regular commit

gogogo/data_fetcher.py

@@ -54,7 +54,7 @@ def get_processed_data():
 
     # 限制为最近一年数据
     end_date = smic_data.index.max()
-    start_date = end_date - pd.Timedelta(days=360)
+    start_date = end_date - pd.Timedelta(days=360*3)
 
     print(f"\n限制回测时间范围: {start_date} 到 {end_date}")
 

gogogo/result_visualizer.py

@@ -2,14 +2,12 @@
 import vectorbt as vbt
 import pandas as pd
 import matplotlib.pyplot as plt
-from strategy_executor import execute_strategy
-from strategy_executor import *
+from strategy_executor import generate_strategy, create_portfolio, create_real_stock_portfolio
 
 # 设置中文显示
 plt.rcParams['font.sans-serif'] = ['SimHei']
 plt.rcParams['axes.unicode_minus'] = False
 
-
 def plot_results(strategy_data, portfolio):
     """绘制结果图表"""
     price_ratio = strategy_data['price_ratio']
@@ -177,10 +175,11 @@ def print_statistics(strategy_data, portfolio):
     print(f"做空信号次数: {short_count}")
     print(f"总信号次数: {total_signals}")
 
+
 def main():
     """主函数"""
-    # 执行策略
-    strategy_data = execute_strategy()
+    # 生成策略
+    strategy_data = generate_strategy()
     
     # 创建基于比率的投资组合（用于分析）
     ratio_portfolio = create_portfolio(strategy_data)
@@ -188,45 +187,18 @@ def main():
     # 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)
     
-    # 绘制基于比率的结果
-    plot_results(strategy_data, ratio_portfolio)
-    
     # 打印基于比率的统计
     print_statistics(strategy_data, ratio_portfolio)
     
     # 打印真实股票组合的统计
-    if stock_portfolio is not None:
-        print("\n" + "="*60)
-        print("=== 真实股票投资组合表现 ===")
-        print("="*60)
-        
-        try:
-            stats = stock_portfolio.stats()
-            print(f"总收益率: {stats.get('Total Return [%]', 'N/A')}%")
-            print(f"年化收益率: {stats.get('Annual Return [%]', 'N/A')}%")
-            print(f"夏普比率: {stats.get('Sharpe Ratio', 'N/A')}")
-            print(f"最大回撤: {stats.get('Max Drawdown [%]', 'N/A')}%")
-            print(f"最终组合价值: {stock_portfolio.value().iloc[-1]:.2f}")
-            print(f"总盈亏: {stock_portfolio.value().iloc[-1] - strategy_data['initial_cash']:.2f}")
-            
-            # 绘制真实股票组合的净值曲线
-            plt.figure(figsize=(12, 6))
-            plt.plot(stock_portfolio.value().index, stock_portfolio.value(), 
-                    label='真实股票组合净值', linewidth=2, color='darkgreen')
-            plt.axhline(y=strategy_data['initial_cash'], color='red', linestyle='--', 
-                       alpha=0.7, label=f'初始资金({strategy_data["initial_cash"]})')
-            plt.title('真实股票配对交易组合净值')
-            plt.ylabel('组合价值')
-            plt.xlabel('日期')
-            plt.legend()
-            plt.grid(True, alpha=0.3)
-            plt.tight_layout()
-            plt.show()
-            
-        except Exception as e:
-            print(f"分析真实股票组合时出错: {e}")
+    print(stock_portfolio['SMIC'].stats())
+    print(stock_portfolio['HHIC'].stats())
     
+    # 绘制基于比率的结果
+    plot_results(strategy_data, ratio_portfolio)
+
     print("程序执行完成!")
 
 if __name__ == "__main__":
-    main()
+    main()
+    

gogogo/strategy_executor.py

@@ -132,8 +132,8 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p
     
     return size_df
 
-def execute_strategy():
-    """执行配对交易策略"""
+def generate_strategy():
+    """生成配对交易策略"""
     # 获取数据
     smic_data, hhic_data = get_processed_data()
     
@@ -257,13 +257,80 @@ def create_real_stock_portfolio(strategy_data):
         traceback.print_exc()
         return None
 
+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
+    返回两个DataFrame：smic_size和hhic_size
+    """
+    # 创建空的size Series
+    smic_size = pd.Series(0.0, index=signals.index, name='SMIC')
+    hhic_size = pd.Series(0.0, index=signals.index, name='HHIC')
+    
+    current_position = 0  # 0: 无仓位, 1: 做多价差, -1: 做空价差
+    
+    for i in range(len(signals)):
+        if i < 20:  # 跳过布林带计算期
+            continue
+            
+        signal = signals.iloc[i]
+        smic_price = close_smic.iloc[i]
+        hhic_price = close_hhic.iloc[i]
+        
+        if signal == 1 and current_position != 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
+            
+            # 做多中芯国际
+            smic_shares = position_value / smic_price
+            smic_size.iloc[i] = smic_shares
+            
+            # 做空华虹半导体
+            hhic_shares = -position_value / hhic_price
+            hhic_size.iloc[i] = hhic_shares
+            
+            current_position = 1
+            
+        elif signal == -1 and current_position != -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）
+            position_value = initial_cash * position_ratio
+            
+            # 做空中芯国际
+            smic_shares = -position_value / smic_price
+            smic_size.iloc[i] = smic_shares
+            
+            # 做多华虹半导体
+            hhic_shares = position_value / hhic_price
+            hhic_size.iloc[i] = hhic_shares
+            
+            current_position = -1
+            
+        elif signal == 0 and current_position != 0:  # 平仓
+            # 平掉所有仓位
+            if current_position == 1:  # 平掉做多价差仓位
+                smic_size.iloc[i] = -smic_size.shift(1).iloc[i] if i > 0 else 0
+                hhic_size.iloc[i] = -hhic_size.shift(1).iloc[i] if i > 0 else 0
+            elif current_position == -1:  # 平掉做空价差仓位
+                smic_size.iloc[i] = -smic_size.shift(1).iloc[i] if i > 0 else 0
+                hhic_size.iloc[i] = -hhic_size.shift(1).iloc[i] if i > 0 else 0
+            
+            current_position = 0
+    
+    # 创建size DataFrame
+    size_df = pd.DataFrame({
+        'SMIC': smic_size,
+        'HHIC': hhic_size
+    })
+    
+    return size_df
+
+
 if __name__ == "__main__":
-    strategy_data = execute_strategy()
+    strategy_data = generate_strategy()
     
     # 测试基于比率的投资组合
     ratio_portfolio = create_portfolio(strategy_data)
     
     # 测试基于真实股票的投资组合
     stock_portfolio = create_real_stock_portfolio(strategy_data)
-    
-    print("策略执行完成!")
+
+    print("策略生成完成!")