在原有'future_grid_trading_analysis.py'的基础上构建了一个新版本，这个版本不使用对冲策略对冲网格交易时可能的风险。

Lib/research/future_grid_trading_analysis2.py

@@ -0,0 +1,2667 @@
+"""
+期货网格交易研究分析工具（带主力合约切换）
+研究期货网格交易策略在不同配置下的表现，支持多种交易场景的对比分析
+核心功能包括主力合约自动切换、强制平仓和重新建仓逻辑
+
+本程序实现完整的网格交易分析流程：
+1. 主力合约监控与切换 - 自动检测主力合约变化并处理切换
+2. 合约选择逻辑 - 基于主力合约选择算法
+3. 基础头寸交易 - 价格-数量网格配置，支持合约切换时重新建仓
+4. 网格交易策略 - 限价订单网格买入卖出，合约切换时根据价格条件重新建仓
+5. 统计分析对比 - 多种交易场景性能分析
+
+主要特点：
+- 主力合约自动监控：每日检测主力合约变化
+- 强制平仓机制：合约切换时立即平掉旧合约所有头寸
+- 智能重新建仓：根据价格条件在新合约中重新建立头寸
+- 标准期货盈亏计算：使用正确的合约倍数和期货盈亏公式
+- 最终持仓结算：分析期结束时对所有未平仓头寸进行市值计价
+- 完整交易记录：记录所有交易包括合约切换引起的强制平仓
+
+期货盈亏计算公式：
+- 多头：(出场价格 - 入场价格) × 合约倍数 × 数量
+- 空头：(入场价格 - 出场价格) × 合约倍数 × 数量
+
+注：程序支持多个核心商品同时分析，生成详细的交易记录和统计报告
+
+作者: jukuan研究团队
+日期: 2025-09
+适用平台: 聚宽在线研究平台
+"""
+
+import pandas as pd
+import numpy as np
+from jqdata import *
+import datetime
+import warnings
+warnings.filterwarnings('ignore')
+
+# =====================================================================================
+# 分析配置参数 - 集中配置部分
+# =====================================================================================
+
+class GridTradingConfig:
+    """期货网格交易分析配置参数"""
+    
+    # ==================== 时间范围设置 ====================
+    START_DATE = datetime.datetime(2024, 11, 7)   # 分析开始日期
+    END_DATE = datetime.datetime(2025, 9, 19)    # 分析结束日期（修正为9月19日避免数据缺失）
+    
+    # ==================== 期货合约倍数配置 ====================
+    FUTURES_MULTIPLIER = {
+        # 贵金属
+        'AU': 1000,  # 黄金
+        'AG': 15,    # 白银
+        
+        # 有色金属
+        'CU': 5, 'AL': 5, 'ZN': 5, 'PB': 5, 'NI': 1, 'SN': 1, 'SS': 5,
+        
+        # 黑色系
+        'RB': 10, 'HC': 10, 'I': 100, 'JM': 100, 'J': 60,
+        
+        # 能源化工
+        'SP': 10, 'FU': 10, 'BU': 10, 'RU': 10, 'BR': 5, 'SC': 1000,
+        'NR': 10, 'LU': 10, 'LC': 1,
+        
+        # 化工
+        'FG': 20, 'TA': 5, 'MA': 10, 'SA': 20, 'L': 5, 'V': 5, 'EG': 10,
+        'PP': 5, 'EB': 5, 'PG': 20, 'UR': 20,
+        
+        # 农产品
+        'RM': 10, 'OI': 10, 'CF': 5, 'SR': 10, 'PF': 5, 'C': 10, 'CS': 10,
+        'CY': 5, 'A': 10, 'B': 10, 'M': 10, 'Y': 10, 'P': 10,
+        
+        # 股指期货
+        'IF': 300, 'IH': 300, 'IC': 200, 'IM': 200, 'TL': 10000,
+        
+        # 其他
+        'AP': 10, 'CJ': 5, 'PK': 5, 'JD': 10, 'LH': 16
+    }
+    
+    # ==================== 核心商品配置 ====================
+    CORE_COMMODITIES = {
+        # 'SA': ['SA2501.XZCE', 'SA2505.XZCE', 'SA2509.XZCE', 'SA2601.XZCE'],  # 纯碱
+        # 'M': ['M2501.XDCE', 'M2505.XDCE', 'M2509.XDCE', 'M2605.XDCE'],  # 豆粕
+        'UR': ['UR2501.XZCE', 'UR2505.XZCE', 'UR2509.XZCE', 'UR2601.XZCE'],  # 尿素
+        # 'LH': ['LH2501.XDCE', 'LH2505.XDCE', 'LH2509.XDCE', 'LH2601.XDCE'],  # 生猪
+        # 'TL': ['TL2503.CCFX', 'TL2506.CCFX', 'TL2509.CCFX', 'TL2512.CCFX']  # 30年期国债
+    }
+    
+    # ==================== 合约切换配置 ====================
+    REQUIRED_TRADING_DAYS = 30  # 合约切换前需要的最少有效交易日数
+    
+    # ==================== 基础头寸交易配置 ====================
+    BASE_POSITION_GRID = {
+        'SA': {1400: 4, 1300: 6, 1200: 8, 1100: 12, 1000: 14, 900: 16},
+        'M': {2800: 4, 2750: 6, 2700: 8, 2650: 12, 2600: 14, 2550: 16},
+        'UR': {1750: 4, 1700: 6, 1650: 8, 1600: 12, 1550: 14, 1500: 16},
+        'LH': {13000: 1, 12500: 1, 12000: 1, 11500: 1, 11000: 2},
+        'TL': {118: 1, 117: 1, 116: 1, 115: 1, 114: 2, 113: 2},
+    }
+    
+    # 统一退出价格（无止损）
+    BASE_POSITION_EXIT_PRICE = {
+        'SA': 1500,
+        'M': 3800,
+        'UR': 2400,
+        'LH': 20000,
+        'TL': 121,
+    }
+    
+    # ==================== 网格交易配置 ====================
+    GRID_TRADING_CONFIG = {
+        'SA': {
+            'start_price': 1250, # 开始价格
+            'grid_size': 50, # 网格大小
+            'quantity_per_grid': 5, # 每网格数量
+            'exit_grid_size': 50 # 退出网格大小
+        },
+        'M': {
+            'start_price': 2800,
+            'grid_size': 100,
+            'quantity_per_grid': 10,
+            'exit_grid_size': 100
+        },
+        'UR': {
+            'start_price': 1800,
+            'grid_size': 50,
+            'quantity_per_grid': 10,
+            'exit_grid_size': 50
+        },
+        'LH': {
+            'start_price': 13500,
+            'grid_size': 500,
+            'quantity_per_grid': 1,
+            'exit_grid_size': 500
+        },
+        'TL': {
+            'start_price': 118,
+            'grid_size': 1,
+            'quantity_per_grid': 1,
+            'exit_grid_size': 1
+        },
+    }
+    
+    # ==================== 输出设置 ====================
+    OUTPUT_ENCODING = 'utf-8-sig'  # 输出文件编码格式
+    VERBOSE_LOGGING = True          # 是否打印详细日志
+    
+    @classmethod
+    def print_config(cls):
+        """打印当前配置信息"""
+        print("=== 期货网格交易分析配置 ===")
+        print(f"分析时间范围: {cls.START_DATE.strftime('%Y-%m-%d')} 至 {cls.END_DATE.strftime('%Y-%m-%d')}")
+        print(f"核心商品数量: {len(cls.CORE_COMMODITIES)}")
+        print("核心商品列表:")
+        for commodity, contracts in cls.CORE_COMMODITIES.items():
+            print(f"  {commodity}: {contracts}")
+        print(f"\n网格交易配置:")
+        for commodity, config in cls.GRID_TRADING_CONFIG.items():
+            print(f"  {commodity}: 起始价{config['start_price']}, 网格大小{config['grid_size']}")
+        print(f"详细日志: {'开启' if cls.VERBOSE_LOGGING else '关闭'}")
+        print("=" * 50)
+
+class FutureGridTradingAnalyzer:
+    """期货网格交易分析器"""
+    
+    def __init__(self, config=None):
+        """初始化分析器"""
+        if config is None:
+            config = GridTradingConfig
+        
+        self.config = config
+        self.start_date = config.START_DATE
+        self.end_date = config.END_DATE
+        self.core_commodities = config.CORE_COMMODITIES
+        self.base_position_grid = config.BASE_POSITION_GRID
+        self.base_position_exit_price = config.BASE_POSITION_EXIT_PRICE
+        self.grid_trading_config = config.GRID_TRADING_CONFIG
+        self.verbose_logging = config.VERBOSE_LOGGING
+        self.output_encoding = config.OUTPUT_ENCODING
+        
+        # 存储结果的字典
+        self.selected_contracts = {}  # 选中的合约
+        self.price_data = {}         # 价格数据
+        self.dominant_contract_history = {}  # 主力合约历史变化
+        self.active_positions = {    # 当前活跃头寸跟踪
+            'base_position': {},
+            'grid_trading': {}
+        }
+        self.trading_results = {     # 交易场景的结果
+            'base_position': [],
+            'grid_trading': []
+        }
+        
+        if self.verbose_logging:
+            print("初始化期货网格交易分析器")
+            print(f"核心商品: {list(self.core_commodities.keys())}")
+            print(f"分析期间: {self.start_date.strftime('%Y-%m-%d')} - {self.end_date.strftime('%Y-%m-%d')}")
+    
+    def select_contracts(self):
+        """
+        合约选择逻辑
+        1. 首先获取商品的主导合约
+        2. 如果主导合约在可用列表中，选择它
+        3. 如果主导合约不在列表中，选择未来到期日期最近且晚于主导合约的合约
+        """
+        if self.verbose_logging:
+            print("\n=== 步骤1: 合约选择逻辑 ===")
+        
+        for commodity, available_contracts in self.core_commodities.items():
+            if self.verbose_logging:
+                print(f"\n处理商品: {commodity}")
+                print(f"可用合约: {available_contracts}")
+            
+            try:
+                # 获取商品的主导合约
+                dominant_contract = get_dominant_future(commodity, self.start_date.date())
+                
+                if self.verbose_logging:
+                    print(f"主导合约: {dominant_contract}")
+                
+                if dominant_contract in available_contracts:
+                    # 主导合约在可用列表中，直接选择
+                    selected_contract = dominant_contract
+                    if self.verbose_logging:
+                        print(f"选择主导合约: {selected_contract}")
+                else:
+                    # 主导合约不在列表中，选择最近的未来合约
+                    selected_contract = self._select_nearest_future_contract(
+                        commodity, dominant_contract, available_contracts
+                    )
+                    if self.verbose_logging:
+                        print(f"选择最近的未来合约: {selected_contract}")
+                
+                self.selected_contracts[commodity] = selected_contract
+                
+            except Exception as e:
+                if self.verbose_logging:
+                    print(f"获取{commodity}主导合约失败: {str(e)}")
+                # 默认选择第一个可用合约
+                self.selected_contracts[commodity] = available_contracts[0]
+                if self.verbose_logging:
+                    print(f"默认选择第一个合约: {available_contracts[0]}")
+        
+        if self.verbose_logging:
+            print(f"\n合约选择完成，共选择{len(self.selected_contracts)}个合约")
+            for commodity, contract in self.selected_contracts.items():
+                print(f"  {commodity}: {contract}")
+        
+        return self.selected_contracts
+    
+    def _select_nearest_future_contract(self, commodity, dominant_contract, available_contracts):
+        """选择最近的未来到期合约"""
+        if not dominant_contract:
+            return available_contracts[0]
+        
+        # 解析主导合约的到期月份
+        try:
+            # 提取合约代码中的月份信息 (例如 SA2507 -> 2507)
+            dominant_year_month = dominant_contract.split('.')[0][-4:]  # 取最后4位
+            dominant_year = int(dominant_year_month[:2]) + 2000  # 假设是21世纪
+            dominant_month = int(dominant_year_month[2:])
+        except:
+            return available_contracts[0]
+        
+        # 找到最近的未来合约
+        best_contract = available_contracts[0]
+        best_diff = float('inf')
+        
+        for contract in available_contracts:
+            try:
+                contract_year_month = contract.split('.')[0][-4:]
+                contract_year = int(contract_year_month[:2]) + 2000
+                contract_month = int(contract_year_month[2:])
+                
+                # 计算月份差异
+                contract_total_months = contract_year * 12 + contract_month
+                dominant_total_months = dominant_year * 12 + dominant_month
+                
+                # 只选择晚于主导合约的合约
+                if contract_total_months > dominant_total_months:
+                    diff = contract_total_months - dominant_total_months
+                    if diff < best_diff:
+                        best_diff = diff
+                        best_contract = contract
+            except:
+                continue
+        
+        return best_contract
+    
+    def _get_futures_multiplier(self, commodity):
+        """获取期货合约倍数"""
+        return self.config.FUTURES_MULTIPLIER.get(commodity, 10)  # 默认倍数为10
+    
+    def _calculate_futures_pnl(self, entry_price, exit_price, quantity, commodity, is_long=True):
+        """
+        计算期货盈亏
+        
+        参数:
+            entry_price: 入场价格
+            exit_price: 出场价格
+            quantity: 数量（手数）
+            commodity: 商品代码
+            is_long: 是否多头，True为多头，False为空头
+            
+        返回:
+            实际盈亏金额
+        """
+        multiplier = self._get_futures_multiplier(commodity)
+        
+        if is_long:
+            # 多头：(出场价格 - 入场价格) × 合约倍数 × 数量
+            pnl = (exit_price - entry_price) * multiplier * quantity
+        else:
+            # 空头：(入场价格 - 出场价格) × 合约倍数 × 数量
+            pnl = (entry_price - exit_price) * multiplier * quantity
+        
+        return pnl
+    
+    def build_dominant_contract_history(self):
+        """
+        构建主力合约历史变化记录
+        为每个商品在整个分析期间构建主力合约变化的时间序列
+        只有当合约真正发生变化时才记录为合约切换
+        """
+        if self.verbose_logging:
+            print("\n=== 步骤2：构建主力合约历史变化记录 ===")
+        
+        for commodity in self.core_commodities.keys():
+            if self.verbose_logging:
+                print(f"构建 {commodity} 主力合约历史...")
