From aea7e7ed625c12cf44bb568b06d3939b56a72480 Mon Sep 17 00:00:00 2001
From: bingyi <iceoray@qq.com>
Date: Sat, 1 Nov 2025 17:15:17 +0800
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---
 OneCurvePairTrading.py  | 397 ++++++++++++++++++++++++++++++++++++++
 OneCurvePairTrading2.py | 409 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 806 insertions(+)
 create mode 100644 OneCurvePairTrading.py
 create mode 100644 OneCurvePairTrading2.py

diff --git a/OneCurvePairTrading.py b/OneCurvePairTrading.py
new file mode 100644
index 0000000..df9f128
--- /dev/null
+++ b/OneCurvePairTrading.py
@@ -0,0 +1,397 @@
+import vectorbt as vbt
+import akshare as ak
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import datetime
+from numba import njit
+from collections import namedtuple
+
+# 设置中文显示
+plt.rcParams['font.sans-serif'] = ['SimHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+# 获取股票数据
+print("正在获取股票数据...")
+stock_00981 = ak.stock_hk_daily(symbol="00981")
+stock_01347 = ak.stock_hk_daily(symbol="01347")
+
+print("中芯国际数据列名:", stock_00981.columns.tolist())
+print("华虹半导体数据列名:", stock_01347.columns.tolist())
+
+# 数据预处理
+def preprocess_data(df, symbol):
+    """预处理股票数据"""
+    df = df.copy()
+    # 检查列名并重命名（如果需要）
+    if 'date' in df.columns:
+        df['date'] = pd.to_datetime(df['date'])
+        df.set_index('date', inplace=True)
+    elif '日期' in df.columns:
+        df['date'] = pd.to_datetime(df['日期'])
+        df.set_index('date', inplace=True)
+        # 重命名中文列为英文
+        rename_dict = {
+            '开盘': 'open',
+            '最高': 'high', 
+            '最低': 'low',
+            '收盘': 'close',
+            '成交量': 'volume'
+        }
+        df = df.rename(columns=rename_dict)
+    else:
+        # 如果已经有英文列名，直接使用
+        df.index = pd.to_datetime(df.index)
+    
+    df = df.sort_index()
+    return df[['open', 'high', 'low', 'close', 'volume']]
+
+# 预处理数据
+smic_data = preprocess_data(stock_00981, "00981")
+hhic_data = preprocess_data(stock_01347, "01347")
+
+print(f"中芯国际原始数据时间范围: {smic_data.index.min()} 到 {smic_data.index.max()}")
+print(f"华虹半导体原始数据时间范围: {hhic_data.index.min()} 到 {hhic_data.index.max()}")
+
+# 限制为最近半年数据
+end_date = smic_data.index.max()
+start_date = end_date - pd.Timedelta(days=365)  
+
+print(f"\n限制回测时间范围: {start_date} 到 {end_date}")
+
+smic_data = smic_data.loc[start_date:end_date]
+hhic_data = hhic_data.loc[start_date:end_date]
+
+print(f"限制后中芯国际数据形状: {smic_data.shape}")
+print(f"限制后华虹半导体数据形状: {hhic_data.shape}")
+
+# 对齐数据时间范围
+common_index = smic_data.index.intersection(hhic_data.index)
+smic_data = smic_data.loc[common_index]
+hhic_data = hhic_data.loc[common_index]
+
+print(f"\n对齐后数据时间范围: {smic_data.index.min()} 到 {smic_data.index.max()}")
+print(f"对齐后数据点数: {len(smic_data)}")
+
+# ========== 创建价格比率作为独立资产 ==========
+print("\n=== 创建价格比率作为独立资产 ===")
+close_smic = smic_data['close']
+close_hhic = hhic_data['close']
+
+# 计算价格比率 - 作为独立的"股票"
+price_ratio = close_smic / close_hhic
+price_ratio.name = 'SMIC_HHIC_RATIO'
+
+print(f"价格比率数据形状: {price_ratio.shape}")
+print(f"价格比率统计:")
+print(f"  均值: {price_ratio.mean():.4f}")
+print(f"  标准差: {price_ratio.std():.4f}")
+print(f"  最小值: {price_ratio.min():.4f}")
+print(f"  最大值: {price_ratio.max():.4f}")
+
+# 设置交易参数
+initial_cash = 100000
