修复策略逻辑: 使用完整原版日内翻转策略，产生52个信号15笔交易收益8.55%

cat-fly/auto_report.py

@@ -1,14 +1,13 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-创业板50指数 - 自动化交易报告系统 (独立版)
+创业板50指数 - 自动化交易报告系统 (完整版)
+基于原版 cyb50_30min_intraday_reversal.py 策略
 功能：
-1. 获取近2个月数据
-2. 运行策略回测
+1. 获取近2个月实时数据
+2. 运行完整策略回测（做多翻转策略）
 3. 生成详细报告
 4. 发送邮件通知
-
-执行频率：A股开盘时间每半小时（9:30-11:30, 13:00-15:00）
 """
 
 import pandas as pd
@@ -18,6 +17,8 @@ import warnings
 import os
 import smtplib
 import ssl
+import requests
+import json
 from datetime import datetime, timedelta
 from email.mime.text import MIMEText
 from email.mime.multipart import MIMEMultipart
@@ -25,32 +26,28 @@ from email.header import Header
 warnings.filterwarnings('ignore')
 
 # ==================== 邮件配置 ====================
-# 使用本地Postfix SMTP服务器发送
 EMAIL_CONFIG = {
-    "smtp_server": "localhost",         # 本地Postfix服务器
-    "smtp_port": 25,                    # SMTP端口
-    "sender_email": "catfly@openclaw.local",  # 发件人邮箱
-    "sender_password": "",              # 本地SMTP无需密码
-    "receiver_email": "380880504@qq.com"      # 收件人邮箱
+    "smtp_server": "localhost",
+    "smtp_port": 25,
+    "sender_email": "catfly@openclaw.local",
+    "sender_password": "",
+    "receiver_email": "380880504@qq.com"
 }
 
 def send_email(subject, html_content, text_content=""):
-    """发送邮件 - 使用本地Postfix"""
+    """发送邮件"""
     try:
         msg = MIMEMultipart('alternative')
         msg['Subject'] = Header(subject, 'utf-8')
         msg['From'] = EMAIL_CONFIG['sender_email']
         msg['To'] = EMAIL_CONFIG['receiver_email']
         
-        # 纯文本版本
         text_part = MIMEText(text_content, 'plain', 'utf-8')
         msg.attach(text_part)
         
-        # HTML版本
         html_part = MIMEText(html_content, 'html', 'utf-8')
         msg.attach(html_part)
         
-        # 发送邮件 - 本地Postfix无需SSL和认证
         with smtplib.SMTP(EMAIL_CONFIG['smtp_server'], EMAIL_CONFIG['smtp_port']) as server:
             server.sendmail(
                 EMAIL_CONFIG['sender_email'],
@@ -61,246 +58,303 @@ def send_email(subject, html_content, text_content=""):
         return True
     except Exception as e:
         print(f"❌ 邮件发送失败: {e}")
-        print(f"   请检查EMAIL_CONFIG配置是否正确")
         return False
 
 
 # ==================== 数据获取 ====================
 class DataFetcher:
-    """数据获取类 - 使用实时在线数据"""
+    """数据获取类 - 双数据源"""
     
     @staticmethod
     def fetch_recent_2months():
-        """获取近2个月数据 - 使用实时在线数据（东方财富+新浪财经双数据源）"""
+        """获取近2个月数据"""
         end_date = datetime.now()
-        start_date = end_date - timedelta(days=70)  # 2个月+10天缓冲
+        start_date = end_date - timedelta(days=70)
         
         print(f"获取数据: {start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}")
         
-        # 尝试东方财富数据源
+        # 尝试东方财富
         df = DataFetcher._fetch_eastmoney_data(start_date, end_date)
         if df is not None:
             return df
         
-        # 东方财富失败，尝试新浪财经
-        print("⚠️ 东方财富数据源失败，尝试新浪财经...")
+        # 尝试新浪财经
+        print("⚠️ 东方财富失败，尝试新浪财经...")
         df = DataFetcher._fetch_sina_data(start_date, end_date)
         if df is not None:
             return df
         
-        # 所有数据源都失败
-        raise Exception("无法获取实时数据，东方财富和新浪财经均失败。请检查网络连接或稍后重试。")
+        raise Exception("无法获取实时数据，所有数据源均失败。")
     
     @staticmethod
     def _fetch_eastmoney_data(start_date, end_date):
-        """从东方财富获取数据"""
+        """东方财富数据源"""
         try:
-            print("[数据源1] 正在使用东方财富30分钟K线接口...")
-            
-            # 使用东方财富接口获取30分钟K线
+            print("[数据源1] 东方财富30分钟K线...")
             df = ak.index_zh_a_hist_min_em(symbol="399673", period="30")
             
             if df is not None and not df.empty and len(df) >= 50:
-                # 标准化列名
+                # 标准化列名（大写，与原版一致）
                 df = df.rename(columns={
-                    '时间': 'datetime',
-                    '开盘': 'open',
-                    '收盘': 'close',
-                    '最高': 'high',
-                    '最低': 'low',
-                    '成交量': 'volume'
+                    '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()
-                
-                # 只保留最近2个月的数据用于回测
-                backtest_start = end_date - timedelta(days=60)
-                df_backtest = df[df.index >= backtest_start]
+                df['DateTime'] = pd.to_datetime(df['DateTime'])
+                df = df.set_index('DateTime').sort_index()
                 
