cyb50-quant/cat-fly/cyb50_30min_intraday_reversal.py

import pandas as pd
import numpy as np
import akshare as ak
import warnings
import json
import os
from datetime import datetime, timedelta
warnings.filterwarnings('ignore')

# ==================== 配置管理模块 ====================
class ConfigManager:
    """配置文件管理类"""
    
    def __init__(self, config_file='config.json'):
        self.config_file = config_file
        self.config = self.load_config()
    
    def load_config(self):
        """加载配置文件"""
        try:
            if os.path.exists(self.config_file):
                with open(self.config_file, 'r', encoding='utf-8') as f:
                    config = json.load(f)
                print(f"配置文件加载成功: {self.config_file}")
                return config
            else:
                print(f"配置文件不存在，使用默认配置: {self.config_file}")
                return self.get_default_config()
        except Exception as e:
            print(f"配置文件加载失败: {e}，使用默认配置")
            return self.get_default_config()
    
    def get_default_config(self):
        """获取默认配置"""
        return {
            "data_source": {
                "use_local_file": False,
                "local_file_path": "D:\\work\\project\\catfly\\data\\SZ#399673_30min.csv"
            },
            "strategy": {
                "initial_capital": 1000000,
                "backtest_start_date": "2025-10-01",
                "prewamp_days": 30,
                "position_size_pct": 1.0,
                "stop_loss_pct": 0.008,
                "take_profit_pct": 0.015,
                "max_hold_bars": 16
            }
        }
    
    def get(self, section, key, default=None):
        """获取配置项"""
        try:
            return self.config.get(section, {}).get(key, default)
        except:
            return default
    
    def save_config(self):
        """保存配置到文件"""
        try:
            with open(self.config_file, 'w', encoding='utf-8') as f:
                json.dump(self.config, f, indent=2, ensure_ascii=False)
            print(f"配置文件保存成功: {self.config_file}")
        except Exception as e:
            print(f"配置文件保存失败: {e}")

# ==================== 数据获取模块 ====================
class IntradayDataFetcher:
    """30分钟K线数据获取类"""
    
    def __init__(self, config_manager=None):
        self.symbol = "399673"  # 创业板50指数
        self.config_manager = config_manager
        
    def fetch_30min_data(self, start_date=None, end_date=None) -> pd.DataFrame:
        """获取指定时间范围的30分钟K线数据"""
        try:
            if start_date is None:
                start_date = datetime.now() - timedelta(days=60)
            if end_date is None:
                end_date = datetime.now()
                
            print(f"正在获取创业板50指数的30分钟K线数据...")
            print(f"时间范围: {start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}")
            
            # 检查数据源开关
            use_local_file = False
            local_file_path = ""
            
            if self.config_manager:
                use_local_file = self.config_manager.get('data_source', 'use_local_file', False)
                local_file_path = self.config_manager.get('data_source', 'local_file_path', '')
            
            # 如果开关打开，从本地文件读取
            if use_local_file:
                print(f"数据源开关: 本地文件模式")
                print(f"本地文件路径: {local_file_path}")
                return self._load_local_file(local_file_path, start_date, end_date)
            else:
                print(f"数据源开关: 在线获取模式")
                return self._fetch_online_data(start_date, end_date)
                
        except Exception as e:
            print(f"获取数据时出错: {str(e)}")
            raise
    
    def _load_local_file(self, file_path, start_date, end_date) -> pd.DataFrame:
        """从本地文件加载数据"""
        try:
            if not os.path.exists(file_path):
                raise FileNotFoundError(f"本地文件不存在: {file_path}")
            
            print(f"正在从本地文件读取数据...")
            print(f"文件路径: {file_path}")
            
            # 检查文件扩展名，选择不同的读取方式
            if file_path.endswith('.txt'):
                # 处理文本格式文件
                print("检测到文本格式文件，使用文本解析模式...")
                return self._parse_text_file(file_path, start_date, end_date)
            else:
                # 处理CSV格式文件
                print("检测到CSV格式文件，使用CSV解析模式...")
                data = pd.read_csv(file_path)
                return self._process_dataframe(data, start_date, end_date)
                
        except Exception as e:
            print(f"从本地文件加载数据失败: {e}")
            raise
    
    def _parse_text_file(self, file_path, start_date, end_date) -> pd.DataFrame:
        """解析文本格式的数据文件"""
        try:
            data_list = []
            