+commission = 0.001  # 0.1% 交易佣金
+position_size = 0.5  # 每次交易仓位比例（现在只交易一个"资产"）
+
+# ========== 基于比率数据计算信号 ==========
+def calculate_ratio_signals(ratio_series, window=20, num_std=2):
+    """
+    基于价格比率计算配对交易信号
+    """
+    # 计算布林带
+    ratio_ma = ratio_series.rolling(window=window).mean()
+    ratio_std = ratio_series.rolling(window=window).std()
+    
+    upper_band = ratio_ma + num_std * ratio_std
+    lower_band = ratio_ma - num_std * ratio_std
+    
+    # 生成交易信号
+    # 1: 做多价差 (买中芯/卖华虹) -> 买入比率
+    # -1: 做空价差 (卖中芯/买华虹) -> 卖空比率
+    # 0: 平仓
+    signals = pd.Series(0, index=ratio_series.index, name='signal')
+    
+    # 当比率突破下轨时做多价差 -> 买入比率
+    long_condition = (ratio_series < lower_band) & (ratio_ma.notna())
+    signals[long_condition] = 1
+    
+    # 当比率突破上轨时做空价差 -> 卖空比率
+    short_condition = (ratio_series > upper_band) & (ratio_ma.notna())
+    signals[short_condition] = -1
+    
+    # 当比率回归均值时平仓
+    close_condition = (ratio_series.between(lower_band, upper_band)) & (signals.shift(1) != 0)
+    signals[close_condition] = 0
+    
+    return signals, ratio_ma, upper_band, lower_band
+
+# 计算信号
+signals, ratio_ma, upper_band, lower_band = calculate_ratio_signals(
+    price_ratio, window=20, num_std=2
+)
+
+print(f"信号计算完成，有效信号数量: {(signals != 0).sum()}")
+
+# ========== 基于信号生成size数据（现在只针对比率） ==========
+def generate_ratio_size(signals, position_size=0.5):
+    """
+    生成比率交易的size数据
+    返回一个与price_ratio相同形状的Series，包含交易数量
+    """
+    # 创建与price_ratio相同形状的size Series，初始为0
+    size_series = pd.Series(0, index=signals.index, name='size')
+    current_position = 0
+    
+    for i in range(len(signals)):
+        if i < 20:  # 跳过布林带计算期
+            continue
+            
+        date = signals.index[i]
+        signal = signals.iloc[i]
+        
+        if signal == 1 and current_position != 1:  # 做多价差 -> 买入比率
+            # 计算头寸规模（基于比率价值）
+            # 这里我们假设比率的"价格"就是其实际值
+            ratio_value = price_ratio.iloc[i]
+            # 买入相当于做多中芯/做空华虹
+            size_series.iloc[i] = position_size  # 正数表示买入比率
+            current_position = 1
+            
+        elif signal == -1 and current_position != -1:  # 做空价差 -> 卖空比率
+            # 卖空相当于做空中芯/做多华虹
+            size_series.iloc[i] = -position_size  # 负数表示卖空比率
+            current_position = -1
+            
+        elif signal == 0 and current_position != 0:  # 平仓
+            size_series.iloc[i] = 0  # 平仓
+            current_position = 0
+    
+    return size_series
+
+# 生成size数据
+size = generate_ratio_size(signals, position_size)
+
+print(f"size数据形状: {size.shape}")
+print(f"非零交易数量: {(size != 0).sum()}")
+
+# ========== 创建投资组合（只基于比率） ==========
+print("创建基于价格比率的投资组合...")
+try:
+    # 将price_ratio转换为DataFrame（vectorbt需要）
+    ratio_close = pd.DataFrame({'RATIO': price_ratio})
+    
+    portfolio = vbt.Portfolio.from_orders(
+        close=ratio_close,  # 只传入比率数据
+        size=size,          # 基于比率的交易信号
+        init_cash=initial_cash,
+        fees=commission,
+        freq='D'
+    )
+    
+    print("基于价格比率的投资组合创建成功!")
+    
+    # 计算配对交易统计
+    print("\n=== 基于价格比率的配对交易策略表现 ===")
+    
+    # 获取组合总价值
+    portfolio_value = portfolio.value()
+    
+    # ========== 绘制基于比率的技术分析图表 ==========
+    print("\n绘制基于比率的技术分析图表...")