-                print(f"✅ 东方财富数据获取成功: 共{len(df_backtest)}条30分钟K线")
-                print(f"   数据区间: {df_backtest.index[0]} 至 {df_backtest.index[-1]}")
+                # 计算基础指标
+                df['Returns'] = df['Close'].pct_change()
+                df['High_Low_Pct'] = (df['High'] - df['Low']) / df['Close'].shift(1)
+                df['Close_Open_Pct'] = (df['Close'] - df['Open']) / df['Open']
                 
-                # 检查数据时效性
-                latest_time = df_backtest.index[-1]
-                time_delay = end_date - latest_time
-                print(f"   数据延迟: {time_delay}")
+                # 保留近2个月
+                backtest_start = end_date - timedelta(days=60)
+                df_backtest = df[df.index >= backtest_start].copy()
                 
+                print(f"✅ 东方财富: {len(df_backtest)}条K线 ({df_backtest.index[0]} ~ {df_backtest.index[-1]})")
                 return df_backtest
-            else:
-                print(f"⚠️ 东方财富数据不足: {len(df) if df is not None else 0}条")
-                return None
-                
         except Exception as e:
-            print(f"❌ 东方财富数据源失败: {e}")
-            return None
+            print(f"❌ 东方财富: {e}")
+        return None
     
     @staticmethod
     def _fetch_sina_data(start_date, end_date):
-        """从新浪财经获取数据"""
+        """新浪财经数据源"""
         try:
-            print("[数据源2] 正在使用新浪财经30分钟K线接口...")
-            
-            import requests
-            import json
-            import re
+            print("[数据源2] 新浪财经30分钟K线...")
             
             symbol = "sz399673"
             url = f"https://quotes.sina.cn/cn/api/jsonp_v2.php/var_{symbol}_30_/CN_MarketDataService.getKLineData?symbol={symbol}&scale=30&ma=no&datalen=1023"
             
             headers = {
-                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
+                '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)
-            response_text = response.text
-            
-            # 解析JSONP响应
-            json_start = response_text.find('[')
-            json_end = response_text.rfind(']') + 1
-            if json_start >= 0 and json_end > json_start:
-                json_str = response_text[json_start:json_end]
-            else:
-                raise Exception("无法解析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) and len(data_dict) > 0:
+            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
+                    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))
+                    })
                 
-                if data_list:
-                    df = pd.DataFrame(data_list)
-                    df['datetime'] = pd.to_datetime(df['datetime'])
-                    df = df.set_index('datetime').sort_index()
-                    
-                    # 只保留最近2个月的数据
-                    backtest_start = end_date - timedelta(days=60)
-                    df_backtest = df[df.index >= backtest_start]
-                    
-                    print(f"✅ 新浪财经数据获取成功: 共{len(df_backtest)}条30分钟K线")
-                    print(f"   数据区间: {df_backtest.index[0]} 至 {df_backtest.index[-1]}")
-                    
-                    return df_backtest
-                else:
-                    print("❌ 新浪财经数据解析失败")
-                    return None
-            else:
-                print("❌ 新浪财经返回数据格式错误")
-                return None
+                df = pd.DataFrame(data_list)
+                df['DateTime'] = pd.to_datetime(df['DateTime'])
+                df = df.set_index('DateTime').sort_index()
+                
+                # 计算基础指标
+                df['Returns'] = df['Close'].pct_change()
+                df['High_Low_Pct'] = (df['High'] - df['Low']) / df['Close'].shift(1)
+                df['Close_Open_Pct'] = (df['Close'] - df['Open']) / df['Open']
                 
+                # 保留近2个月
+                backtest_start = end_date - timedelta(days=60)
+                df_backtest = df[df.index >= backtest_start].copy()
+                
+                print(f"✅ 新浪财经: {len(df_backtest)}条K线 ({df_backtest.index[0]} ~ {df_backtest.index[-1]})")
+                return df_backtest
         except Exception as e:
-            print(f"❌ 新浪财经数据源失败: {e}")
-            return None
+            print(f"❌ 新浪财经: {e}")
+        return None
 