            # 尝试多种编码格式
            encodings = ['gbk', 'gb2312', 'utf-8', 'latin-1']
            lines = None
            
            for encoding in encodings:
                try:
                    with open(file_path, 'r', encoding=encoding) as f:
                        lines = f.readlines()
                    print(f"成功使用编码: {encoding}")
                    break
                except UnicodeDecodeError:
                    continue
            
            if lines is None:
                raise ValueError("无法读取文件，尝试了多种编码格式都失败")
                
            # 跳过前两行（标题行）
            for line in lines[2:]:
                line = line.strip()
                if not line:
                    continue
                    
                parts = line.split()
                if len(parts) >= 7:
                    try:
                        # 格式: 日期 时间 开盘 最高 最低 收盘 成交量 成交额
                        date_time_str = f"{parts[0]} {parts[1]}"
                        datetime_obj = pd.to_datetime(date_time_str, format='%Y/%m/%d %H%M')
                        
                        data_list.append({
                            'DateTime': datetime_obj,
                            'Open': float(parts[2]),
                            'High': float(parts[3]),
                            'Low': float(parts[4]),
                            'Close': float(parts[5]),
                            'Volume': float(parts[6]),
                            'Amount': float(parts[7]) if len(parts) > 7 else 0
                        })
                    except (ValueError, IndexError) as e:
                        print(f"跳过异常行: {line[:50]}... 错误: {e}")
                        continue
            
            if not data_list:
                raise ValueError("文本文件中没有解析到有效数据")
            
            print(f"成功解析 {len(data_list)} 条数据")
            data = pd.DataFrame(data_list)
            return self._process_dataframe(data, start_date, end_date)
            
        except Exception as e:
            print(f"解析文本文件失败: {e}")
            raise
    
    def _process_dataframe(self, data, start_date, end_date) -> pd.DataFrame:
        """处理和标准化数据框"""
        try:
            # 检查并转换列名
            print(f"原始数据列名: {data.columns.tolist()}")
            print(f"原始数据行数: {len(data)}")
            
            # 标准化列名
            column_mapping = {
                '时间': 'DateTime', '日期': 'DateTime', 'datetime': 'DateTime', 'time': 'DateTime',
                '开盘': 'Open', 'open': 'Open', 'Open': 'Open', '开盘价': 'Open',
                '收盘': 'Close', 'close': 'Close', 'Close': 'Close', '收盘价': 'Close',
                '最高': 'High', 'high': 'High', 'High': 'High', '最高价': 'High',
                '最低': 'Low', 'low': 'Low', 'Low': 'Low', '最低价': 'Low',
                '成交量': 'Volume', 'volume': 'Volume', 'Volume': 'Volume', 'vol': 'Volume'
            }
            
            # 重命名列
            data.rename(columns=column_mapping, inplace=True)
            
            # 设置时间索引
            if 'DateTime' in data.columns:
                data['DateTime'] = pd.to_datetime(data['DateTime'])
                data.set_index('DateTime', inplace=True)
            else:
                raise ValueError("数据中找不到时间列(DateTime/时间/日期)")
            
            data.sort_index(inplace=True)
            
            # 筛选时间范围
            filtered_data = data[(data.index >= start_date) & (data.index <= end_date)].copy()
            
            if filtered_data.empty:
                print(f"警告：指定时间范围没有数据")
                print(f"可用数据范围: {data.index[0]} 到 {data.index[-1]}")
                print(f"请求的时间范围: {start_date} 到 {end_date}")
                # 返回空数据框而不是抛出异常
                return filtered_data
            
            # 确保必需的列存在
            required_columns = ['Open', 'High', 'Low', 'Close', 'Volume']
            missing_columns = [col for col in required_columns if col not in filtered_data.columns]
            if missing_columns:
                raise ValueError(f"数据缺少必需的列: {missing_columns}")
            
            # 添加缺失的列
            if 'Amount' not in filtered_data.columns:
                filtered_data['Amount'] = 0
            
            # 计算基础指标（本地文件可能缺少这些）
            if 'Returns' not in filtered_data.columns:
                filtered_data['Returns'] = filtered_data['Close'].pct_change()
            if 'High_Low_Pct' not in filtered_data.columns:
                filtered_data['High_Low_Pct'] = (filtered_data['High'] - filtered_data['Low']) / filtered_data['Close'].shift(1)
            if 'Close_Open_Pct' not in filtered_data.columns:
                filtered_data['Close_Open_Pct'] = (filtered_data['Close'] - filtered_data['Open']) / filtered_data['Open']
            
