赚钱机器指标分析

finance/money-machine-A.py

@@ -0,0 +1,304 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime
+
+try:
+    from WindPy import w
+    WIND_AVAILABLE = True
+    # 启动Wind
+    w.start()
+    if not w.isconnected():
+        WIND_AVAILABLE = False
+        print("❌ 请先启动Wind金融终端")
+    else:
+        print("✅ Wind连接成功")
+except ImportError:
+    WIND_AVAILABLE = False
+    print("❌ 未安装WindPy库")
+
+# 设置中文字体
+plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
+plt.rcParams['axes.unicode_minus'] = False
+
+def get_ten_year_financial_data(symbol):
+    """
+    使用ED-10Y参数获取十年财务数据
+    """
+    if not WIND_AVAILABLE:
+        print("Wind不可用")
+        return None
+    
+    # 转换为Wind代码格式
+    if symbol.startswith('6'):
+        wind_code = f"{symbol}.SH"
+    else:
+        wind_code = f"{symbol}.SZ"
+    
+    try:
+        end_date = datetime.now().strftime('%Y-%m-%d')
+        
+        print(f"📊 获取 {wind_code} 十年财务数据...")
+        print(f"查询参数: ED-10Y 到 {end_date}")
+        
+        # 获取资本支出数据 - 使用您提供的参数格式
+        print("获取资本支出数据...")
+        capex_data = w.wsd(wind_code, "cash_pay_acq_const_fiolta", "ED-10Y", end_date, "unit=1;rptType=1;Period=Y;Days=Alldays")
+        
+        # 获取净利润数据 - 使用相同参数格式
+        print("获取净利润数据...")
+        profit_data = w.wsd(wind_code, "net_profit_is", "ED-10Y", end_date, "unit=1;rptType=1;Period=Y;Days=Alldays")
+        
+        print(f"资本支出数据点: {len(capex_data.Data[0]) if capex_data.Data else 0}")
+        print(f"净利润数据点: {len(profit_data.Data[0]) if profit_data.Data else 0}")
+        
+        # 检查数据错误代码
+        print(f"资本支出错误代码: {capex_data.ErrorCode}")
+        print(f"净利润错误代码: {profit_data.ErrorCode}")
+        
+        # 创建包含所有数据的DataFrame
+        data_list = []
+        
+        if capex_data.Data and profit_data.Data:
+            # 确定数据长度
+            data_length = min(len(capex_data.Times), len(capex_data.Data[0]), len(profit_data.Data[0]))
+            print(f"有效数据长度: {data_length}")
+            
+            for i in range(data_length):
+                date = capex_data.Times[i]
+                capex_value = capex_data.Data[0][i]
+                profit_value = profit_data.Data[0][i]
+                
+                # 计算比率（如果数据有效）- 改为百分比
+                ratio = None
+                is_capex_valid = capex_value is not None and not np.isnan(capex_value)
+                is_profit_valid = profit_value is not None and not np.isnan(profit_value) and profit_value != 0
+                
+                if is_capex_valid and is_profit_valid:
+                    ratio = (abs(capex_value) / profit_value) * 100  # 乘以100转换为百分比
+                
+                data_list.append({
+                    'year': date.year,
+                    'report_date': date,
+                    'capital_expenditure': capex_value,
+                    'net_profit': profit_value,
+                    'capex_to_profit_ratio_pct': ratio,  # 改为百分比
+                    'is_capex_valid': is_capex_valid,
+                    'is_profit_valid': is_profit_valid,
+                    'is_ratio_valid': ratio is not None
+                })
+        else:
+            print("❌ 数据获取失败")
+            return None
+        
+        df = pd.DataFrame(data_list)
+        
+        print(f"✅ 成功获取 {len(df)} 年数据")
+        return df, capex_data, profit_data
+        
+    except Exception as e:
+        print(f"❌ 数据获取失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return None, None, None
+
+def print_ten_year_analysis(df, symbol):
+    """打印十年数据分析"""
+    print(f"\n{'='*80}")
+    print(f"📊 {symbol} - 十年财务数据分析报告")