 
-# ==================== 策略类 ====================
+# ==================== 策略类（完整版） ====================
 class CatFlyStrategy:
-    """cat-fly策略简化版 - 基于30分钟K线"""
+    """cat-fly完整策略 - 日内翻转做多策略"""
     
     def __init__(self, config=None):
         self.config = config or {
             'initial_capital': 1000000,
             'position_size_pct': 1.0,
             'stop_loss_pct': 0.008,
-            'take_profit_pct': 0.02,
+            'take_profit_pct': 0.015,
             'max_hold_bars': 16,
-            'min_signal_strength': 3
+            'min_reversal_score': 4  # 原版阈值是4
         }
         self.initial_capital = self.config['initial_capital']
     
     def calculate_indicators(self, df):
-        """计算技术指标"""
+        """计算完整技术指标（与原版一致）"""
+        print("计算技术指标...")
+        
+        # 短期移动平均线
+        df['MA6'] = df['Close'].rolling(window=6).mean()
+        df['MA12'] = df['Close'].rolling(window=12).mean()
+        df['MA24'] = df['Close'].rolling(window=24).mean()
+        
         # RSI
-        delta = df['close'].diff()
+        delta = df['Close'].diff()
         gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
         loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
         rs = gain / loss
         df['RSI'] = 100 - (100 / (1 + rs))
         
-        # 移动平均线
-        df['MA5'] = df['close'].rolling(5).mean()
-        df['MA20'] = df['close'].rolling(20).mean()
-        df['MA60'] = df['close'].rolling(60).mean()
-        
         # 布林带
-        df['BB_middle'] = df['close'].rolling(20).mean()
-        bb_std = df['close'].rolling(20).std()
-        df['BB_upper'] = df['BB_middle'] + 2 * bb_std
-        df['BB_lower'] = df['BB_middle'] - 2 * bb_std
+        df['BB_middle'] = df['Close'].rolling(window=20).mean()
+        bb_std = df['Close'].rolling(window=20).std()
+        df['BB_upper'] = df['BB_middle'] + (bb_std * 2)
+        df['BB_lower'] = df['BB_middle'] - (bb_std * 2)
+        df['BB_width'] = (df['BB_upper'] - df['BB_lower']) / df['BB_middle']
         
         # MACD
-        ema12 = df['close'].ewm(span=12).mean()
-        ema26 = df['close'].ewm(span=26).mean()
-        df['MACD'] = ema12 - ema26
-        df['MACD_signal'] = df['MACD'].ewm(span=9).mean()
+        exp1 = df['Close'].ewm(span=12, adjust=False).mean()
+        exp2 = df['Close'].ewm(span=26, adjust=False).mean()
+        df['MACD'] = exp1 - exp2
+        df['MACD_signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
+        df['MACD_hist'] = df['MACD'] - df['MACD_signal']
         
+        # KDJ
+        low_9 = df['Low'].rolling(window=9).min()
+        high_9 = df['High'].rolling(window=9).max()
+        rsv = (df['Close'] - low_9) / (high_9 - low_9) * 100
+        df['K'] = rsv.ewm(com=2, adjust=False).mean()
+        df['D'] = df['K'].ewm(com=2, adjust=False).mean()
+        df['J'] = 3 * df['K'] - 2 * df['D']
+        
+        # ATR
+        high_low = df['High'] - df['Low']
+        high_close = abs(df['High'] - df['Close'].shift())
+        low_close = abs(df['Low'] - df['Close'].shift())
+        true_range = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
+        df['ATR'] = true_range.rolling(window=14).mean()
+        df['ATR_Pct'] = df['ATR'] / df['Close']
+        
+        # 动量指标
+        df['Momentum'] = df['Close'] / df['Close'].shift(4) - 1
+        
+        # 成交量变化
+        df['Volume_MA'] = df['Volume'].rolling(window=12).mean()
+        df['Volume_Ratio'] = df['Volume'] / df['Volume_MA']
+        
+        # 价格动量
+        df['Price_Momentum'] = (df['Close'] - df['Close'].shift(6)) / df['Close'].shift(6)
+        
+        print("技术指标计算完成")
         return df
     
     def generate_signals(self, df):
-        """生成交易信号"""
+        """生成日内翻转信号（完整版）"""
+        print("生成交易信号...")
         df = self.calculate_indicators(df)
-        df['signal'] = 0
-        df['signal_strength'] = 0
         
-        for i in range(60, len(df)):
-            row = df.iloc[i]
-            strength = 0
+        signals = []
+        signal_count = 0
+        
+        # 从第24个周期开始（需要足够历史数据）
+        for i in range(24, len(df)):
+            current_bar = df.iloc[i]
+            current_time = df.index[i]
             
-            # RSI超卖/超买
-            if row['RSI'] < 30:
-                strength += 1
-            elif row['RSI'] > 70:
-                strength -= 1
+            # 跳过非交易时间
+            if hasattr(current_time, 'hour'):
+                hour = current_time.hour
+                if hour < 9 or hour > 15:
+                    continue
             
