549 lines
19 KiB
Python
549 lines
19 KiB
Python
import vectorbt as vbt
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import akshare as ak
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import datetime
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from numba import njit
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from collections import namedtuple
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# 设置中文显示
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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# 获取股票数据
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print("正在获取股票数据...")
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stock_00981 = ak.stock_hk_daily(symbol="00981")
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stock_01347 = ak.stock_hk_daily(symbol="01347")
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print("中芯国际数据列名:", stock_00981.columns.tolist())
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print("华虹半导体数据列名:", stock_01347.columns.tolist())
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# 数据预处理
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def preprocess_data(df, symbol):
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"""预处理股票数据"""
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df = df.copy()
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# 检查列名并重命名（如果需要）
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if 'date' in df.columns:
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df['date'] = pd.to_datetime(df['date'])
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df.set_index('date', inplace=True)
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elif '日期' in df.columns:
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df['date'] = pd.to_datetime(df['日期'])
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df.set_index('date', inplace=True)
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# 重命名中文列为英文
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rename_dict = {
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'开盘': 'open',
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'最高': 'high',
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'最低': 'low',
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'收盘': 'close',
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'成交量': 'volume'
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}
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df = df.rename(columns=rename_dict)
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else:
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# 如果已经有英文列名，直接使用
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df.index = pd.to_datetime(df.index)
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df = df.sort_index()
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return df[['open', 'high', 'low', 'close', 'volume']]
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# 预处理数据
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smic_data = preprocess_data(stock_00981, "00981")
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hhic_data = preprocess_data(stock_01347, "01347")
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print(f"中芯国际原始数据时间范围: {smic_data.index.min()} 到 {smic_data.index.max()}")
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print(f"华虹半导体原始数据时间范围: {hhic_data.index.min()} 到 {hhic_data.index.max()}")
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# 限制为最近三年数据
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end_date = smic_data.index.max()
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start_date = end_date - pd.Timedelta(days=3*365) # 最近三年
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print(f"\n限制回测时间范围: {start_date} 到 {end_date}")
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smic_data = smic_data.loc[start_date:end_date]
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hhic_data = hhic_data.loc[start_date:end_date]
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print(f"限制后中芯国际数据形状: {smic_data.shape}")
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print(f"限制后华虹半导体数据形状: {hhic_data.shape}")
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# 对齐数据时间范围
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common_index = smic_data.index.intersection(hhic_data.index)
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smic_data = smic_data.loc[common_index]
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hhic_data = hhic_data.loc[common_index]
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print(f"\n对齐后数据时间范围: {smic_data.index.min()} 到 {smic_data.index.max()}")
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print(f"对齐后数据点数: {len(smic_data)}")
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# 设置交易参数
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initial_cash = 100000
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commission = 0.001 # 0.1% 交易佣金
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position_size = 0.1 # 每次交易仓位比例
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# 计算配对交易信号
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def calculate_pair_signals(price1, price2, window=20, num_std=2):
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"""
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计算配对交易信号
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"""
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# 计算价格比率
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ratio = price1 / price2
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# 计算布林带
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ratio_ma = ratio.rolling(window=window).mean()
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ratio_std = ratio.rolling(window=window).std()
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upper_band = ratio_ma + num_std * ratio_std
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lower_band = ratio_ma - num_std * ratio_std
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# 生成交易信号
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# 1: 做多价差 (买中芯/卖华虹)
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# -1: 做空价差 (卖中芯/买华虹)
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# 0: 平仓
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signals = pd.Series(0, index=ratio.index, name='signal')
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# 当比率突破下轨时做多价差
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long_condition = (ratio < lower_band) & (ratio_ma.notna())
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signals[long_condition] = 1