+    print(f"{'='*80}")
+    
+    print(f"\n📈 数据完整性统计:")
+    total_years = len(df)
+    valid_capex = df['is_capex_valid'].sum()
+    valid_profit = df['is_profit_valid'].sum()
+    valid_ratio = df['is_ratio_valid'].sum()
+    
+    print(f"总年份数: {total_years}")
+    print(f"有效资本支出年份: {valid_capex} ({valid_capex/total_years*100:.1f}%)")
+    print(f"有效净利润年份: {valid_profit} ({valid_profit/total_years*100:.1f}%)")
+    print(f"有效比率年份: {valid_ratio} ({valid_ratio/total_years*100:.1f}%)")
+    
+    print(f"\n🔍 详细年度数据:")
+    for i, row in df.iterrows():
+        year = row['year']
+        capex_str = f"{row['capital_expenditure']/1e8:8.2f}亿" if row['is_capex_valid'] else "      NaN"
+        profit_str = f"{row['net_profit']/1e8:8.2f}亿" if row['is_profit_valid'] else "      NaN"
+        ratio_str = f"{row['capex_to_profit_ratio_pct']:6.2f}%" if row['is_ratio_valid'] else "   NaN"  # 改为百分比显示
+        
+        capex_status = "✅" if row['is_capex_valid'] else "❌"
+        profit_status = "✅" if row['is_profit_valid'] else "❌"
+        ratio_status = "✅" if row['is_ratio_valid'] else "❌"
+        
+        print(f"  {year}年: 资本支出={capex_str} {capex_status}, "
+              f"净利润={profit_str} {profit_status}, 比率={ratio_str} {ratio_status}")
+
+def analyze_valid_ten_year_data(df, symbol):
+    """分析十年有效数据"""
+    valid_df = df[df['is_ratio_valid']].copy()
+    
+    if valid_df.empty:
+        print(f"\n❌ {symbol} 无有效比率数据")
+        return None
+    
+    print(f"\n📊 {symbol} - 有效数据分析 (共{len(valid_df)}年):")
+    print("-" * 60)
+    
+    for i, row in valid_df.iterrows():
+        year = row['year']
+        capex = row['capital_expenditure'] / 1e8
+        profit = row['net_profit'] / 1e8
+        ratio = row['capex_to_profit_ratio_pct']  # 已经是百分比
+        
+        print(f"  {year}年: 资本支出 {capex:6.2f}亿 / 净利润 {profit:6.2f}亿 = 比率 {ratio:.2f}%")  # 改为百分比显示
+    
+    # 统计信息
+    capex_mean = valid_df['capital_expenditure'].mean() / 1e8
+    profit_mean = valid_df['net_profit'].mean() / 1e8
+    ratio_mean = valid_df['capex_to_profit_ratio_pct'].mean()
+    ratio_std = valid_df['capex_to_profit_ratio_pct'].std()
+    ratio_min = valid_df['capex_to_profit_ratio_pct'].min()
+    ratio_max = valid_df['capex_to_profit_ratio_pct'].max()
+    
+    print(f"\n💡 统计摘要:")
+    print(f"  平均资本支出: {capex_mean:.2f} 亿元")
+    print(f"  平均净利润: {profit_mean:.2f} 亿元")
+    print(f"  平均比率: {ratio_mean:.2f}%")  # 改为百分比显示
+    print(f"  比率标准差: {ratio_std:.2f}%")  # 改为百分比显示
+    print(f"  比率范围: {ratio_min:.2f}% - {ratio_max:.2f}%")  # 改为百分比显示
+    
+    return valid_df
+
+def plot_ten_year_analysis(valid_df, symbol):
+    """绘制十年分析图表"""
+    if valid_df is None or valid_df.empty:
+        print("❌ 无有效数据可绘制")
+        return
+    
+    # 提取数据
+    years = valid_df['year'].tolist()
+    capex = valid_df['capital_expenditure'] / 1e8  # 转换为亿元
+    profit = valid_df['net_profit'] / 1e8  # 转换为亿元
+    ratios = valid_df['capex_to_profit_ratio_pct']  # 已经是百分比
+    
+    # 创建图表
+    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 15))
+    
+    x = np.arange(len(years))
+    
+    # 1. 资本支出和净利润对比
+    bars1 = ax1.bar(x - 0.2, capex, 0.4, label='资本支出(亿元)', alpha=0.8, color='#FF6B6B')
+    bars2 = ax1.bar(x + 0.2, profit, 0.4, label='净利润(亿元)', alpha=0.8, color='#4ECDC4')
+    ax1.set_title(f'{symbol} - 十年资本支出 vs 净利润', fontsize=14, fontweight='bold')
+    ax1.set_xticks(x)
+    ax1.set_xticklabels(years, rotation=45)
+    ax1.legend()
+    ax1.grid(True, alpha=0.3)
+    
+    # 添加数值标签
+    for bar in bars1:
+        height = bar.get_height()
+        ax1.text(bar.get_x() + bar.get_width()/2., height, f'{height:.1f}', 
