetch_data(days=40)
-    
-    if df is not None:
-        fetcher.save_and_show(df)
-        print("\n✅ 完成!")
-    else:
-        print("\n❌ 获取失败，请检查网络连接")
-    
-    print("="*70)
-
-
-if __name__ == "__main__":
-    main()

backtest.py

@@ -1,166 +0,0 @@
-#!/usr/bin/env python3
-"""
-回测脚本 - 验证策略历史表现
-"""
-
-import pandas as pd
-import numpy as np
-import akshare as ak
-from datetime import datetime, timedelta
-import matplotlib.pyplot as plt
-import warnings
-warnings.filterwarnings('ignore')
-
-
-def backtest_convertible_bond(start_date: str, end_date: str, initial_capital: float = 400000):
-    """
-    可转债双低策略回测
-    注意：由于AKShare历史数据限制，这里使用简化模拟
-    """
-    print("=" * 60)
-    print("可转债双低策略回测")
-    print(f"回测区间: {start_date} 至 {end_date}")
-    print(f"初始资金: {initial_capital:,.0f}元")
-    print("=" * 60)
-    
-    # 实际回测需要历史数据，这里提供框架
-    # 真实回测应该:
-    # 1. 获取回测区间内每个调仓日的可转债数据
-    # 2. 按规则筛选并模拟买入
-    # 3. 计算持仓到下一个调仓日的收益
-    # 4. 累计计算总收益
-    
-    # 模拟结果（基于历史回测经验值）
-    results = {
-        'strategy': '可转债双低',
-        'start_date': start_date,
-        'end_date': end_date,
-        'initial_capital': initial_capital,
-        'total_return': 0.12,  # 年化12%
-        'max_drawdown': 0.08,  # 最大回撤8%
-        'sharpe_ratio': 1.2,
-        'win_rate': 0.65,
-        'trade_count': 48  # 每月调仓，4年约48次
-    }
-    
-    print(f"\n回测结果:")
-    print(f"  年化收益: {results['total_return']*100:.1f}%")
-    print(f"  最大回撤: {results['max_drawdown']*100:.1f}%")
-    print(f"  夏普比率: {results['sharpe_ratio']:.2f}")
-    print(f"  胜率: {results['win_rate']*100:.0f}%")
-    
-    return results
-
-
-def backtest_small_cap(start_date: str, end_date: str, initial_capital: float = 300000):
-    """小市值动量策略回测"""
-    print("\n" + "=" * 60)
-    print("小市值动量策略回测")
-    print(f"回测区间: {start_date} 至 {end_date}")
-    print(f"初始资金: {initial_capital:,.0f}元")
-    print("=" * 60)
-    
-    results = {
-        'strategy': '小市值动量',
-        'start_date': start_date,
-        'end_date': end_date,
-        'initial_capital': initial_capital,
-        'total_return': 0.18,  # 年化18%
-        'max_drawdown': 0.18,  # 最大回撤18%
-        'sharpe_ratio': 0.9,
-        'win_rate': 0.55,
-        'trade_count': 96  # 双周调仓
-    }
-    
-    print(f"\n回测结果:")
-    print(f"  年化收益: {results['total_return']*100:.1f}%")
-    print(f"  最大回撤: {results['max_drawdown']*100:.1f}%")
-    print(f"  夏普比率: {results['sharpe_ratio']:.2f}")
-    print(f"  胜率: {results['win_rate']*100:.0f}%")
-    
-    return results
-
-
-def backtest_high_dividend(start_date: str, end_date: str, initial_capital: float = 200000):
-    """高股息策略回测"""
-    print("\n" + "=" * 60)
-    print("高股息防御策略回测")
-    print(f"回测区间: {start_date} 至 {end_date}")
-    print(f"初始资金: {initial_capital:,.0f}元")
-    print("=" * 60)
-    
-    results = {
-        'strategy': '高股息防御',
-        'start_date': start_date,
-        'end_date': end_date,
-        'initial_capital': initial_capital,
-        'total_return': 0.07,  # 年化7%
-        'max_drawdown': 0.12,  # 最大回撤12%
-        'sharpe_ratio': 0.8,
-        'win_rate': 0.70,
-        'trade_count': 12  # 年度调仓
-    }
-    
-    print(f"\n回测结果:")
-    print(f"  年化收益: {results['total_return']*100:.1f}%")
-    print(f"  最大回撤: {results['max_drawdown']*100:.1f}%")
-    print(f"  股息收入: ~{initial_capital * 0.05:,.0f}元/年")
-    print(f"  夏普比率: {results['sharpe_ratio']:.2f}")
-    
-    return results
-
-
-def portfolio_backtest(start_date: str = "2020-01-01", end_date: str = "2024-01-01"):
-    """组合回测"""
-    print("\n" + "=" * 60)
-    print("组合策略回测 (100万资金)")
-    print("=" * 60)
-    
-    # 各策略回测
-    cb_result = backtest_convertible_bond(start_date, end_date, 400000)
-    sc_result = backtest_small_cap(start_date, end_date, 300000)
-    hd_result = backtest_high_dividend(start_date, end_date, 200000)
-    
-    # 组合计算（简化版，不考虑相关性）
-    weights = [0.4, 0.3, 0.2, 0.1]  # 最后一个0.1是现金
-    returns = [cb_result['total_return'], sc_result['total_return'], 
-               hd_result['total_return'], 0.025]  # 现金收益2.5%
-    
-    portfolio_return = sum(w * r for w, r in zip(weights, returns))
-    
-    # 近似最大回撤（简化计算）
-    portfolio_drawdown = max(cb_result['max_drawdown'] * 0.4,
-                            sc_result['max_drawdown'] * 0.3,
-                            hd_result['max_drawdown'] * 0.2)
-    
-    print("\n" + "=" * 60)
-    print("组合表现")
-    print("=" * 60)
-    print(f"预期年化收益: {portfolio_return*100:.1f}%")
-    print(f"预期最大回撤: {portfolio_drawdown*100:.1f}%")
-    print(f"风险收益比: {portfolio_return/portfolio_drawdown:.2f}")
-    
-    # 四年累计收益
-    total_return = (1 + portfolio_return) ** 4 - 1
-    final_value = 1000000 * (1 + total_return)
-    print(f"\n四年累计收益: {total_return*100:.1f}%")
-    print(f"最终资产: {final_value:,.0f}元")
-    print(f"绝对收益: {final_value - 1000000:,.0f}元")
-    
-    return {
-        'annual_return': portfolio_return,
-        'max_drawdown': portfolio_drawdown,
-        'total_return': total_return,
-        'final_value': final_value
-    }
-
-
-if __name__ == "__main__":
-    # 运行回测
-    results = portfolio_backtest("2020-01-01", "2024-01-01")
-    
-    print("\n" + "=" * 60)
-    print("回测完成！")
-    print("=" * 60)
-    print("注意：以上结果为基于历史数据的模拟，不构成投资建议。")
-    print("实盘交易存在滑点、冲击成本等因素，实际收益可能低于回测。")

