From 77fbebe23192886d3768673fcb0295b72483738f Mon Sep 17 00:00:00 2001
From: bingyi <iceoray@qq.com>
Date: Sat, 1 Nov 2025 22:49:53 +0800
Subject: [PATCH] =?UTF-8?q?=E6=95=B0=E6=8D=AE=E8=8E=B7=E5=8F=96=EF=BC=8C?=
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---
 .gitignore                  |   3 +-
 gogogo/data_fetcher.py      |  79 +++++++++++++
 gogogo/result_visualizer.py | 225 ++++++++++++++++++++++++++++++++++++
 gogogo/strategy_executor.py | 122 +++++++++++++++++++
 4 files changed, 428 insertions(+), 1 deletion(-)
 create mode 100644 gogogo/data_fetcher.py
 create mode 100644 gogogo/result_visualizer.py
 create mode 100644 gogogo/strategy_executor.py

diff --git a/.gitignore b/.gitignore
index 3699812..6094eef 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
-**/*.csv
\ No newline at end of file
+**/*.csv
+__pycache__/
diff --git a/gogogo/data_fetcher.py b/gogogo/data_fetcher.py
new file mode 100644
index 0000000..2e8419e
--- /dev/null
+++ b/gogogo/data_fetcher.py
@@ -0,0 +1,79 @@
+# data_fetcher.py
+import akshare as ak
+import pandas as pd
+import datetime
+
+def fetch_stock_data():
+    """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())
+    
+    return stock_00981, stock_01347
+
+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']]
+
+def get_processed_data():
+    """获取并处理数据"""
+    # 获取原始数据
+    stock_00981, stock_01347 = fetch_stock_data()
+    
+    # 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()}")
+
+    # 限制为最近一年数据
+    end_date = smic_data.index.max()
+    start_date = end_date - pd.Timedelta(days=360)
+
+    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)}")
+    
+    return smic_data, hhic_data
+
+if __name__ == "__main__":
+    smic_data, hhic_data = get_processed_data()
+    print("数据获取和预处理完成!")
\ No newline at end of file
diff --git a/gogogo/result_visualizer.py b/gogogo/result_visualizer.py
new file mode 100644
index 0000000..32cb739
--- /dev/null
+++ b/gogogo/result_visualizer.py
@@ -0,0 +1,225 @@
+# result_visualizer.py
+import vectorbt as vbt
+import pandas as pd
+import matplotlib.pyplot as plt
+from strategy_executor import execute_strategy
+
+# 设置中文显示
+plt.rcParams['font.sans-serif'] = ['SimHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+def create_portfolio(strategy_data):
+    """创建投资组合"""
+    print("创建基于价格比率的投资组合...")
+    
+    price_ratio = strategy_data['price_ratio']
+    size = strategy_data['size']
+    initial_cash = strategy_data['initial_cash']
+    commission = strategy_data['commission']
+    
+    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!")
+        return portfolio
+        
+    except Exception as e:
+        print(f"创建投资组合时出错: {e}")
+        import traceback
+        traceback.print_exc()
+        return None
+
+def plot_results(strategy_data, portfolio):
+    """绘制结果图表"""
+    price_ratio = strategy_data['price_ratio']
+    signals = strategy_data['signals']
+    ratio_ma = strategy_data['ratio_ma']
+    upper_band = strategy_data['upper_band']
+    lower_band = strategy_data['lower_band']
+    initial_cash = strategy_data['initial_cash']
+    
+    # ========== 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)
+
+    # 3. 仓位变化
+    positions = signals.replace({1: '做多', -1: '做空', 0: '空仓'})
+    axes[2].plot(positions.index, positions, label='仓位状态', linewidth=2, color='darkorange', drawstyle='steps-post')
+    axes[2].set_title('仓位变化')
+    axes[2].set_ylabel('仓位')
+    axes[2].legend()
+    axes[2].grid(True, alpha=0.3)
+    
+    # 4. 组合净值
+    if portfolio is not None:
+        portfolio_value = portfolio.value()
+        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()
+
+def print_statistics(strategy_data, portfolio):
+    """打印统计结果"""
+    price_ratio = strategy_data['price_ratio']
+    signals = strategy_data['signals']
+    
+    print("\n=== 基于价格比率的配对交易策略表现 ===")
+    
+    if portfolio is not None:
+        # ========== 使用vectorbt进行专业分析 ==========
+        print("\n=== VectorBT 专业分析（基于价格比率） ===")
+        
+        try:
+            # 选择第一列（也是唯一的一列）
+            portfolio_single = portfolio['RATIO']
+            
+            print(portfolio_single.stats())
+
+            # 绘制vectorbt图表
+            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}")
+        
+        # ========== 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}")
+
+def main():
+    """主函数"""
+    # 执行策略
+    strategy_data = execute_strategy()
+    
+    # 创建投资组合
+    portfolio = create_portfolio(strategy_data)
+    
+    # 绘制结果
+    plot_results(strategy_data, portfolio)
+    
+    # 打印统计
+    print_statistics(strategy_data, portfolio)
+    
+    print("程序执行完成!")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/gogogo/strategy_executor.py b/gogogo/strategy_executor.py
new file mode 100644
index 0000000..3de9ccd
--- /dev/null
+++ b/gogogo/strategy_executor.py
@@ -0,0 +1,122 @@
+# strategy_executor.py
+import pandas as pd
+import numpy as np
+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
+    """
+    # 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
+
+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
+    """
+    # 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
+            
+        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
+            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
+
+def execute_strategy():
+    """执行配对交易策略"""
+    # 获取数据
+    smic_data, hhic_data = get_processed_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
+
+    # 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()}")
+
+    # 生成size数据
+    size = generate_ratio_size(signals, price_ratio, position_size)
+
+    print(f"size数据形状: {size.shape}")
+    print(f"非零交易数量: {(size != 0).sum()}")
+    
+    return {
+        'price_ratio': price_ratio,
+        'signals': signals,
+        'size': size,
+        'ratio_ma': ratio_ma,
+        'upper_band': upper_band,
+        'lower_band': lower_band,
+        'initial_cash': initial_cash,
+        'commission': commission,
+        'smic_data': smic_data,
+        'hhic_data': hhic_data
+    }
+
+if __name__ == "__main__":
+    strategy_data = execute_strategy()
+    print("策略执行完成!")
\ No newline at end of file