JUKUAN/Lib/research/future_grid_trading_analysis2.py

"""
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")