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@@ -0,0 +1,992 @@
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+import pandas as pd
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+import numpy as np
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+import akshare as ak
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+import warnings
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+import json
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+import os
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+from datetime import datetime, timedelta
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+warnings.filterwarnings('ignore')
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+
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+# ==================== 配置管理模块 ====================
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+class ConfigManager:
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+ """配置文件管理类"""
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+
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+ def __init__(self, config_file='config.json'):
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+ self.config_file = config_file
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+ self.config = self.load_config()
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+
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+ def load_config(self):
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+ """加载配置文件"""
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+ try:
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+ if os.path.exists(self.config_file):
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+ with open(self.config_file, 'r', encoding='utf-8') as f:
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+ config = json.load(f)
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+ print(f"配置文件加载成功: {self.config_file}")
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+ return config
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+ else:
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+ print(f"配置文件不存在，使用默认配置: {self.config_file}")
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+ return self.get_default_config()
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+ except Exception as e:
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+ print(f"配置文件加载失败: {e}，使用默认配置")
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+ return self.get_default_config()
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+
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+ def get_default_config(self):
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+ """获取默认配置"""
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+ return {
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+ "data_source": {
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+ "use_local_file": False,
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+ "local_file_path": "D:\\work\\project\\catfly\\data\\SZ#399673_30min.csv"
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+ },
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+ "strategy": {
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+ "initial_capital": 1000000,
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+ "backtest_start_date": "2025-10-01",
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+ "prewamp_days": 30,
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+ "position_size_pct": 1.0,
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+ "stop_loss_pct": 0.008,
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+ "take_profit_pct": 0.015,
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+ "max_hold_bars": 16
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+ }
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+ }
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+
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+ def get(self, section, key, default=None):
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+ """获取配置项"""
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+ try:
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+ return self.config.get(section, {}).get(key, default)
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+ except:
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+ return default
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+
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+ def save_config(self):
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+ """保存配置到文件"""
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+ try:
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+ with open(self.config_file, 'w', encoding='utf-8') as f:
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+ json.dump(self.config, f, indent=2, ensure_ascii=False)
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+ print(f"配置文件保存成功: {self.config_file}")
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+ except Exception as e:
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+ print(f"配置文件保存失败: {e}")
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+
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+# ==================== 数据获取模块 ====================
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+class IntradayDataFetcher:
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+ """30分钟K线数据获取类"""
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+
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+ def __init__(self, config_manager=None):
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+ self.symbol = "399673" # 创业板50指数
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+ self.config_manager = config_manager
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+
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+ def fetch_30min_data(self, start_date=None, end_date=None) -> pd.DataFrame:
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+ """获取指定时间范围的30分钟K线数据"""
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+ try:
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+ if start_date is None:
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+ start_date = datetime.now() - timedelta(days=60)
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+ if end_date is None:
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+ end_date = datetime.now()
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+
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+ print(f"正在获取创业板50指数的30分钟K线数据...")
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+ print(f"时间范围: {start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}")
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+
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+ # 检查数据源开关
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+ use_local_file = False
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+ local_file_path = ""
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+
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+ if self.config_manager:
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+ use_local_file = self.config_manager.get('data_source', 'use_local_file', False)
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+ local_file_path = self.config_manager.get('data_source', 'local_file_path', '')
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+
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+ # 如果开关打开，从本地文件读取
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+ if use_local_file:
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+ print(f"数据源开关: 本地文件模式")
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+ print(f"本地文件路径: {local_file_path}")
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+ return self._load_local_file(local_file_path, start_date, end_date)
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+ else:
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+ print(f"数据源开关: 在线获取模式")
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+ return self._fetch_online_data(start_date, end_date)
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+
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+ except Exception as e:
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+ print(f"获取数据时出错: {str(e)}")
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+ raise
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+
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+ def _load_local_file(self, file_path, start_date, end_date) -> pd.DataFrame:
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+ """从本地文件加载数据"""
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+ try:
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+ if not os.path.exists(file_path):
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+ raise FileNotFoundError(f"本地文件不存在: {file_path}")
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+
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+ print(f"正在从本地文件读取数据...")
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+ print(f"文件路径: {file_path}")
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+
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+ # 检查文件扩展名，选择不同的读取方式
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+ if file_path.endswith('.txt'):
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+ # 处理文本格式文件
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+ print("检测到文本格式文件，使用文本解析模式...")
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+ return self._parse_text_file(file_path, start_date, end_date)
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+ else:
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+ # 处理CSV格式文件
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+ print("检测到CSV格式文件，使用CSV解析模式...")