+                ha='center', va='bottom', fontsize=8)
+    
+    for bar in bars2:
+        height = bar.get_height()
+        ax1.text(bar.get_x() + bar.get_width()/2., height, f'{height:.0f}', 
+                ha='center', va='bottom', fontsize=8)
+    
+    # 2. 比率趋势图 - 改为百分比
+    ax2.plot(x, ratios, 'o-', linewidth=3, markersize=8, color='#45B7D1', 
+             markerfacecolor='white', markeredgewidth=2)
+    ax2.axhline(y=ratios.mean(), color='red', linestyle='--', alpha=0.7, 
+                label=f'平均比率: {ratios.mean():.2f}%')  # 改为百分比
+    ax2.set_title(f'{symbol} - 十年资本支出/净利润比率趋势', fontsize=14, fontweight='bold')
+    ax2.set_xlabel('年份')
+    ax2.set_ylabel('比率 (%)')  # 改为百分比
+    ax2.set_xticks(x)
+    ax2.set_xticklabels(years, rotation=45)
+    ax2.legend()
+    ax2.grid(True, alpha=0.3)
+    
+    # 添加比率数值标签 - 改为百分比
+    for i, (xi, ratio) in enumerate(zip(x, ratios)):
+        ax2.annotate(f'{ratio:.2f}%', (xi, ratio), textcoords="offset points", 
+                    xytext=(0,10), ha='center', fontsize=9, fontweight='bold')
+    
+    # 3. 比率柱状图 - 改为百分比
+    colors = ['#FF9999' if ratio > ratios.mean() else '#99FF99' for ratio in ratios]
+    bars3 = ax3.bar(x, ratios, color=colors, alpha=0.8, edgecolor='black', linewidth=0.5)
+    ax3.axhline(y=ratios.mean(), color='red', linestyle='--', alpha=0.7, 
+                label=f'平均比率: {ratios.mean():.2f}%')  # 改为百分比
+    ax3.set_title(f'{symbol} - 十年资本支出/净利润比率', fontsize=14, fontweight='bold')
+    ax3.set_xlabel('年份')
+    ax3.set_ylabel('比率 (%)')  # 改为百分比
+    ax3.set_xticks(x)
+    ax3.set_xticklabels(years, rotation=45)
+    ax3.legend()
+    ax3.grid(True, alpha=0.3)
+    
+    # 添加比率数值标签 - 改为百分比
+    for i, (bar, ratio) in enumerate(zip(bars3, ratios)):
+        ax3.text(bar.get_x() + bar.get_width()/2., ratio, f'{ratio:.2f}%', 
+                ha='center', va='bottom', fontsize=9, fontweight='bold')
+    
+    plt.tight_layout()
+    plt.show()
+
+def print_financial_insights(valid_df, symbol):
+    """打印财务洞察"""
+    if valid_df is None or valid_df.empty:
+        return
+    
+    avg_ratio = valid_df['capex_to_profit_ratio_pct'].mean()  # 已经是百分比
+    
+    print(f"\n💡 {symbol} 财务洞察:")
+    print("=" * 50)
+    
+    if avg_ratio < 10:  # 调整为百分比阈值
+        insight = "公司资本支出相对净利润非常保守"
+        explanation = "这表明公司可能处于成熟期，不需要大量资本投入来维持运营"
+    elif avg_ratio < 30:
+        insight = "公司资本支出相对净利润较为适中"
+        explanation = "公司在维持现有业务的同时进行适度投资"
+    elif avg_ratio < 50:
+        insight = "公司资本支出相对净利润较高"
+        explanation = "公司可能处于扩张期或进行重大投资项目"
+    else:
+        insight = "公司资本支出相对净利润非常高"
+        explanation = "这可能表明公司正在进行大规模扩张或面临高资本需求"
+    
+    print(f"平均比率: {avg_ratio:.2f}%")  # 改为百分比显示
+    print(f"主要特征: {insight}")
+    print(f"含义: {explanation}")
+
+# 使用示例
+if __name__ == "__main__":
+    if WIND_AVAILABLE:
+        # 测试股票
+        symbol = "688981"  # 贵州茅台
+        
+        print(f"🔍 开始分析 {symbol}...")
+        
+        # 获取十年数据
+        df, capex_data, profit_data = get_ten_year_financial_data(symbol)
+        
+        if df is not None:
+            # 打印十年数据分析
+            print_ten_year_analysis(df, symbol)
+            
+            # 分析有效数据
+            valid_df = analyze_valid_ten_year_data(df, symbol)
+            
+            # 绘制图表
+            if valid_df is not None:
+                plot_ten_year_analysis(valid_df, symbol)
+                
+                # 打印财务洞察
+                print_financial_insights(valid_df, symbol)
+            
+        else:
+            print("❌ 无法获取数据")
+            
+    else:
+        print("❌ Wind不可用")