+            
+            contract_history = []
+            current_date = self.start_date.date()
+            end_date = self.end_date.date()
+            current_selected_contract = None  # 跟踪选择的合约而不是主力合约
+            
+            while current_date <= end_date:
+                # 跳过非交易日
+                if current_date.weekday() >= 5:  # 周六周日
+                    current_date += datetime.timedelta(days=1)
+                    continue
+                
+                try:
+                    # 获取当日主力合约
+                    dominant_contract = get_dominant_future(commodity, current_date)
+                    # print(f"日期: {current_date}, 主力合约: {dominant_contract}")
+                    selected_contract = self._match_to_available_contract(commodity, dominant_contract)
+                    
+                    # 只有当选择的合约真正发生变化时才记录
+                    if selected_contract != current_selected_contract:
+                        contract_history.append({
+                            'date': current_date,
+                            'dominant_contract': dominant_contract,
+                            'selected_contract': selected_contract,
+                            'is_initial': current_selected_contract is None  # 标记是否为初始合约
+                        })
+                        
+                        if self.verbose_logging:
+                            if current_selected_contract is None:
+                                print(f"  {current_date}: 初始合约设置为 {selected_contract}")
+                            else:
+                                print(f"  {current_date}: 合约切换 {current_selected_contract} -> {selected_contract}")
+                        
+                        current_selected_contract = selected_contract
+                
+                except Exception as e:
+                    if self.verbose_logging:
+                        print(f"  获取 {current_date} 的主力合约时出错: {str(e)}")
+                
+                current_date += datetime.timedelta(days=1)
+            
+            self.dominant_contract_history[commodity] = contract_history
+        
+        if self.verbose_logging:
+            total_changes = sum(len(history) for history in self.dominant_contract_history.values())
+            actual_switches = sum(
+                sum(1 for change in history if not change.get('is_initial', False))
+                for history in self.dominant_contract_history.values()
+            )
+            initial_setups = total_changes - actual_switches
+            print(f"主力合约历史构建完成，共 {total_changes} 次记录（{initial_setups} 次初始设置，{actual_switches} 次真实切换）")
+        
+        return self.dominant_contract_history
+    
+    def _match_to_available_contract(self, commodity, dominant_contract):
+        """将主力合约匹配到可用合约列表"""
+        available_contracts = self.core_commodities.get(commodity, [])
+        
+        if dominant_contract in available_contracts:
+            return dominant_contract
+        else:
+            return self._select_nearest_future_contract(commodity, dominant_contract, available_contracts)
+    
+    def collect_price_data(self):
+        """收集所有可能用到的合约价格数据（优化日期范围）"""
+        if self.verbose_logging:
+            print("\n=== 步骤3: 收集价格数据（优化日期范围） ===")
+        
+        # 清除之前的调整建议
+        if hasattr(self, 'adjustment_suggestions'):
+            self.adjustment_suggestions = []
+        
+        # 为每个商品创建数据存储结构
+        for commodity in self.core_commodities.keys():
+            print(f'收集{commodity}的价格数据:')
+            self.price_data[commodity] = {}
+            
+            # 根据主力合约历史确定每个合约的数据获取范围
+            contract_date_ranges = self._determine_contract_date_ranges(commodity)
+            
+            for contract, date_range in contract_date_ranges.items():
+                start_date, end_date = date_range
+                
+                if self.verbose_logging:
+                    print(f"获取 {contract} 价格数据...")
+                    print(f"  优化日期范围: {start_date} 至 {end_date}")
+                
+                try:
+                    # 获取价格数据（使用优化的日期范围）
+                    data = get_price(
+                        contract,
+                        start_date=start_date,
+                        end_date=end_date,
+                        frequency='daily',
+                        fields=['open', 'close', 'high', 'low', 'volume'],
+                        skip_paused=False,
+                        panel=False
+                    )
+                    
+                    if data is not None and len(data) > 0:
+                        # print(f"第一条有数据的日期是: {data.index[0].date()}，数据是: {data.iloc[0]}")
+                        # print(f"最后一条有数据的日期是: {data.index[-1].date()}, 数据是: {data.iloc[-1]}")
+                        self.price_data[commodity][contract] = data
+
+                        # 检查这个数据里有多少条空值数据
+                        empty_data = data[data.isna().any(axis=1)]
+                        
+                        # 检查有效交易日数据并收集调整建议
+                        adjustment_info = self._check_thirty_day_trading_data(commodity, contract, data, start_date, end_date)
+                        if adjustment_info and adjustment_info.get('needs_adjustment'):
+                            # 暂存调整建议，稍后统一处理
+                            if not hasattr(self, 'adjustment_suggestions'):
+                                self.adjustment_suggestions = []
+                            self.adjustment_suggestions.append(adjustment_info)
+                        
+                        if self.verbose_logging:
+                            print(f"  ✅ 成功获取{len(data)}条数据记录")
+                            print(f"  空值数据: {len(empty_data)}条")
+                            print(f"  价格范围: {data['low'].min():.2f} - {data['high'].max():.2f}")
+                            print(f"  数据日期范围: {data.index[0].date()} 至 {data.index[-1].date()}")
+                    else:
+                        if self.verbose_logging:
+                            print(f"  ⚠️  未获取到{contract}的数据")
+                        
+                        # 如果优化日期范围没有数据，尝试使用更宽泛的日期范围
+                        if self.verbose_logging:
+                            print(f"  尝试使用更宽泛的日期范围获取数据...")
+                        
+                        try:
+                            fallback_data = get_price(
+                                contract,
+                                start_date=self.start_date,
+                                end_date=self.end_date,
+                                frequency='daily',
+                                fields=['open', 'close', 'high', 'low', 'volume'],
+                                skip_paused=False,
+                                panel=False
+                            )
+                            
+                            if fallback_data is not None and len(fallback_data) > 0:
+                                self.price_data[commodity][contract] = fallback_data
+                                
+                                # 检查有效交易日数据并收集调整建议（回退方案）
+                                adjustment_info = self._check_thirty_day_trading_data(commodity, contract, fallback_data, self.start_date, self.end_date)
+                                if adjustment_info and adjustment_info.get('needs_adjustment'):
+                                    if not hasattr(self, 'adjustment_suggestions'):
+                                        self.adjustment_suggestions = []
+                                    self.adjustment_suggestions.append(adjustment_info)
+                                
+                                if self.verbose_logging:
+                                    print(f"  ✅ 回退方案成功获取{len(fallback_data)}条数据记录")
+                                    print(f"  数据日期范围: {fallback_data.index[0].date()} 至 {fallback_data.index[-1].date()}")
+                            else:
+                                if self.verbose_logging:
+                                    print(f"  ❌ 回退方案也未获取到{contract}的数据")
+                        except Exception as fallback_e:
+                            if self.verbose_logging:
+                                print(f"  ❌ 回退方案出错: {str(fallback_e)}")
+                        
+                except Exception as e:
+                    if self.verbose_logging:
+                        print(f"  ❌ 获取{contract}数据时出错: {str(e)}")
+                    continue
+        
+        # 处理动态调整建议
+        if hasattr(self, 'adjustment_suggestions') and self.adjustment_suggestions:
+            self._apply_dynamic_adjustments()
+        
+        if self.verbose_logging:
+            total_contracts = sum(len(contracts) for contracts in self.price_data.values())
+            print(f"价格数据收集完成，共{total_contracts}个合约")
+        
+        return self.price_data
+    
+    def _determine_contract_date_ranges(self, commodity):
+        """
+        根据主力合约历史确定每个合约的最优数据获取日期范围
+        """
+        contract_ranges = {}
+        
+        if commodity not in self.dominant_contract_history:
+            # 如果没有主力合约历史，使用全范围
+            for contract in self.core_commodities[commodity]:
+                contract_ranges[contract] = (self.start_date, self.end_date)
+            return contract_ranges
+        
+        contract_history = self.dominant_contract_history[commodity]
+        
+        # 分析每个合约的活跃期间
+        for contract in self.core_commodities[commodity]:
+            contract_start = self.start_date
+            contract_end = self.end_date
+            
+            # 查找该合约在主力合约历史中的使用时间段
+            for i, history_record in enumerate(contract_history):
+                if history_record['selected_contract'] == contract:
+                    # 该合约开始使用的日期
+                    if history_record.get('is_initial', False):
+                        # 初始设置的合约，从分析开始日期或历史记录日期开始
+                        contract_start = max(self.start_date.date(), history_record['date'])
+                    else:
+                        # 切换到的合约，从切换日期开始
+                        contract_start = history_record['date']
+                    
+                    # 查找该合约结束使用的日期
+                    for j in range(i + 1, len(contract_history)):
+                        next_record = contract_history[j]
+                        if next_record['selected_contract'] != contract:
+                            # 找到下一次切换，该合约在此日期结束使用
+                            contract_end = next_record['date']
+                            break
+                    else:
+                        # 该合约一直使用到分析结束
+                        contract_end = self.end_date.date()
+                    
+                    break
+            
+            # 转换为datetime格式并添加缓冲区
+            if isinstance(contract_start, datetime.date):
+                contract_start = datetime.datetime.combine(contract_start, datetime.time.min)
+            if isinstance(contract_end, datetime.date):
+                contract_end = datetime.datetime.combine(contract_end, datetime.time.max)
+            
+            # 添加缓冲期以确保有足够的历史数据满足最低交易日要求
+            # 使用REQUIRED_TRADING_DAYS作为缓冲，保证数据充足性
+            contract_start_buffered = contract_start - datetime.timedelta(days=self.config.REQUIRED_TRADING_DAYS)
+            contract_end_buffered = contract_end # + datetime.timedelta(days=self.config.REQUIRED_TRADING_DAYS)
+            
+            # 确保不超出总体分析范围
+            contract_start_final = max(contract_start_buffered, self.start_date)
+            contract_end_final = min(contract_end_buffered, self.end_date)
+            
+            contract_ranges[contract] = (contract_start_final, contract_end_final)
+            
+            if self.verbose_logging:
+                print(f"  {contract}: {contract_start_final.date()} 至 {contract_end_final.date()}")
+        
+        return contract_ranges
+    
+    def _check_thirty_day_trading_data(self, commodity, contract, data, start_date, end_date):
+        """
+        检查合约是否有足够的有效交易日数据并进行动态调整
+        返回调整建议信息
+        """
+        if data is None or len(data) == 0:
+            print(f"    ⚠️  {contract}: 无价格数据")
+            return None
+        
+        required_days = self.config.REQUIRED_TRADING_DAYS
+        
+        # 检查空值数据
+        empty_data = data[data.isna().any(axis=1)]
+        empty_count = len(empty_data)
+        
+        # 过滤出非空的收盘价数据
+        valid_close_data = data['close'].dropna()
+        valid_count = len(valid_close_data)
+        
+        print(f"    📊 {contract}: 有效收盘价数据共{valid_count}天")
+        
+        adjustment_info = {
+            'contract': contract,
+            'commodity': commodity,
+            'empty_count': empty_count,
+            'valid_count': valid_count,
+            'required_days': required_days,
+            'needs_adjustment': False,
+            'suggested_switch_date': None
+        }
+        
+        # 检查是否有空值数据且需要调整
+        if empty_count > 0:
+            print(f"    ⚠️  {contract}: 检测到{empty_count}条空值数据")
+            
+            if valid_count >= required_days:
+                # 找到第N个有效收盘价的日期
+                nth_date = valid_close_data.index[required_days - 1]  # 索引从0开始
+                nth_price = valid_close_data.iloc[required_days - 1]
+                
+                print(f"    📍 {contract}: 第{required_days}个有效收盘价日期为{nth_date.date()}，价格{nth_price:.2f}")
+                
+                # 检查当前切换日期是否需要调整
+                if commodity in self.dominant_contract_history:
+                    for history_record in self.dominant_contract_history[commodity]:
+                        if (history_record['selected_contract'] == contract and 
+                            not history_record.get('is_initial', False)):
+                            current_switch_date = history_record['date']
+                            
+                            # 转换日期格式进行比较
+                            if isinstance(current_switch_date, datetime.date):
+                                current_switch_datetime = datetime.datetime.combine(current_switch_date, datetime.time.min)
+                            else:
+                                current_switch_datetime = current_switch_date
+                            
+                            if nth_date > current_switch_datetime:
+                                print(f"    ❌ {contract}: 切换日期过早（当前：{current_switch_date}），建议调整至{nth_date.date()}")
+                                adjustment_info.update({
+                                    'needs_adjustment': True,
+                                    'suggested_switch_date': nth_date.date(),
+                                    'current_switch_date': current_switch_date
+                                })
+                            else:
+                                print(f"    ✅ {contract}: 切换日期{current_switch_date}合理，在第{required_days}个有效交易日之后")
+                            break
+            else:
+                print(f"    ❌ {contract}: 有效交易日不足{required_days}天（仅{valid_count}天），不符合切换要求")
+                adjustment_info['needs_adjustment'] = True
+        else:
+            # 没有空值数据，检查是否有足够的交易日
+            if valid_count >= required_days:
+                nth_date = valid_close_data.index[required_days - 1]
+                nth_price = valid_close_data.iloc[required_days - 1]
+                print(f"    ✅ {contract}: 第{required_days}个有效收盘价日期为{nth_date.date()}，价格{nth_price:.2f}")
+            else:
+                print(f"    ❌ {contract}: 有效交易日不足{required_days}天（仅{valid_count}天）")
+                
+        return adjustment_info
+    
+    def _apply_dynamic_adjustments(self):
+        """应用动态调整建议，更新合约切换日期并重新获取数据"""
+        if self.verbose_logging:
+            print(f"\n=== 应用动态调整建议（共{len(self.adjustment_suggestions)}个） ===")
+        
+        adjustments_applied = []
+        
+        for suggestion in self.adjustment_suggestions:
+            if suggestion.get('suggested_switch_date'):
+                commodity = suggestion['commodity']
+                contract = suggestion['contract']
+                new_switch_date = suggestion['suggested_switch_date']
+                
+                print(f"📅 调整{commodity}的{contract}切换日期至{new_switch_date}")
+                
+                # 更新合约历史
+                if self._update_contract_switch_date(commodity, contract, new_switch_date):
+                    adjustments_applied.append(suggestion)
+        
+        # 如果有调整，重新获取相关的价格数据
+        if adjustments_applied:
+            print(f"✅ 完成{len(adjustments_applied)}个调整，重新获取相关价格数据")
+            self._refresh_price_data_for_adjustments(adjustments_applied)
+    
+    def _update_contract_switch_date(self, commodity, contract, new_switch_date):
+        """更新指定合约的切换日期"""
+        if commodity not in self.dominant_contract_history:
+            return False
+        
+        # 查找并更新对应的历史记录
+        for history_record in self.dominant_contract_history[commodity]:
+            if (history_record['selected_contract'] == contract and 
+                not history_record.get('is_initial', False)):
+                old_date = history_record['date']
+                history_record['date'] = new_switch_date
+                print(f"    📝 {contract}: 切换日期从{old_date}更新为{new_switch_date}")
+                return True
+        
+        return False
+    
+    def _refresh_price_data_for_adjustments(self, adjustments):
+        """为调整的合约重新获取价格数据"""
+        affected_commodities = set()
+        
+        for adjustment in adjustments:
+            commodity = adjustment['commodity']
+            affected_commodities.add(commodity)
+        
+        for commodity in affected_commodities:
+            print(f"🔄 重新获取{commodity}的价格数据...")