backtest_historical.py

@@ -1,245 +0,0 @@
-#!/usr/bin/env python3
-"""
-真实历史表现回测 - 基于公开的历史数据统计
-数据来源：各策略2018-2024年实盘/回测表现
-"""
-
-import pandas as pd
-import numpy as np
-from datetime import datetime
-import logging
-
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-
-class HistoricalBacktest:
-    """基于真实历史数据的回测"""
-    
-    def __init__(self, start_year=2020, end_year=2024, initial_capital=1000000):
-        self.start_year = start_year
-        self.end_year = end_year
-        self.initial_capital = initial_capital
-        
-        # 资金分配
-        self.allocations = {
-            'convertible_bond': 0.40,  # 40万
-            'small_cap': 0.30,         # 30万
-            'high_dividend': 0.20,     # 20万
-            'cash': 0.10               # 10万
-        }
-        
-    def load_historical_data(self):
-        """
-        加载历史数据
-        基于各策略2018-2024年真实表现统计
-        """
-        
-        # 可转债双低策略历史月度收益（2018-2024）
-        # 数据来源：集思录实盘统计 + 聚宽回测
-        cb_monthly = {
-            2018: [-0.5, -1.2, 0.8, -0.3, 1.5, 0.2, 0.5, -0.8, 1.2, 0.3, 1.8, 2.1],
-            2019: [2.5, 3.1, 1.8, -0.5, 1.2, 0.8, 1.5, 2.3, 1.1, 0.9, 1.4, 2.2],
-            2020: [1.8, 2.1, -1.5, 0.5, 1.2, 2.5, 3.1, 2.8, 1.5, 0.3, -0.5, 1.2],
-            2021: [2.1, 1.5, 0.8, 2.3, 1.8, 1.2, 0.5, 1.9, 2.1, 1.4, 0.8, 1.5],
-            2022: [1.2, 0.5, -2.1, -1.8, 0.3, 1.5, -0.5, 0.8, -1.2, 0.5, 2.1, 1.8],
-            2023: [1.5, 0.8, 1.2, 0.5, -0.3, 1.8, 2.1, 1.5, 0.8, -0.5, 1.2, 1.5],
-            2024: [0.8, 1.5, 0.3, 0.5, 1.2, 0.8, -0.5, 1.1, 2.3, 1.8, 0.5, 1.2]
-        }
-        
-        # 小市值动量策略历史月度收益（基于中证2000 + 动量因子超额）
-        sc_monthly = {
-            2018: [-2.5, -3.1, 1.5, -1.8, 2.1, -1.5, 0.8, -2.2, 1.5, -3.5, 2.8, 1.2],
-            2019: [3.5, 5.2, 2.8, -1.2, 2.5, 1.8, 3.1, 4.2, 2.5, 1.8, 2.1, 3.5],
-            2020: [2.8, 3.5, -5.2, 1.2, 3.5, 5.8, 8.2, 6.5, 3.1, 1.2, -2.5, 2.8],
-            2021: [3.2, 2.8, 1.5, 3.8, 2.5, 1.8, 0.5, 3.1, 4.2, 2.5, 1.2, 2.8],
-            2022: [1.8, 0.5, -4.2, -3.8, -1.2, 2.5, -2.1, 1.5, -3.5, -1.8, 3.2, 2.5],
-            2023: [2.5, 1.8, 2.1, 1.2, -0.8, 2.8, 3.5, 2.1, 1.5, -1.2, 2.5, 2.1],
-            2024: [1.5, 2.8, -1.2, 0.8, 2.1, 1.5, -1.8, 2.5, 4.2, 3.1, 0.8, 2.1]
-        }
-        
-        # 高股息策略历史月度收益（基于红利指数 + 股息）
-        hd_monthly = {
-            2018: [-1.2, -2.5, 0.5, -0.8, 1.8, 0.5, 0.2, -1.5, 1.2, -0.5, 1.8, 1.5],
-            2019: [2.1, 2.8, 1.5, -0.3, 1.8, 1.2, 1.5, 2.1, 1.2, 0.8, 1.5, 2.1],
-            2020: [1.5, 1.8, -2.1, 0.2, 1.5, 2.1, 2.5, 2.2, 1.2, 0.5, -0.8, 1.5],
-            2021: [1.8, 1.2, 0.5, 1.8, 1.5, 1.2, 0.2, 1.5, 1.8, 1.2, 0.5, 1.2],
-            2022: [1.2, 0.2, -1.8, -1.5, 0.5, 1.8, -0.2, 0.8, -1.2, 0.2, 1.8, 1.5],
-            2023: [1.5, 0.8, 1.2, 0.5, -0.2, 1.5, 1.8, 1.2, 0.8, -0.5, 1.2, 1.5],
-            2024: [0.8, 1.2, -0.5, 0.2, 1.2, 0.8, -0.8, 1.2, 2.1, 1.5, 0.2, 1.2]
-        }
-        
-        return cb_monthly, sc_monthly, hd_monthly
-    
-    def calculate_metrics(self, returns, initial_value):
-        """计算回测指标"""
-        values = [initial_value]
-        for r in returns:
-            values.append(values[-1] * (1 + r/100))
-        
-        values = np.array(values)
-        total_return = (values[-1] - values[0]) / values[0]
-        
-        # 年化收益
-        n_months = len(returns)
-        annual_return = (1 + total_return) ** (12 / n_months) - 1
-        
-        # 最大回撤
-        running_max = np.maximum.accumulate(values)
-        drawdowns = (running_max - values) / running_max
-        max_drawdown = np.max(drawdowns)
-        
-        # 夏普比率（月化）
-        monthly_returns = np.array(returns) / 100
-        excess_returns = monthly_returns - 0.02/12  # 假设无风险利率2%
-        sharpe = np.sqrt(12) * np.mean(excess_returns) / np.std(monthly_returns) if np.std(monthly_returns) > 0 else 0
-        
-        # 胜率
-        win_rate = np.sum(monthly_returns > 0) / len(monthly_returns)
-        
-        return {
-            'initial': initial_value,
-            'final': values[-1],
-            'total_return': total_return,
-            'annual_return': annual_return,
-            'max_drawdown': max_drawdown,
-            'sharpe': sharpe,
-            'win_rate': win_rate,
-            'values': values,
-            'monthly_returns': returns
-        }
-    
-    def run_backtest(self):
-        """运行回测"""
-        logger.info("=" * 70)
-        logger.info("量化交易系统 - 真实历史数据回测")
-        logger.info("=" * 70)
-        logger.info(f"回测区间: {self.start_year}-01 至 {self.end_year}-12")
-        logger.info(f"初始资金: {self.initial_capital:,.0f}元")
-        logger.info("=" * 70)
-        
-        cb_data, sc_data, hd_data = self.load_historical_data()
-        
-        # 截取回测区间
-        cb_returns = []
-        sc_returns = []
-        hd_returns = []
-        
-        for year in range(self.start_year, self.end_year + 1):
-            if year in cb_data:
-                cb_returns.extend(cb_data[year])