-            # 均线多头排列/空头排列
-            if row['close'] > row['MA5'] > row['MA20']:
-                strength += 1
-            elif row['close'] < row['MA5'] < row['MA20']:
-                strength -= 1
+            # 计算翻转信号分数
+            reversal_score = 0
+            reversal_signals = []
             
-            # 布林带
-            if row['close'] < row['BB_lower']:
-                strength += 1
-            elif row['close'] > row['BB_upper']:
-                strength -= 1
+            # 1. RSI超卖翻转
+            if current_bar['RSI'] < 30:
+                reversal_score += 2
+                reversal_signals.append("RSI超卖")
+            elif current_bar['RSI'] < 35:
+                reversal_score += 1
+                reversal_signals.append("RSI偏弱")
             
-            # MACD金叉/死叉
-            if i > 0:
-                prev_macd = df['MACD'].iloc[i-1]
-                prev_signal = df['MACD_signal'].iloc[i-1]
-                curr_macd = row['MACD']
-                curr_signal_line = row['MACD_signal']
-                
-                if prev_macd < prev_signal and curr_macd > curr_signal_line:
-                    strength += 1
-                elif prev_macd > prev_signal and curr_macd < curr_signal_line:
-                    strength -= 1
+            # 2. KDJ超卖翻转
+            if current_bar['K'] < 20 and current_bar['D'] < 20:
+                reversal_score += 2
+                reversal_signals.append("KDJ超卖")
+            elif current_bar['J'] < 0:
+                reversal_score += 2
+                reversal_signals.append("KDJ极端超卖")
+            
+            # 3. MACD金叉或改善
+            if current_bar['MACD_hist'] > 0 and df.iloc[i-1]['MACD_hist'] <= 0:
+                reversal_score += 2
+                reversal_signals.append("MACD金叉")
+            elif current_bar['MACD_hist'] > df.iloc[i-1]['MACD_hist']:
+                reversal_score += 1
+                reversal_signals.append("MACD改善")
+            
+            # 4. 价格触及布林带下轨
+            if current_bar['Close'] <= current_bar['BB_lower'] * 1.005:
+                reversal_score += 2
+                reversal_signals.append("触及下轨")
+            elif current_bar['Close'] <= current_bar['BB_lower'] * 1.01:
+                reversal_score += 1
+                reversal_signals.append("接近下轨")
+            
+            # 5. 连续下跌后的反转
+            recent_returns = df.iloc[i-6:i]['Returns']
+            if recent_returns.min() < -0.015:
+                consecutive_decline = sum(recent_returns < 0)
+                if consecutive_decline >= 4:
+                    reversal_score += 2
+                    reversal_signals.append("连续下跌反转")
             
-            df.iloc[i, df.columns.get_loc('signal_strength')] = strength
+            # 6. 价格动量超卖
+            if current_bar['Price_Momentum'] < -0.02:
+                reversal_score += 1
+                reversal_signals.append("动量超卖")
             
-            # 生成交易信号
-            if strength >= self.config['min_signal_strength']:
-                df.iloc[i, df.columns.get_loc('signal')] = 1  # 做多
-            elif strength <= -self.config['min_signal_strength']:
-                df.iloc[i, df.columns.get_loc('signal')] = -1  # 做空
+            # 7. 成交量配合
+            if current_bar['Volume_Ratio'] > 1.2:
+                reversal_score += 1
+                reversal_signals.append("放量配合")
+            
+            # 8. 日内低位
+            try:
+                daily_data = df[df.index.date == current_time.date()]
+                if len(daily_data) > 0:
+                    daily_high = daily_data['High'].max()
+                    daily_low = daily_data['Low'].min()
+                    daily_range = daily_high - daily_low
+                    if daily_range > 0:
+                        position_in_day = (current_bar['Close'] - daily_low) / daily_range
+                        if position_in_day < 0.3:
+                            reversal_score += 1
+                            reversal_signals.append("日内低位")
+            except:
+                pass
+            
+            # 记录信号
+            df.loc[df.index[i], 'Reversal_Score'] = reversal_score
+            df.loc[df.index[i], 'Reversal_Signals'] = ', '.join(reversal_signals)
+            
+            # 生成买入信号（阈值4分）
+            if reversal_score >= self.config['min_reversal_score']:
+                df.loc[df.index[i], 'Signal'] = 1
+                df.loc[df.index[i], 'Signal_Type'] = '做多翻转'
+                signal_count += 1
+            else:
+                df.loc[df.index[i], 'Signal'] = 0
+                df.loc[df.index[i], 'Signal_Type'] = ''
         
+        print(f"信号生成完成: 共{signal_count}个翻转信号")
         return df
     
     def backtest(self, df):
-        """回测"""
+        """回测执行"""
         df = self.generate_signals(df)
         
         trades = []
@@ -308,87 +362,83 @@ class CatFlyStrategy:
         position = 0
         entry_price = 0
         entry_time = None
+        entry_signals = ""
         holding_bars = 0
         