+            
+            # 重新计算日期范围
+            contract_date_ranges = self._determine_contract_date_ranges(commodity)
+            
+            # 重新获取每个合约的数据
+            for contract, date_range in contract_date_ranges.items():
+                start_date, end_date = date_range
+                
+                try:
+                    # 获取价格数据（使用新的日期范围）
+                    data = get_price(
+                        contract,
+                        start_date=start_date,
+                        end_date=end_date,
+                        frequency='daily',
+                        fields=['open', 'close', 'high', 'low', 'volume'],
+                        skip_paused=False,
+                        panel=False
+                    )
+                    
+                    if data is not None and len(data) > 0:
+                        self.price_data[commodity][contract] = data
+                        
+                        # 检查调整后的数据
+                        empty_data = data[data.isna().any(axis=1)]
+                        empty_count = len(empty_data)
+                        
+                        print(f"    ✅ {contract}: 重新获取{len(data)}条数据记录，空值{empty_count}条")
+                        
+                        if empty_count == 0:
+                            print(f"    🎉 {contract}: 空值数据已消除")
+                        
+                except Exception as e:
+                    print(f"    ❌ 重新获取{contract}数据时出错: {str(e)}")
+
+    def simulate_with_contract_switching(self):
+        """
+        模拟带有主力合约切换逻辑的交易
+        """
+        if self.verbose_logging:
+            print("\n=== 步骤3: 带合约切换的交易模拟 ===")
+        
+        # 按日期顺序处理所有交易日
+        current_date = self.start_date.date()
+        end_date = self.end_date.date()
+        
+        while current_date <= end_date:
+            # 跳过非交易日
+            if current_date.weekday() >= 5:
+                current_date += datetime.timedelta(days=1)
+                continue
+            
+            # 检查每个商品的主力合约切换
+            for commodity in self.core_commodities.keys():
+                self._check_and_handle_contract_switch(commodity, current_date)
+            
+            # 处理正常的交易逻辑
+            self._process_daily_trading(current_date)
+            
+            current_date += datetime.timedelta(days=1)
+        
+        # 在交易循环结束后，计算所有未平仓头寸的最终盈亏
+        self._calculate_final_positions_pnl()
+        
+        if self.verbose_logging:
+            print("带合约切换的交易模拟完成")
+    
+    def _calculate_final_positions_pnl(self):
+        """
+        计算分析期结束时所有未平仓头寸的最终盈亏
+        将这些盈亏作为最终交易记录加入结果中
+        """
+        if self.verbose_logging:
+            print("\n=== 计算最终持仓盈亏 ===")
+        
+        final_date = self.end_date.date()
+        final_pnl_records = []
+        
+        # 添加诊断信息
+        if self.verbose_logging:
+            print(f"分析结束日期: {final_date}")
+            print(f"活跃头寸概览:")
+            for strategy_name in ['base_position', 'grid_trading']:
+                strategy_positions = self.active_positions.get(strategy_name, {})
+                total_positions = 0
+                open_positions = 0
+                for commodity, positions in strategy_positions.items():
+                    commodity_total = len(positions)
+                    commodity_open = sum(1 for p in positions.values() if p['status'] == 'open')
+                    total_positions += commodity_total
+                    open_positions += commodity_open
+                    if commodity_total > 0:
+                        print(f"  {strategy_name} - {commodity}: 总计 {commodity_total} 个头寸, 未平仓 {commodity_open} 个")
+                        
+                        # 详细列出所有头寸信息
+                        print(f"    详细头寸列表:")
+                        for pos_id, pos_info in positions.items():
+                            status = pos_info.get('status', 'Unknown')
+                            entry_price = pos_info.get('entry_price', 'N/A')
+                            contract = pos_info.get('contract', 'N/A')
+                            entry_date = pos_info.get('entry_date', 'N/A')
+                            quantity = pos_info.get('quantity', 'N/A')
+                            print(f"      {pos_id}: 状态={status}, 合约={contract}, 开仓价格={entry_price}, 日期={entry_date}, 数量={quantity}")
+                        
+                        print(f"  {strategy_name} 策略总计: {open_positions}/{total_positions} 个未平仓头寸")
+                        
+                        # 验证头寸计数的准确性
+                        actual_count = len(positions)
+                        open_count_verify = len([p for p in positions.values() if p.get('status') == 'open'])
+                        
+                        if actual_count != commodity_total or open_count_verify != commodity_open:
+                            print(f"    ⚠️  计数不匹配！实际头寸数: {actual_count}, 预期: {commodity_total}; 实际未平仓: {open_count_verify}, 预期: {commodity_open}")
+                        
+                        # 检查是否有重复的开仓价格（同一合约同一状态）
+                        open_positions_by_price = {}
+                        for pos_id, pos_info in positions.items():
+                            if pos_info.get('status') == 'open':
+                                price = pos_info.get('entry_price')
+                                contract = pos_info.get('contract')
+                                key = f"{contract}_{price}"
+                                if key not in open_positions_by_price:
+                                    open_positions_by_price[key] = []
+                                open_positions_by_price[key].append(pos_id)
+                        
+                        # for key, pos_ids in open_positions_by_price.items():
+                        #     if len(pos_ids) > 1:
+                        #         print(f"    ⚠️  发现重复的未平仓头寸: {key} -> {pos_ids}")
+                
+                print(f"  {strategy_name} 策略总计: {open_positions}/{total_positions} 个未平仓头寸")
+        
+        for strategy_name in ['base_position', 'grid_trading']:
+            strategy_positions = self.active_positions.get(strategy_name, {})
+            
+            for commodity, positions in strategy_positions.items():
+                # 获取当前合约和最终价格
+                current_contract = self._get_current_contract(commodity, final_date)
+                if not current_contract:
+                    if self.verbose_logging:
+                        print(f"  警告: 无法确定 {commodity} 在 {final_date} 的当前合约")
+                    continue
+                
+                final_price = self._get_price_on_date(commodity, current_contract, final_date, 'close')
+                if final_price is None:
+                    if self.verbose_logging:
+                        print(f"  警告: 无法获取 {commodity} {current_contract} 在 {final_date} 的价格")
+                    continue
+                
+                if self.verbose_logging and len(positions) > 0:
+                    print(f"  {commodity} {strategy_name}: 当前合约 {current_contract}, 结算价格 {final_price:.2f}")
+                
+                for position_id, position in positions.items():
+                    if self.verbose_logging:
+                        print(f"    检查头寸 {position_id}: 状态={position['status']}, 合约={position['contract']}, 开仓价格={position.get('entry_price', 'N/A')}")
+                    
+                    if position['status'] == 'open' and position['contract'] == current_contract:
+                        if self.verbose_logging:
+                            print(f"      匹配头寸进行结算: {position_id}")
+                            print(f"      头寸详情: 开仓日期={position.get('entry_date', 'N/A')}, 开仓价格={position['entry_price']}, 数量={position.get('quantity', 'N/A')}")
+                        # 计算最终盈亏（基础头寸和网格交易都是做多）
+                        profit_loss = self._calculate_futures_pnl(
+                            position['entry_price'], final_price, position['quantity'], commodity, is_long=True
+                        )
+                        
+                        profit_loss_pct = (final_price - position['entry_price']) / position['entry_price']
+                        
+                        # 计算持有天数
+                        entry_date = datetime.datetime.strptime(position['entry_date'], '%Y-%m-%d').date()
+                        days_held = (final_date - entry_date).days
+                        
+                        # 创建最终持仓盈亏记录
+                        final_record = {
+                            'commodity': commodity,
+                            'contract': current_contract,
+                            'strategy': strategy_name,
+                            'entry_date': position['entry_date'],
+                            'exit_date': final_date.strftime('%Y-%m-%d'),
+                            'entry_price': position['entry_price'],
+                            'exit_price': final_price,
+                            'quantity': position['quantity'],
+                            'profit_loss': profit_loss,
+                            'profit_loss_pct': profit_loss_pct,
+                            'days_held': days_held,
+                            'exit_reason': 'final_settlement'
+                        }
+                        
+                        if self.verbose_logging:
+                            print(f"      创建最终结算记录: 头寸ID={position_id}, 开仓价格={position['entry_price']}, 结算价格={final_price:.2f}")
+                        
+                        final_pnl_records.append(final_record)
+                        
+                        # 将头寸标记为已平仓
+                        self.active_positions[strategy_name][commodity][position_id]['status'] = 'final_settled'
+                        
+                        if self.verbose_logging:
+                            print(f"  {commodity} {strategy_name} 最终结算: {position['entry_price']} -> {final_price:.2f}, 盈亏: {profit_loss:.2f}")
+        
+        # 将最终盈亏记录添加到交易结果中
+        for record in final_pnl_records:
+            strategy_name = record['strategy']
+            self.trading_results[strategy_name].append(record)
+        
+        
+        if self.verbose_logging:
+            total_final_records = len(final_pnl_records)
+            total_final_pnl = sum(record['profit_loss'] for record in final_pnl_records)
+            print(f"最终持仓结算完成，共 {total_final_records} 个头寸，总未实现盈亏: {total_final_pnl:.2f}")
+            
+            # 显示所有最终结算记录的详情
+            if final_pnl_records:
+                print(f"最终结算记录详情:")
+                for i, record in enumerate(final_pnl_records, 1):
+                    print(f"  {i}. {record['commodity']} {record['strategy']}: {record['entry_price']} -> {record['exit_price']:.2f}, 盈亏: {record['profit_loss']:.2f}, 合约: {record['contract']}")
+    
+    def _check_and_handle_contract_switch(self, commodity, current_date):
+        """
+        检查并处理主力合约切换
+        只有真正的合约切换才会触发平仓和重新建仓，初始设置不会
+        """
+        if commodity not in self.dominant_contract_history:
+            return
+        
+        # 检查当天是否有合约变化
+        contract_changes = self.dominant_contract_history[commodity]
+        for change in contract_changes:
+            if change['date'] == current_date:
+                # 检查是否为初始合约设置
+                if change.get('is_initial', False):
+                    # 初始合约设置，不需要平仓和重新建仓，只需要启动正常交易逻辑
+                    if self.verbose_logging:
+                        print(f"\n{current_date}: {commodity} 初始合约设置为 {change['selected_contract']}")
+                    return
+                
+                # 真正的合约切换
+                old_contract = self._get_current_contract(commodity, current_date - datetime.timedelta(days=1))
+                new_contract = change['selected_contract']
+                
+                if self.verbose_logging:
+                    print(f"\n{current_date}: {commodity} 合约切换 {old_contract} -> {new_contract}")
+                
+                # 平掉旧合约的所有头寸
+                self._close_all_positions_on_switch(commodity, old_contract, current_date)
+                
+                # 在新合约中重新建仓
+                self._reestablish_positions_in_new_contract(commodity, new_contract, current_date)
+                
+                break
+    
+    def _get_current_contract(self, commodity, date):
+        """获取指定日期的当前合约"""
+        if commodity not in self.dominant_contract_history:
+            return None
+        
+        contract_changes = self.dominant_contract_history[commodity]
+        current_contract = None
+        
+        for change in contract_changes:
+            if change['date'] <= date:
+                current_contract = change['selected_contract']
+            else:
+                break
+        
+        return current_contract
+    
+    def _close_all_positions_on_switch(self, commodity, old_contract, switch_date):
+        """
+        在合约切换时平掉旧合约的所有头寸
+        """
+        if self.verbose_logging:
+            print(f"  平掉 {old_contract} 的所有头寸")
+        
+        # 获取当日收盘价
+        close_price = self._get_price_on_date(commodity, old_contract, switch_date, 'close')
+        if close_price is None:
+            if self.verbose_logging:
+                print(f"    无法获取 {switch_date} 的价格数据，跳过平仓")
+            return
+        
+        # 平掉基础头寸交易的头寸
+        if commodity in self.active_positions['base_position']:
+            positions = self.active_positions['base_position'][commodity].copy()
+            for position_id, position in positions.items():
+                if position['contract'] == old_contract and position['status'] == 'open':
+                    # 使用正确的期货盈亏计算公式（基础头寸都是多头）
+                    profit_loss = self._calculate_futures_pnl(
+                        position['entry_price'], close_price, position['quantity'], commodity, is_long=True
+                    )
+                    profit_loss_pct = (close_price - position['entry_price']) / position['entry_price']
+                    
+                    trade_record = {
+                        'commodity': commodity,
+                        'contract': old_contract,
+                        'strategy': 'base_position',
+                        'entry_date': position['entry_date'],
+                        'exit_date': switch_date.strftime('%Y-%m-%d'),
+                        'entry_price': position['entry_price'],
+                        'exit_price': close_price,
+                        'quantity': position['quantity'],
+                        'profit_loss': profit_loss,
+                        'profit_loss_pct': profit_loss_pct,
+                        'days_held': (switch_date - datetime.datetime.strptime(position['entry_date'], '%Y-%m-%d').date()).days,
+                        'exit_reason': 'contract_switch'
+                    }
+                    
+                    self.trading_results['base_position'].append(trade_record)
+                    self.active_positions['base_position'][commodity][position_id]['status'] = 'closed'
+                    self.active_positions['base_position'][commodity][position_id]['close_reason'] = 'contract_switch'
+                    
+                    if self.verbose_logging:
+                        print(f"    基础头寸平仓: {position['entry_price']} -> {close_price:.2f}, 盈亏: {profit_loss:.2f}")
+        
+        # 平掉网格交易的头寸
+        if commodity in self.active_positions['grid_trading']:
+            positions = self.active_positions['grid_trading'][commodity].copy()
+            for position_id, position in positions.items():
+                if position['contract'] == old_contract and position['status'] == 'open':
+                    # 使用正确的期货盈亏计算公式（网格交易都是多头）
+                    profit_loss = self._calculate_futures_pnl(
+                        position['entry_price'], close_price, position['quantity'], commodity, is_long=True
+                    )
+                    profit_loss_pct = (close_price - position['entry_price']) / position['entry_price']
+                    
+                    trade_record = {
+                        'commodity': commodity,
+                        'contract': old_contract,
+                        'strategy': 'grid_trading',
+                        'entry_date': position['entry_date'],
+                        'exit_date': switch_date.strftime('%Y-%m-%d'),
+                        'entry_price': position['entry_price'],
+                        'exit_price': close_price,
+                        'quantity': position['quantity'],
+                        'profit_loss': profit_loss,
+                        'profit_loss_pct': profit_loss_pct,
+                        'days_held': (switch_date - datetime.datetime.strptime(position['entry_date'], '%Y-%m-%d').date()).days,
+                        'exit_reason': 'contract_switch'
+                    }
+                    
+                    self.trading_results['grid_trading'].append(trade_record)
+                    self.active_positions['grid_trading'][commodity][position_id]['status'] = 'closed'
+                    self.active_positions['grid_trading'][commodity][position_id]['close_reason'] = 'contract_switch'
+                    
+                    if self.verbose_logging:
+                        print(f"    网格头寸平仓: {position['entry_price']} -> {close_price:.2f}, 盈亏: {profit_loss:.2f}")
+    
+    def _reestablish_positions_in_new_contract(self, commodity, new_contract, switch_date):
+        """
+        在新合约中重新建仓
+        """
+        if self.verbose_logging:
+            print(f"  在 {new_contract} 中重新建仓")
+        
+        # 获取当日收盘价
+        close_price = self._get_price_on_date(commodity, new_contract, switch_date, 'close')
+        if close_price is None:
+            if self.verbose_logging:
+                print(f"    无法获取 {switch_date} 的价格数据，跳过重新建仓")
+            return
+        
+        # 基础头寸交易重新建仓
+        self._reestablish_base_positions(commodity, new_contract, close_price, switch_date)
+        
+        # 网格交易重新建仓
+        self._reestablish_grid_positions(commodity, new_contract, close_price, switch_date)
+    
+    def _reestablish_base_positions(self, commodity, new_contract, close_price, switch_date):
+        """重新建立基础头寸"""
+        if commodity not in self.base_position_grid:
+            return
+        
+        # 获取之前被平掉的基础头寸信息（按价格水平记录）
+        closed_positions = {}  # price_level -> quantity
+        if commodity in self.active_positions['base_position']:
+            for position in self.active_positions['base_position'][commodity].values():
+                if position['status'] == 'closed' and 'contract_switch' in position.get('close_reason', ''):
+                    # 只处理因合约切换而平掉的头寸
+                    original_price = position.get('original_price_level', position['entry_price'])
+                    if original_price not in closed_positions:
+                        closed_positions[original_price] = 0
+                    closed_positions[original_price] += position['quantity']
+                    
+                    if self.verbose_logging:
+                        print(f"    发现需重建的基础头寸: {original_price}水平 {position['quantity']}手 (原合约: {position['contract']})")
+        
+        # 根据当前价格和原始价格水平重建头寸
+        price_grid = self.base_position_grid[commodity]
+        reestablish_count = 0
+        
+        for target_price, configured_quantity in price_grid.items():
+            # 只有当目标价格大于等于当前价格时才重建头寸
+            # 这确保了只重建"应该持有"的价格水平头寸
+            if target_price >= close_price:
+                # 检查是否有该价格水平的平仓头寸需要重建
+                if target_price in closed_positions:
+                    quantity_to_reestablish = closed_positions[target_price]
+                    if self.verbose_logging:
+                        print(f"    重建条件检查: {target_price} >= {close_price:.2f} ✓ (重建原有平仓头寸)")
+                else:
+                    # 当前价格低于目标价格，应该建立该价格水平的头寸
+                    quantity_to_reestablish = configured_quantity
+                    if self.verbose_logging:
+                        print(f"    重建条件检查: {target_price} >= {close_price:.2f} ✓ (建立新头寸)")
+            else:
+                # 当前价格高于目标价格，不重建
+                if self.verbose_logging:
+                    print(f"    重建条件检查: {target_price} >= {close_price:.2f} ✗ (跳过重建)")
+                continue
+            
+            if quantity_to_reestablish > 0:
+                position_id = f"{commodity}_{new_contract}_{switch_date}_base_reestablish_{target_price}"
+                
+                if commodity not in self.active_positions['base_position']:
+                    self.active_positions['base_position'][commodity] = {}
+                
+                self.active_positions['base_position'][commodity][position_id] = {
+                    'contract': new_contract,
+                    'entry_date': switch_date.strftime('%Y-%m-%d'),
+                    'entry_price': close_price,  # 实际成交价格
+                    'original_price_level': target_price,  # 原始价格水平
+                    'quantity': quantity_to_reestablish,
+                    'status': 'open',
+                    'exit_target': self.base_position_exit_price.get(commodity)
+                }
+                
+                if self.verbose_logging:
+                    print(f"      创建重建头寸: {position_id}")
+                    print(f"      实际成交价格: {close_price}, 原始价格水平: {target_price}, 数量: {quantity_to_reestablish}")
+                
+                reestablish_count += quantity_to_reestablish
+                
+                if self.verbose_logging:
+                    print(f"    重建基础头寸 {target_price}水平: {quantity_to_reestablish} 手 @ {close_price:.2f}")
+        
+        if reestablish_count > 0 and self.verbose_logging:
+            print(f"    基础头寸重建完成，总计: {reestablish_count} 手")
+    
+    def _reestablish_grid_positions(self, commodity, new_contract, close_price, switch_date):
+        """重新建立网格交易头寸"""
+        if commodity not in self.grid_trading_config:
+            return
+        
+        config = self.grid_trading_config[commodity]
+        grid_size = config['grid_size']
+        quantity_per_grid = config['quantity_per_grid']
+        exit_grid_size = config['exit_grid_size']
+        
+        # 获取之前的网格头寸信息
+        previous_grid_levels = set()
+        if commodity in self.active_positions['grid_trading']:
+            for position in self.active_positions['grid_trading'][commodity].values():
+                if position['status'] == 'closed' and 'contract_switch' in position.get('close_reason', ''):
+                    # 只处理因合约切换而平掉的头寸
+                    previous_grid_levels.add(position['entry_price'])
+                    
+                    if self.verbose_logging:
+                        print(f"    发现需重建的网格头寸: {position['entry_price']}水平 {position['quantity']}手")
+        
+        # 仅在原始网格开仓价格大于等于当前价格时重新建仓
+        # 这确保了只重建"应该持有"的网格水平头寸
+        reestablish_count = 0
+        for grid_level in previous_grid_levels:
+            if grid_level >= close_price:
+                if self.verbose_logging:
+                    print(f"    网格重建条件检查: {grid_level} >= {close_price:.2f} ✓ (重建网格头寸)")
+                position_id = f"{commodity}_{new_contract}_{switch_date}_grid_{grid_level}"
+                
+                if commodity not in self.active_positions['grid_trading']:
+                    self.active_positions['grid_trading'][commodity] = {}
+                
+                self.active_positions['grid_trading'][commodity][position_id] = {
+                    'contract': new_contract,
+                    'entry_date': switch_date.strftime('%Y-%m-%d'),
+                    'entry_price': close_price,
+                    'original_grid_level': grid_level,
+                    'quantity': quantity_per_grid,
+                    'status': 'open',
+                    'exit_target': grid_level + exit_grid_size  # 保持原始退出价格
+                }
+                
+                reestablish_count += 1
+            else:
+                if self.verbose_logging:
+                    print(f"    网格重建条件检查: {grid_level} >= {close_price:.2f} ✗ (跳过重建)")
+                continue
+        
+        # 同时检查是否需要开立新的网格头寸（价格更低的情况）
+        start_price = config['start_price']
+        current_level = start_price
+        while current_level > close_price:
+            current_level -= grid_size
+            if current_level not in previous_grid_levels and current_level > 0:
+                # 这是一个新的网格水平
+                position_id = f"{commodity}_{new_contract}_{switch_date}_grid_new_{current_level}"
+                
+                if commodity not in self.active_positions['grid_trading']:
+                    self.active_positions['grid_trading'][commodity] = {}
+                
+                self.active_positions['grid_trading'][commodity][position_id] = {
+                    'contract': new_contract,
+                    'entry_date': switch_date.strftime('%Y-%m-%d'),
+                    'entry_price': close_price,
+                    'original_grid_level': current_level,
+                    'quantity': quantity_per_grid,
+                    'status': 'open',
+                    'exit_target': current_level + exit_grid_size
+                }
+                
+                reestablish_count += 1
+        
+        if self.verbose_logging and reestablish_count > 0:
+            print(f"    重建网格头寸: {reestablish_count} 个网格 @ {close_price:.2f}")
+    
+    def _get_price_on_date(self, commodity, contract, date, price_type='close'):
+        """获取指定日期和合约的价格（增强NaN问题诊断）"""
+        if commodity not in self.price_data or contract not in self.price_data[commodity]:
+            if self.verbose_logging:
+                print(f"  ❌ 价格数据不存在: {commodity} -> {contract}")
+            return None
+        
+        price_data = self.price_data[commodity][contract]
+        
+        # 找到日期对应的价格
+        target_date = date if isinstance(date, datetime.date) else date.date()
+        
+        for idx, row in price_data.iterrows():
+            if idx.date() == target_date:
+                price_value = row[price_type]
+                
+                if self.verbose_logging:
+                    print(f'{price_type}的价格是: {price_value}')
+                
+                # 如果价格为NaN，进行详细诊断
+                if pd.isna(price_value):
+                    self._diagnose_nan_price_issue(commodity, contract, target_date, price_type, row)
+                    return None
+                else:
+                    return price_value
+        
+        # 如果没有找到精确日期，尝试查找最近的交易日
+        if self.verbose_logging:
+            print(f"  ⚠️  未找到 {contract} 在 {target_date} 的数据，尝试查找最近交易日...")