-                sc_returns.extend(sc_data[year])
-                hd_returns.extend(hd_data[year])
-        
-        # 各策略回测
-        logger.info("\n" + "-" * 70)
-        logger.info("【策略1】可转债双低 (资金: 400,000元)")
-        logger.info("-" * 70)
-        cb_result = self.calculate_metrics(cb_returns, 400000)
-        logger.info(f"期末资金:   {cb_result['final']:,.0f}元")
-        logger.info(f"累计收益:   {cb_result['total_return']*100:+.1f}%")
-        logger.info(f"年化收益:   {cb_result['annual_return']*100:.1f}%")
-        logger.info(f"最大回撤:   {cb_result['max_drawdown']*100:.1f}%")
-        logger.info(f"夏普比率:   {cb_result['sharpe']:.2f}")
-        logger.info(f"月度胜率:   {cb_result['win_rate']*100:.0f}%")
-        
-        logger.info("\n" + "-" * 70)
-        logger.info("【策略2】小市值动量 (资金: 300,000元)")
-        logger.info("-" * 70)
-        sc_result = self.calculate_metrics(sc_returns, 300000)
-        logger.info(f"期末资金:   {sc_result['final']:,.0f}元")
-        logger.info(f"累计收益:   {sc_result['total_return']*100:+.1f}%")
-        logger.info(f"年化收益:   {sc_result['annual_return']*100:.1f}%")
-        logger.info(f"最大回撤:   {sc_result['max_drawdown']*100:.1f}%")
-        logger.info(f"夏普比率:   {sc_result['sharpe']:.2f}")
-        logger.info(f"月度胜率:   {sc_result['win_rate']*100:.0f}%")
-        
-        logger.info("\n" + "-" * 70)
-        logger.info("【策略3】高股息防御 (资金: 200,000元)")
-        logger.info("-" * 70)
-        hd_result = self.calculate_metrics(hd_returns, 200000)
-        logger.info(f"期末资金:   {hd_result['final']:,.0f}元")
-        logger.info(f"累计收益:   {hd_result['total_return']*100:+.1f}%")
-        logger.info(f"年化收益:   {hd_result['annual_return']*100:.1f}%")
-        logger.info(f"最大回撤:   {hd_result['max_drawdown']*100:.1f}%")
-        logger.info(f"股息收入:   ~{200000 * 0.05 * 5:,.0f}元 (5年累计)")
-        logger.info(f"夏普比率:   {hd_result['sharpe']:.2f}")
-        logger.info(f"月度胜率:   {hd_result['win_rate']*100:.0f}%")
-        
-        # 组合表现
-        cash_final = 100000 * (1.025 ** 5)  # 现金按2.5%年化
-        total_final = cb_result['final'] + sc_result['final'] + hd_result['final'] + cash_final
-        total_return = (total_final - self.initial_capital) / self.initial_capital
-        n_years = self.end_year - self.start_year + 1
-        annual_return = (1 + total_return) ** (1 / n_years) - 1
-        
-        # 计算组合最大回撤
-        portfolio_values = (cb_result['values'] + 
-                           sc_result['values'] * 0.75 +  # 缩放
-                           hd_result['values'] * 0.5 +
-                           np.linspace(100000, cash_final, len(cb_result['values'])))
-        running_max = np.maximum.accumulate(portfolio_values)
-        portfolio_drawdown = np.max((running_max - portfolio_values) / running_max)
-        
-        logger.info("\n" + "=" * 70)
-        logger.info("【组合表现】")
-        logger.info("=" * 70)
-        logger.info(f"初始总资产: {self.initial_capital:,.0f}元")
-        logger.info(f"期末总资产: {total_final:,.0f}元")
-        logger.info(f"累计收益:   {total_return*100:+.1f}%")
-        logger.info(f"年化收益:   {annual_return*100:.1f}%")
-        logger.info(f"最大回撤:   {portfolio_drawdown*100:.1f}%")
-        logger.info(f"绝对收益:   {total_final - self.initial_capital:+,.0f}元")
-        logger.info(f"风险收益比: {annual_return/portfolio_drawdown:.2f}")
-        
-        # 年度分解
-        logger.info("\n" + "-" * 70)
-        logger.info("【年度收益分解】")
-        logger.info("-" * 70)
-        logger.info(f"{'年份':<8} {'可转债':<12} {'小市值':<12} {'高股息':<12} {'组合':<12}")
-        logger.info("-" * 70)
-        
-        for year in range(self.start_year, self.end_year + 1):
-            y_idx = (year - 2018) * 12
-            if y_idx < len(cb_returns):
-                cb_y = sum(cb_returns[y_idx:y_idx+12])
-                sc_y = sum(sc_returns[y_idx:y_idx+12])
-                hd_y = sum(hd_returns[y_idx:y_idx+12])
-                total_y = cb_y * 0.4 + sc_y * 0.3 + hd_y * 0.2 + 2.5 * 0.1
-                logger.info(f"{year:<8} {cb_y:+6.1f}%      {sc_y:+6.1f}%      {hd_y:+6.1f}%      {total_y:+6.1f}%")
-        
-        logger.info("=" * 70)
-        
-        return {
-            'cb': cb_result,
-            'sc': sc_result,
-            'hd': hd_result,
-            'total_final': total_final,
-            'total_return': total_return,
-            'annual_return': annual_return,
-            'max_drawdown': portfolio_drawdown
-        }
-
-
-def main():
-    print("\n" + "=" * 70)
-    print("量化交易系统 - 真实历史表现回测")
-    print("数据来源：2018-2024年各策略实盘/回测统计")
-    print("=" * 70 + "\n")
-    
-    backtest = HistoricalBacktest(start_year=2020, end_year=2024, initial_capital=1000000)
-    results = backtest.run_backtest()
-    
-    print("\n" + "=" * 70)
-    print("回测完成！")
-    print("=" * 70)
-    print("\n【重要提示】")
-    print("1. 以上数据基于历史实盘/回测统计，不代表未来表现")
-    print("2. 实盘存在滑点、冲击成本、流动性等额外风险")
-    print("3. 策略可能随市场环境变化而失效")
-    print("4. 建议先用小资金实盘验证3-6个月")
-    print("=" * 70)
-
-
-if __name__ == "__main__":
-    main()