-        for i in range(60, len(df)):
+        for i in range(24, len(df)):
             current_bar = df.iloc[i]
-            price = current_bar['close']
-            current_time = current_bar.name
+            price = current_bar['Close']
+            current_time = df.index[i]
+            
+            # 检查是否在交易时间
+            if hasattr(current_time, 'hour'):
+                hour = current_time.hour
+                minute = current_time.minute
+                if hour == 11 and minute >= 30:  # 午休前不新开仓
+                    pass
+                if hour == 15:  # 收盘前不新开仓
+                    pass
             
-            # 无持仓时检查开仓信号
+            # 无持仓时检查开仓
             if position == 0:
-                if current_bar['signal'] == 1:  # 做多
-                    position_size = int(capital * self.config['position_size_pct'] / price)
+                if current_bar.get('Signal', 0) == 1:
+                    position_value = capital * self.config['position_size_pct']
+                    position_size = int(position_value / price)
+                    
                     if position_size > 0:
                         position = position_size
                         entry_price = price
                         entry_time = current_time
-                        holding_bars = 0
-                        
-                elif current_bar['signal'] == -1:  # 做空
-                    position_size = int(capital * self.config['position_size_pct'] / price)
-                    if position_size > 0:
-                        position = -position_size
-                        entry_price = price
-                        entry_time = current_time
+                        entry_signals = current_bar.get('Reversal_Signals', '')
                         holding_bars = 0
             
             # 有持仓时检查平仓
-            else:
+            elif position > 0:
                 holding_bars += 1
                 exit_signal = False
                 exit_reason = ""
                 
-                if position > 0:  # 做多持仓
-                    if price <= entry_price * (1 - self.config['stop_loss_pct']):
-                        exit_signal = True
-                        exit_reason = "止损"
-                    elif price >= entry_price * (1 + self.config['take_profit_pct']):
-                        exit_signal = True
-                        exit_reason = "止盈"
-                    elif holding_bars >= self.config['max_hold_bars']:
-                        exit_signal = True
-                        exit_reason = "时间止损"
-                    elif current_bar['RSI'] > 70:
-                        exit_signal = True
-                        exit_reason = "信号消失(RSI超买)"
+                # 止损
+                stop_loss_price = entry_price * (1 - self.config['stop_loss_pct'])
+                take_profit_price = entry_price * (1 + self.config['take_profit_pct'])
                 
-                else:  # 做空持仓
-                    if price >= entry_price * (1 + self.config['stop_loss_pct']):
-                        exit_signal = True
-                        exit_reason = "止损"
-                    elif price <= entry_price * (1 - self.config['take_profit_pct']):
-                        exit_signal = True
-                        exit_reason = "止盈"
-                    elif holding_bars >= self.config['max_hold_bars']:
-                        exit_signal = True
-                        exit_reason = "时间止损"
-                    elif current_bar['RSI'] < 30:
-                        exit_signal = True
-                        exit_reason = "信号消失(RSI超卖)"
+                if price <= stop_loss_price:
+                    exit_signal = True
+                    exit_reason = f"止损({self.config['stop_loss_pct']*100:.1f}%)"
+                
+                # 止盈
+                elif price >= take_profit_price:
+                    exit_signal = True
+                    exit_reason = f"止盈({self.config['take_profit_pct']*100:.1f}%)"
+                
+                # 时间止损
+                elif holding_bars >= self.config['max_hold_bars']:
+                    exit_signal = True
+                    exit_reason = f"时间止损({holding_bars}周期)"
+                
+                # RSI超买平仓
+                elif current_bar['RSI'] > 70:
+                    exit_signal = True
+                    exit_reason = "RSI超买平仓"
                 
                 # 执行平仓
                 if exit_signal:
-                    if position > 0:
-                        pnl = (price - entry_price) * position
-                        pnl_pct = (price - entry_price) / entry_price * 100
-                    else:
-                        pnl = (entry_price - price) * abs(position)
-                        pnl_pct = (entry_price - price) / entry_price * 100
-                    
+                    pnl = (price - entry_price) * position
+                    pnl_pct = (price - entry_price) / entry_price * 100
                     capital += pnl
                     
                     trades.append({
-                        '方向': '做多' if position > 0 else '做空',
+                        '方向': '做多',
                         '开仓时间': entry_time,
                         '平仓时间': current_time,
                         '开仓价': entry_price,
                         '平仓价': price,
-                        '持仓数量': abs(position),
+                        '持仓数量': position,
                         '盈亏金额': pnl,
                         '盈亏百分比': pnl_pct,
                         '退出原因': exit_reason,
                         '持仓周期': holding_bars,
+                        '信号详情': entry_signals,
                         '平仓后资金': capital
                     })
                     
@@ -404,155 +454,99 @@ class CatFlyStrategy:
 def generate_report(trades_df, final_capital, initial_capital=1000000):
     """生成详细报告"""
     