+    
+    portfolio['RATIO'].plot().show()
+
+    # 创建详细的技术分析图表
+    fig, axes = plt.subplots(4, 1, figsize=(15, 16))
+    
+    # 1. 价格比率和布林带 + 交易信号
+    axes[0].plot(price_ratio.index, price_ratio, label='价格比率(中芯/华虹)', linewidth=1.5, color='blue')
+    axes[0].plot(price_ratio.index, ratio_ma, label='移动平均', linewidth=1, alpha=0.7, color='orange')
+    axes[0].plot(price_ratio.index, upper_band, label='上轨', linewidth=1, alpha=0.7, linestyle='--', color='red')
+    axes[0].plot(price_ratio.index, lower_band, label='下轨', linewidth=1, alpha=0.7, linestyle='--', color='green')
+    axes[0].set_title('中芯国际-华虹半导体价格比率 (配对交易标的)')
+    axes[0].set_ylabel('价格比率')
+    axes[0].legend()
+    axes[0].grid(True, alpha=0.3)
+    
+    # 标记交易信号
+    long_signals = signals[signals == 1]
+    short_signals = signals[signals == -1]
+    
+    if len(long_signals) > 0:
+        axes[0].scatter(long_signals.index, price_ratio[long_signals.index], 
+                       color='green', marker='^', s=80, label='买入比率(做多中芯/做空华虹)', zorder=5)
+    if len(short_signals) > 0:
+        axes[0].scatter(short_signals.index, price_ratio[short_signals.index], 
+                       color='red', marker='v', s=80, label='卖空比率(做空中芯/做多华虹)', zorder=5)
+    
+    # 2. 交易信号
+    axes[1].plot(signals.index, signals, label='交易信号', linewidth=2, color='purple', drawstyle='steps-post')
+    axes[1].axhline(y=1, color='green', linestyle='--', alpha=0.5, label='买入信号')
+    axes[1].axhline(y=-1, color='red', linestyle='--', alpha=0.5, label='卖出信号')
+    axes[1].axhline(y=0, color='gray', linestyle='-', alpha=0.3)
+    axes[1].set_title('交易信号时序')
+    axes[1].set_ylabel('信号')
+    axes[1].set_ylim(-1.5, 1.5)
+    axes[1].legend()
+    axes[1].grid(True, alpha=0.3)
+    
+    
+    # 4. 组合净值
+    if len(portfolio_value) > 0:
+        axes[3].plot(portfolio_value.index, portfolio_value, label='组合净值', linewidth=2, color='darkblue')
+        # 标记初始资金线
+        axes[3].axhline(y=initial_cash, color='red', linestyle='--', alpha=0.7, label=f'初始资金({initial_cash})')
+        axes[3].set_title('基于价格比率的配对交易组合净值')
+        axes[3].set_ylabel('组合价值')
+        axes[3].set_xlabel('日期')
+        axes[3].legend()
+        axes[3].grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.show()
+    
+    # ========== 使用vectorbt进行专业分析 ==========
+    print("\n=== VectorBT 专业分析（基于价格比率） ===")
+    
+    # 1. 组合详细分析
+    try:
+        fig = portfolio.plot(subplots=[
+            'orders',       # 订单
+            'trade_pnl',    # 交易盈亏
+            'cum_returns',  # 累积收益
+            'drawdowns'     # 回撤
+        ])
+        fig.update_layout(
+            title='基于价格比率的配对交易详细分析',
+            height=800
+        )
+        fig.show()
+    except Exception as e:
+        print(f"详细分析绘制失败: {e}")
+    
+    # 2. 组合价值变化
+    try:
+        fig = portfolio_value.vbt.plot(
+            title='基于价格比率的配对交易组合价值'
+        )
+        fig.update_layout(
+            xaxis_title='日期',
+            yaxis_title='组合价值'
+        )
+        fig.show()
+    except Exception as e:
+        print(f"组合价值变化绘制失败: {e}")
+    
+    # 3. 累积收益
+    try:
+        cumulative_returns = portfolio.cumulative_returns()
+        fig = cumulative_returns.vbt.plot(
+            title='基于价格比率的配对交易累积收益率'
+        )
+        fig.update_layout(
+            xaxis_title='日期',
+            yaxis_title='累积收益'
+        )
+        fig.show()
+    except Exception as e:
+        print(f"累积收益绘制失败: {e}")
+    
+    # 4. 回撤分析
+    try:
+        drawdown = portfolio.drawdown()