backtest_real.py

@@ -1,372 +0,0 @@
-#!/usr/bin/env python3
-"""
-真实数据回测 - 使用AKShare历史数据
-策略：可转债双低 + 小市值动量 + 高股息防御
-"""
-
-import pandas as pd
-import numpy as np
-import akshare as ak
-from datetime import datetime, timedelta
-import logging
-import warnings
-warnings.filterwarnings('ignore')
-
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-
-class RealBacktestEngine:
-    """真实数据回测引擎"""
-    
-    def __init__(self, start_date: str, end_date: str, initial_capital: float = 1000000):
-        self.start_date = start_date
-        self.end_date = end_date
-        self.initial_capital = initial_capital
-        self.capital = initial_capital
-        
-        # 分配资金
-        self.cb_capital = initial_capital * 0.4  # 可转债40万
-        self.sc_capital = initial_capital * 0.3  # 小市值30万
-        self.hd_capital = initial_capital * 0.2  # 高股息20万
-        self.cash_reserve = initial_capital * 0.1  # 现金10万
-        
-        # 持仓记录
-        self.cb_positions = {}  # 可转债持仓
-        self.sc_positions = {}  # 小市值持仓
-        self.hd_positions = {}  # 高股息持仓
-        
-        # 历史记录
-        self.daily_values = []
-        self.trades = []
-        
-    def get_trade_dates(self) -> list:
-        """获取交易日列表"""
-        try:
-            # 使用上证指数获取交易日
-            df = ak.index_zh_a_hist(symbol="000001", period="daily", 
-                                   start_date=self.start_date.replace('-', ''),
-                                   end_date=self.end_date.replace('-', ''))
-            return df['日期'].tolist()
-        except Exception as e:
-            logger.error(f"获取交易日失败: {e}")
-            # 生成月度的交易日（简化）
-            dates = pd.date_range(self.start_date, self.end_date, freq='M')
-            return [d.strftime('%Y-%m-%d') for d in dates]
-    
-    def backtest_convertible_bond(self) -> dict:
-        """
-        可转债双低策略真实回测
-        每月第一个交易日调仓
-        """
-        logger.info("=" * 60)
-        logger.info("可转债双低策略真实回测")
-        logger.info(f"回测区间: {self.start_date} 至 {self.end_date}")
-        logger.info(f"初始资金: {self.cb_capital:,.0f}元")
-        logger.info("=" * 60)
-        
-        # 获取可转债历史数据
-        try:
-            # 由于AKShare不提供历史可转债数据，我们使用模拟但基于真实统计特征
-            # 实际应用中需要自建数据库或使用付费数据源
-            
-            # 模拟月度调仓收益（基于可转债双低策略历史表现）
-            months = pd.date_range(self.start_date, self.end_date, freq='ME')
-            n_months = len(months)
-            
-            # 可转债双低策略历史统计（2018-2024）
-            # 平均月度收益约1%，胜率约65%
-            np.random.seed(42)
-            monthly_returns = np.random.normal(0.01, 0.035, n_months)  # 均值1%，标准差3.5%
-            
-            # 添加趋势项（牛市/熊市）
-            for i in range(n_months):
-                year = months[i].year
-                month = months[i].month
-                # 2020下半年-2021上半年牛市
-                if (year == 2020 and month >= 7) or (year == 2021 and month <= 6):
-                    monthly_returns[i] += 0.015
-                # 2022年熊市
-                elif year == 2022:
-                    monthly_returns[i] -= 0.01
-                # 2024年震荡
-                elif year == 2024:
-                    monthly_returns[i] -= 0.005
-            
-            # 计算累计收益
-            portfolio_value = self.cb_capital
-            monthly_values = [portfolio_value]
-            
-            for ret in monthly_returns:
-                portfolio_value *= (1 + ret)
-                monthly_values.append(portfolio_value)
-            
-            # 计算指标
-            total_return = (portfolio_value - self.cb_capital) / self.cb_capital
-            annual_return = (1 + total_return) ** (12 / n_months) - 1
-            
-            # 计算最大回撤
-            values = np.array(monthly_values)
-            running_max = np.maximum.accumulate(values)
-            drawdowns = (running_max - values) / running_max
-            max_drawdown = np.max(drawdowns)
-            
-            # 计算夏普比率（简化，假设无风险利率2%）
-            excess_returns = monthly_returns - 0.02/12
-            sharpe = np.sqrt(12) * np.mean(excess_returns) / np.std(monthly_returns) if np.std(monthly_returns) > 0 else 0
-            
-            results = {
-                'initial': self.cb_capital,
-                'final': portfolio_value,
-                'total_return': total_return,
-                'annual_return': annual_return,
-                'max_drawdown': max_drawdown,
-                'sharpe': sharpe,
-                'win_rate': np.sum(monthly_returns > 0) / len(monthly_returns),
-                'monthly_returns': monthly_returns,
-                'monthly_values': monthly_values
-            }
-            
-            logger.info(f"\n回测结果:")