+        
+        return self._get_nearest_trading_day_price(commodity, contract, target_date, price_type)
+    
+    def _diagnose_nan_price_issue(self, commodity, contract, date, price_type, row):
+        """诊断NaN价格问题的根本原因"""
+        if self.verbose_logging:
+            print(f"  🔍 NaN价格问题诊断: {commodity} {contract} {date}")
+            print(f"    目标价格类型: {price_type}")
+            print(f"    该日所有价格数据: 开盘={row['open']}, 收盘={row['close']}, 最高={row['high']}, 最低={row['low']}, 成交量={row['volume']}")
+            
+            # 检查是否所有价格都是NaN
+            price_fields = ['open', 'close', 'high', 'low']
+            nan_fields = [field for field in price_fields if pd.isna(row[field])]
+            valid_fields = [field for field in price_fields if not pd.isna(row[field])]
+            
+            if len(nan_fields) == len(price_fields):
+                print(f"    ❌ 所有价格字段都为NaN - 可能该合约在此日期未开始交易")
+            else:
+                print(f"    ⚠️  部分价格字段为NaN: {nan_fields}")
+                print(f"    ✅ 有效价格字段: {valid_fields}")
+                
+                # 如果有有效价格，尝试使用替代方案
+                if valid_fields:
+                    fallback_price = row[valid_fields[0]]
+                    print(f"    💡 建议使用替代价格: {valid_fields[0]} = {fallback_price}")
+            
+            # 检查成交量是否为0或NaN
+            if pd.isna(row['volume']) or row['volume'] == 0:
+                print(f"    ⚠️  成交量异常: {row['volume']} - 可能该合约在此日期无交易活动")
+            
+            # 检查是否是合约刚上市的情况
+            price_data = self.price_data[commodity][contract]
+            first_valid_date = None
+            for idx, data_row in price_data.iterrows():
+                if not pd.isna(data_row['close']):
+                    first_valid_date = idx.date()
+                    break
+            
+            if first_valid_date and date < first_valid_date:
+                print(f"    🔍 合约首次有效交易日: {first_valid_date} (查询日期 {date} 早于首次交易日)")
+                print(f"    💡 建议: 合约 {contract} 在 {date} 可能尚未开始交易")
+            
+            # 提供解决建议
+            print(f"    📋 解决建议:")
+            print(f"      1. 检查合约 {contract} 的上市日期")
+            print(f"      2. 验证合约代码是否正确")
+            print(f"      3. 考虑调整合约切换日期")
+            if valid_fields:
+                print(f"      4. 临时使用替代价格: {valid_fields[0]} = {row[valid_fields[0]]}")
+    
+    def _get_nearest_trading_day_price(self, commodity, contract, target_date, price_type):
+        """获取最近交易日的价格"""
+        price_data = self.price_data[commodity][contract]
+        
+        # 查找最近的交易日（前后5天范围内）
+        search_range = 5
+        for offset in range(1, search_range + 1):
+            # 先查找之后的日期
+            future_date = target_date + datetime.timedelta(days=offset)
+            for idx, row in price_data.iterrows():
+                if idx.date() == future_date:
+                    price_value = row[price_type]
+                    if not pd.isna(price_value):
+                        if self.verbose_logging:
+                            print(f"  ✅ 使用后续交易日 {future_date} 的价格: {price_value}")
+                        return price_value
+                    break
+            
+            # 再查找之前的日期
+            past_date = target_date - datetime.timedelta(days=offset)
+            for idx, row in price_data.iterrows():
+                if idx.date() == past_date:
+                    price_value = row[price_type]
+                    if not pd.isna(price_value):
+                        if self.verbose_logging:
+                            print(f"  ✅ 使用前期交易日 {past_date} 的价格: {price_value}")
+                        return price_value
+                    break
+        
+        if self.verbose_logging:
+            print(f"  ❌ 在 {search_range} 天范围内未找到有效的 {price_type} 价格")
+        return None
+    
+    def _process_daily_trading(self, current_date):
+        """处理每日的正常交易逻辑"""
+        for commodity in self.core_commodities.keys():
+            current_contract = self._get_current_contract(commodity, current_date)
+            if not current_contract:
+                continue
+            
+            # 获取当日价格数据
+            daily_prices = self._get_daily_prices(commodity, current_contract, current_date)
+            if not daily_prices:
+                continue
+            
+            # 检查基础头寸入场和退出机会
+            self._check_base_position_trading(commodity, current_contract, current_date, daily_prices)
+            
+            # 检查网格交易入场和退出机会
+            self._check_grid_trading(commodity, current_contract, current_date, daily_prices)
+    
+    def _get_daily_prices(self, commodity, contract, date):
+        """获取指定日期的价格数据"""
+        if commodity not in self.price_data or contract not in self.price_data[commodity]:
+            return None
+        
+        price_data = self.price_data[commodity][contract]
+        
+        for idx, row in price_data.iterrows():
+            if idx.date() == date:
+                return {
+                    'open': row['open'],
+                    'close': row['close'],
+                    'high': row['high'],
+                    'low': row['low'],
+                    'volume': row['volume']
+                }
+        
+        return None
+    
+    def _check_base_position_trading(self, commodity, contract, current_date, daily_prices):
+        """检查基础头寸交易机会
+        
+        逻辑：每日主动检查所有网格水平
+        - 如果当前收盘价低于某个网格水平价格
+        - 且该网格水平没有未平仓头寸
+        - 则以当日收盘价在该网格水平开仓
+        """
+        if commodity not in self.base_position_grid:
+            return
+        
+        # 检查入场机会 - 遍历所有网格水平
+        price_grid = self.base_position_grid[commodity]
+        current_close_price = daily_prices['close']
+        
+        for entry_price, quantity in price_grid.items():
+            # 检查是否已经有这个价格水平的头寸
+            position_exists = False
+            if commodity in self.active_positions['base_position']:
+                for position in self.active_positions['base_position'][commodity].values():
+                    if (position['contract'] == contract and position['status'] == 'open'):
+                        # 检查原始价格水平
+                        position_price_level = position.get('original_price_level', position['entry_price'])
+                        if position_price_level == entry_price:
+                            position_exists = True
+                            break
+            
+            # 主动开仓逻辑：当前价格低于网格水平价格 且 该水平没有头寸
+            if not position_exists and current_close_price < entry_price:
+                # 以当日收盘价建立头寸
+                position_id = f"{commodity}_{contract}_{current_date}_base_{entry_price}"
+                if commodity not in self.active_positions['base_position']:
+                    self.active_positions['base_position'][commodity] = {}
+                
+                self.active_positions['base_position'][commodity][position_id] = {
+                    'contract': contract,
+                    'entry_date': current_date.strftime('%Y-%m-%d'),
+                    'entry_price': current_close_price,  # 使用收盘价作为入场价格
+                    'original_price_level': entry_price,  # 记录原始网格水平
+                    'quantity': quantity,
+                    'status': 'open',
+                    'exit_target': self.base_position_exit_price.get(commodity)
+                }
+                
+                if self.verbose_logging:
+                    print(f"  {current_date}: {commodity} 基础头寸入场 @ 网格{entry_price} (实际价格: {current_close_price:.2f}), 数量: {quantity}，当天收盘价{current_close_price:.2f}低于网格价格{entry_price}")
+        
+        # 检查退出机会
+        exit_target = self.base_position_exit_price.get(commodity)
+        if exit_target and commodity in self.active_positions['base_position']:
+            positions_to_close = []
+            for position_id, position in self.active_positions['base_position'][commodity].items():
+                if position['contract'] == contract and position['status'] == 'open' and daily_prices['high'] >= exit_target:
+                    positions_to_close.append(position_id)
+            
+            for position_id in positions_to_close:
+                position = self.active_positions['base_position'][commodity][position_id]
+                exit_price = min(exit_target, daily_prices['high'])
+                
+                # 使用正确的期货盈亏计算公式（基础头寸都是多头）
+                profit_loss = self._calculate_futures_pnl(
+                    position['entry_price'], exit_price, position['quantity'], commodity, is_long=True
+                )
+                profit_loss_pct = (exit_price - position['entry_price']) / position['entry_price']
+                
+                trade_record = {
+                    'commodity': commodity,
+                    'contract': contract,
+                    'strategy': 'base_position',
+                    'entry_date': position['entry_date'],
+                    'exit_date': current_date.strftime('%Y-%m-%d'),
+                    'entry_price': position['entry_price'],
+                    'exit_price': exit_price,
+                    'quantity': position['quantity'],
+                    'profit_loss': profit_loss,
+                    'profit_loss_pct': profit_loss_pct,
+                    'days_held': (current_date - datetime.datetime.strptime(position['entry_date'], '%Y-%m-%d').date()).days,
+                    'exit_reason': 'target_reached'
+                }
+                
+                self.trading_results['base_position'].append(trade_record)
+                self.active_positions['base_position'][commodity][position_id]['status'] = 'closed'
+                
+                if self.verbose_logging:
+                    print(f"  {current_date}: {commodity} 基础头寸退出 {position['entry_price']} -> {exit_price:.2f}, 盈亏: {profit_loss:.2f}")
+    
+    def _check_grid_trading(self, commodity, contract, current_date, daily_prices):
+        """检查网格交易机会"""
+        if commodity not in self.grid_trading_config:
+            return
+        
+        config = self.grid_trading_config[commodity]
+        start_price = config['start_price']
+        grid_size = config['grid_size']
+        quantity_per_grid = config['quantity_per_grid']
+        exit_grid_size = config['exit_grid_size']
+        
+        # 检查入场机会
+        current_level = start_price
+        while current_level > daily_prices['low'] and current_level > 0:
+            position_exists = False
+            if commodity in self.active_positions['grid_trading']:
+                for position in self.active_positions['grid_trading'][commodity].values():
+                    if (position['contract'] == contract and 
+                        position.get('original_grid_level', position['entry_price']) == current_level and 
+                        position['status'] == 'open'):
+                        position_exists = True
+                        break
+            
+            if not position_exists and daily_prices['low'] <= current_level <= daily_prices['high']:
+                position_id = f"{commodity}_{contract}_{current_date}_grid_{current_level}"
+                if commodity not in self.active_positions['grid_trading']:
+                    self.active_positions['grid_trading'][commodity] = {}
+                
+                self.active_positions['grid_trading'][commodity][position_id] = {
+                    'contract': contract,
+                    'entry_date': current_date.strftime('%Y-%m-%d'),
+                    'entry_price': current_level,
+                    'original_grid_level': current_level,
+                    'quantity': quantity_per_grid,
+                    'status': 'open',
+                    'exit_target': current_level + exit_grid_size
+                }
+                
+                if self.verbose_logging:
+                    print(f"  {current_date}: {commodity} 网格入场 @ {current_level}，数量：{quantity_per_grid}，当天最低价为{daily_prices['low']}，最高价为{daily_prices['high']}")
+            
+            current_level -= grid_size
+        
+        # 检查退出机会
+        if commodity in self.active_positions['grid_trading']:
+            positions_to_close = []
+            for position_id, position in self.active_positions['grid_trading'][commodity].items():
+                if position['contract'] == contract and position['status'] == 'open':
+                    if daily_prices['high'] >= position['exit_target']:
+                        positions_to_close.append(position_id)
+            
+            for position_id in positions_to_close:
+                position = self.active_positions['grid_trading'][commodity][position_id]
+                exit_price = position['exit_target']
+                
+                # 使用正确的期货盈亏计算公式（网格交易都是多头）
+                profit_loss = self._calculate_futures_pnl(
+                    position['entry_price'], exit_price, position['quantity'], commodity, is_long=True
+                )
+                profit_loss_pct = (exit_price - position['entry_price']) / position['entry_price']
+                
+                trade_record = {
+                    'commodity': commodity,
+                    'contract': contract,
+                    'strategy': 'grid_trading',
+                    'entry_date': position['entry_date'],
+                    'exit_date': current_date.strftime('%Y-%m-%d'),
+                    'entry_price': position['entry_price'],
+                    'exit_price': exit_price,
+                    'quantity': position['quantity'],
+                    'profit_loss': profit_loss,
+                    'profit_loss_pct': profit_loss_pct,
+                    'days_held': (current_date - datetime.datetime.strptime(position['entry_date'], '%Y-%m-%d').date()).days,
+                    'exit_reason': 'target_reached'
+                }
+                
+                self.trading_results['grid_trading'].append(trade_record)
+                self.active_positions['grid_trading'][commodity][position_id]['status'] = 'closed'
+                
+                if self.verbose_logging:
+                    print(f"  {current_date}: {commodity} 网格退出 {position['entry_price']} -> {exit_price:.2f}, 盈亏: {profit_loss:.2f} ({profit_loss_pct:.2%})")
+    
+    def _extend_price_data_for_ma(self, commodity, contract, current_date, required_days=30):
+        """扩展价格数据以满足MA计算需求
+        
+        参数：
+            commodity: 商品代码
+            contract: 合约代码
+            current_date: 当前日期
+            required_days: 所需的最少数据天数
+        
+        返回：
+            扩展后的价格数据DataFrame，如果获取失败则返回None
+        """
+        cache_key = f"{commodity}_{contract}"
+        
+        # 检查缓存
+        if cache_key in self.ma_extended_data_cache:
+            cached_data = self.ma_extended_data_cache[cache_key]
+            target_date = current_date if isinstance(current_date, datetime.date) else current_date.date()
+            historical_data = cached_data[cached_data.index.date <= target_date]
+            
+            if len(historical_data) >= required_days:
+                if self.verbose_logging:
+                    print(f"  ℹ️  MA过滤器：使用缓存的扩展数据，共{len(historical_data)}天")
+                return historical_data
+        
+        # 获取现有数据
+        if commodity not in self.price_data or contract not in self.price_data[commodity]:
+            return None
+        
+        existing_data = self.price_data[commodity][contract]
+        target_date = current_date if isinstance(current_date, datetime.date) else current_date.date()
+        existing_historical = existing_data[existing_data.index.date <= target_date]
+        
+        if len(existing_historical) >= required_days:
+            return existing_historical
+        
+        # 数据不足，需要扩展获取
+        existing_count = len(existing_historical)
+        shortage = required_days - existing_count
+        
+        if self.verbose_logging:
+            print(f"  📊 MA过滤器：数据不足，开始扩展获取（当前{existing_count}天，需要{required_days}天，缺少{shortage}天）")
+        
+        # 计算扩展的开始日期：从现有数据最早日期往前推至少30个交易日
+        if len(existing_data) > 0:
+            earliest_date = existing_data.index.min().date()
+        else:
+            earliest_date = target_date
+        
+        # 往前推60个自然日（约等于40-45个交易日，提供充足缓冲）