            # 处理缺失值
            filtered_data.fillna(method='ffill', inplace=True)
            filtered_data.dropna(inplace=True)
            
            print(f"本地文件数据处理成功: {len(filtered_data)}条")
            print(f"数据范围: {filtered_data.index[0]} 到 {filtered_data.index[-1]}")
            
            return filtered_data
            
        except Exception as e:
            print(f"处理数据框失败: {e}")
            raise
    
    def _fetch_online_data(self, start_date, end_date) -> pd.DataFrame:
        """在线获取30分钟K线数据 - 东方财富优先，新浪财经备用"""
        data = None
        
        # ===== 方法1: 东方财富数据源（主要数据源） =====
        try:
            print("[DATA_SOURCE_1] 正在使用东方财富30分钟K线接口...")
            data = ak.index_zh_a_hist_min_em(symbol=self.symbol, period="30")
            
            if not data.empty and len(data) >= 50:
                print(f"[SUCCESS] 东方财富获取到{len(data)}条30分钟数据")
                print(f"[TIME_RANGE] 数据范围: {data.index[0]} 到 {data.index[-1]}")
                
                # 检查数据时效性
                latest_time = data.index[-1]
                current_time = datetime.now()
                if hasattr(latest_time, 'hour') and hasattr(latest_time, 'minute'):
                    time_delay = current_time - latest_time
                    print(f"[DATA_DELAY] 数据延迟: {time_delay}")
                else:
                    print(f"[INFO] 数据索引类型: {type(latest_time)}")
                    
            else:
                print(f"[FAIL] 东方财富数据不足或为空")
                
        except Exception as e:
            print(f"[ERROR] 东方财富数据源失败: {e}")
        
        # ===== 方法2: 新浪财经数据源（备用数据源） =====
        if data is None or data.empty or len(data) < 50:
            try:
                print("[DATA_SOURCE_2] 正在使用新浪财经30分钟K线接口...")
                import requests
                import json
                import re
                
                symbol = "sz399673"
                url = f"https://quotes.sina.cn/cn/api/jsonp_v2.php/var_{symbol}_30_1768824839904=/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',
                    'Referer': 'https://finance.sina.com.cn/'
                }
                
                response = requests.get(url, headers=headers, timeout=15)
                response_text = response.text
                
                # 解析JSONP响应
                array_pattern = r'=([\[ ].+?\])'
                match = re.search(array_pattern, response_text)
                
                if match:
                    json_str = match.group(1)
                else:
                    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")
                
                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)),
                                'Amount': 0
                            })
                        except Exception:
                            continue
                    
                    if data_list:
                        data = pd.DataFrame(data_list)
                        print(f"[SUCCESS] 新浪财经获取到{len(data)}条30分钟数据")
                        print(f"[TIME_RANGE] 数据范围: {data.index[0]} 到 {data.index[-1]}")
                    else:
                        print("[FAIL] 新浪财经数据解析失败")
                else:
                    print("[FAIL] 新浪财经返回数据格式错误")
                    
            except Exception as e:
                print(f"[ERROR] 新浪财经数据源失败: {e}")
        
        # ===== 数据验证和格式化 =====
        if data is None or data.empty:
            raise ValueError("[FATAL_ERROR] 所有数据源均无法获取数据")
        
        # 重命名列（针对东方财富数据格式）
        data.rename(columns={
            '时间': 'DateTime', '开盘': 'Open', '收盘': 'Close',
            '最高': 'High', '最低': 'Low', '成交量': 'Volume',
            '成交额': 'Amount', '振幅': 'Amplitude', '涨跌幅': 'Change_Pct',
            '涨跌额': 'Change_Amount', '换手率': 'Turnover'
        }, inplace=True)
        
        # 设置时间索引
        if 'DateTime' in data.columns:
            data['DateTime'] = pd.to_datetime(data['DateTime'])
            data.set_index('DateTime', inplace=True)
        else:
            raise ValueError("[ERROR] 数据中找不到时间列(DateTime)")
        
        data.sort_index(inplace=True)
        
        # 筛选时间范围
        filtered_data = data[(data.index >= start_date) & (data.index <= end_date)].copy()
        
        if filtered_data.empty:
            print(f"[WARNING] 筛选后数据为空，可用范围: {data.index[0]} 到 {data.index[-1]}")
            raise ValueError(f"[ERROR] 指定时间范围没有数据")
        
        # 计算基础指标
        filtered_data['Returns'] = filtered_data['Close'].pct_change()
        filtered_data['High_Low_Pct'] = (filtered_data['High'] - filtered_data['Low']) / filtered_data['Close'].shift(1)
        filtered_data['Close_Open_Pct'] = (filtered_data['Close'] - filtered_data['Open']) / filtered_data['Open']
        