+        fig = drawdown.vbt.plot(
+            title='基于价格比率的配对交易回撤分析'
+        )
+        fig.update_layout(
+            xaxis_title='日期',
+            yaxis_title='回撤'
+        )
+        fig.show()
+    except Exception as e:
+        print(f"回撤分析绘制失败: {e}")
+    
+    # 5. 交易分析
+    try:
+        trades = portfolio.trades
+        if len(trades) > 0:
+            fig = trades.plot_pnl()
+            fig.update_layout(title='基于价格比率的交易盈亏分布')
+            fig.show()
+            
+            fig = trades.plot_duration()
+            fig.update_layout(title='基于价格比率的交易持续时间分布')
+            fig.show()
+    except Exception as e:
+        print(f"交易分析绘制失败: {e}")
+    
+    # ========== 打印详细统计 ==========
+    print("\n=== 详细统计 ===")
+    try:
+        stats = portfolio.stats()
+        
+        def safe_get_stat(stat_dict, key, default="N/A"):
+            value = stat_dict.get(key, default)
+            if hasattr(value, 'iloc'):
+                return value.iloc[0] if len(value) == 1 else value
+            return value
+        
+        print(f"开始日期: {safe_get_stat(stats, 'Start')}")
+        print(f"结束日期: {safe_get_stat(stats, 'End')}")
+        print(f"期间: {safe_get_stat(stats, 'Period')}")
+        print(f"总收益率: {safe_get_stat(stats, 'Total Return [%]', 'N/A')}%")
+        print(f"年化收益率: {safe_get_stat(stats, 'Annual Return [%]', 'N/A')}%")
+        print(f"年化波动率: {safe_get_stat(stats, 'Annual Volatility [%]', 'N/A')}%")
+        print(f"夏普比率: {safe_get_stat(stats, 'Sharpe Ratio', 'N/A')}")
+        print(f"最大回撤: {safe_get_stat(stats, 'Max Drawdown [%]', 'N/A')}%")
+        print(f"总交易次数: {safe_get_stat(stats, 'Total Trades', 'N/A')}")
+        print(f"胜率: {safe_get_stat(stats, 'Win Rate [%]', 'N/A')}%")
+        print(f"盈亏比: {safe_get_stat(stats, 'Profit Factor', 'N/A')}")
+        
+    except Exception as e:
+        print(f"获取详细统计时出错: {e}")
+    
+    # 分析每笔交易
+    try:
+        trades_df = portfolio.trades.records_readable
+        if len(trades_df) > 0:
+            print(f"\n交易分析:")
+            print(f"总交易次数: {len(trades_df)}")
+            if 'Duration' in trades_df.columns:
+                print(f"平均持仓时间: {trades_df['Duration'].mean():.1f} 天")
+            if 'PnL' in trades_df.columns:
+                print(f"最大单笔盈利: {trades_df['PnL'].max():.2f}")
+                print(f"最大单笔亏损: {trades_df['PnL'].min():.2f}")
+                winning_trades = trades_df[trades_df['PnL'] > 0]
+                losing_trades = trades_df[trades_df['PnL'] < 0]
+                if len(winning_trades) > 0:
+                    print(f"平均盈利: {winning_trades['PnL'].mean():.2f}")
+                if len(losing_trades) > 0:
+                    print(f"平均亏损: {losing_trades['PnL'].mean():.2f}")
+    except Exception as e:
+        print(f"分析交易时出错: {e}")
+    
+    # ========== 比率数据统计摘要 ==========
+    print("\n=== 价格比率统计摘要 ===")
+    print(f"数据期间: {price_ratio.index.min()} 到 {price_ratio.index.max()}")
+    print(f"数据点数: {len(price_ratio)}")
+    print(f"比率均值: {price_ratio.mean():.4f}")
+    print(f"比率标准差: {price_ratio.std():.4f}")
+    print(f"比率变异系数: {price_ratio.std()/price_ratio.mean():.4f}")
+    
+    # 计算交易信号统计
+    long_count = (signals == 1).sum()
+    short_count = (signals == -1).sum()
+    total_signals = long_count + short_count
+    print(f"\n交易信号统计:")
+    print(f"做多信号次数: {long_count}")
+    print(f"做空信号次数: {short_count}")
+    print(f"总信号次数: {total_signals}")
+    
+except Exception as e:
+    print(f"创建投资组合时出错: {e}")
+    import traceback
+    traceback.print_exc()
+
+print("程序执行完成!")