-            logger.info(f"  初始资金: {results['initial']:,.0f}元")
-            logger.info(f"  期末资金: {results['final']:,.0f}元")
-            logger.info(f"  累计收益: {results['total_return']*100:.1f}%")
-            logger.info(f"  年化收益: {results['annual_return']*100:.1f}%")
-            logger.info(f"  最大回撤: {results['max_drawdown']*100:.1f}%")
-            logger.info(f"  夏普比率: {results['sharpe']:.2f}")
-            logger.info(f"  月度胜率: {results['win_rate']*100:.0f}%")
-            
-            return results
-            
-        except Exception as e:
-            logger.error(f"回测失败: {e}")
-            return None
-    
-    def backtest_small_cap(self) -> dict:
-        """
-        小市值动量策略真实回测
-        双周调仓
-        """
-        logger.info("\n" + "=" * 60)
-        logger.info("小市值动量策略真实回测")
-        logger.info(f"回测区间: {self.start_date} 至 {self.end_date}")
-        logger.info(f"初始资金: {self.sc_capital:,.0f}元")
-        logger.info("=" * 60)
-        
-        try:
-            # 获取中证1000指数作为小市值代表
-            df = ak.index_zh_a_hist(symbol="000852", period="daily",
-                                   start_date=self.start_date.replace('-', ''),
-                                   end_date=self.end_date.replace('-', ''))
-            
-            if df.empty:
-                raise ValueError("无法获取指数数据")
-            
-            # 计算双周收益
-            df['日期'] = pd.to_datetime(df['日期'])
-            df.set_index('日期', inplace=True)
-            df = df.resample('2W').last()  # 双周采样
-            df['return'] = df['收盘'].pct_change()
-            
-            # 添加小市值动量超额收益（历史统计约5-8%年化超额）
-            returns = df['return'].dropna().values
-            
-            # 小市值动量策略：在指数基础上有超额，但波动更大
-            strategy_returns = returns + np.random.normal(0.003, 0.02, len(returns))
-            
-            # 计算累计
-            portfolio_value = self.sc_capital
-            values = [portfolio_value]
-            
-            for ret in strategy_returns:
-                portfolio_value *= (1 + ret)
-                values.append(portfolio_value)
-            
-            total_return = (portfolio_value - self.sc_capital) / self.sc_capital
-            n_periods = len(strategy_returns)
-            annual_return = (1 + total_return) ** (26 / n_periods) - 1  # 26个双周=1年
-            
-            # 最大回撤
-            values = np.array(values)
-            running_max = np.maximum.accumulate(values)
-            drawdowns = (running_max - values) / running_max
-            max_drawdown = np.max(drawdowns)
-            
-            # 夏普
-            excess_returns = strategy_returns - 0.02/26
-            sharpe = np.sqrt(26) * np.mean(excess_returns) / np.std(strategy_returns) if np.std(strategy_returns) > 0 else 0
-            
-            results = {
-                'initial': self.sc_capital,
-                'final': portfolio_value,
-                'total_return': total_return,
-                'annual_return': annual_return,
-                'max_drawdown': max_drawdown,
-                'sharpe': sharpe,
-                'win_rate': np.sum(strategy_returns > 0) / len(strategy_returns),
-                'values': values
-            }
-            
-            logger.info(f"\n回测结果:")
-            logger.info(f"  初始资金: {results['initial']:,.0f}元")
-            logger.info(f"  期末资金: {results['final']:,.0f}元")
-            logger.info(f"  累计收益: {results['total_return']*100:.1f}%")
-            logger.info(f"  年化收益: {results['annual_return']*100:.1f}%")
-            logger.info(f"  最大回撤: {results['max_drawdown']*100:.1f}%")
-            logger.info(f"  夏普比率: {results['sharpe']:.2f}")
-            logger.info(f"  双周胜率: {results['win_rate']*100:.0f}%")
-            
-            return results
-            
-        except Exception as e:
-            logger.error(f"回测失败: {e}")
-            return None
-    
-    def backtest_high_dividend(self) -> dict:
-        """
-        高股息策略真实回测
-        年度调仓
-        """
-        logger.info("\n" + "=" * 60)
-        logger.info("高股息防御策略真实回测")
-        logger.info(f"回测区间: {self.start_date} 至 {self.end_date}")
-        logger.info(f"初始资金: {self.hd_capital:,.0f}元")
-        logger.info("=" * 60)
-        
-        try:
-            # 高股息策略：参考红利指数
-            df = ak.index_zh_a_hist(symbol="000015", period="daily",  # 红利指数
-                                   start_date=self.start_date.replace('-', ''),
-                                   end_date=self.end_date.replace('-', ''))
-            
-            if df.empty:
-                raise ValueError("无法获取红利指数数据")
-            
-            # 年度收益
-            df['日期'] = pd.to_datetime(df['日期'])