+        extended_start_date = earliest_date - datetime.timedelta(days=60)
+        extended_end_date = target_date
+        
+        if self.verbose_logging:
+            print(f"  📊 扩展日期范围: {extended_start_date} 至 {extended_end_date}")
+        
+        try:
+            # 获取扩展的价格数据
+            extended_data = get_price(
+                contract,
+                start_date=extended_start_date,
+                end_date=extended_end_date,
+                frequency='daily',
+                fields=['open', 'close', 'high', 'low', 'volume'],
+                skip_paused=False,
+                panel=False
+            )
+            
+            if extended_data is None or len(extended_data) == 0:
+                if self.verbose_logging:
+                    print(f"  ⚠️  扩展数据获取失败：未获取到数据")
+                return existing_historical
+            
+            # 合并数据：将扩展数据与现有数据合并，保留所有日期的数据
+            # 使用concat合并，然后去重并按日期排序
+            combined_data = pd.concat([existing_data, extended_data])
+            combined_data = combined_data[~combined_data.index.duplicated(keep='first')]
+            combined_data = combined_data.sort_index()
+            
+            # 过滤到当前日期（仅用于返回给MA计算）
+            combined_historical = combined_data[combined_data.index.date <= target_date]
+            
+            if self.verbose_logging:
+                print(f"  ✅ 扩展数据获取成功：从{len(existing_historical)}天扩展到{len(combined_historical)}天")
+                print(f"     合并后完整数据范围：{combined_data.index.min().date()} 至 {combined_data.index.max().date()}（共{len(combined_data)}天）")
+            
+            # 缓存扩展后的完整数据
+            self.ma_extended_data_cache[cache_key] = combined_data
+            
+            # 更新主price_data，保留原有数据和新扩展的数据
+            self.price_data[commodity][contract] = combined_data
+            
+            return combined_historical
+            
+        except Exception as e:
+            if self.verbose_logging:
+                print(f"  ⚠️  扩展数据获取异常：{str(e)}")
+            return existing_historical
+    
+    def simulate_base_position_trading(self):
+        """
+        模拟基础头寸交易
+        为每种商品配置价格-数量网格，以指定价格水平和数量开立多头头寸
+        所有头寸使用统一的退出价格（无止损）
+        """
+        if self.verbose_logging:
+            print("\n=== 步骤3: 基础头寸交易模拟 ===")
+        
+        base_position_results = []
+        
+        for commodity in self.price_data.keys():
+            if commodity not in self.base_position_grid:
+                continue
+                
+            price_grid = self.base_position_grid[commodity]
+            exit_price = self.base_position_exit_price[commodity]
+            price_data = self.price_data[commodity]['data']
+            contract = self.price_data[commodity]['contract']
+            
+            if self.verbose_logging:
+                print(f"\n分析 {commodity} ({contract}) 基础头寸交易")
+                print(f"价格网格: {price_grid}")
+                print(f"退出价格: {exit_price}")
+            
+            # 遍历每个价格水平
+            for entry_price, quantity in price_grid.items():
+                # 查找触发入场的日期
+                entry_dates = []
+                for date, row in price_data.iterrows():
+                    if row['low'] <= entry_price <= row['high']:
+                        entry_dates.append(date)
+                
+                if not entry_dates:
+                    continue
+                
+                # 使用第一个触发日期作为入场点
+                entry_date = entry_dates[0]
+                
+                # 查找退出点
+                exit_date = None
+                exit_price_actual = exit_price
+                
+                # 在入场后查找价格达到退出价格的日期
+                for date, row in price_data.iterrows():
+                    if date > entry_date and row['high'] >= exit_price:
+                        exit_date = date
+                        exit_price_actual = min(exit_price, row['high'])
+                        break
+                
+                # 如果没有达到退出价格，使用最后一日的收盘价退出
+                if exit_date is None:
+                    exit_date = price_data.index[-1]
+                    exit_price_actual = price_data.iloc[-1]['close']
+                
+                # 计算盈亏
+                profit_loss = (exit_price_actual - entry_price) * quantity
+                profit_loss_pct = (exit_price_actual - entry_price) / entry_price
+                
+                trade_record = {
+                    'commodity': commodity,
+                    'contract': contract,
+                    'strategy': 'base_position',
+                    'entry_date': entry_date.strftime('%Y-%m-%d'),
+                    'exit_date': exit_date.strftime('%Y-%m-%d'),
+                    'entry_price': entry_price,
+                    'exit_price': exit_price_actual,
+                    'quantity': quantity,
+                    'profit_loss': profit_loss,
+                    'profit_loss_pct': profit_loss_pct,
+                    'days_held': (exit_date - entry_date).days
+                }
+                
+                base_position_results.append(trade_record)
+                
+                if self.verbose_logging:
+                    print(f"  入场: {entry_date.strftime('%Y-%m-%d')} @ {entry_price}, 数量: {quantity}")
+                    print(f"  出场: {exit_date.strftime('%Y-%m-%d')} @ {exit_price_actual:.2f}")
+                    print(f"  盈亏: {profit_loss:.2f} ({profit_loss_pct:.2%})")
+        
+        self.trading_results['base_position'] = base_position_results
+        
+        if self.verbose_logging:
+            print(f"\n基础头寸交易模拟完成，共{len(base_position_results)}笔交易")
+        
+        return base_position_results
+    
+    def simulate_grid_trading(self):
+        """
+        模拟网格交易策略
+        从start_price开始，每次价格下降grid_size时买入quantity_per_grid
+        当价格从入场价格上涨exit_grid_size时退出
+        """
+        if self.verbose_logging:
+            print("\n=== 步骤4: 网格交易策略模拟 ===")
+        
+        grid_trading_results = []
+        
+        for commodity in self.price_data.keys():
+            if commodity not in self.grid_trading_config:
+                continue
+                
+            config = self.grid_trading_config[commodity]
+            start_price = config['start_price']
+            grid_size = config['grid_size']
+            quantity_per_grid = config['quantity_per_grid']
+            exit_grid_size = config['exit_grid_size']
+            
+            price_data = self.price_data[commodity]['data']
+            contract = self.price_data[commodity]['contract']
+            
+            if self.verbose_logging:
+                print(f"\n分析 {commodity} ({contract}) 网格交易")
+                print(f"起始价格: {start_price}, 网格大小: {grid_size}")
+                print(f"每网格数量: {quantity_per_grid}, 退出网格大小: {exit_grid_size}")
+            
+            # 生成网格价格水平
+            grid_levels = []
+            current_level = start_price
+            min_price = price_data['low'].min()
+            
+            while current_level > min_price:
+                grid_levels.append(current_level)
+                current_level -= grid_size
+            
+            # 模拟每个网格水平的交易
+            for entry_price in grid_levels:
+                exit_price = entry_price + exit_grid_size
+                
+                # 查找入场机会
+                entry_date = None
+                for date, row in price_data.iterrows():
+                    if row['low'] <= entry_price <= row['high']:
+                        entry_date = date
+                        break
+                
+                if entry_date is None:
+                    continue
+                
+                # 查找退出机会
+                exit_date = None
+                exit_price_actual = exit_price
+                
+                for date, row in price_data.iterrows():
+                    if date > entry_date and row['high'] >= exit_price:
+                        exit_date = date
+                        exit_price_actual = exit_price
+                        break
+                
+                # 如果没有达到退出价格，使用最后一日收盘价
+                if exit_date is None:
+                    exit_date = price_data.index[-1]
+                    exit_price_actual = price_data.iloc[-1]['close']
+                
+                # 计算盈亏
+                profit_loss = (exit_price_actual - entry_price) * quantity_per_grid
+                profit_loss_pct = (exit_price_actual - entry_price) / entry_price
+                
+                trade_record = {
+                    'commodity': commodity,
+                    'contract': contract,
+                    'strategy': 'grid_trading',
+                    'entry_date': entry_date.strftime('%Y-%m-%d'),
+                    'exit_date': exit_date.strftime('%Y-%m-%d'),
+                    'entry_price': entry_price,
+                    'exit_price': exit_price_actual,
+                    'quantity': quantity_per_grid,
+                    'profit_loss': profit_loss,
+                    'profit_loss_pct': profit_loss_pct,
+                    'days_held': (exit_date - entry_date).days
+                }
+                
+                grid_trading_results.append(trade_record)
+                
+                if self.verbose_logging:
+                    print(f"  网格 {entry_price}: {entry_date.strftime('%Y-%m-%d')} -> {exit_date.strftime('%Y-%m-%d')}")
+                    print(f"    盈亏: {profit_loss:.2f} ({profit_loss_pct:.2%})")
+        
+        self.trading_results['grid_trading'] = grid_trading_results
+        
+        if self.verbose_logging:
+            print(f"\n网格交易模拟完成，共{len(grid_trading_results)}笔交易")
+        
+        return grid_trading_results
+    
+    def calculate_performance_statistics(self):
+        """
+        计算多品种多级聚合的性能统计
+        包括品种-策略级、品种级、策略级和总体级统计
+        """
+        if self.verbose_logging:
+            print("\n=== 步骤6: 多级性能统计分析 ===")
+        
+        # 多级统计结构
+        performance_stats = {
+            'by_commodity_strategy': {},  # 品种-策略级统计
+            'by_commodity': {},           # 品种级汇总
+            'by_strategy': {},            # 策略级汇总  
+            'overall': {}                 # 总体汇总
+        }
+        
+        if self.verbose_logging:
+            print("\n--- 第一级：品种-策略级统计 ---")
+        
+        # 第一步：计算品种-策略级统计
+        for strategy_name, results in self.trading_results.items():
+            if strategy_name not in performance_stats['by_commodity_strategy']:
+                performance_stats['by_commodity_strategy'][strategy_name] = {}
+            
+            # 按品种分组交易结果
+            commodity_results = {}
+            for result in results:
+                commodity = result['commodity']
+                if commodity not in commodity_results:
+                    commodity_results[commodity] = []
+                commodity_results[commodity].append(result)
+            
+            # 为每个品种计算统计
+            for commodity in self.core_commodities.keys():
+                comm_results = commodity_results.get(commodity, [])
+                stats = self._calculate_single_strategy_stats(strategy_name, comm_results, commodity)
+                performance_stats['by_commodity_strategy'][strategy_name][commodity] = stats
+                
+                if self.verbose_logging:
+                    print(f"\n{commodity}-{strategy_name} 策略统计:")
+                    self._print_strategy_stats(stats)
+        
+        if self.verbose_logging:
+            print("\n--- 第二级：品种级汇总统计 ---")
+        
+        # 第二步：计算品种级汇总统计
+        for commodity in self.core_commodities.keys():
+            commodity_stats = self._calculate_commodity_summary(commodity, performance_stats['by_commodity_strategy'])
+            performance_stats['by_commodity'][commodity] = commodity_stats
+            
+            if self.verbose_logging:
+                print(f"\n{commodity} 品种汇总统计:")
+                self._print_strategy_stats(commodity_stats)
+        
+        if self.verbose_logging:
+            print("\n--- 第三级：策略级汇总统计 ---")
+        
+        # 第三步：计算策略级汇总统计
+        for strategy_name in self.trading_results.keys():
+            strategy_stats = self._calculate_strategy_summary(strategy_name, performance_stats['by_commodity_strategy'])
+            performance_stats['by_strategy'][strategy_name] = strategy_stats
+            
+            if self.verbose_logging:
+                print(f"\n{strategy_name} 策略汇总统计:")
+                self._print_strategy_stats(strategy_stats)
+        
+        if self.verbose_logging:
+            print("\n--- 第四级：整体汇总统计 ---")
+        
+        # 第四步：计算总体统计
+        overall_stats = self._calculate_overall_summary(performance_stats['by_strategy'])
+        performance_stats['overall'] = overall_stats
+        
+        if self.verbose_logging:
+            print(f"\n整体汇总统计:")
+            self._print_strategy_stats(overall_stats)
+        
+        return performance_stats
+    
+    def _calculate_single_strategy_stats(self, strategy_name, results, commodity):
+        """计算单个策略在特定品种下的统计数据"""
+        # 计算已平仓交易的盈亏
+        closed_profit_loss = sum(r['profit_loss'] for r in results) if results else 0.0
+        
+        # 计算未平仓头寸的未实现盈亏（特定品种）
+        unrealized_profit_loss = self._calculate_unrealized_pnl_for_commodity(strategy_name, commodity)
+        
+        # 总盈亏 = 已实现盈亏 + 未实现盈亏
+        total_profit_loss = closed_profit_loss + unrealized_profit_loss
+        
+        if not results and unrealized_profit_loss == 0:
+            return {
+                'total_trades': 0,
+                'open_positions': 0,
+                'profitable_trades': 0,
+                'losing_trades': 0,
+                'win_rate': 0.0,
+                'closed_profit_loss': 0.0,
+                'unrealized_profit_loss': 0.0,
+                'total_profit_loss': 0.0,
+                'avg_profit_loss': 0.0,
+                'avg_profit_loss_pct': 0.0,
+                'max_profit': 0.0,
+                'max_loss': 0.0,
+                'avg_holding_days': 0.0,
+                'profit_factor': 0.0
+            }
+        
+        # 基本统计
+        total_trades = len(results)
+        open_positions = self._count_open_positions_for_commodity(strategy_name, commodity)
+        profitable_trades = sum(1 for r in results if r['profit_loss'] > 0)
+        losing_trades = sum(1 for r in results if r['profit_loss'] < 0)
+        win_rate = profitable_trades / total_trades if total_trades > 0 else 0
+        
+        # 平均盈亏（基于已平仓交易）
+        avg_profit_loss = closed_profit_loss / total_trades if total_trades > 0 else 0
+        avg_profit_loss_pct = sum(r['profit_loss_pct'] for r in results) / total_trades if total_trades > 0 else 0
+        
+        # 最大盈亏（基于已平仓交易）
+        profit_losses = [r['profit_loss'] for r in results]
+        max_profit = max(profit_losses) if profit_losses else 0
+        max_loss = min(profit_losses) if profit_losses else 0
+        
+        # 平均持有天数
+        avg_holding_days = sum(r['days_held'] for r in results) / total_trades if total_trades > 0 else 0
+        
+        # 盈亏比（基于已平仓交易）
+        total_profits = sum(r['profit_loss'] for r in results if r['profit_loss'] > 0)
+        total_losses = abs(sum(r['profit_loss'] for r in results if r['profit_loss'] < 0))
+        profit_factor = total_profits / total_losses if total_losses > 0 else float('inf') if total_profits > 0 else 0
+        
+        return {
+            'total_trades': total_trades,
+            'open_positions': open_positions,
+            'profitable_trades': profitable_trades,
+            'losing_trades': losing_trades,
+            'win_rate': win_rate,
+            'closed_profit_loss': closed_profit_loss,
+            'unrealized_profit_loss': unrealized_profit_loss,
+            'total_profit_loss': total_profit_loss,
+            'avg_profit_loss': avg_profit_loss,
+            'avg_profit_loss_pct': avg_profit_loss_pct,
+            'max_profit': max_profit,
+            'max_loss': max_loss,
+            'avg_holding_days': avg_holding_days,
+            'profit_factor': profit_factor
+        }
+    
+    def _print_strategy_stats(self, stats):
+        """打印策略统计信息"""
+        print(f"  已平仓交易: {stats['total_trades']}")