\ No newline at end of file
diff --git a/OneCurvePairTrading2.py b/OneCurvePairTrading2.py
new file mode 100644
index 0000000..5adca07
--- /dev/null
+++ b/OneCurvePairTrading2.py
@@ -0,0 +1,409 @@
+import vectorbt as vbt
+import akshare as ak
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import datetime
+from numba import njit
+from collections import namedtuple
+
+# 设置中文显示
+plt.rcParams['font.sans-serif'] = ['SimHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+# 获取股票数据
+print("正在获取股票数据...")
+stock_00981 = ak.stock_hk_daily(symbol="00981")
+stock_01347 = ak.stock_hk_daily(symbol="01347")
+
+print("中芯国际数据列名:", stock_00981.columns.tolist())
+print("华虹半导体数据列名:", stock_01347.columns.tolist())
+
+# 数据预处理
+def preprocess_data(df, symbol):
+    """预处理股票数据"""
+    df = df.copy()
+    # 检查列名并重命名（如果需要）
+    if 'date' in df.columns:
+        df['date'] = pd.to_datetime(df['date'])
+        df.set_index('date', inplace=True)
+    elif '日期' in df.columns:
+        df['date'] = pd.to_datetime(df['日期'])
+        df.set_index('date', inplace=True)
+        # 重命名中文列为英文
+        rename_dict = {
+            '开盘': 'open',
+            '最高': 'high', 
+            '最低': 'low',
+            '收盘': 'close',
+            '成交量': 'volume'
+        }
+        df = df.rename(columns=rename_dict)
+    else:
+        # 如果已经有英文列名，直接使用
+        df.index = pd.to_datetime(df.index)
+    
+    df = df.sort_index()
+    return df[['open', 'high', 'low', 'close', 'volume']]
+
+# 预处理数据
+smic_data = preprocess_data(stock_00981, "00981")
+hhic_data = preprocess_data(stock_01347, "01347")
+
+print(f"中芯国际原始数据时间范围: {smic_data.index.min()} 到 {smic_data.index.max()}")
+print(f"华虹半导体原始数据时间范围: {hhic_data.index.min()} 到 {hhic_data.index.max()}")
+
+# 限制为最近半年数据
+end_date = smic_data.index.max()
+start_date = end_date - pd.Timedelta(days=360)  # 最近半年
+
+print(f"\n限制回测时间范围: {start_date} 到 {end_date}")
+
+smic_data = smic_data.loc[start_date:end_date]
+hhic_data = hhic_data.loc[start_date:end_date]
+
+print(f"限制后中芯国际数据形状: {smic_data.shape}")
+print(f"限制后华虹半导体数据形状: {hhic_data.shape}")
+
+# 对齐数据时间范围
+common_index = smic_data.index.intersection(hhic_data.index)
+smic_data = smic_data.loc[common_index]
+hhic_data = hhic_data.loc[common_index]
+
+print(f"\n对齐后数据时间范围: {smic_data.index.min()} 到 {smic_data.index.max()}")
+print(f"对齐后数据点数: {len(smic_data)}")
+
+# ========== 创建价格比率作为独立资产 ==========
+print("\n=== 创建价格比率作为独立资产 ===")
+close_smic = smic_data['close']
+close_hhic = hhic_data['close']
+
+# 计算价格比率 - 作为独立的"股票"
+price_ratio = close_smic / close_hhic
+price_ratio.name = 'SMIC_HHIC_RATIO'
+
+print(f"价格比率数据形状: {price_ratio.shape}")
+print(f"价格比率统计:")
+print(f"  均值: {price_ratio.mean():.4f}")
+print(f"  标准差: {price_ratio.std():.4f}")
+print(f"  最小值: {price_ratio.min():.4f}")
+print(f"  最大值: {price_ratio.max():.4f}")
+
+# 设置交易参数
+initial_cash = 100000
+commission = 0.001  # 0.1% 交易佣金
+position_size = 0.5  # 每次交易仓位比例（现在只交易一个"资产"）
+