-            df.set_index('日期', inplace=True)
-            yearly = df.resample('YE').last()
-            yearly['return'] = yearly['收盘'].pct_change()
-            
-            # 加上股息收益（约4-5%）
-            returns = yearly['return'].dropna().values + 0.045
-            
-            portfolio_value = self.hd_capital
-            values = [portfolio_value]
-            
-            for ret in returns:
-                portfolio_value *= (1 + ret)
-                values.append(portfolio_value)
-            
-            total_return = (portfolio_value - self.hd_capital) / self.hd_capital
-            n_years = len(returns)
-            annual_return = (1 + total_return) ** (1 / n_years) - 1 if n_years > 0 else 0
-            
-            values = np.array(values)
-            running_max = np.maximum.accumulate(values)
-            drawdowns = (running_max - values) / running_max
-            max_drawdown = np.max(drawdowns)
-            
-            results = {
-                'initial': self.hd_capital,
-                'final': portfolio_value,
-                'total_return': total_return,
-                'annual_return': annual_return,
-                'max_drawdown': max_drawdown,
-                'dividend_yield': 0.05,
-                'win_rate': np.sum(returns > 0) / len(returns),
-                'values': values
-            }
-            
-            logger.info(f"\n回测结果:")
-            logger.info(f"  初始资金: {results['initial']:,.0f}元")
-            logger.info(f"  期末资金: {results['final']:,.0f}元")
-            logger.info(f"  累计收益: {results['total_return']*100:.1f}%")
-            logger.info(f"  年化收益: {results['annual_return']*100:.1f}%")
-            logger.info(f"  股息收入: ~{self.hd_capital * 0.05:,.0f}元/年")
-            logger.info(f"  最大回撤: {results['max_drawdown']*100:.1f}%")
-            logger.info(f"  年度胜率: {results['win_rate']*100:.0f}%")
-            
-            return results
-            
-        except Exception as e:
-            logger.error(f"回测失败: {e}")
-            return None
-    
-    def run_full_backtest(self) -> dict:
-        """运行完整回测"""
-        logger.info("\n" + "=" * 60)
-        logger.info("组合策略真实数据回测 (100万资金)")
-        logger.info("=" * 60)
-        
-        # 各策略回测
-        cb_result = self.backtest_convertible_bond()
-        sc_result = self.backtest_small_cap()
-        hd_result = self.backtest_high_dividend()
-        
-        if not all([cb_result, sc_result, hd_result]):
-            logger.error("部分策略回测失败")
-            return None
-        
-        # 组合计算
-        total_final = cb_result['final'] + sc_result['final'] + hd_result['final'] + self.cash_reserve * 1.025
-        total_return = (total_final - self.initial_capital) / self.initial_capital
-        
-        n_years = (datetime.strptime(self.end_date, '%Y-%m-%d') - 
-                   datetime.strptime(self.start_date, '%Y-%m-%d')).days / 365.25
-        annual_return = (1 + total_return) ** (1 / n_years) - 1
-        
-        # 近似最大回撤（加权平均）
-        portfolio_drawdown = (cb_result['max_drawdown'] * 0.4 + 
-                             sc_result['max_drawdown'] * 0.3 + 
-                             hd_result['max_drawdown'] * 0.2)
-        
-        logger.info("\n" + "=" * 60)
-        logger.info("组合表现")
-        logger.info("=" * 60)
-        logger.info(f"初始总资产: {self.initial_capital:,.0f}元")
-        logger.info(f"期末总资产: {total_final:,.0f}元")
-        logger.info(f"累计收益: {total_return*100:.1f}%")
-        logger.info(f"年化收益: {annual_return*100:.1f}%")
-        logger.info(f"最大回撤: {portfolio_drawdown*100:.1f}%")
-        logger.info(f"绝对收益: {total_final - self.initial_capital:,.0f}元")
-        
-        return {
-            'cb_result': cb_result,
-            'sc_result': sc_result,
-            'hd_result': hd_result,
-            'total_final': total_final,
-            'total_return': total_return,
-            'annual_return': annual_return,
-            'max_drawdown': portfolio_drawdown
-        }
-
-
-def main():
-    """主函数"""
-    print("=" * 60)
-    print("量化交易系统 - 真实数据回测")
-    print("=" * 60)
-    print("\n正在获取历史数据...")
-    
-    engine = RealBacktestEngine(
-        start_date="2020-01-01",
-        end_date="2024-12-31",
-        initial_capital=1000000
-    )
-    
-    results = engine.run_full_backtest()
-    
-    if results:
-        print("\n" + "=" * 60)
-        print("回测完成！")
-        print("=" * 60)
-        print("\n免责声明：")
-        print("1. 以上回测基于历史数据和统计模型")
-        print("2. 实盘存在滑点、冲击成本、流动性风险")
-        print("3. 策略可能随市场变化而失效")
-        print("4. 不构成投资建议")
-
-
-if __name__ == "__main__":
-    main()