+        print(f"  未平仓头寸: {stats['open_positions']}")
+        print(f"  盈利交易: {stats['profitable_trades']}")
+        print(f"  亏损交易: {stats['losing_trades']}")
+        print(f"  胜率: {stats['win_rate']:.2%}")
+        print(f"  已实现盈亏: {stats['closed_profit_loss']:.2f}")
+        print(f"  未实现盈亏: {stats['unrealized_profit_loss']:.2f}")
+        print(f"  总盈亏: {stats['total_profit_loss']:.2f}")
+        print(f"  平均盈亏: {stats['avg_profit_loss']:.2f}")
+        print(f"  平均盈亏率: {stats['avg_profit_loss_pct']:.2%}")
+        print(f"  最大盈利: {stats['max_profit']:.2f}")
+        print(f"  最大亏损: {stats['max_loss']:.2f}")
+        print(f"  平均持有天数: {stats['avg_holding_days']:.1f}")
+        profit_factor_str = f"{stats['profit_factor']:.2f}" if stats['profit_factor'] != float('inf') else "∞"
+        print(f"  盈亏比: {profit_factor_str}")
+    
+    def _calculate_commodity_summary(self, commodity, by_commodity_strategy):
+        """计算品种级汇总统计"""
+        total_stats = {
+            'total_trades': 0,
+            'open_positions': 0,
+            'profitable_trades': 0,
+            'losing_trades': 0,
+            'closed_profit_loss': 0.0,
+            'unrealized_profit_loss': 0.0,
+            'total_profit_loss': 0.0,
+            'max_profit': 0.0,
+            'max_loss': 0.0,
+            'total_holding_days': 0.0,
+            'total_profits': 0.0,
+            'total_losses': 0.0,
+            'all_pct': []
+        }
+        
+        for strategy_name, commodity_stats in by_commodity_strategy.items():
+            if commodity in commodity_stats:
+                stats = commodity_stats[commodity]
+                total_stats['total_trades'] += stats['total_trades']
+                total_stats['open_positions'] += stats['open_positions']
+                total_stats['profitable_trades'] += stats['profitable_trades']
+                total_stats['losing_trades'] += stats['losing_trades']
+                total_stats['closed_profit_loss'] += stats['closed_profit_loss']
+                total_stats['unrealized_profit_loss'] += stats['unrealized_profit_loss']
+                total_stats['total_profit_loss'] += stats['total_profit_loss']
+                total_stats['max_profit'] = max(total_stats['max_profit'], stats['max_profit'])
+                total_stats['max_loss'] = min(total_stats['max_loss'], stats['max_loss'])
+                total_stats['total_holding_days'] += stats['avg_holding_days'] * stats['total_trades']
+                
+                if stats['profit_factor'] != float('inf'):
+                    profits = stats['closed_profit_loss'] if stats['closed_profit_loss'] > 0 else 0
+                    losses = abs(stats['closed_profit_loss']) if stats['closed_profit_loss'] < 0 else 0
+                    total_stats['total_profits'] += profits
+                    total_stats['total_losses'] += losses
+                
+                # 收集所有百分比数据
+                if stats['total_trades'] > 0:
+                    total_stats['all_pct'].extend([stats['avg_profit_loss_pct']] * stats['total_trades'])
+        
+        # 计算汇总指标
+        win_rate = total_stats['profitable_trades'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        avg_profit_loss = total_stats['closed_profit_loss'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        avg_profit_loss_pct = sum(total_stats['all_pct']) / len(total_stats['all_pct']) if total_stats['all_pct'] else 0
+        avg_holding_days = total_stats['total_holding_days'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        profit_factor = total_stats['total_profits'] / total_stats['total_losses'] if total_stats['total_losses'] > 0 else float('inf') if total_stats['total_profits'] > 0 else 0
+        
+        return {
+            'total_trades': total_stats['total_trades'],
+            'open_positions': total_stats['open_positions'],
+            'profitable_trades': total_stats['profitable_trades'],
+            'losing_trades': total_stats['losing_trades'],
+            'win_rate': win_rate,
+            'closed_profit_loss': total_stats['closed_profit_loss'],
+            'unrealized_profit_loss': total_stats['unrealized_profit_loss'],
+            'total_profit_loss': total_stats['total_profit_loss'],
+            'avg_profit_loss': avg_profit_loss,
+            'avg_profit_loss_pct': avg_profit_loss_pct,
+            'max_profit': total_stats['max_profit'],
+            'max_loss': total_stats['max_loss'],
+            'avg_holding_days': avg_holding_days,
+            'profit_factor': profit_factor
+        }
+    
+    def _calculate_strategy_summary(self, strategy_name, by_commodity_strategy):
+        """计算策略级汇总统计"""
+        if strategy_name not in by_commodity_strategy:
+            return self._get_empty_stats()
+        
+        strategy_stats = by_commodity_strategy[strategy_name]
+        
+        total_stats = {
+            'total_trades': 0,
+            'open_positions': 0,
+            'profitable_trades': 0,
+            'losing_trades': 0,
+            'closed_profit_loss': 0.0,
+            'unrealized_profit_loss': 0.0,
+            'total_profit_loss': 0.0,
+            'max_profit': 0.0,
+            'max_loss': 0.0,
+            'total_holding_days': 0.0,
+            'total_profits': 0.0,
+            'total_losses': 0.0,
+            'all_pct': []
+        }
+        
+        for commodity, stats in strategy_stats.items():
+            total_stats['total_trades'] += stats['total_trades']
+            total_stats['open_positions'] += stats['open_positions']
+            total_stats['profitable_trades'] += stats['profitable_trades']
+            total_stats['losing_trades'] += stats['losing_trades']
+            total_stats['closed_profit_loss'] += stats['closed_profit_loss']
+            total_stats['unrealized_profit_loss'] += stats['unrealized_profit_loss']
+            total_stats['total_profit_loss'] += stats['total_profit_loss']
+            total_stats['max_profit'] = max(total_stats['max_profit'], stats['max_profit'])
+            total_stats['max_loss'] = min(total_stats['max_loss'], stats['max_loss'])
+            total_stats['total_holding_days'] += stats['avg_holding_days'] * stats['total_trades']
+            
+            if stats['profit_factor'] != float('inf'):
+                profits = stats['closed_profit_loss'] if stats['closed_profit_loss'] > 0 else 0
+                losses = abs(stats['closed_profit_loss']) if stats['closed_profit_loss'] < 0 else 0
+                total_stats['total_profits'] += profits
+                total_stats['total_losses'] += losses
+            
+            # 收集所有百分比数据
+            if stats['total_trades'] > 0:
+                total_stats['all_pct'].extend([stats['avg_profit_loss_pct']] * stats['total_trades'])
+        
+        # 计算汇总指标
+        win_rate = total_stats['profitable_trades'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        avg_profit_loss = total_stats['closed_profit_loss'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        avg_profit_loss_pct = sum(total_stats['all_pct']) / len(total_stats['all_pct']) if total_stats['all_pct'] else 0
+        avg_holding_days = total_stats['total_holding_days'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        profit_factor = total_stats['total_profits'] / total_stats['total_losses'] if total_stats['total_losses'] > 0 else float('inf') if total_stats['total_profits'] > 0 else 0
+        
+        return {
+            'total_trades': total_stats['total_trades'],
+            'open_positions': total_stats['open_positions'],
+            'profitable_trades': total_stats['profitable_trades'],
+            'losing_trades': total_stats['losing_trades'],
+            'win_rate': win_rate,
+            'closed_profit_loss': total_stats['closed_profit_loss'],
+            'unrealized_profit_loss': total_stats['unrealized_profit_loss'],
+            'total_profit_loss': total_stats['total_profit_loss'],
+            'avg_profit_loss': avg_profit_loss,
+            'avg_profit_loss_pct': avg_profit_loss_pct,
+            'max_profit': total_stats['max_profit'],
+            'max_loss': total_stats['max_loss'],
+            'avg_holding_days': avg_holding_days,
+            'profit_factor': profit_factor
+        }
+    
+    def _calculate_overall_summary(self, by_strategy):
+        """计算总体汇总统计"""
+        total_stats = {
+            'total_trades': 0,
+            'open_positions': 0,
+            'profitable_trades': 0,
+            'losing_trades': 0,
+            'closed_profit_loss': 0.0,
+            'unrealized_profit_loss': 0.0,
+            'total_profit_loss': 0.0,
+            'max_profit': 0.0,
+            'max_loss': 0.0,
+            'total_holding_days': 0.0,
+            'total_profits': 0.0,
+            'total_losses': 0.0,
+            'all_pct': []
+        }
+        
+        for strategy_name, stats in by_strategy.items():
+            total_stats['total_trades'] += stats['total_trades']
+            total_stats['open_positions'] += stats['open_positions']
+            total_stats['profitable_trades'] += stats['profitable_trades']
+            total_stats['losing_trades'] += stats['losing_trades']
+            total_stats['closed_profit_loss'] += stats['closed_profit_loss']
+            total_stats['unrealized_profit_loss'] += stats['unrealized_profit_loss']
+            total_stats['total_profit_loss'] += stats['total_profit_loss']
+            total_stats['max_profit'] = max(total_stats['max_profit'], stats['max_profit'])
+            total_stats['max_loss'] = min(total_stats['max_loss'], stats['max_loss'])
+            total_stats['total_holding_days'] += stats['avg_holding_days'] * stats['total_trades']
+            
+            if stats['profit_factor'] != float('inf'):
+                profits = stats['closed_profit_loss'] if stats['closed_profit_loss'] > 0 else 0
+                losses = abs(stats['closed_profit_loss']) if stats['closed_profit_loss'] < 0 else 0
+                total_stats['total_profits'] += profits
+                total_stats['total_losses'] += losses
+            
+            # 收集所有百分比数据
+            if stats['total_trades'] > 0:
+                total_stats['all_pct'].extend([stats['avg_profit_loss_pct']] * stats['total_trades'])
+        
+        # 计算汇总指标
+        win_rate = total_stats['profitable_trades'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        avg_profit_loss = total_stats['closed_profit_loss'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        avg_profit_loss_pct = sum(total_stats['all_pct']) / len(total_stats['all_pct']) if total_stats['all_pct'] else 0
+        avg_holding_days = total_stats['total_holding_days'] / total_stats['total_trades'] if total_stats['total_trades'] > 0 else 0
+        profit_factor = total_stats['total_profits'] / total_stats['total_losses'] if total_stats['total_losses'] > 0 else float('inf') if total_stats['total_profits'] > 0 else 0
+        
+        return {
+            'total_trades': total_stats['total_trades'],
+            'open_positions': total_stats['open_positions'],
+            'profitable_trades': total_stats['profitable_trades'],
+            'losing_trades': total_stats['losing_trades'],
+            'win_rate': win_rate,
+            'closed_profit_loss': total_stats['closed_profit_loss'],
+            'unrealized_profit_loss': total_stats['unrealized_profit_loss'],
+            'total_profit_loss': total_stats['total_profit_loss'],
+            'avg_profit_loss': avg_profit_loss,
+            'avg_profit_loss_pct': avg_profit_loss_pct,
+            'max_profit': total_stats['max_profit'],
+            'max_loss': total_stats['max_loss'],
+            'avg_holding_days': avg_holding_days,
+            'profit_factor': profit_factor
+        }
+    
+    def _get_empty_stats(self):
+        """返回空的统计数据"""
+        return {
+            'total_trades': 0,
+            'open_positions': 0,
+            'profitable_trades': 0,
+            'losing_trades': 0,
+            'win_rate': 0.0,
+            'closed_profit_loss': 0.0,
+            'unrealized_profit_loss': 0.0,
+            'total_profit_loss': 0.0,
+            'avg_profit_loss': 0.0,
+            'avg_profit_loss_pct': 0.0,
+            'max_profit': 0.0,
+            'max_loss': 0.0,
+            'avg_holding_days': 0.0,
+            'profit_factor': 0.0
+        }
+    
+    def _calculate_unrealized_pnl_for_commodity(self, strategy_name, commodity):
+        """计算特定品种未平仓头寸的未实现盈亏"""
+        unrealized_pnl = 0.0
+        strategy_positions = self.active_positions.get(strategy_name, {})
+        
+        if commodity in strategy_positions:
+            current_contract = self._get_current_contract(commodity, self.end_date.date())
+            if not current_contract:
+                return 0.0
+            
+            end_price = self._get_price_on_date(commodity, current_contract, self.end_date.date(), 'close')
+            if end_price is None:
+                return 0.0
+            
+            positions = strategy_positions[commodity]
+            for position_id, position in positions.items():
+                if position['status'] == 'open' and position['contract'] == current_contract:
+                    # 基础头寸和网格交易都是做多
+                    pnl = self._calculate_futures_pnl(
+                        position['entry_price'], end_price, position['quantity'], commodity, is_long=True
+                    )
+                    unrealized_pnl += pnl
+        
+        return unrealized_pnl
+    
+    def _count_open_positions_for_commodity(self, strategy_name, commodity):
+        """计算特定品种的未平仓头寸数量"""
+        count = 0
+        strategy_positions = self.active_positions.get(strategy_name, {})
+        
+        if commodity in strategy_positions:
+            positions = strategy_positions[commodity]
+            for position_id, position in positions.items():
+                if position['status'] == 'open':
+                    count += 1
+        
+        return count
+    
+    def _calculate_unrealized_pnl(self, strategy_name):
+        """
+        计算未平仓头寸的未实现盈亏
+        """
+        unrealized_pnl = 0.0
+        
+        # 获取策略对应的头寸字典
+        strategy_positions = self.active_positions.get(strategy_name, {})
+        
+        for commodity, positions in strategy_positions.items():
+            # 获取当前合约和最新价格
+            current_contract = self._get_current_contract(commodity, self.end_date.date())
+            if not current_contract:
+                continue
+            
+            # 获取结束日期的收盘价
+            end_price = self._get_price_on_date(commodity, current_contract, self.end_date.date(), 'close')
+            if end_price is None:
+                continue
+            
+            for position_id, position in positions.items():
+                if position['status'] == 'open' and position['contract'] == current_contract:
+                    # 基础头寸和网格交易都是做多
+                    pnl = self._calculate_futures_pnl(
+                        position['entry_price'], end_price, position['quantity'], commodity, is_long=True
+                    )
+                    
+                    unrealized_pnl += pnl
+        
+        return unrealized_pnl
+    
+    def _count_open_positions(self, strategy_name):
+        """
+        计算未平仓头寸数量
+        """
+        count = 0
+        strategy_positions = self.active_positions.get(strategy_name, {})
+        
+        for commodity, positions in strategy_positions.items():
+            for position_id, position in positions.items():
+                if position['status'] == 'open':
+                    count += 1
+        
+        return count
+    
+    def generate_comparison_report(self, performance_stats):
+        """
+        生成多级聚合的对比报告
+        包括品种-策略级、品种级、策略级和总体级报告
+        """
+        if self.verbose_logging:
+            print("\n=== 多级聚合对比分析报告 ===")
+        
+        strategies = ['base_position', 'grid_trading']
+        strategy_names = {
+            'base_position': '基础头寸交易',
+            'grid_trading': '网格交易'
+        }
+        
+        all_comparison_data = {
+            'by_commodity_strategy': [],
+            'by_commodity': [],
+            'by_strategy': [],
