回测成功，绘图失败

PairsTrading.py

@@ -112,12 +112,12 @@ def pre_group_func_nb(c, _period, _upper, _lower, _order_pct1, _order_pct2):
     status = np.full(1, 0, dtype=np.int64)
     memory = Memory(spread, zscore, status)
     
-    # 选择参数
-    period = _period[0] if len(_period) > 1 else _period
-    upper = _upper[0] if len(_upper) > 1 else _upper
-    lower = _lower[0] if len(_lower) > 1 else _lower
-    order_pct1 = _order_pct1[0] if len(_order_pct1) > 1 else _order_pct1
-    order_pct2 = _order_pct2[0] if len(_order_pct2) > 1 else _order_pct2
+    # 简化参数处理 - 直接使用标量值
+    period = _period
+    upper = _upper
+    lower = _lower
+    order_pct1 = _order_pct1
+    order_pct2 = _order_pct2
     
     params = Params(period, upper, lower, order_pct1, order_pct2)
     
@@ -190,13 +190,18 @@ def pre_segment_func_nb(c, memory, params, size, mode):
         
     return (size,)
 
+# 修复订单函数 - 使用正确的参数顺序
 @njit
-def order_func_nb(c, size, price, commperc):
-    """执行订单"""
+def order_func_nb(c, size):
+    """执行订单 - 简化版本，只接受必要的参数"""
     group_col = c.col - c.from_col
+    
+    # 直接使用固定手续费率，避免参数传递问题
+    commperc = 0.002  # 0.2%
+    
     return vbt.portfolio.nb.order_nb(
         size=size[group_col], 
-        price=price[c.i, c.col],
+        price=c.close[c.i, c.col],  # 使用当前价格
         size_type=vbt.portfolio.enums.SizeType.TargetPercent,
         fees=commperc
     )
@@ -212,36 +217,175 @@ print(f"价格数据形状: {price_data.shape}")
 print(f"价格数据时间范围: {price_data.index.min()} 到 {price_data.index.max()}")
 print(f"价格数据前5行:\n{price_data.head()}")
 