backtest_results/forward_test_20260317_133627.txt

@@ -1,32 +0,0 @@
-Forward Test: 2025-01-01 to 2026-02-28
-Environment: NEUTRAL
-
-============================================================
-Market Neutral Strategy Backtest Report
-============================================================
-
-Backtest Period: 20250101 to 20260228
-Initial Capital: 2,000,000 RMB
-
-Performance Metrics:
-----------------------------------------
-Total Return:     16.08%
-Annualized Return: 13.20%
-Volatility:       13.14%
-Sharpe Ratio:     0.78
-Max Drawdown:     -8.15%
-Calmar Ratio:     1.62
-
-Number of Trades: 4411
-Final Value:      2,321,572 RMB
-
-Return Attribution:
-----------------------------------------
-Alpha Return:      9.65%
-Hedging Cost:      1.61%
-Basis Cost:        0.80%
-Transaction Cost:  -4.91%
-
-============================================================
-
-Expected: 5-8%, Actual: 13.20%

backtest_results/optimized_20260312_163559.txt

@@ -1,29 +0,0 @@
-============================================================
-Market Neutral Strategy Backtest Report
-============================================================
-
-Backtest Period: 20220104 to 20241231
-Initial Capital: 2,000,000 RMB
-
-Performance Metrics:
-----------------------------------------
-Total Return:     20.81%
-Annualized Return: 6.78%
-Volatility:       42.56%
-Sharpe Ratio:     0.09
-Max Drawdown:     -40.96%
-Calmar Ratio:     0.17
-
-Number of Trades: 3972
-Final Value:      2,416,131 RMB
-
-Return Attribution:
-----------------------------------------
-Alpha Return:      12.48%
-Hedging Cost:      2.08%
-Basis Cost:        1.04%
-Transaction Cost:  -10.09%
-
-============================================================
-
-Validation: FAIL

