配对交易最初代码(不能运行版本)

comtom_indicator_example.py

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+import vectorbt as vbt
+import akshare as ak
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import datetime
+
+stock_00981 = ak.stock_hk_daily(symbol="00981")
+stock_01347 = ak.stock_hk_daily(symbol="01347")
+
+# 数据预处理 - 设置日期索引
+def prepare_data(df):
+    """准备数据，设置日期索引"""
+    df = df.copy()
+    df['date'] = pd.to_datetime(df['date'])
+    df = df.set_index('date')
+    return df
+
+stock_00981 = prepare_data(stock_00981)
+stock_01347 = prepare_data(stock_01347)
+
+stock_00981_close = stock_00981['close']
+print("中芯国际最近5个交易日收盘价:")
+print(stock_00981_close.tail(5))
+
+def custom_indicator(close, rsi_window=14, ma_window=50):
+    # 计算日线RSI
+    rsi = vbt.RSI.run(close, window=rsi_window).rsi
+    
+    # 计算日线移动平均
+    ma = vbt.MA.run(close, window=ma_window).ma
+    
+    # 确保所有数组都是numpy数组，避免pandas索引问题
+    close_np = close.to_numpy() if hasattr(close, 'to_numpy') else close
+    rsi_np = rsi.to_numpy() if hasattr(rsi, 'to_numpy') else rsi
+    ma_np = ma.to_numpy() if hasattr(ma, 'to_numpy') else ma
+    
+    # 使用numpy数组进行计算
+    trend = np.where(rsi_np > 70, -1, 0)
+    trend = np.where((rsi_np < 30) & (close_np < ma_np), 1, trend)
+    return trend
+
+# 创建自定义指标 - 移除 keep_pd=True
+ind = vbt.IndicatorFactory(
+    class_name="Combination",
+    short_name="comb",
+    input_names=["close"],
+    param_names=["rsi_window", "ma_window"],
+    output_names=["value"]
+).from_apply_func(
+    custom_indicator,
+    rsi_window=14,
+    ma_window=50
+    # 移除 keep_pd=True
+)
+
+# 运行指标
+res = ind.run(
+    stock_00981_close,
+    rsi_window=21,
+    ma_window=[21,50,100]
+)
+
+print("指标结果:")
+print(res.value)
+
+# 生成交易信号
+entries = res.value == 1.0
+exits = res.value == -1.0
+
+print(f"买入信号数量: {entries.sum().sum()}")
+print(f"卖出信号数量: {exits.sum().sum()}")
+
+# 如果结果有多列，选择第一列
+if entries.ndim > 1 and entries.shape[1] > 1:
+    entries = entries.iloc[:, 0]
+    exits = exits.iloc[:, 0]
+    print("使用第一组参数")
+
+# 创建投资组合
+pf = vbt.Portfolio.from_signals(stock_00981_close, entries, exits, freq='D')
+
+# 绘图
+try:
+    pf.plot().show()
+except Exception as e:
+    print(f"绘图错误: {e}")
+    # 简化绘图
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
+    
+    # 价格和信号
+    ax1.plot(stock_00981_close.index, stock_00981_close.values, label='Close Price', color='blue')
+    
+    # 买入信号
+    buy_signals = entries.fillna(False)
+    if buy_signals.any():
+        buy_dates = stock_00981_close.index[buy_signals]
+        buy_prices = stock_00981_close[buy_signals]
+        ax1.scatter(buy_dates, buy_prices, color='green', marker='^', s=100, label='Buy')
+    
+    # 卖出信号
+    sell_signals = exits.fillna(False)
+    if sell_signals.any():
+        sell_dates = stock_00981_close.index[sell_signals]
+        sell_prices = stock_00981_close[sell_signals]
+        ax1.scatter(sell_dates, sell_prices, color='red', marker='v', s=100, label='Sell')
+    
+    ax1.set_title('Price and Trading Signals')
+    ax1.legend()
+    ax1.grid(True)
+    
+    # 投资组合价值
+    portfolio_value = pf.value()
+    ax2.plot(portfolio_value.index, portfolio_value.values, label='Portfolio Value', color='orange')
+    ax2.set_title('Portfolio Value')
+    ax2.legend()
+    ax2.grid(True)
+    
+    plt.tight_layout()
+    plt.show()
+
+print("-------------------")
+print("投资组合统计:")
+stats = pf.stats()
+print(stats)
+print(f"总收益率: {pf.total_return():.2%}")
+print("-------------------")