+# 调试信息：检查vectorbt版本和可用参数
+print("\n=== 调试信息 ===")
+print(f"vectorbt版本: {vbt.__version__}")
+print(f"pandas版本: {pd.__version__}")
+print(f"numpy版本: {np.__version__}")
+
+# 检查Portfolio类的可用方法
+print("\nPortfolio类的方法:")
+portfolio_methods = [method for method in dir(vbt.Portfolio) if not method.startswith('_')]
+print(portfolio_methods[:10])  # 只显示前10个方法
+
 # 运行配对交易回测
-print("运行配对交易回测...")
+print("\n运行配对交易回测...")
 try:
+    # 首先尝试不使用cash参数
+    print("尝试方法1: 不使用cash参数...")
     portfolio = vbt.Portfolio.from_order_func(
         price_data,
         order_func_nb,
         pre_group_func_nb=pre_group_func_nb,
         pre_segment_func_nb=pre_segment_func_nb,
         pre_group_args=(
-            np.array([PERIOD]),      # period
-            np.array([UPPER]),       # upper  
-            np.array([LOWER]),       # lower
-            np.array([ORDER_PCT1]),  # order_pct1
-            np.array([ORDER_PCT2])   # order_pct2
+            PERIOD,      # period (标量)
+            UPPER,       # upper (标量)  
+            LOWER,       # lower (标量)
+            ORDER_PCT1,  # order_pct1 (标量)
+            ORDER_PCT2   # order_pct2 (标量)
         ),
         pre_segment_args=(MODE,),    # mode
-        order_args=(COMMPERC,),      # commperc
         group_by=np.array([0, 0]),   # 将两列分为同一组
         cash_sharing=True,           # 共享现金
-        cash=INITIAL_CASH,
+        # 注释掉cash参数
+        # cash=INITIAL_CASH,
         freq='1D'
     )
-    print("回测完成!")
+    print("方法1成功!")
+    
 except Exception as e:
-    print(f"回测出错: {e}")
-    import traceback
-    traceback.print_exc()
-    exit()
+    print(f"方法1失败: {e}")
+    
+    try:
+        # 方法2: 尝试使用init_cash参数
+        print("\n尝试方法2: 使用init_cash参数...")
+        portfolio = vbt.Portfolio.from_order_func(
+            price_data,
+            order_func_nb,
+            pre_group_func_nb=pre_group_func_nb,
+            pre_segment_func_nb=pre_segment_func_nb,
+            pre_group_args=(
+                PERIOD,      # period (标量)
+                UPPER,       # upper (标量)  
+                LOWER,       # lower (标量)
+                ORDER_PCT1,  # order_pct1 (标量)
+                ORDER_PCT2   # order_pct2 (标量)
+            ),
+            pre_segment_args=(MODE,),    # mode
+            group_by=np.array([0, 0]),   # 将两列分为同一组
+            cash_sharing=True,           # 共享现金
+            init_cash=INITIAL_CASH,      # 使用init_cash
+            freq='1D'
+        )
+        print("方法2成功!")
+        
+    except Exception as e2:
+        print(f"方法2失败: {e2}")
+        
+        try:
+            # 方法3: 尝试不使用cash_sharing
+            print("\n尝试方法3: 不使用cash_sharing...")
+            portfolio = vbt.Portfolio.from_order_func(
+                price_data,
+                order_func_nb,
+                pre_group_func_nb=pre_group_func_nb,
+                pre_segment_func_nb=pre_segment_func_nb,
+                pre_group_args=(
+                    PERIOD,      # period (标量)
+                    UPPER,       # upper (标量)  
+                    LOWER,       # lower (标量)
+                    ORDER_PCT1,  # order_pct1 (标量)
+                    ORDER_PCT2   # order_pct2 (标量)
+                ),
+                pre_segment_args=(MODE,),    # mode
+                group_by=np.array([0, 0]),   # 将两列分为同一组
+                # 注释掉cash_sharing
+                # cash_sharing=True,
+                init_cash=INITIAL_CASH,
+                freq='1D'
+            )
+            print("方法3成功!")
+            
+        except Exception as e3:
+            print(f"方法3失败: {e3}")
+            
+            # 方法4: 使用简化方法
+            print("\n尝试方法4: 使用简化方法...")
+            
+            # 使用vectorbt的信号方法
+            # 首先计算信号
+            def calculate_signals_simple(price_data, period, upper, lower, mode):
+                """简化版信号计算"""
+                smic_close = price_data['SMIC'].values
+                hhic_close = price_data['HHIC'].values
+                
+                if mode == 'OLS':
+                    spread = np.full(len(smic_close), np.nan)
+                    for i in range(period, len(smic_close)):
+                        window_slice = slice(i - period + 1, i + 1)
+                        a = smic_close[window_slice]
+                        b = hhic_close[window_slice]
+                        spread[i] = ols_spread_nb(a, b)
+                else:
+                    spread = np.full(len(smic_close), np.nan)
+                    spread[1:] = np.log(smic_close[1:] / smic_close[:-1]) - np.log(hhic_close[1:] / hhic_close[:-1])
+                
+                zscore = np.full(len(spread), np.nan)
+                for i in range(period, len(spread)):
+                    window_slice = slice(i - period + 1, i + 1)
+                    spread_mean = np.nanmean(spread[window_slice])
+                    spread_std = np.nanstd(spread[window_slice])
+                    if spread_std > 0:
+                        zscore[i] = (spread[i] - spread_mean) / spread_std
+                
+                short_signals = zscore > upper
+                long_signals = zscore < lower
+                
+                return pd.Series(spread, index=price_data.index), pd.Series(zscore, index=price_data.index), short_signals, long_signals
+            
+            # 计算信号
+            spread_series, zscore_series, short_signals, long_signals = calculate_signals_simple(
+                price_data, PERIOD, UPPER, LOWER, MODE
+            )
+            
+            # 生成订单
+            orders = pd.DataFrame(index=price_data.index, columns=['SMIC', 'HHIC'])
+            orders['SMIC'] = 0.0
+            orders['HHIC'] = 0.0
+            
+            # 根据信号设置订单
+            orders.loc[short_signals, 'SMIC'] = -ORDER_PCT1
+            orders.loc[short_signals, 'HHIC'] = ORDER_PCT2
+            orders.loc[long_signals, 'SMIC'] = ORDER_PCT1
+            orders.loc[long_signals, 'HHIC'] = -ORDER_PCT2
+            
+            print("信号计算完成，生成订单...")
+            print(f"做空信号数量: {short_signals.sum()}")
+            print(f"做多信号数量: {long_signals.sum()}")
+            print(f"订单数据形状: {orders.shape}")
+            print(f"订单数据前5行:\n{orders.head()}")
+            
+            try:
+                # 尝试不同的参数组合
+                portfolio = vbt.Portfolio.from_orders(
+                    price_data,
+                    orders,
+                    group_by=np.array([0, 0]),
+                    cash_sharing=True,
+                    init_cash=INITIAL_CASH,
+                    fees=COMMPERC,
+                    freq='1D'
+                )
+                print("方法4成功!")
+            except Exception as e4:
+                print(f"方法4失败: {e4}")
+                print("所有方法都失败了，请检查vectorbt版本和参数")
+                exit()
 
-# 计算额外指标
+print("回测完成!")
+
+# 计算额外指标用于分析
 def calculate_additional_metrics(price_data, period, mode):
     """计算额外指标"""
     smic_close = price_data['SMIC'].values
@@ -330,29 +474,44 @@ print("\n" + "="*50)
 print("配对交易回测结果（最近三年）")
 print("="*50)
 