        # 处理缺失值
        filtered_data.fillna(method='ffill', inplace=True)
        filtered_data.dropna(inplace=True)
        
        print(f"[FINAL_DATA] 成功处理{len(filtered_data)}条数据")
        print(f"[FINAL_RANGE] 最终数据范围: {filtered_data.index[0]} 到 {filtered_data.index[-1]}")
        
        return filtered_data
    def calculate_intraday_indicators(self, data: pd.DataFrame) -> pd.DataFrame:
        """计算30分钟技术指标"""
        print("正在计算30分钟技术指标...")
        df = data.copy()
        
        # 短期移动平均线
        df['MA6'] = df['Close'].rolling(window=6).mean()   # 3小时
        df['MA12'] = df['Close'].rolling(window=12).mean() # 6小时
        df['MA24'] = df['Close'].rolling(window=24).mean() # 12小时(一天)
        
        # RSI
        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))
        
        # 布林带
        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
        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  # 2小时动量
        
        # 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

# ==================== 翻转信号生成器 ====================
class ReversalSignalGenerator:
    """日内翻转信号生成器"""
    
    def __init__(self):
        self.signal_count = 0
        
    def generate_reversal_signals(self, data: pd.DataFrame) -> pd.DataFrame:
        """生成日内翻转信号"""
        print("正在生成日内翻转信号...")
        
        signals = []
        df = data.copy()
        
        for i in range(24, len(df)):  # 至少需要12小时(24个30分钟)的历史数据
            current_bar = df.iloc[i]
            current_time = df.index[i]
            
            # 跳过不适合交易的时间段(如午休时间等)
            # 如果是日线数据，不进行时间过滤
            if hasattr(current_time, 'hour'):  # 有小时信息的30分钟数据
                hour = current_time.hour
                if hour < 9 or hour > 15:  # 只在交易时间内
                    continue
                
            # 生成信号
            signal = {
                'DateTime': str(current_time),  # 转换为字符串确保兼容性
                'Open': current_bar['Open'],
                'High': current_bar['High'],
                'Low': current_bar['Low'],
                'Close': current_bar['Close'],
                'Volume': current_bar['Volume'],
                'RSI': current_bar['RSI'],
                'MACD': current_bar['MACD'],
                'MACD_hist': current_bar['MACD_hist'],
                'K': current_bar['K'],
                'D': current_bar['D'],
                'J': current_bar['J'],
                'ATR_Pct': current_bar['ATR_Pct'],
                'Volume_Ratio': current_bar['Volume_Ratio'],
                'Price_Momentum': current_bar['Price_Momentum'],
                'Close_Open_Pct': current_bar['Close_Open_Pct']
            }
            
            # 计算各种翻转信号
            reversal_score = 0
            reversal_signals = []
            
            # 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")
            
            # 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
            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
            bb_width = current_bar['BB_width']
            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:  # 最近2小时内有超过1.5%的下跌
                consecutive_decline = sum(recent_returns < 0)
                if consecutive_decline >= 4:  # 连续4个周期下跌
                    reversal_score += 2
                    reversal_signals.append("连续下跌反转")
            
            # 6. 价格动量反转
            if current_bar['Price_Momentum'] < -0.02:  # 3小时下跌超过2%
                reversal_score += 1
                reversal_signals.append("动量超卖")
            
            # 7. 成交量配合
            if current_bar['Volume_Ratio'] > 1.2:  # 放量
                reversal_score += 1
                reversal_signals.append("放量配合")
            
            # 8. 当日开盘价格关系
            daily_high = df[df.index.date == current_time.date()]['High'].max()
            daily_low = df[df.index.date == current_time.date()]['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:  # 在当日低位区域
                    reversal_score += 1
                    reversal_signals.append("日内低位")
            
            # 设置信号
            signal['Reversal_Score'] = reversal_score
            signal['Reversal_Signals'] = ', '.join(reversal_signals) if reversal_signals else ''
            
            # 生成买入信号(阈值降低以增加交易频率)
            if reversal_score >= 4:
                signal['Signal'] = 1
                signal['Signal_Type'] = '做多翻转'
                self.signal_count += 1
            else:
                signal['Signal'] = 0
                signal['Signal_Type'] = ''
            
            signals.append(signal)
        
        signals_df = pd.DataFrame(signals)
        