+# ========== 基于比率数据计算信号 ==========
+def calculate_ratio_signals(ratio_series, window=20, num_std=2):
+    """
+    基于价格比率计算配对交易信号
+    """
+    # 计算布林带
+    ratio_ma = ratio_series.rolling(window=window).mean()
+    ratio_std = ratio_series.rolling(window=window).std()
+    
+    upper_band = ratio_ma + num_std * ratio_std
+    lower_band = ratio_ma - num_std * ratio_std
+    
+    # 生成交易信号
+    # 1: 做多价差 (买中芯/卖华虹) -> 买入比率
+    # -1: 做空价差 (卖中芯/买华虹) -> 卖空比率
+    # 0: 平仓
+    signals = pd.Series(0, index=ratio_series.index, name='signal')
+    
+    # 当比率突破下轨时做多价差 -> 买入比率
+    long_condition = (ratio_series < lower_band) & (ratio_ma.notna())
+    signals[long_condition] = 1
+    
+    # 当比率突破上轨时做空价差 -> 卖空比率
+    short_condition = (ratio_series > upper_band) & (ratio_ma.notna())
+    signals[short_condition] = -1
+    
+    # 当比率回归均值时平仓
+    close_condition = (ratio_series.between(lower_band, upper_band)) & (signals.shift(1) != 0)
+    signals[close_condition] = 0
+    
+    return signals, ratio_ma, upper_band, lower_band
+
+# 计算信号
+signals, ratio_ma, upper_band, lower_band = calculate_ratio_signals(
+    price_ratio, window=20, num_std=2
+)
+
+print(f"信号计算完成，有效信号数量: {(signals != 0).sum()}")
+
+# ========== 基于信号生成size数据（现在只针对比率） ==========
+def generate_ratio_size(signals, position_size=0.5):
+    """
+    生成比率交易的size数据
+    返回一个与price_ratio相同形状的Series，包含交易数量
+    """
+    # 创建与price_ratio相同形状的size Series，初始为0
+    size_series = pd.Series(0, index=signals.index, name='size')
+    current_position = 0
+    
+    for i in range(len(signals)):
+        if i < 20:  # 跳过布林带计算期
+            continue
+            
+        date = signals.index[i]
+        signal = signals.iloc[i]
+        
+        if signal == 1 and current_position != 1:  # 做多价差 -> 买入比率
+            # 计算头寸规模（基于比率价值）
+            # 这里我们假设比率的"价格"就是其实际值
+            ratio_value = price_ratio.iloc[i]
+            # 买入相当于做多中芯/做空华虹
+            size_series.iloc[i] = position_size  # 正数表示买入比率
+            current_position = 1
+            
+        elif signal == -1 and current_position != -1:  # 做空价差 -> 卖空比率
+            # 卖空相当于做空中芯/做多华虹
+            size_series.iloc[i] = -position_size  # 负数表示卖空比率
+            current_position = -1
+            
+        elif signal == 0 and current_position != 0:  # 平仓
+            size_series.iloc[i] = 0  # 平仓
+            current_position = 0
+    
+    return size_series
+
+# 生成size数据
+size = generate_ratio_size(signals, position_size)
+
+print(f"size数据形状: {size.shape}")
+print(f"非零交易数量: {(size != 0).sum()}")
+
+# ========== 创建投资组合（只基于比率） ==========
+print("创建基于价格比率的投资组合...")
+try:
+    # 将price_ratio转换为DataFrame（vectorbt需要）
+    ratio_close = pd.DataFrame({'RATIO': price_ratio})
+    
+    portfolio = vbt.Portfolio.from_orders(
+        close=ratio_close,  # 只传入比率数据
+        size=size,          # 基于比率的交易信号
+        init_cash=initial_cash,
+        fees=commission,
+        freq='D'
+    )
+    
+    print("基于价格比率的投资组合创建成功!")