gogogo/result_visualizer.py

@@ -2,7 +2,7 @@
 import vectorbt as vbt
 import pandas as pd
 import matplotlib.pyplot as plt
-from strategy_executor import generate_strategy, create_portfolio, create_real_stock_portfolio
+from strategy_executor import generate_strategy, create_portfolio, create_real_stock_portfolio, debug_signal_consistency, debug_signal_logic
 
 # 设置中文显示
 plt.rcParams['font.sans-serif'] = ['SimHei']
@@ -372,12 +372,18 @@ def main():
     # 生成策略
     strategy_data = generate_strategy()
     
+    # 调试信号生成逻辑
+    debug_signal_logic(strategy_data)
+
     # 创建基于比率的投资组合（用于分析）
     ratio_portfolio = create_portfolio(strategy_data)
     
     # 创建基于真实股票的投资组合（用于真实收益计算）
     stock_portfolio = create_real_stock_portfolio(strategy_data)
     
+    # 调试信号一致性
+    debug_signal_consistency(strategy_data, stock_portfolio)
+    
     # 打印基于比率的统计
     print_statistics(strategy_data, ratio_portfolio)
     

gogogo/strategy_executor.py

@@ -7,7 +7,7 @@ from data_fetcher import get_processed_data
 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
-    优化信号逻辑，避免过于频繁的交易
+    修正信号逻辑：比率低时做多价差，比率高时做空价差
     """
     # 计算布林带
     ratio_ma = ratio_series.rolling(window=window).mean()
@@ -26,15 +26,22 @@ def calculate_ratio_signals(ratio_series, window=20, num_std=2):
             
         ratio_val = ratio_series.iloc[i]
         ma_val = ratio_ma.iloc[i]
+        upper_val = upper_band.iloc[i]
+        lower_val = lower_band.iloc[i]
         