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+ data = pd.read_csv(file_path)
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+ return self._process_dataframe(data, start_date, end_date)
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+
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+ except Exception as e:
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+ print(f"从本地文件加载数据失败: {e}")
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+ raise
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+
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+ def _parse_text_file(self, file_path, start_date, end_date) -> pd.DataFrame:
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+ """解析文本格式的数据文件"""
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+ try:
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+ data_list = []
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+
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+ # 尝试多种编码格式
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+ encodings = ['gbk', 'gb2312', 'utf-8', 'latin-1']
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+ lines = None
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+
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+ for encoding in encodings:
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+ try:
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+ with open(file_path, 'r', encoding=encoding) as f:
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+ lines = f.readlines()
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+ print(f"成功使用编码: {encoding}")
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+ break
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+ except UnicodeDecodeError:
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+ continue
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+
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+ if lines is None:
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+ raise ValueError("无法读取文件，尝试了多种编码格式都失败")
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+
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+ # 跳过前两行（标题行）
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+ for line in lines[2:]:
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+ line = line.strip()
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+ if not line:
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+ continue
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+
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+ parts = line.split()
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+ if len(parts) >= 7:
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+ try:
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+ # 格式: 日期 时间 开盘 最高 最低 收盘 成交量 成交额
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+ date_time_str = f"{parts[0]} {parts[1]}"
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+ datetime_obj = pd.to_datetime(date_time_str, format='%Y/%m/%d %H%M')
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+
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+ data_list.append({
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+ 'DateTime': datetime_obj,
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+ 'Open': float(parts[2]),
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+ 'High': float(parts[3]),
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+ 'Low': float(parts[4]),
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+ 'Close': float(parts[5]),
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+ 'Volume': float(parts[6]),
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+ 'Amount': float(parts[7]) if len(parts) > 7 else 0
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+ })
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+ except (ValueError, IndexError) as e:
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+ print(f"跳过异常行: {line[:50]}... 错误: {e}")
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+ continue
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+
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+ if not data_list:
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+ raise ValueError("文本文件中没有解析到有效数据")
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+
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+ print(f"成功解析 {len(data_list)} 条数据")
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+ data = pd.DataFrame(data_list)
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+ return self._process_dataframe(data, start_date, end_date)
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+
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+ except Exception as e:
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+ print(f"解析文本文件失败: {e}")
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+ raise
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+
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+ def _process_dataframe(self, data, start_date, end_date) -> pd.DataFrame:
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+ """处理和标准化数据框"""
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+ try:
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+ # 检查并转换列名
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+ print(f"原始数据列名: {data.columns.tolist()}")
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+ print(f"原始数据行数: {len(data)}")
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+
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+ # 标准化列名
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+ column_mapping = {
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+ '时间': 'DateTime', '日期': 'DateTime', 'datetime': 'DateTime', 'time': 'DateTime',
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+ '开盘': 'Open', 'open': 'Open', 'Open': 'Open', '开盘价': 'Open',
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+ '收盘': 'Close', 'close': 'Close', 'Close': 'Close', '收盘价': 'Close',
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+ '最高': 'High', 'high': 'High', 'High': 'High', '最高价': 'High',
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+ '最低': 'Low', 'low': 'Low', 'Low': 'Low', '最低价': 'Low',
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+ '成交量': 'Volume', 'volume': 'Volume', 'Volume': 'Volume', 'vol': 'Volume'
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+ }
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+
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+ # 重命名列
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+ data.rename(columns=column_mapping, inplace=True)
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+
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+ # 设置时间索引
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+ if 'DateTime' in data.columns:
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+ data['DateTime'] = pd.to_datetime(data['DateTime'])
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+ data.set_index('DateTime', inplace=True)
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+ else:
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+ raise ValueError("数据中找不到时间列(DateTime/时间/日期)")
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+
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+ data.sort_index(inplace=True)
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+
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+ # 筛选时间范围
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+ filtered_data = data[(data.index >= start_date) & (data.index <= end_date)].copy()
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+
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+ if filtered_data.empty:
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+ print(f"警告：指定时间范围没有数据")
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+ print(f"可用数据范围: {data.index[0]} 到 {data.index[-1]}")
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+ print(f"请求的时间范围: {start_date} 到 {end_date}")
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+ # 返回空数据框而不是抛出异常
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+ return filtered_data
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+
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+ # 确保必需的列存在
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+ required_columns = ['Open', 'High', 'Low', 'Close', 'Volume']
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+ missing_columns = [col for col in required_columns if col not in filtered_data.columns]
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+ if missing_columns:
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+ raise ValueError(f"数据缺少必需的列: {missing_columns}")
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+
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+ # 添加缺失的列
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+ if 'Amount' not in filtered_data.columns:
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+ filtered_data['Amount'] = 0
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+
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+ # 计算基础指标（本地文件可能缺少这些）
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+ if 'Returns' not in filtered_data.columns:
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+ filtered_data['Returns'] = filtered_data['Close'].pct_change()
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+ if 'High_Low_Pct' not in filtered_data.columns:
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+ filtered_data['High_Low_Pct'] = (filtered_data['High'] - filtered_data['Low']) / filtered_data['Close'].shift(1)
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+ if 'Close_Open_Pct' not in filtered_data.columns:
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+ filtered_data['Close_Open_Pct'] = (filtered_data['Close'] - filtered_data['Open']) / filtered_data['Open']
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+
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+ # 处理缺失值
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+ filtered_data.fillna(method='ffill', inplace=True)
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+ filtered_data.dropna(inplace=True)
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+
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+ print(f"本地文件数据处理成功: {len(filtered_data)}条")
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+ print(f"数据范围: {filtered_data.index[0]} 到 {filtered_data.index[-1]}")
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+
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+ return filtered_data
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+
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+ except Exception as e:
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+ print(f"处理数据框失败: {e}")
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+ raise
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+
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+ def _fetch_online_data(self, start_date, end_date) -> pd.DataFrame:
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+ """在线获取30分钟K线数据 - 东方财富优先，新浪财经备用"""
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+ data = None
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+
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+ # ===== 方法1: 东方财富数据源（主要数据源） =====
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+ try:
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+ print("[DATA_SOURCE_1] 正在使用东方财富30分钟K线接口...")