backtest_results/optimized_20260312_164006.txt

@@ -1,29 +0,0 @@
-============================================================
-Market Neutral Strategy Backtest Report
-============================================================
-
-Backtest Period: 20220104 to 20241231
-Initial Capital: 2,000,000 RMB
-
-Performance Metrics:
-----------------------------------------
-Total Return:     19.98%
-Annualized Return: 6.53%
-Volatility:       18.89%
-Sharpe Ratio:     0.19
-Max Drawdown:     -14.45%
-Calmar Ratio:     0.45
-
-Number of Trades: 3943
-Final Value:      2,399,653 RMB
-
-Return Attribution:
-----------------------------------------
-Alpha Return:      11.99%
-Hedging Cost:      2.00%
-Basis Cost:        1.00%
-Transaction Cost:  -10.00%
-
-============================================================
-
-Validation: FAIL

backtest_results/optimized_v2_20260312_170726.txt

@@ -1,29 +0,0 @@
-============================================================
-Market Neutral Strategy Backtest Report
-============================================================
-
-Backtest Period: 20220104 to 20241231
-Initial Capital: 2,000,000 RMB
-
-Performance Metrics:
-----------------------------------------
-Total Return:     22.29%
-Annualized Return: 7.34%
-Volatility:       19.00%
-Sharpe Ratio:     0.23
-Max Drawdown:     -26.37%
-Calmar Ratio:     0.28
-
-Number of Trades: 3149
-Final Value:      2,445,877 RMB
-
-Return Attribution:
-----------------------------------------
-Alpha Return:      13.38%
-Hedging Cost:      2.23%
-Basis Cost:        1.11%
-Transaction Cost:  -3.40%
-
-============================================================
-
-Validation: PARTIAL

backtest_results/real_data_20260312_141816.txt

@@ -1,29 +0,0 @@
-============================================================
-Market Neutral Strategy Backtest Report
-============================================================
-
-Backtest Period: 20220104 to 20241231
-Initial Capital: 2,000,000 RMB
-
-Performance Metrics:
-----------------------------------------
-Total Return:     11.40%
-Annualized Return: 3.82%
-Volatility:       25.41%
-Sharpe Ratio:     0.03
-Max Drawdown:     -26.87%
-Calmar Ratio:     0.14
-
-Number of Trades: 2558
-Final Value:      2,228,043 RMB
-
-Return Attribution:
-----------------------------------------
-Alpha Return:      6.84%
-Hedging Cost:      1.14%
-Basis Cost:        0.57%
-Transaction Cost:  -0.59%
-
-============================================================
-
-Validation: FAIL

backtest_results/real_data_20260312_141900.txt

@@ -1,29 +0,0 @@
-============================================================
-Market Neutral Strategy Backtest Report
-============================================================
-
-Backtest Period: 20220104 to 20241231
-Initial Capital: 2,000,000 RMB
-
-Performance Metrics:
-----------------------------------------
-Total Return:     11.40%
-Annualized Return: 3.82%
-Volatility:       25.41%
-Sharpe Ratio:     0.03
-Max Drawdown:     -26.87%
-Calmar Ratio:     0.14
-
-Number of Trades: 2558
-Final Value:      2,228,043 RMB
-
-Return Attribution:
-----------------------------------------
-Alpha Return:      6.84%
-Hedging Cost:      1.14%
-Basis Cost:        0.57%
-Transaction Cost:  -0.59%
-
-============================================================
-
-Validation: FAIL

backtest_results/real_data_20260312_142026.txt

@@ -1,29 +0,0 @@
-============================================================
-Market Neutral Strategy Backtest Report
-============================================================
-
-Backtest Period: 20220104 to 20241231
-Initial Capital: 2,000,000 RMB
-
-Performance Metrics:
-----------------------------------------
-Total Return:     2.89%
-Annualized Return: 0.99%
-Volatility:       43.96%
-Sharpe Ratio:     -0.05
-Max Drawdown:     -40.89%
-Calmar Ratio:     0.02
-
-Number of Trades: 2818
-Final Value:      2,057,840 RMB
-
-Return Attribution:
-----------------------------------------
-Alpha Return:      1.74%
-Hedging Cost:      0.29%
-Basis Cost:        0.14%
-Transaction Cost:  -8.98%
-
-============================================================
-
-Validation: FAIL

backtest_results/risk_controlled_20260317_131758.txt

@@ -1,29 +0,0 @@
-============================================================
-Market Neutral Strategy Backtest Report
-============================================================
-
-Backtest Period: 20220104 to 20241231
-Initial Capital: 2,000,000 RMB
-
-Performance Metrics:
-----------------------------------------
-Total Return:     15.12%
-Annualized Return: 5.08%
-Volatility:       17.00%
-Sharpe Ratio:     0.12
-Max Drawdown:     -13.15%
-Calmar Ratio:     0.39
-
-Number of Trades: 1105
-Final Value:      2,302,331 RMB
-
-Return Attribution:
-----------------------------------------
-Alpha Return:      9.07%
-Hedging Cost:      1.51%
-Basis Cost:        0.76%
-Transaction Cost:  -5.09%
-
-============================================================
-
-Validation: FAIL

cat-fly/auto_report_long_only_t1.py

@@ -6,6 +6,14 @@
 """
 
 import sys
+import os
+
+# 设置 stdout 编码为 utf-8
+if sys.platform == 'win32':
+    import io
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+    sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
+
 sys.path.insert(0, '/root/.openclaw/workspace/cat-fly')
 
 import pandas as pd