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# 当比率突破上轨时做空价差
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short_condition = (ratio > upper_band) & (ratio_ma.notna())
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signals[short_condition] = -1
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# 当比率回归均值时平仓
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close_condition = (ratio.between(lower_band, upper_band)) & (signals.shift(1) != 0)
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signals[close_condition] = 0
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return signals, ratio, ratio_ma, upper_band, lower_band
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# 计算信号
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close_smic = smic_data['close']
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close_hhic = hhic_data['close']
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signals, ratio, ratio_ma, upper_band, lower_band = calculate_pair_signals(
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close_smic, close_hhic, window=20, num_std=2
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)
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print(f"信号计算完成，有效信号数量: {(signals != 0).sum()}")
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# 创建组合数据
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close = pd.DataFrame({
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'00981': close_smic,
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'01347': close_hhic
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})
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# 基于信号生成size数据
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def generate_pair_size(signals, close_smic, close_hhic, position_size=0.1):
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"""
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生成配对交易的size数据
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返回一个与close相同形状的DataFrame，包含每只股票的交易数量
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"""
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# 创建与close相同形状的size DataFrame，初始为0
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size_df = pd.DataFrame(0, index=close.index, columns=close.columns)
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current_position = 0
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for i in range(len(signals)):
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if i < 20: # 跳过布林带计算期
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continue
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date = signals.index[i]
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signal = signals.iloc[i]
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if signal == 1 and current_position != 1: # 做多价差
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# 买中芯，卖华虹
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smic_price = close_smic.iloc[i]
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hhic_price = close_hhic.iloc[i]
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# 计算头寸规模
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smic_shares = int((initial_cash * position_size) / smic_price)
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hhic_shares = int((initial_cash * position_size) / hhic_price)
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size_df.loc[date, '00981'] = smic_shares # 买入中芯
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size_df.loc[date, '01347'] = -hhic_shares # 卖空华虹
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current_position = 1
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elif signal == -1 and current_position != -1: # 做空价差
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# 卖中芯，买华虹
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smic_price = close_smic.iloc[i]
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hhic_price = close_hhic.iloc[i]
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# 计算头寸规模
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smic_shares = int((initial_cash * position_size) / smic_price)
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hhic_shares = int((initial_cash * position_size) / hhic_price)
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size_df.loc[date, '00981'] = -smic_shares # 卖空中芯
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size_df.loc[date, '01347'] = hhic_shares # 买入华虹
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current_position = -1
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elif signal == 0 and current_position != 0: # 平仓
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# 平掉所有头寸
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size_df.loc[date, '00981'] = 0 # 平仓中芯
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size_df.loc[date, '01347'] = 0 # 平仓华虹
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current_position = 0
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return size_df
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# 生成size数据
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size = generate_pair_size(signals, close_smic, close_hhic, position_size)
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print(f"size数据形状: {size.shape}")
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print(f"非零交易数量 - 中芯国际: {(size['00981'] != 0).sum()}, 华虹半导体: {(size['01347'] != 0).sum()}")
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# 创建投资组合
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print("创建投资组合...")
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try:
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portfolio = vbt.Portfolio.from_orders(
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close=close,
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size=size,
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init_cash=initial_cash,
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fees=commission,
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freq='D'
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)
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print("投资组合创建成功!")
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# 计算配对交易统计
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print("\n=== 配对交易策略表现 ===")
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# 获取组合总价值
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portfolio_value = portfolio.value()
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# 绘制结果
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print("\n绘制图表...")