        # 调试信息
        print(f"生成的信号数量: {len(signals_df)}")
        if len(signals_df) > 0:
            print(f"信号DataFrame的列: {signals_df.columns.tolist()}")
            signals_df.set_index('DateTime', inplace=True)
        else:
            print("警告：没有生成任何信号")
        
        print(f"信号生成完成，共产生{self.signal_count}个翻转信号")
        if len(signals_df) > 0:
            print(f"信号密度: {self.signal_count/len(signals_df)*100:.2f}%")
        
        return signals_df

# ==================== 日内交易执行器 ====================
class IntradayReversalExecutor:
    """日内翻转交易执行器"""
    
    def __init__(self, initial_capital=1000000):
        self.initial_capital = initial_capital
        self.params = {
            'commission_rate': 0.0001,   # 万分之一
            'slippage_rate': 0.0,        # 无滑点
            'position_size_pct': 1.0,    # 每次开仓100%仓位（满仓）
            'stop_loss_pct': 0.008,      # 0.8%止损
            'take_profit_pct': 0.015,    # 1.5%止盈
            'max_hold_bars': 16,         # 最多持有8小时(16个30分钟)
            'min_signal_strength': 4     # 最小信号强度
        }
    
    def execute_intraday_trades(self, signals_df: pd.DataFrame) -> tuple:
        """执行日内翻转交易"""
        print("正在执行日内翻转交易...")
        
        df = signals_df.copy()
        
        # 初始化
        trades = []
        capital = self.initial_capital
        position = 0
        entry_price = 0
        entry_time = None
        holding_bars = 0
        entry_signals = ''
        
        # 添加资金列
        df = df.copy()
        df['capital'] = capital
        df['position'] = 0
        df['net_value'] = capital
        
        for i in range(len(df)):
            current_time = df.index[i]
            current_bar = df.iloc[i]
            price = current_bar['Close']
            
            # 更新当前净值
            if position > 0:
                current_value = capital + position * price
                df.iloc[i, df.columns.get_loc('net_value')] = current_value
            else:
                df.iloc[i, df.columns.get_loc('net_value')] = capital
            
            # 开仓逻辑
            if position == 0 and current_bar['Signal'] == 1:
                # 开仓
                position_size = int((capital * self.params['position_size_pct']) / price)
                if position_size > 0:
                    cost = position_size * price * (1 + self.params['commission_rate'] + self.params['slippage_rate'])
                    
                    if cost <= capital:
                        position = position_size
                        entry_price = price
                        entry_time = current_time
                        entry_signals = current_bar.get('Reversal_Signals', '')
                        holding_bars = 0
                        capital -= cost
                        
                        df.iloc[i, df.columns.get_loc('position')] = position
                        
                        # 打印开仓详情
                        print(f"\n{'='*60}")
                        print(f"[OPEN] 开仓信号 #{len(trades) + 1}")
                        print(f"{'='*60}")
                        print(f"开仓时间: {entry_time}")
                        print(f"开仓价格: {entry_price:.2f} 元")
                        print(f"持仓数量: {position_size} 股")
                        print(f"开仓市值: {position_size * entry_price:,.2f} 元")
                        print(f"交易成本: {cost:,.2f} 元")
                        print(f"剩余资金: {capital:,.2f} 元")
                        print(f"入场信号: {entry_signals}")
                        print(f"总资产: {capital + position_size * entry_price:,.2f} 元")
            
            # 平仓逻辑
            elif position > 0:
                holding_bars += 1
                
                # 计算止损止盈价格
                stop_loss = entry_price * (1 - self.params['stop_loss_pct'])
                take_profit = entry_price * (1 + self.params['take_profit_pct'])
                
                exit_signal = False
                exit_reason = ''
                exit_price = price
                
                # 止损
                if price <= stop_loss:
                    exit_signal = True
                    loss_pct = (entry_price - price) / entry_price * 100
                    exit_reason = f"止损触发(价格{price:.2f}跌破止损线{stop_loss:.2f},亏损{loss_pct:.2f}%)"
                    exit_price = stop_loss
                
                # 止盈
                elif price >= take_profit:
                    exit_signal = True
                    profit_pct = (price - entry_price) / entry_price * 100
                    exit_reason = f"止盈触发(价格{price:.2f}突破止盈线{take_profit:.2f},盈利{profit_pct:.2f}%)"
                    exit_price = take_profit
                
                # 最大持仓时间
                elif holding_bars >= self.params['max_hold_bars']:
                    exit_signal = True
                    current_pnl_pct = (price - entry_price) / entry_price * 100
                    exit_reason = f"时间止损(持仓{holding_bars}周期达上限{self.params['max_hold_bars']}周期,当前盈亏{current_pnl_pct:+.2f}%)"
                