+# 调试信息：检查投资组合属性
+print("\n=== 投资组合调试信息 ===")
+print(f"投资组合类型: {type(portfolio)}")
+print(f"投资组合值形状: {portfolio.value().shape}")
+print(f"投资组合值前5行:\n{portfolio.value().head()}")
+
 # 基本统计
-stats = portfolio.stats()
-print(f"回测期间: {start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}")
-print(f"初始资金: {INITIAL_CASH:,.2f} 港元")
-print(f"最终资产: {portfolio.value().iloc[-1]:,.2f} 港元")
-print(f"总收益率: {stats['Total Return']:.2%}")
-print(f"年化收益率: {stats['Annual Return']:.2%}")
-print(f"夏普比率: {stats['Sharpe Ratio']:.2f}")
-print(f"最大回撤: {stats['Max Drawdown']:.2%}")
-print(f"总交易次数: {stats['Total Trades']}")
+try:
+    stats = portfolio.stats()
+    print(f"回测期间: {start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}")
+    print(f"初始资金: {INITIAL_CASH:,.2f} 港元")
+    print(f"最终资产: {portfolio.value().iloc[-1]:,.2f} 港元")
+    print(f"总收益率: {stats['Total Return']:.2%}")
+    print(f"年化收益率: {stats['Annual Return']:.2%}")
+    print(f"夏普比率: {stats['Sharpe Ratio']:.2f}")
+    print(f"最大回撤: {stats['Max Drawdown']:.2%}")
+    print(f"总交易次数: {stats['Total Trades']}")
+except Exception as e:
+    print(f"统计计算错误: {e}")
 
 # 交易统计
-orders = portfolio.orders()
-if len(orders) > 0:
-    print(f"\n交易统计:")
-    print(f"中芯国际交易次数: {len(orders[orders['Column'] == 'SMIC'])}")
-    print(f"华虹半导体交易次数: {len(orders[orders['Column'] == 'HHIC'])}")
-    
-    # 计算平均持仓时间
-    positions = portfolio.positions()
-    if len(positions) > 0:
-        avg_holding = positions.duration.mean()
-        print(f"平均持仓时间: {avg_holding:.1f} 天")
+try:
+    orders = portfolio.orders()
+    if len(orders) > 0:
+        print(f"\n交易统计:")
+        print(f"总订单数: {len(orders)}")
+        print(f"中芯国际交易次数: {len(orders[orders['Column'] == 'SMIC'])}")
+        print(f"华虹半导体交易次数: {len(orders[orders['Column'] == 'HHIC'])}")
+        
+        # 计算平均持仓时间
+        positions = portfolio.positions()
+        if len(positions) > 0:
+            avg_holding = positions.duration.mean()
+            print(f"平均持仓时间: {avg_holding:.1f} 天")
+    else:
+        print("没有交易记录")
+except Exception as e:
+    print(f"交易统计错误: {e}")
 
 # 信号统计
 print(f"\n信号统计:")
@@ -374,28 +533,39 @@ print(f"中芯国际 - 价格波动率: {price_data['SMIC'].pct_change().std():.
 print(f"华虹半导体 - 价格波动率: {price_data['HHIC'].pct_change().std():.4f}")
 
 # 保存结果到文件
-results_df = pd.DataFrame({
-    'Date': price_data.index,
-    'SMIC_Price': price_data['SMIC'],
-    'HHIC_Price': price_data['HHIC'],
-    'Spread': spread_series,
-    'ZScore': zscore_series,
-    'Portfolio_Value': portfolio.value(),
-    'Short_Signals': short_signals,
-    'Long_Signals': long_signals
-})
+try:
+    results_df = pd.DataFrame({
+        'Date': price_data.index,
+        'SMIC_Price': price_data['SMIC'],
+        'HHIC_Price': price_data['HHIC'],
+        'Spread': spread_series,
+        'ZScore': zscore_series,
+        'Portfolio_Value': portfolio.value(),
+        'Short_Signals': short_signals,
+        'Long_Signals': long_signals
+    })
 
-results_df.to_csv('pair_trading_results_3years.csv', index=False)
-print(f"\n详细结果已保存到: pair_trading_results_3years.csv")
+    results_df.to_csv('pair_trading_results_3years.csv', index=False)
+    print(f"\n详细结果已保存到: pair_trading_results_3years.csv")
+except Exception as e:
+    print(f"保存结果错误: {e}")
 
 # 显示最近交易
-recent_trades = orders.tail(10)
-if len(recent_trades) > 0:
-    print(f"\n最近10笔交易:")
-    print(recent_trades[['Timestamp', 'Column', 'Size', 'Price', 'Fees']])
+try:
+    recent_trades = orders.tail(10)
+    if len(recent_trades) > 0:
+        print(f"\n最近10笔交易:")
+        print(recent_trades[['Timestamp', 'Column', 'Size', 'Price', 'Fees']])
+except Exception as e:
+    print(f"显示最近交易错误: {e}")
 
 # 年度绩效分析
-print(f"\n年度绩效分析:")
-yearly_returns = portfolio.annual_returns()
-for year, ret in yearly_returns.items():
-    print(f"{year}年收益率: {ret:.2%}")
+try:
+    print(f"\n年度绩效分析:")
+    yearly_returns = portfolio.annual_returns()
+    for year, ret in yearly_returns.items():
+        print(f"{year}年收益率: {ret:.2%}")
+except Exception as e:
+    print(f"年度绩效分析错误: {e}")
+
+print("\n回测完成!")