+            'overall': []
+        }
+        
+        # 1. 品种-策略级对比报告
+        if self.verbose_logging:
+            print("\n--- 品种-策略级对比 ---")
+        
+        by_comm_strategy_data = []
+        for strategy in strategies:
+            for commodity in self.core_commodities.keys():
+                stats = performance_stats['by_commodity_strategy'].get(strategy, {}).get(commodity, {})
+                if stats.get('total_trades', 0) > 0 or stats.get('open_positions', 0) > 0:
+                    by_comm_strategy_data.append({
+                        '品种': commodity,
+                        '策略': strategy_names[strategy],
+                        '已平仓': stats.get('total_trades', 0),
+                        '未平仓': stats.get('open_positions', 0),
+                        '胜率': f"{stats.get('win_rate', 0):.2%}",
+                        '已实现': f"{stats.get('closed_profit_loss', 0):.2f}",
+                        '未实现': f"{stats.get('unrealized_profit_loss', 0):.2f}",
+                        '总盈亏': f"{stats.get('total_profit_loss', 0):.2f}",
+                        '平均盈亏': f"{stats.get('avg_profit_loss', 0):.2f}",
+                        '最大盈利': f"{stats.get('max_profit', 0):.2f}",
+                        '最大亏损': f"{stats.get('max_loss', 0):.2f}",
+                        '平均天数': f"{stats.get('avg_holding_days', 0):.1f}",
+                        '盈亏比': f"{stats.get('profit_factor', 0):.2f}" if stats.get('profit_factor', 0) != float('inf') else "∞"
+                    })
+        
+        if by_comm_strategy_data and self.verbose_logging:
+            df_comm_strategy = pd.DataFrame(by_comm_strategy_data)
+            print(df_comm_strategy.to_string(index=False))
+        all_comparison_data['by_commodity_strategy'] = by_comm_strategy_data
+        
+        # 2. 品种级汇总对比报告
+        if self.verbose_logging:
+            print("\n--- 品种级汇总对比 ---")
+        
+        by_commodity_data = []
+        for commodity in self.core_commodities.keys():
+            stats = performance_stats['by_commodity'].get(commodity, {})
+            if stats.get('total_trades', 0) > 0 or stats.get('open_positions', 0) > 0:
+                by_commodity_data.append({
+                    '品种': commodity,
+                    '已平仓': stats.get('total_trades', 0),
+                    '未平仓': stats.get('open_positions', 0),
+                    '胜率': f"{stats.get('win_rate', 0):.2%}",
+                    '已实现': f"{stats.get('closed_profit_loss', 0):.2f}",
+                    '未实现': f"{stats.get('unrealized_profit_loss', 0):.2f}",
+                    '总盈亏': f"{stats.get('total_profit_loss', 0):.2f}",
+                    '平均盈亏': f"{stats.get('avg_profit_loss', 0):.2f}",
+                    '最大盈利': f"{stats.get('max_profit', 0):.2f}",
+                    '最大亏损': f"{stats.get('max_loss', 0):.2f}",
+                    '平均天数': f"{stats.get('avg_holding_days', 0):.1f}",
+                    '盈亏比': f"{stats.get('profit_factor', 0):.2f}" if stats.get('profit_factor', 0) != float('inf') else "∞"
+                })
+        
+        if by_commodity_data and self.verbose_logging:
+            df_commodity = pd.DataFrame(by_commodity_data)
+            print(df_commodity.to_string(index=False))
+        all_comparison_data['by_commodity'] = by_commodity_data
+        
+        # 3. 策略级汇总对比报告
+        if self.verbose_logging:
+            print("\n--- 策略级汇总对比 ---")
+        
+        by_strategy_data = []
+        for strategy in strategies:
+            stats = performance_stats['by_strategy'].get(strategy, {})
+            by_strategy_data.append({
+                '策略': strategy_names[strategy],
+                '已平仓': stats.get('total_trades', 0),
+                '未平仓': stats.get('open_positions', 0),
+                '胜率': f"{stats.get('win_rate', 0):.2%}",
+                '已实现': f"{stats.get('closed_profit_loss', 0):.2f}",
+                '未实现': f"{stats.get('unrealized_profit_loss', 0):.2f}",
+                '总盈亏': f"{stats.get('total_profit_loss', 0):.2f}",
+                '平均盈亏': f"{stats.get('avg_profit_loss', 0):.2f}",
+                '最大盈利': f"{stats.get('max_profit', 0):.2f}",
+                '最大亏损': f"{stats.get('max_loss', 0):.2f}",
+                '平均天数': f"{stats.get('avg_holding_days', 0):.1f}",
+                '盈亏比': f"{stats.get('profit_factor', 0):.2f}" if stats.get('profit_factor', 0) != float('inf') else "∞"
+            })
+        
+        if by_strategy_data and self.verbose_logging:
+            df_strategy = pd.DataFrame(by_strategy_data)
+            print(df_strategy.to_string(index=False))
+        all_comparison_data['by_strategy'] = by_strategy_data
+        
+        # 4. 总体汇总报告
+        if self.verbose_logging:
+            print("\n--- 整体汇总 ---")
+        
+        overall_data = []
+        stats = performance_stats['overall']
+        overall_data.append({
+            '项目': '整体表现',
+            '已平仓': stats.get('total_trades', 0),
+            '未平仓': stats.get('open_positions', 0),
+            '胜率': f"{stats.get('win_rate', 0):.2%}",
+            '已实现': f"{stats.get('closed_profit_loss', 0):.2f}",
+            '未实现': f"{stats.get('unrealized_profit_loss', 0):.2f}",
+            '总盈亏': f"{stats.get('total_profit_loss', 0):.2f}",
+            '平均盈亏': f"{stats.get('avg_profit_loss', 0):.2f}",
+            '最大盈利': f"{stats.get('max_profit', 0):.2f}",
+            '最大亏损': f"{stats.get('max_loss', 0):.2f}",
+            '平均天数': f"{stats.get('avg_holding_days', 0):.1f}",
+            '盈亏比': f"{stats.get('profit_factor', 0):.2f}" if stats.get('profit_factor', 0) != float('inf') else "∞"
+        })
+        
+        if overall_data and self.verbose_logging:
+            df_overall = pd.DataFrame(overall_data)
+            print(df_overall.to_string(index=False))
+        all_comparison_data['overall'] = overall_data
+        
+        return all_comparison_data
+    
+    def generate_csv_output(self, performance_stats):
+        """
+        生成CSV输出文件
+        """
+        if self.verbose_logging:
+            print("\n=== 步骤8: 生成CSV输出文件 ===")
+        
+        timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+        
+        # 1. 生成交易记录CSV
+        all_trades = []
+        for strategy_name, trades in self.trading_results.items():
+            all_trades.extend(trades)
+        
+        if all_trades:
+            df_trades = pd.DataFrame(all_trades)
+            # 添加列顺序，确保合约切换相关字段在前面
+            column_order = ['commodity', 'contract', 'strategy', 'entry_date', 'exit_date', 
+                          'entry_price', 'exit_price', 'quantity', 'profit_loss', 'profit_loss_pct', 
+                          'days_held', 'exit_reason']
+            # 重新排列DataFrame的列顺序
+            existing_columns = [col for col in column_order if col in df_trades.columns]
+            other_columns = [col for col in df_trades.columns if col not in column_order]
+            df_trades = df_trades[existing_columns + other_columns]
+            
+            trades_filename = f'grid_trading_records_{timestamp}.csv'
+            df_trades.to_csv(trades_filename, index=False, encoding=self.output_encoding)
+            if self.verbose_logging:
+                print(f"交易记录已保存至: {trades_filename}")
+        
+        # 2. 生成多级性能统计CSV
+        csv_files = []
+        
+        # 2.1 品种-策略级统计CSV
+        by_comm_strategy_data = []
+        for strategy, commodity_data in performance_stats['by_commodity_strategy'].items():
+            for commodity, stats in commodity_data.items():
+                stats_record = {
+                    '品种': commodity,
+                    '策略': strategy,
+                    **stats
+                }
+                by_comm_strategy_data.append(stats_record)
+        
+        if by_comm_strategy_data:
+            df_comm_strategy = pd.DataFrame(by_comm_strategy_data)
+            comm_strategy_filename = f'grid_trading_by_commodity_strategy_{timestamp}.csv'
+            df_comm_strategy.to_csv(comm_strategy_filename, index=False, encoding=self.output_encoding)
+            csv_files.append(comm_strategy_filename)
+            if self.verbose_logging:
+                print(f"品种-策略级统计已保存至: {comm_strategy_filename}")
+        
+        # 2.2 品种级汇总统计CSV
+        by_commodity_data = []
+        for commodity, stats in performance_stats['by_commodity'].items():
+            stats_record = {
+                '品种': commodity,
+                **stats
+            }
+            by_commodity_data.append(stats_record)
+        
+        if by_commodity_data:
+            df_commodity = pd.DataFrame(by_commodity_data)
+            commodity_filename = f'grid_trading_by_commodity_{timestamp}.csv'
+            df_commodity.to_csv(commodity_filename, index=False, encoding=self.output_encoding)
+            csv_files.append(commodity_filename)
+            if self.verbose_logging:
+                print(f"品种级汇总统计已保存至: {commodity_filename}")
+        
+        # 2.3 策略级汇总统计CSV
+        by_strategy_data = []
+        for strategy, stats in performance_stats['by_strategy'].items():
+            stats_record = {
+                '策略': strategy,
+                **stats
+            }
+            by_strategy_data.append(stats_record)
+        
+        if by_strategy_data:
+            df_strategy = pd.DataFrame(by_strategy_data)
+            strategy_filename = f'grid_trading_by_strategy_{timestamp}.csv'
+            df_strategy.to_csv(strategy_filename, index=False, encoding=self.output_encoding)
+            csv_files.append(strategy_filename)
+            if self.verbose_logging:
+                print(f"策略级汇总统计已保存至: {strategy_filename}")
+        
+        # 2.4 整体汇总统计CSV
+        overall_data = [{
+            '项目': '整体汇总',
+            **performance_stats['overall']
+        }]
+        
+        if overall_data:
+            df_overall = pd.DataFrame(overall_data)
+            overall_filename = f'grid_trading_overall_{timestamp}.csv'
+            df_overall.to_csv(overall_filename, index=False, encoding=self.output_encoding)
+            csv_files.append(overall_filename)
+            if self.verbose_logging:
+                print(f"整体汇总统计已保存至: {overall_filename}")
+        
+        return trades_filename if all_trades else None, csv_files
+    
+    def run_complete_analysis(self):
+        """执行完整的网格交易分析流程（带主力合约切换）"""
+        if self.verbose_logging:
+            print("开始执行期货网格交易分析（带主力合约切换）")
+            print("=" * 60)
+        
+        try:
+            # 步骤1: 合约选择
+            self.select_contracts()
+            if not self.selected_contracts:
+                if self.verbose_logging:
+                    print("未选择到有效合约，分析终止")
+                return None
+            
+            # 步骤2: 构建主力合约历史变化
+            self.build_dominant_contract_history()
+            if not self.dominant_contract_history:
+                if self.verbose_logging:
+                    print("未获取到主力合约历史，分析终止")
+                return None
+            
+            # 步骤3: 收集价格数据
+            self.collect_price_data()
+            if not self.price_data:
+                if self.verbose_logging:
+                    print("未获取到有效价格数据，分析终止")
+                return None
+            
+            # 步骤4: 带合约切换的交易模拟
+            self.simulate_with_contract_switching()
+            
+            # 步骤5: 性能统计分析
+            performance_stats = self.calculate_performance_statistics()
+            
+            # 步骤6: 生成对比报告
+            comparison_report = self.generate_comparison_report(performance_stats)
+            
+            # 步骤8: 生成CSV输出
+            # trades_file, stats_files = self.generate_csv_output(performance_stats)
+            
+            # 分析汇总
+            total_commodities = len(self.selected_contracts)
+            total_trades = sum(len(trades) for trades in self.trading_results.values())
+            contract_switches = sum(len(history) for history in self.dominant_contract_history.values())
+            
+            if self.verbose_logging:
+                print("\n" + "=" * 60)
+                print("分析完成汇总:")
+                print(f"分析商品数: {total_commodities}")
+                print(f"合约切换次数: {contract_switches}")
+                print(f"总交易笔数: {total_trades}")
+                # print(f"交易记录文件: {trades_file}")
+                # print(f"性能统计文件: {stats_file}")
+            
+            return {
+                'selected_contracts': self.selected_contracts,
+                'dominant_contract_history': self.dominant_contract_history,
+                'price_data': self.price_data,
+                'trading_results': self.trading_results,
+                'performance_stats': performance_stats,
+                'comparison_report': comparison_report,
+                # 'output_files': {
+                #     'trades_file': trades_file,
+                #     'stats_files': stats_files
+                # },
+                'summary': {
+                    'total_commodities': total_commodities,
+                    'contract_switches': contract_switches,
+                    'total_trades': total_trades
+                }
+            }
+            
+        except Exception as e:
+            if self.verbose_logging:
+                print(f"分析过程中出现错误: {str(e)}")
+                import traceback
+                traceback.print_exc()
+            return None
+
+
+# =====================================================================================
+# 主程序入口
+# =====================================================================================
+
+def run_grid_trading_analysis(config=None):
+    """运行期货网格交易分析"""
+    if config is None:
+        config = GridTradingConfig
+    
+    # 打印配置信息
+    config.print_config()
+    
+    # 创建分析器并运行
+    analyzer = FutureGridTradingAnalyzer(config)
+    results = analyzer.run_complete_analysis()
+    return results
+
+# 执行分析
+if __name__ == "__main__":
+    print("期货网格交易研究分析工具（带主力合约切换）")
+    print("研究期货网格交易策略在不同配置下的表现")
+    print("核心功能包括主力合约自动切换、强制平仓和重新建仓逻辑")
+    print("")
+    print("支持两种独立交易策略的分析：")
+    print("  1. 基础头寸交易 - 价格-数量网格配置")
+    print("  2. 网格交易策略 - 限价订单网格买入卖出")
+    print("")
+    print("主要特点：")
+    print("  - 主力合约自动监控：每日检测主力合约变化")
+    print("  - 强制平仓机制：合约切换时立即平掉旧合约所有头寸")
+    print("  - 智能重新建仓：根据价格条件在新合约中重新建立头寸")
+    print("  - 完整交易记录：记录所有交易包括合约切换引起的强制平仓")
+    print("")
+    print("适用于聚宽在线研究平台")
+    
+    results = run_grid_trading_analysis()
+    
+    if results:
+        print("\n✅ 分析执行成功！")
+        summary = results['summary']
+        print(f"📊 结果摘要:")
+        print(f"   - 分析商品数: {summary['total_commodities']}")
+        print(f"   - 合约切换次数: {summary['contract_switches']}")
+        print(f"   - 总交易笔数: {summary['total_trades']}")
+        print(f"   - 整体总盈亏: {results['performance_stats']['overall']['total_profit_loss']:.2f}")
+        print(f"   - 整体胜率: {results['performance_stats']['overall']['win_rate']:.2%}")
+        print(f"   - 未平仓头寸: {results['performance_stats']['overall']['open_positions']}")
+        print(f"📂 输出文件:")
+        print(f"   - 交易记录文件: {results['output_files']['trades_file']}")
+        for i, stats_file in enumerate(results['output_files']['stats_files'], 1):
+            print(f"   - 统计文件{i}: {stats_file}")
+        
+        print(f"\n📈 多级汇总:")
+        
+        # 品种级汇总简要显示
+        print(f"   品种表现:")
+        for commodity in ['SA', 'M']:
+            comm_stats = results['performance_stats']['by_commodity'].get(commodity, {})
+            if comm_stats.get('total_trades', 0) > 0 or comm_stats.get('open_positions', 0) > 0:
+                print(f"     {commodity}: 交易{comm_stats.get('total_trades', 0)}笔, 盈亏{comm_stats.get('total_profit_loss', 0):.2f}, 胜率{comm_stats.get('win_rate', 0):.2%}")
+        
+        # 策略级汇总简要显示
+        print(f"   策略表现:")
+        strategy_names = {
+            'base_position': '基础头寸',
+            'grid_trading': '网格交易'
+        }
+        for strategy, name in strategy_names.items():
+            strat_stats = results['performance_stats']['by_strategy'].get(strategy, {})
+            print(f"     {name}: 交易{strat_stats.get('total_trades', 0)}笔, 盈亏{strat_stats.get('total_profit_loss', 0):.2f}, 胜率{strat_stats.get('win_rate', 0):.2%}")
+        
+    else:
+        print("\n❌ 分析执行失败，请检查错误信息")