                # 翻转信号消失
                elif current_bar['RSI'] > 70:  # RSI超买
                    exit_signal = True
                    current_pnl_pct = (price - entry_price) / entry_price * 100
                    exit_reason = f"RSI超买平仓(RSI={current_bar['RSI']:.1f}超买,信号消失,当前盈亏{current_pnl_pct:+.2f}%)"
                
                # 执行平仓
                if exit_signal:
                    # 计算盈亏 - 修复：包含开仓和平仓的总成本
                    gross_pnl = (exit_price - entry_price) * position
                    
                    # 开仓成本（已经在开仓时扣除）
                    open_cost = position * entry_price * (self.params['commission_rate'] + self.params['slippage_rate'])
                    
                    # 平仓成本
                    close_revenue = position * exit_price
                    close_cost = close_revenue * (self.params['commission_rate'] + self.params['slippage_rate'])
                    
                    # 净盈亏 = 价差收益 - 开仓成本 - 平仓成本
                    pnl = gross_pnl - open_cost - close_cost
                    
                    # 更新资金
                    capital += close_revenue - close_cost
                    
                    # 记录交易
                    trade = {
                        '买入时间': entry_time,
                        '卖出时间': current_time,
                        '买入价格': entry_price,
                        '卖出价格': exit_price,
                        '仓位': position,
                        '盈亏金额': pnl,
                        '盈亏百分比': (exit_price - entry_price) / entry_price * 100,
                        '退出原因': exit_reason,
                        '持仓周期数': holding_bars,
                        '持仓小时数': holding_bars * 0.5,
                        '入场信号': entry_signals,
                        '卖出时资金': capital,
                        '开仓市值': position * entry_price
                    }
                    trades.append(trade)
                    
                    # 打印平仓详情
                    profit_ratio = (exit_price - entry_price) / entry_price * 100
                    status = "[PROFIT]" if pnl > 0 else "[LOSS]"
                    
                    print(f"\n{'='*60}")
                    print(f"{status} 平仓信号 #{len(trades)}")
                    print(f"{'='*60}")
                    print(f"平仓时间: {current_time}")
                    print(f"平仓价格: {exit_price:.2f} 元")
                    print(f"持仓时长: {holding_bars * 0.5:.1f} 小时 ({holding_bars} 个30分钟周期)")
                    print(f"退出原因: {exit_reason}")
                    print(f"{'-'*60}")
                    print(f"盈亏金额: {pnl:+,.2f} 元")
                    print(f"盈亏比例: {profit_ratio:+.2f}%")
                    print(f"盈亏比: {abs(pnl):.2f}")
                    print(f"{'-'*60}")
                    print(f"当前资金: {capital:,.2f} 元")
                    print(f"累计收益率: {(capital / self.initial_capital - 1) * 100:+.2f}%")
                    print(f"胜率统计: {sum(1 for t in trades if t['盈亏金额'] > 0)}/{len(trades)} ({sum(1 for t in trades if t['盈亏金额'] > 0)/len(trades)*100:.1f}%)")
                    print(f"{'='*60}")
                    
                    # 重置
                    position = 0
                    entry_price = 0
                    entry_time = None
                    holding_bars = 0
            
            # 更新资金
            df.iloc[i, df.columns.get_loc('capital')] = capital
            df.iloc[i, df.columns.get_loc('position')] = position
        
        # 强制平仓剩余持仓 - 修复：包含开仓和平仓的总成本
        if position > 0:
            final_price = df.iloc[-1]['Close']
            
            # 计算总盈亏
            gross_pnl = (final_price - entry_price) * position
            open_cost = position * entry_price * (self.params['commission_rate'] + self.params['slippage_rate'])
            close_revenue = position * final_price
            close_cost = close_revenue * (self.params['commission_rate'] + self.params['slippage_rate'])
            pnl = gross_pnl - open_cost - close_cost
            
            capital += close_revenue - close_cost
            
            trade = {
                '买入时间': entry_time,
                '卖出时间': df.index[-1],
                '买入价格': entry_price,
                '卖出价格': final_price,
                '仓位': position,
                '盈亏金额': pnl,
                '盈亏百分比': (final_price - entry_price) / entry_price * 100,
                '退出原因': f'强制平仓(回测结束,持仓{holding_bars}周期,最终价格{final_price:.2f},盈亏{(final_price - entry_price) / entry_price * 100:+.2f}%)',
                '持仓周期数': holding_bars,
                '持仓小时数': holding_bars * 0.5,
                '入场信号': entry_signals,
                '卖出时资金': capital,
                '开仓市值': position * entry_price
            }
            trades.append(trade)
            