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+ data = ak.index_zh_a_hist_min_em(symbol=self.symbol, period="30")
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+
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+ if not data.empty and len(data) >= 50:
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+ print(f"[SUCCESS] 东方财富获取到{len(data)}条30分钟数据")
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+ print(f"[TIME_RANGE] 数据范围: {data.index[0]} 到 {data.index[-1]}")
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+
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+ # 检查数据时效性
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+ latest_time = data.index[-1]
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+ current_time = datetime.now()
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+ if hasattr(latest_time, 'hour') and hasattr(latest_time, 'minute'):
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+ time_delay = current_time - latest_time
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+ print(f"[DATA_DELAY] 数据延迟: {time_delay}")
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+ else:
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+ print(f"[INFO] 数据索引类型: {type(latest_time)}")
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+
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+ else:
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+ print(f"[FAIL] 东方财富数据不足或为空")
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+
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+ except Exception as e:
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+ print(f"[ERROR] 东方财富数据源失败: {e}")
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+
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+ # ===== 方法2: 新浪财经数据源（备用数据源） =====
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+ if data is None or data.empty or len(data) < 50:
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+ try:
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+ print("[DATA_SOURCE_2] 正在使用新浪财经30分钟K线接口...")
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+ import requests
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+ import json
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+ import re
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+
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+ symbol = "sz399673"
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+ 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"
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+
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+ headers = {
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+ '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',
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+ 'Referer': 'https://finance.sina.com.cn/'
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+ }
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+
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+ response = requests.get(url, headers=headers, timeout=15)
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+ response_text = response.text
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+
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+ # 解析JSONP响应
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+ array_pattern = r'=([\[ ].+?\])'
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+ match = re.search(array_pattern, response_text)
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+
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+ if match:
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+ json_str = match.group(1)
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+ else:
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+ json_start = response_text.find('[')
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+ json_end = response_text.rfind(']') + 1
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+ if json_start >= 0 and json_end > json_start:
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+ json_str = response_text[json_start:json_end]
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+ else:
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+ raise Exception("无法解析JSONP响应")
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+
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+ data_dict = json.loads(json_str)
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+
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+ if data_dict and isinstance(data_dict, list):
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+ data_list = []
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+ for item in data_dict:
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+ try:
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+ data_list.append({
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+ 'DateTime': item.get('day'),
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+ 'Open': float(item.get('open', 0)),
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+ 'High': float(item.get('high', 0)),
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+ 'Low': float(item.get('low', 0)),
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+ 'Close': float(item.get('close', 0)),
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+ 'Volume': float(item.get('volume', 0)),
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+ 'Amount': 0
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+ })
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+ except Exception:
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+ continue
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+
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+ if data_list:
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+ data = pd.DataFrame(data_list)
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+ print(f"[SUCCESS] 新浪财经获取到{len(data)}条30分钟数据")
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+ print(f"[TIME_RANGE] 数据范围: {data.index[0]} 到 {data.index[-1]}")
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+ else:
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+ print("[FAIL] 新浪财经数据解析失败")
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+ else:
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+ print("[FAIL] 新浪财经返回数据格式错误")
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+
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+ except Exception as e:
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+ print(f"[ERROR] 新浪财经数据源失败: {e}")
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+
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+ # ===== 数据验证和格式化 =====
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+ if data is None or data.empty:
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+ raise ValueError("[FATAL_ERROR] 所有数据源均无法获取数据")
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+
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+ # 重命名列（针对东方财富数据格式）
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+ 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()
|