+    
+    # 计算配对交易统计
+    print("\n=== 基于价格比率的配对交易策略表现 ===")
+    
+    # 获取组合总价值
+    portfolio_value = portfolio.value()
+    
+
+    
+    # ========== 使用vectorbt进行专业分析 ==========
+    print("\n=== VectorBT 专业分析（基于价格比率） ===")
+    
+    # 修复：使用正确的列选择方法
+    try:
+        # 选择第一列（也是唯一的一列）
+        portfolio_single = portfolio['RATIO']
+        
+        fig = portfolio_single.plot(subplots=[
+            'orders',       # 订单
+            'trade_pnl',    # 交易盈亏
+            'cum_returns',  # 累积收益
+            'drawdowns'     # 回撤
+        ])
+        fig.update_layout(
+            title='基于价格比率的配对交易详细分析',
+            height=800
+        )
+        fig.show()
+    except Exception as e:
+        print(f"详细分析绘制失败: {e}")
+    
+    # 2. 组合价值变化
+    try:
+        fig = portfolio_value.vbt.plot(
+            title='基于价格比率的配对交易组合价值'
+        )
+        fig.update_layout(
+            xaxis_title='日期',
+            yaxis_title='组合价值'
+        )
+        fig.show()
+    except Exception as e:
+        print(f"组合价值变化绘制失败: {e}")
+    
+    # 3. 累积收益
+    try:
+        cumulative_returns = portfolio.cumulative_returns()
+        fig = cumulative_returns.vbt.plot(
+            title='基于价格比率的配对交易累积收益率'
+        )
+        fig.update_layout(
+            xaxis_title='日期',
+            yaxis_title='累积收益'
+        )
+        fig.show()
+    except Exception as e:
+        print(f"累积收益绘制失败: {e}")
+    
+    # 4. 回撤分析
+    try:
+        drawdown = portfolio.drawdown()
+        fig = drawdown.vbt.plot(
+            title='基于价格比率的配对交易回撤分析'
+        )
+        fig.update_layout(
+            xaxis_title='日期',
+            yaxis_title='回撤'
+        )
+        fig.show()
+    except Exception as e:
+        print(f"回撤分析绘制失败: {e}")
+    
+    # 5. 交易分析 - 修复：使用正确的列选择
+    try:
+        trades = portfolio['RATIO'].trades
+        if len(trades) > 0:
+            fig = trades.plot_pnl()
+            fig.update_layout(title='基于价格比率的交易盈亏分布')
+            fig.show()
+            
+            fig = trades.plot_duration()
+            fig.update_layout(title='基于价格比率的交易持续时间分布')
+            fig.show()
+    except Exception as e:
+        print(f"交易分析绘制失败: {e}")
+    
+    # 6. 订单流分析 - 修复：使用正确的列选择
+    try:
+        fig = portfolio['RATIO'].orders.plot()
+        fig.update_layout(title='基于价格比率的订单流分析')
+        fig.show()
+    except Exception as e:
+        print(f"订单流分析绘制失败: {e}")
+    
+    # ========== 绘制基于比率的技术分析图表 ==========
+    print("\n绘制基于比率的技术分析图表...")
+    
+    # 创建详细的技术分析图表
+    fig, axes = plt.subplots(4, 1, figsize=(15, 16))
+    
+    # 1. 价格比率和布林带 + 交易信号
+    axes[0].plot(price_ratio.index, price_ratio, label='价格比率(中芯/华虹)', linewidth=1.5, color='blue')
+    axes[0].plot(price_ratio.index, ratio_ma, label='移动平均', linewidth=1, alpha=0.7, color='orange')
+    axes[0].plot(price_ratio.index, upper_band, label='上轨', linewidth=1, alpha=0.7, linestyle='--', color='red')
+    axes[0].plot(price_ratio.index, lower_band, label='下轨', linewidth=1, alpha=0.7, linestyle='--', color='green')
+    axes[0].set_title('中芯国际-华虹半导体价格比率 (配对交易标的)')
+    axes[0].set_ylabel('价格比率')
+    axes[0].legend()
+    axes[0].grid(True, alpha=0.3)
+    
+    # 标记交易信号
+    long_signals = signals[signals == 1]
+    short_signals = signals[signals == -1]
+    
+    if len(long_signals) > 0:
+        axes[0].scatter(long_signals.index, price_ratio[long_signals.index], 
+                       color='green', marker='^', s=80, label='买入比率(做多中芯/做空华虹)', zorder=5)
+    if len(short_signals) > 0:
+        axes[0].scatter(short_signals.index, price_ratio[short_signals.index], 
+                       color='red', marker='v', s=80, label='卖空比率(做空中芯/做多华虹)', zorder=5)
+    
+    # 2. 交易信号
+    axes[1].plot(signals.index, signals, label='交易信号', linewidth=2, color='purple', drawstyle='steps-post')