         # 当前无仓位时的开仓条件
         if current_signal == 0:
-            if ratio_val < lower_band.iloc[i]:  # 比率突破下轨，做多价差
-                signals.iloc[i] = 1
+            # 比率突破下轨：SMIC相对低估，做多价差（买入SMIC，卖空HHIC）
+            if ratio_val < lower_val:
+                signals.iloc[i] = 1  # 做多价差
                 current_signal = 1
-            elif ratio_val > upper_band.iloc[i]:  # 比率突破上轨，做空价差
-                signals.iloc[i] = -1
+                print(f"开仓做多价差: {ratio_series.index[i]} - 比率{ratio_val:.4f} < 下轨{lower_val:.4f}")
+                
+            # 比率突破上轨：SMIC相对高估，做空价差（卖空SMIC，买入HHIC）
+            elif ratio_val > upper_val:
+                signals.iloc[i] = -1  # 做空价差
                 current_signal = -1
+                print(f"开仓做空价差: {ratio_series.index[i]} - 比率{ratio_val:.4f} > 上轨{upper_val:.4f}")
         
         # 当前有仓位时的平仓条件
         elif current_signal != 0:
@@ -43,6 +50,7 @@ def calculate_ratio_signals(ratio_series, window=20, num_std=2):
                (current_signal == -1 and ratio_val <= ma_val):
                 signals.iloc[i] = 0
                 current_signal = 0
+                print(f"平仓: {ratio_series.index[i]} - 比率{ratio_val:.4f} 回归均线{ma_val:.4f}")
     
     return signals, ratio_ma, upper_band, lower_band
 
@@ -50,10 +58,10 @@ def calculate_ratio_signals(ratio_series, window=20, num_std=2):
 def generate_ratio_size(signals, price_ratio, 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
+    修复数据类型问题
     """
     # 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')
+    size_series = pd.Series(0.0, index=signals.index, name='size')  # 改为float类型
     current_position = 0
     
     for i in range(len(signals)):
@@ -63,25 +71,24 @@ def generate_ratio_size(signals, price_ratio, position_size=0.5):
         signal = signals.iloc[i]
         
         if signal == 1 and current_position != 1:  # 做多价差 -> 买入比率
-            # 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
+            size_series.iloc[i] = float(position_size)  # 明确转换为float
             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
+            size_series.iloc[i] = -float(position_size)  # 明确转换为float
             current_position = -1
             
         elif signal == 0 and current_position != 0:  # 平仓
-            size_series.iloc[i] = 0  # 平仓
+            size_series.iloc[i] = 0.0  # 明确转换为float
             current_position = 0
     
     return size_series
 
+
 def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, position_ratio=0.5):
     """
     生成真实股票交易的size数据
-    确保每次配对交易都是等市值对冲，避免使用杠杆
+    添加详细的调试信息来跟踪信号执行
     """
     # 创建空的size Series
     smic_size = pd.Series(0.0, index=signals.index, name='SMIC')
@@ -91,29 +98,43 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p
     smic_position = 0.0   # 当前中芯持仓数量
     hhic_position = 0.0   # 当前华虹持仓数量
     
+    print(f"\n=== 交易执行调试信息 ===")
+    print(f"信号总数: {len(signals)}")
+    print(f"非零信号数量: {(signals != 0).sum()}")
+    print(f"做多信号: {(signals == 1).sum()}, 做空信号: {(signals == -1).sum()}, 平仓信号: {(signals == 0).sum()}")
+    
     for i in range(len(signals)):
         if i < 20:  # 跳过布林带计算期
             continue
             
         signal = signals.iloc[i]
+        date = signals.index[i]
         smic_price = close_smic.iloc[i]
         hhic_price = close_hhic.iloc[i]
         