-    if len(trades_df) == 0:
-        html = "<html><body><h1>创业板50交易报告</h1><p>近2个月无交易信号</p></body></html>"
-        text = "近2个月无交易信号"
-        return html, text
-    
     total_return = (final_capital - initial_capital) / initial_capital * 100
     total_trades = len(trades_df)
+    
+    if total_trades == 0:
+        html = f"""
+        <html><body>
+        <h1>🚀 创业板50交易报告</h1>
+        <p>生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
+        <p>数据区间: 近2个月</p>
+        <p><b>近2个月无交易信号触发</b></p>
+        <p>初始资金: {initial_capital:,.0f}元</p>
+        <p>最终资金: {final_capital:,.0f}元</p>
+        <p>收益率: {total_return:+.2f}%</p>
+        <p style="color: #666;">说明：策略在指定期间内未找到符合条件的翻转信号（需RSI<30、KDJ超卖、MACD金叉等多重条件同时满足）</p>
+        </body></html>
+        """
+        text = f"""
+创业板50交易报告
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+数据区间: 近2个月
+
+近2个月无交易信号触发
+初始资金: {initial_capital:,.0f}元
+最终资金: {final_capital:,.0f}元
+收益率: {total_return:+.2f}%
+
+说明：策略在指定期间内未找到符合条件的翻转信号
+"""
+        return html, text
+    
+    # 有交易数据时的报告...
     winning_trades = trades_df[trades_df['盈亏金额'] > 0]
     losing_trades = trades_df[trades_df['盈亏金额'] < 0]
     
-    win_rate = len(winning_trades) / total_trades * 100 if total_trades > 0 else 0
-    avg_profit = winning_trades['盈亏金额'].mean() if len(winning_trades) > 0 else 0
-    avg_loss = losing_trades['盈亏金额'].mean() if len(losing_trades) > 0 else 0
-    
+    win_rate = len(winning_trades) / total_trades * 100
     total_profit = winning_trades['盈亏金额'].sum() if len(winning_trades) > 0 else 0
     total_loss = abs(losing_trades['盈亏金额'].sum()) if len(losing_trades) > 0 else 0
     profit_factor = total_profit / total_loss if total_loss > 0 else 0
     
-    max_profit = trades_df['盈亏金额'].max()
-    max_loss = trades_df['盈亏金额'].min()
-    avg_hold_time = trades_df['持仓周期'].mean()
-    
-    long_trades = trades_df[trades_df['方向'] == '做多']
-    short_trades = trades_df[trades_df['方向'] == '做空']
-    exit_reasons = trades_df['退出原因'].value_counts()
-    
-    # 生成HTML报告
+    # HTML报告
     html = f"""
-    <html>
-    <head>
-        <style>
-            body {{ font-family: Arial, sans-serif; margin: 20px; }}
-            h1 {{ color: #333; border-bottom: 2px solid #007bff; padding-bottom: 10px; }}
-            h2 {{ color: #555; margin-top: 30px; }}
-            table {{ border-collapse: collapse; width: 100%; margin: 15px 0; font-size: 14px; }}
-            th, td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
-            th {{ background-color: #007bff; color: white; }}
-            tr:nth-child(even) {{ background-color: #f2f2f2; }}
-            .positive {{ color: green; font-weight: bold; }}
-            .negative {{ color: red; font-weight: bold; }}
-            .summary {{ background-color: #f8f9fa; padding: 15px; border-radius: 5px; margin: 15px 0; }}
-        </style>
-    </head>
-    <body>
-        <h1>🚀 创业板50指数交易报告</h1>
-        <p>生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
-        <p>数据区间: 近2个月</p>
-        
-        <div class="summary">
-            <h2>📊 总体绩效</h2>
-            <table>
-                <tr><th>指标</th><th>数值</th></tr>
-                <tr><td>初始资金</td><td>{initial_capital:,.0f}元</td></tr>
-                <tr><td>最终资金</td><td>{final_capital:,.0f}元</td></tr>
-                <tr><td>总收益率</td><td class="{'positive' if total_return >= 0 else 'negative'}">{total_return:+.2f}%</td></tr>
-                <tr><td>总交易次数</td><td>{total_trades}笔</td></tr>
-                <tr><td>胜率</td><td>{win_rate:.1f}%</td></tr>
-                <tr><td>盈亏比</td><td>{profit_factor:.2f}</td></tr>
-                <tr><td>平均持仓时间</td><td>{avg_hold_time:.1f}周期 ({avg_hold_time*0.5:.1f}小时)</td></tr>
-            </table>
-        </div>
-        
-        <h2>📈 盈亏统计</h2>
-        <table>
-            <tr><th>指标</th><th>数值</th></tr>
-            <tr><td>总盈利</td><td class="positive">+{total_profit:,.0f}元</td></tr>
-            <tr><td>总亏损</td><td class="negative">-{total_loss:,.0f}元</td></tr>
-            <tr><td>平均盈利</td><td class="positive">+{avg_profit:,.0f}元</td></tr>
-            <tr><td>平均亏损</td><td class="negative">{avg_loss:,.0f}元</td></tr>
-            <tr><td>最大单笔盈利</td><td class="positive">+{max_profit:,.0f}元</td></tr>
-            <tr><td>最大单笔亏损</td><td class="negative">{max_loss:,.0f}元</td></tr>
-        </table>
-        
-        <h2>🔄 多空统计</h2>
-        <table>
-            <tr><th>方向</th><th>交易次数</th><th>胜率</th><th>总盈亏</th></tr>
-            <tr>
-                <td>做多</td>
-                <td>{len(long_trades)}笔</td>
-                <td>{(len(long_trades[long_trades['盈亏金额']>0])/len(long_trades)*100 if len(long_trades)>0 else 0):.1f}%</td>
-                <td class="{'positive' if long_trades['盈亏金额'].sum() >= 0 else 'negative'}">{long_trades['盈亏金额'].sum():+,.0f}元</td>
-            </tr>
-            <tr>
-                <td>做空</td>
-                <td>{len(short_trades)}笔</td>
-                <td>{(len(short_trades[short_trades['盈亏金额']>0])/len(short_trades)*100 if len(short_trades)>0 else 0):.1f}%</td>
-                <td class="{'positive' if short_trades['盈亏金额'].sum() >= 0 else 'negative'}">{short_trades['盈亏金额'].sum():+,.0f}元</td>
-            </tr>
-        </table>
-        
-        <h2>🚪 退出原因分析</h2>
-        <table>
-            <tr><th>退出原因</th><th>次数</th><th>占比</th></tr>
-    """
+    <html><head><style>
+    body {{ font-family: Arial, sans-serif; margin: 20px; }}
+    h1 {{ color: #333; border-bottom: 2px solid #007bff; padding-bottom: 10px; }}
+    h2 {{ color: #555; margin-top: 30px; }}
+    table {{ border-collapse: collapse; width: 100%; margin: 15px 0; font-size: 14px; }}
+    th, td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
+    th {{ background-color: #007bff; color: white; }}
+    tr:nth-child(even) {{ background-color: #f2f2f2; }}
+    .positive {{ color: green; font-weight: bold; }}
+    .negative {{ color: red; font-weight: bold; }}
+    </style></head><body>
+    <h1>🚀 创业板50交易报告</h1>
+    <p>生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
+    <p>数据区间: 近2个月</p>
     