+    axes[1].axhline(y=1, color='green', linestyle='--', alpha=0.5, label='买入信号')
+    axes[1].axhline(y=-1, color='red', linestyle='--', alpha=0.5, label='卖出信号')
+    axes[1].axhline(y=0, color='gray', linestyle='-', alpha=0.3)
+    axes[1].set_title('交易信号时序')
+    axes[1].set_ylabel('信号')
+    axes[1].set_ylim(-1.5, 1.5)
+    axes[1].legend()
+    axes[1].grid(True, alpha=0.3)
+
+    
+    # 4. 组合净值
+    if len(portfolio_value) > 0:
+        axes[3].plot(portfolio_value.index, portfolio_value, label='组合净值', linewidth=2, color='darkblue')
+        # 标记初始资金线
+        axes[3].axhline(y=initial_cash, color='red', linestyle='--', alpha=0.7, label=f'初始资金({initial_cash})')
+        axes[3].set_title('基于价格比率的配对交易组合净值')
+        axes[3].set_ylabel('组合价值')
+        axes[3].set_xlabel('日期')
+        axes[3].legend()
+        axes[3].grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.show()
+
+    # ========== 打印详细统计 ==========
+    print("\n=== 详细统计 ===")
+    try:
+        # 使用单列统计
+        stats = portfolio['RATIO'].stats()
+        
+        def safe_get_stat(stat_dict, key, default="N/A"):
+            value = stat_dict.get(key, default)
+            if hasattr(value, 'iloc'):
+                return value.iloc[0] if len(value) == 1 else value
+            return value
+        
+        print(f"开始日期: {safe_get_stat(stats, 'Start')}")
+        print(f"结束日期: {safe_get_stat(stats, 'End')}")
+        print(f"期间: {safe_get_stat(stats, 'Period')}")
+        print(f"总收益率: {safe_get_stat(stats, 'Total Return [%]', 'N/A')}%")
+        print(f"年化收益率: {safe_get_stat(stats, 'Annual Return [%]', 'N/A')}%")
+        print(f"年化波动率: {safe_get_stat(stats, 'Annual Volatility [%]', 'N/A')}%")
+        print(f"夏普比率: {safe_get_stat(stats, 'Sharpe Ratio', 'N/A')}")
+        print(f"最大回撤: {safe_get_stat(stats, 'Max Drawdown [%]', 'N/A')}%")
+        print(f"总交易次数: {safe_get_stat(stats, 'Total Trades', 'N/A')}")
+        print(f"胜率: {safe_get_stat(stats, 'Win Rate [%]', 'N/A')}%")
+        print(f"盈亏比: {safe_get_stat(stats, 'Profit Factor', 'N/A')}")
+        
+    except Exception as e:
+        print(f"获取详细统计时出错: {e}")
+    
+    # 分析每笔交易
+    try:
+        trades_df = portfolio['RATIO'].trades.records_readable
+        if len(trades_df) > 0:
+            print(f"\n交易分析:")
+            print(f"总交易次数: {len(trades_df)}")
+            if 'Duration' in trades_df.columns:
+                print(f"平均持仓时间: {trades_df['Duration'].mean():.1f} 天")
+            if 'PnL' in trades_df.columns:
+                print(f"最大单笔盈利: {trades_df['PnL'].max():.2f}")
+                print(f"最大单笔亏损: {trades_df['PnL'].min():.2f}")
+                winning_trades = trades_df[trades_df['PnL'] > 0]
+                losing_trades = trades_df[trades_df['PnL'] < 0]
+                if len(winning_trades) > 0:
+                    print(f"平均盈利: {winning_trades['PnL'].mean():.2f}")
+                if len(losing_trades) > 0:
+                    print(f"平均亏损: {losing_trades['PnL'].mean():.2f}")
+    except Exception as e:
+        print(f"分析交易时出错: {e}")
+    
+    # ========== 比率数据统计摘要 ==========
+    print("\n=== 价格比率统计摘要 ===")
+    print(f"数据期间: {price_ratio.index.min()} 到 {price_ratio.index.max()}")
+    print(f"数据点数: {len(price_ratio)}")
+    print(f"比率均值: {price_ratio.mean():.4f}")
+    print(f"比率标准差: {price_ratio.std():.4f}")
+    print(f"比率变异系数: {price_ratio.std()/price_ratio.mean():.4f}")
+    
+    # 计算交易信号统计
+    long_count = (signals == 1).sum()
+    short_count = (signals == -1).sum()
+    total_signals = long_count + short_count
+    print(f"\n交易信号统计:")
+    print(f"做多信号次数: {long_count}")
+    print(f"做空信号次数: {short_count}")
+    print(f"总信号次数: {total_signals}")
+    
+except Exception as e:
+    print(f"创建投资组合时出错: {e}")
+    import traceback
+    traceback.print_exc()
+
+print("程序执行完成!")
\ No newline at end of file