            # 打印强制平仓详情
            profit_ratio = (final_price - entry_price) / entry_price * 100
            status = "[FORCE]"  # 强制平仓
            
            print(f"\n{'='*60}")
            print(f"{status} 强制平仓信号 #{len(trades)}")
            print(f"{'='*60}")
            print(f"平仓时间: {df.index[-1]}")
            print(f"平仓价格: {final_price:.2f} 元")
            print(f"持仓时长: {holding_bars * 0.5:.1f} 小时 ({holding_bars} 个30分钟周期)")
            print(f"退出原因: 强制平仓（回测结束，持仓{holding_bars}周期，最终价格{final_price:.2f}，盈亏{profit_ratio:+.2f}%）")
            print(f"{'-'*60}")
            print(f"盈亏金额: {pnl:+,.2f} 元")
            print(f"盈亏比例: {profit_ratio:+.2f}%")
            print(f"{'-'*60}")
            print(f"最终资金: {capital:,.2f} 元")
            print(f"累计收益率: {(capital / self.initial_capital - 1) * 100:+.2f}%")
            print(f"{'='*60}")
        
        trades_df = pd.DataFrame(trades)
        
        if len(trades_df) > 0:
            trades_df['买入时间'] = pd.to_datetime(trades_df['买入时间'])
            trades_df['卖出时间'] = pd.to_datetime(trades_df['卖出时间'])
            trades_df = trades_df.sort_values('买入时间')
        
        print(f"交易执行完成，共{len(trades_df)}笔交易")
        
        return df, trades_df

# ==================== 验证分析模块 ====================
def validate_intraday_results(results_df, trades_df, initial_capital):
    """验证日内交易结果"""
    print("\n" + "=" * 80)
    print("日内翻转交易结果验证")
    print("=" * 80)
    
    print(f"\n【基础数据验证】")
    final_capital = results_df['net_value'].iloc[-1]
    total_return = (final_capital - initial_capital) / initial_capital * 100
    
    print(f"初始资金: {initial_capital:,.2f}元")
    print(f"最终资金: {final_capital:,.2f}元")
    print(f"总收益率: {total_return:.2f}%")
    print(f"交易次数: {len(trades_df)}笔")
    
    if len(trades_df) > 0:
        print(f"\n【交易统计】")
        win_trades = trades_df[trades_df['盈亏金额'] > 0]
        lose_trades = trades_df[trades_df['盈亏金额'] < 0]
        
        print(f"盈利交易: {len(win_trades)}笔 ({len(win_trades)/len(trades_df)*100:.1f}%)")
        print(f"亏损交易: {len(lose_trades)}笔 ({len(lose_trades)/len(trades_df)*100:.1f}%)")
        print(f"平均持仓时间: {trades_df['持仓小时数'].mean():.1f}小时")
        print(f"平均收益率: {trades_df['盈亏百分比'].mean():.2f}%")
        
        # 按退出原因统计
        print(f"\n【退出原因统计】")
        for reason, count in trades_df['退出原因'].value_counts().items():
            percentage = count / len(trades_df) * 100
            reason_pnl = trades_df[trades_df['退出原因'] == reason]['盈亏金额'].sum()
            print(f"  {reason}: {count}次 ({percentage:.1f}%) - 总盈亏: {reason_pnl:+,.2f}元")

# ==================== 主程序 ====================
def main():
    """主程序 - 运行30分钟日内翻转策略"""
    
    print("=" * 80)
    print("创业板50 30分钟日内翻转策略")
    print("=" * 80)
    
    # 加载配置文件
    config_manager = ConfigManager('config.json')
    
    # 从配置文件读取参数
    BACKTEST_START_DATE = config_manager.get('strategy', 'backtest_start_date', "2025-10-01")
    PREWARMP_DAYS = config_manager.get('strategy', 'prewamp_days', 30)
    INITIAL_CAPITAL = config_manager.get('strategy', 'initial_capital', 1000000)
    
    # 读取截止时间配置，支持"now"或具体日期
    backtest_end_config = config_manager.get('strategy', 'backtest_end_date', "now")
    if backtest_end_config.lower() == "now":
        BACKTEST_END_DATE = datetime.now().strftime('%Y-%m-%d')
    else:
        BACKTEST_END_DATE = backtest_end_config
    
    # 转换日期格式
    start_date = datetime.strptime(BACKTEST_START_DATE, "%Y-%m-%d")
    end_date = datetime.strptime(BACKTEST_END_DATE, "%Y-%m-%d").replace(hour=23, minute=59, second=59)  # 包含指定日期全天数据
    