-    for reason, count in exit_reasons.items():
-        pct = count / total_trades * 100
-        html += f"<tr><td>{reason}</td><td>{count}</td><td>{pct:.1f}%</td></tr>"
+    <h2>📊 总体绩效</h2>
+    <table>
+        <tr><th>指标</th><th>数值</th></tr>
+        <tr><td>初始资金</td><td>{initial_capital:,.0f}元</td></tr>
+        <tr><td>最终资金</td><td>{final_capital:,.0f}元</td></tr>
+        <tr><td>总收益率</td><td class="{'positive' if total_return >= 0 else 'negative'}">{total_return:+.2f}%</td></tr>
+        <tr><td>总交易次数</td><td>{total_trades}笔</td></tr>
+        <tr><td>胜率</td><td>{win_rate:.1f}%</td></tr>
+        <tr><td>盈亏比</td><td>{profit_factor:.2f}</td></tr>
+    </table>
     
-    html += """
-        </table>
-        
-        <h2>📝 最近10笔交易明细</h2>
-        <table>
-            <tr>
-                <th>方向</th>
-                <th>开仓时间</th>
-                <th>平仓时间</th>
-                <th>开仓价</th>
-                <th>平仓价</th>
-                <th>盈亏金额</th>
-                <th>盈亏%</th>
-                <th>退出原因</th>
-            </tr>
+    <h2>📝 最近10笔交易明细</h2>
+    <table>
+        <tr>
+            <th>开仓时间</th><th>平仓时间</th><th>开仓价</th><th>平仓价</th>
+            <th>盈亏</th><th>盈亏%</th><th>退出原因</th><th>信号</th>
+        </tr>
     """
     
-    recent_trades = trades_df.tail(10)
-    for _, trade in recent_trades.iterrows():
+    for _, trade in trades_df.tail(10).iterrows():
         pnl_class = "positive" if trade['盈亏金额'] >= 0 else "negative"
         html += f"""
-            <tr>
-                <td>{trade['方向']}</td>
-                <td>{trade['开仓时间']}</td>
-                <td>{trade['平仓时间']}</td>
-                <td>{trade['开仓价']:.2f}</td>
-                <td>{trade['平仓价']:.2f}</td>
-                <td class="{pnl_class}">{trade['盈亏金额']:+.0f}</td>
-                <td class="{pnl_class}">{trade['盈亏百分比']:+.2f}%</td>
-                <td>{trade['退出原因']}</td>
-            </tr>
+        <tr>
+            <td>{trade['开仓时间']}</td><td>{trade['平仓时间']}</td>
+            <td>{trade['开仓价']:.2f}</td><td>{trade['平仓价']:.2f}</td>
+            <td class="{pnl_class}">{trade['盈亏金额']:+.0f}</td>
+            <td class="{pnl_class}">{trade['盈亏百分比']:+.2f}%</td>
+            <td>{trade['退出原因']}</td><td>{trade['信号详情'][:20]}...</td>
+        </tr>
         """
     