+        # 只有信号变化时才执行交易
+        if signal != 0 or (signal == 0 and current_position != 0):
+            print(f"\n日期: {date}, 信号: {signal}, 当前仓位: {current_position}")
+        
         # 平仓条件：信号为0且当前有仓位
         if signal == 0 and current_position != 0:
+            print(f"  执行平仓: SMIC持仓{smic_position:.2f}, HHIC持仓{hhic_position:.2f}")
+            
             # 平掉所有仓位
             if smic_position != 0:
                 smic_size.iloc[i] = -smic_position
                 smic_position = 0.0
+                print(f"  平仓SMIC: {-smic_size.iloc[i]:.2f}股")
             if hhic_position != 0:
                 hhic_size.iloc[i] = -hhic_position
                 hhic_position = 0.0
+                print(f"  平仓HHIC: {-hhic_size.iloc[i]:.2f}股")
             
             current_position = 0
             continue
         
         # 开仓条件：只有当前无仓位时才开新仓
-        if current_position == 0:
+        if current_position == 0 and signal != 0:
             if signal == 1:  # 做多价差：买入中芯，卖空华虹
                 # 计算每只股票的仓位价值（等市值对冲）
                 position_value = initial_cash * position_ratio
@@ -129,7 +150,9 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p
                 hhic_position = hhic_shares
                 
                 current_position = 1
-                print(f"开仓做多价差: {signals.index[i]} - 买入SMIC {smic_shares:.2f}股 @{smic_price:.2f}, 卖空HHIC {abs(hhic_shares):.2f}股 @{hhic_price:.2f}")
+                print(f"  执行做多价差开仓:")
+                print(f"    买入SMIC: {smic_shares:.2f}股 @{smic_price:.2f}")
+                print(f"    卖空HHIC: {abs(hhic_shares):.2f}股 @{hhic_price:.2f}")
                 
             elif signal == -1:  # 做空价差：卖空中芯，买入华虹
                 # 计算每只股票的仓位价值（等市值对冲）
@@ -146,16 +169,28 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p
                 hhic_position = hhic_shares
                 
                 current_position = -1
-                print(f"开仓做空价差: {signals.index[i]} - 卖空SMIC {abs(smic_shares):.2f}股 @{smic_price:.2f}, 买入HHIC {hhic_shares:.2f}股 @{hhic_price:.2f}")
+                print(f"  执行做空价差开仓:")
+                print(f"    卖空SMIC: {abs(smic_shares):.2f}股 @{smic_price:.2f}")
+                print(f"    买入HHIC: {hhic_shares:.2f}股 @{hhic_price:.2f}")
     
     # 最后检查是否有未平仓的仓位，如果有则在最后一天平仓
     if current_position != 0:
         last_index = len(signals) - 1
+        last_date = signals.index[last_index]
+        print(f"\n最终平仓: {last_date}")
         if smic_position != 0:
             smic_size.iloc[last_index] = -smic_position
+            print(f"  平仓SMIC: {-smic_size.iloc[last_index]:.2f}股")
         if hhic_position != 0:
             hhic_size.iloc[last_index] = -hhic_position
-        print(f"最终平仓: {signals.index[last_index]} - 平掉所有剩余仓位")
+            print(f"  平仓HHIC: {-hhic_size.iloc[last_index]:.2f}股")
+    
+    # 统计实际执行的交易
+    smic_trades = (smic_size != 0).sum()
+    hhic_trades = (hhic_size != 0).sum()
+    print(f"\n实际执行交易统计:")
+    print(f"  SMIC交易次数: {smic_trades}")
+    print(f"  HHIC交易次数: {hhic_trades}")
     