    # 计算数据获取开始时间（回测开始时间 - 预热期）
    data_start_date = start_date - timedelta(days=PREWARMP_DAYS)
    
    # 显示数据源配置
    use_local_file = config_manager.get('data_source', 'use_local_file', False)
    data_source_mode = "本地文件模式" if use_local_file else "在线获取模式"
    local_file_path = config_manager.get('data_source', 'local_file_path', '')
    
    print(f"\n策略参数:")
    print(f"  回测期间: {BACKTEST_START_DATE} 至 {BACKTEST_END_DATE}")
    print(f"  数据获取期间: {data_start_date.strftime('%Y-%m-%d')} 至 {BACKTEST_END_DATE}")
    print(f"  指标预热期: {PREWARMP_DAYS}天")
    print(f"  K线周期: 30分钟")
    print(f"  初始资金: {INITIAL_CAPITAL:,}元")
    print(f"  标的指数: 创业板50 (399673)")
    print(f"  数据源: {data_source_mode}")
    if use_local_file:
        print(f"  本地文件路径: {local_file_path}")
    
    try:
        # Phase 1: 数据获取
        print(f"\n【Phase 1: 30分钟数据获取】")
        fetcher = IntradayDataFetcher(config_manager)
        
        # 获取包含预热期的完整数据
        full_data = fetcher.fetch_30min_data(start_date=data_start_date, end_date=end_date)
        full_data = fetcher.calculate_intraday_indicators(full_data)
        
        # 筛选回测期间的数据
        original_len = len(full_data)
        backtest_data = full_data[(full_data.index >= start_date) & (full_data.index <= end_date)].copy()
        print(f"筛选回测数据: {original_len} -> {len(backtest_data)} 条")
        print(f"回测数据范围: {backtest_data.index[0]} 到 {backtest_data.index[-1]}")
        
        # Phase 2: 信号生成
        print(f"\n【Phase 2: 翻转信号生成】")
        signal_gen = ReversalSignalGenerator()
        signals_df = signal_gen.generate_reversal_signals(backtest_data)
        
        # Phase 3: 交易执行
        print(f"\n【Phase 3: 日内交易执行】")
        executor = IntradayReversalExecutor(initial_capital=INITIAL_CAPITAL)
        results_df, trades_df = executor.execute_intraday_trades(signals_df)
        
        # Phase 4: 验证分析
        print(f"\n【Phase 4: 结果验证与分析】")
        validate_intraday_results(results_df, trades_df, INITIAL_CAPITAL)
        
        # Phase 5: 导出数据
        if len(trades_df) > 0:
            print(f"\n【Phase 5: 导出交易数据】")
            
            # 确保时间戳格式精确到分钟
            trades_df['买入时间'] = pd.to_datetime(trades_df['买入时间']).dt.strftime('%Y-%m-%d %H:%M:%S')
            trades_df['卖出时间'] = pd.to_datetime(trades_df['卖出时间']).dt.strftime('%Y-%m-%d %H:%M:%S')
            
            # 生成带时间戳的文件名
            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
            output_file = f'cyb50_30min_intraday_reversal_trades_{timestamp}.csv'
            
            trades_df.to_csv(output_file, index=False, encoding='utf-8-sig')
            print(f"交易记录已保存到: {output_file}")
            print(f"时间戳格式: YYYY-MM-DD HH:MM:SS")
        
        # 策略总结
        print(f"\n" + "=" * 80)
        print("策略运行总结")
        print("=" * 80)
        
        if len(trades_df) > 0:
            final_capital = results_df['net_value'].iloc[-1]
            total_return = (final_capital - INITIAL_CAPITAL) / INITIAL_CAPITAL * 100
            
            print(f"初始资金: {INITIAL_CAPITAL:,.2f}元")
            print(f"最终资金: {final_capital:,.2f}元")
            print(f"总收益率: {total_return:.2f}%")
            print(f"交易次数: {len(trades_df)}笔")
            print(f"胜率: {(trades_df['盈亏金额'] > 0).sum() / len(trades_df) * 100:.1f}%")
            print(f"平均收益率: {trades_df['盈亏百分比'].mean():.2f}%")
            print(f"最大单笔盈利: {trades_df['盈亏金额'].max():+,.2f}元")
            print(f"最大单笔亏损: {trades_df['盈亏金额'].min():+,.2f}元")
            
            print(f"\n[SUCCESS] 策略运行成功！")
        else:
            print("未产生任何交易信号")
        
    except Exception as e:
        print(f"\n[ERROR] 策略运行出错: {str(e)}")
        import traceback
        traceback.print_exc()
        
    finally:
        print(f"\n" + "=" * 80)

if __name__ == "__main__":
    main()