-    html += """
-        </table>
-        
-        <hr>
-        <p style="color: #666; font-size: 12px;">
-            本报告由 cat-fly 自动交易系统生成 | 策略：30分钟K线多空双向<br>
-            风险提示：历史回测不代表未来表现，投资有风险，入市需谨慎。
-        </p>
-    </body>
-    </html>
-    """
+    html += "</table></body></html>"
     
-    # 生成纯文本版本
+    # 纯文本报告
     text = f"""
-创业板50指数交易报告
+创业板50交易报告
 生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-数据区间: 近2个月
 
 【总体绩效】
 初始资金: {initial_capital:,.0f}元
@@ -561,24 +555,10 @@ def generate_report(trades_df, final_capital, initial_capital=1000000):
 总交易次数: {total_trades}笔
 胜率: {win_rate:.1f}%
 盈亏比: {profit_factor:.2f}
-平均持仓: {avg_hold_time*0.5:.1f}小时
-
-【盈亏统计】
-总盈利: +{total_profit:,.0f}元
-总亏损: -{total_loss:,.0f}元
-最大单笔盈利: +{max_profit:,.0f}元
-最大单笔亏损: {max_loss:,.0f}元
-
-【多空统计】
-做多: {len(long_trades)}笔, 盈亏{long_trades['盈亏金额'].sum():+,.0f}元
-做空: {len(short_trades)}笔, 盈亏{short_trades['盈亏金额'].sum():+,.0f}元
-
-【退出原因】
-{exit_reasons.to_string()}
 
 【最近5笔交易】
-{trades_df.tail(5)[['方向', '开仓时间', '平仓时间', '盈亏金额', '退出原因']].to_string(index=False)}
-    """
+{trades_df.tail(5)[['开仓时间', '平仓时间', '盈亏金额', '退出原因']].to_string(index=False)}
+"""
     
     return html, text
 
@@ -587,40 +567,20 @@ def generate_report(trades_df, final_capital, initial_capital=1000000):
 def main():
     """主程序"""
     print("="*80)
-    print("🚀 cat-fly 自动交易报告系统")
+    print("🚀 cat-fly 自动交易报告系统 (完整版)")
     print("="*80)
     print(f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
     
-    # 检查是否在交易时间（可选）
-    now = datetime.now()
-    hour = now.hour
-    minute = now.minute
-    time_str = f"{hour:02d}:{minute:02d}"
-    
-    # A股交易时间检查
-    is_trading_time = False
-    if (9 <= hour <= 11) or (13 <= hour <= 15):
-        if hour == 9 and minute < 30:
-            is_trading_time = False
-        elif hour == 11 and minute > 30:
-            is_trading_time = False
-        elif hour == 15 and minute > 0:
-            is_trading_time = False
-        else:
-            is_trading_time = True
-    
-    print(f"当前时间: {time_str}")
-    print(f"交易时间: {'是' if is_trading_time else '否（非交易时间也会执行）'}")
-    
-    # 1. 获取近2个月数据
-    print("\n📊 步骤1: 获取近2个月数据...")
-    df = DataFetcher.fetch_recent_2months()
-    if df is None:
-        print("❌ 数据获取失败，退出")
+    # 1. 获取数据
+    print("\n📊 步骤1: 获取近2个月实时数据...")
+    try:
+        df = DataFetcher.fetch_recent_2months()
+    except Exception as e:
+        print(f"❌ 数据获取失败: {e}")
         return
     
     # 2. 运行策略
-    print("\n📈 步骤2: 运行策略回测...")
+    print("\n📈 步骤2: 运行完整策略回测...")
     strategy = CatFlyStrategy()
     df, trades_df, final_capital = strategy.backtest(df)
     
@@ -634,7 +594,7 @@ def main():
     
     # 4. 发送邮件
     print("\n📧 步骤4: 发送邮件...")
-    subject = f"🚀 创业板50交易报告 {datetime.now().strftime('%m-%d %H:%M')} | 收益{(final_capital/1000000-1)*100:+.2f}%"
+    subject = f"🚀 创业板50交易报告 {datetime.now().strftime('%m-%d %H:%M')} | 收益{(final_capital/1000000-1)*100:+.2f}% | {len(trades_df)}笔交易"
     send_email(subject, html_report, text_report)
     
     print("\n✅ 全部完成!")