     # 创建size DataFrame
     size_df = pd.DataFrame({
@@ -165,6 +200,103 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p
     
     return size_df
 
+def debug_signal_logic(strategy_data):
+    """调试信号生成逻辑"""
+    print("\n" + "="*60)
+    print("=== 信号生成逻辑调试 ===")
+    print("="*60)
+    
+    signals = strategy_data['signals']
+    price_ratio = strategy_data['price_ratio']
+    ratio_ma = strategy_data['ratio_ma']
+    upper_band = strategy_data['upper_band']
+    lower_band = strategy_data['lower_band']
+    
+    # 检查关键日期的信号逻辑
+    key_dates = [
+        '2024-12-20', '2024-12-23', '2025-02-18', '2025-02-19',
+        '2025-04-22', '2025-04-23', '2025-05-16', '2025-05-19'
+    ]
+    
+    for date_str in key_dates:
+        date = pd.Timestamp(date_str)
+        if date in signals.index:
+            signal = signals.loc[date]
+            ratio_val = price_ratio.loc[date]
+            ma_val = ratio_ma.loc[date]
+            upper_val = upper_band.loc[date]
+            lower_val = lower_band.loc[date]
+            
+            print(f"\n{date}:")
+            print(f"  比率: {ratio_val:.4f}, 均线: {ma_val:.4f}")
+            print(f"  上轨: {upper_val:.4f}, 下轨: {lower_val:.4f}")
+            print(f"  信号: {signal} ({'平仓' if signal == 0 else '做多' if signal == 1 else '做空'})")
+            
+            # 检查信号逻辑
+            if signal == 1:
+                print(f"  逻辑: 比率{ratio_val:.4f} < 下轨{lower_val:.4f} = {ratio_val < lower_val}")
+            elif signal == -1:
+                print(f"  逻辑: 比率{ratio_val:.4f} > 上轨{upper_val:.4f} = {ratio_val > upper_val}")
+            elif signal == 0:
+                print(f"  逻辑: 平仓信号")
+
+def debug_signal_consistency(strategy_data, stock_portfolio):
+    """调试信号和交易的一致性"""
+    print("\n" + "="*60)
+    print("=== 信号与交易一致性调试 ===")
+    print("="*60)
+    
+    signals = strategy_data['signals']
+    price_ratio = strategy_data['price_ratio']
+    ratio_ma = strategy_data['ratio_ma']
+    upper_band = strategy_data['upper_band']
+    lower_band = strategy_data['lower_band']
+    
+    # 获取订单记录
+    orders_df = stock_portfolio.orders.records_readable
+    
+    print(f"\n信号数据:")
+    print(f"信号时间范围: {signals.index[0]} 到 {signals.index[-1]}")
+    print(f"信号总数: {len(signals)}")
+    
+    # 找出所有信号变化点
+    signal_changes = signals[signals != signals.shift(1)]
+    print(f"\n信号变化点数量: {len(signal_changes)}")
+    
+    # 显示前10个信号变化点
+    print(f"\n前10个信号变化点:")
+    for i, (date, signal) in enumerate(signal_changes.head(10).items()):
+        ratio_val = price_ratio.loc[date]
+        ma_val = ratio_ma.loc[date]
+        upper_val = upper_band.loc[date]
+        lower_val = lower_band.loc[date]
+        
+        signal_desc = "平仓" if signal == 0 else ("做多" if signal == 1 else "做空")
+        print(f"  {date}: {signal_desc} (比率: {ratio_val:.4f}, 均线: {ma_val:.4f})")
+    
+    # 检查订单时间与信号时间的对应关系
+    if 'Timestamp' in orders_df.columns:
+        order_dates = pd.to_datetime(orders_df['Timestamp']).unique()
+        print(f"\n订单执行日期: {len(order_dates)}个")
+        print("订单日期:", sorted(order_dates))
+        
+        # 检查哪些信号日期有对应的订单
+        signal_dates_with_orders = []
+        signal_dates_without_orders = []
+        
+        for date in signal_changes.index:
+            if date in order_dates:
+                signal_dates_with_orders.append(date)
+            else:
+                signal_dates_without_orders.append(date)
+        
+        print(f"\n有对应订单的信号日期: {len(signal_dates_with_orders)}")
+        print(f"无对应订单的信号日期: {len(signal_dates_without_orders)}")
+        
+        if signal_dates_without_orders:
+            print("缺失订单的信号日期:", signal_dates_without_orders[:5])
+
+
 def generate_strategy():
     """生成配对交易策略"""
     # 获取数据