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# 1. 价格比率和交易信号
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fig, axes = plt.subplots(3, 1, figsize=(15, 12))
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# 价格比率图
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axes[0].plot(ratio.index, ratio, label='价格比率(中芯/华虹)', linewidth=1)
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axes[0].plot(ratio.index, ratio_ma, label='移动平均', linewidth=1, alpha=0.7)
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axes[0].plot(ratio.index, upper_band, label='上轨', linewidth=1, alpha=0.7, linestyle='--', color='red')
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axes[0].plot(ratio.index, lower_band, label='下轨', linewidth=1, alpha=0.7, linestyle='--', color='green')
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axes[0].set_title('中芯国际-华虹半导体价格比率')
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axes[0].set_ylabel('价格比率')
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axes[0].legend()
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axes[0].grid(True, alpha=0.3)
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# 标记交易信号
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long_signals = signals[signals == 1]
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short_signals = signals[signals == -1]
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if len(long_signals) > 0:
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axes[0].scatter(long_signals.index, ratio[long_signals.index],
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color='green', marker='^', s=50, label='做多信号', zorder=5)
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if len(short_signals) > 0:
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axes[0].scatter(short_signals.index, ratio[short_signals.index],
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color='red', marker='v', s=50, label='做空信号', zorder=5)
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# 累积收益图
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if len(portfolio_value) > 0:
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axes[1].plot(portfolio_value.index, portfolio_value, label='组合净值', linewidth=1)
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axes[1].set_title('组合净值曲线')
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axes[1].set_ylabel('组合价值')
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axes[1].grid(True, alpha=0.3)
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# 回撤图
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drawdown = portfolio.drawdown()
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if len(drawdown) > 0:
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axes[2].plot(drawdown.index, drawdown, label='回撤', linewidth=1, color='red')
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axes[2].set_title('回撤')
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axes[2].set_ylabel('回撤')
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axes[2].set_xlabel('日期')
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axes[2].grid(True, alpha=0.3)
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plt.tight_layout()
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plt.show()
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# ========== 修复：vectorbt自带的可视化 ==========
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print("\n=== VectorBT 专业可视化 ===")
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# 1. 组合价值、持仓和交易的可视化 - 分别绘制每只股票
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print("\n绘制中芯国际分析...")
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try:
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# 中芯国际
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fig1 = portfolio['00981'].plot(subplots=[
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'orders', # 订单
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'trade_pnl', # 交易盈亏
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'cum_returns', # 累积收益
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'drawdowns' # 回撤
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])
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fig1.update_layout(
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title='中芯国际配对交易分析',
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height=800
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)
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fig1.show()
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except Exception as e:
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print(f"中芯国际分析绘制失败: {e}")
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print("\n绘制华虹半导体分析...")
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try:
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# 华虹半导体
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fig2 = portfolio['01347'].plot(subplots=[
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'orders', # 订单
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'trade_pnl', # 交易盈亏
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'cum_returns', # 累积收益
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'drawdowns' # 回撤
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])
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fig2.update_layout(
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title='华虹半导体配对交易分析',
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height=800
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)
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fig2.show()
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except Exception as e:
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print(f"华虹半导体分析绘制失败: {e}")
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# 2. 资产价值变化 - 使用组合总价值
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print("\n绘制组合总价值变化...")
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try:
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fig = portfolio_value.vbt.plot(
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title='配对交易组合总价值'
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)
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fig.update_layout(
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xaxis_title='日期',
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yaxis_title='组合价值'
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)
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fig.show()
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except Exception as e:
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print(f"组合价值变化绘制失败: {e}")
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# 3. 累积收益 - 使用组合总收益
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print("\n绘制组合累积收益...")
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try:
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cumulative_returns = portfolio.cumulative_returns()
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# 如果是多列，取第一列或平均值
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if hasattr(cumulative_returns, 'columns') and len(cumulative_returns.columns) > 1:
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cumulative_returns = cumulative_returns.mean(axis=1)
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fig = cumulative_returns.vbt.plot(
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title='配对交易累积收益率'
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)
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fig.update_layout(
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xaxis_title='日期',
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yaxis_title='累积收益'
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)
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fig.show()
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except Exception as e:
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print(f"累积收益绘制失败: {e}")
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# 4. 回撤分析 - 使用组合总回撤
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print("\n绘制组合回撤分析...")
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try:
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# 如果是多列，取第一列或平均值
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if hasattr(drawdown, 'columns') and len(drawdown.columns) > 1:
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drawdown = drawdown.mean(axis=1)
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fig = drawdown.vbt.plot(
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title='配对交易回撤分析'
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)
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fig.update_layout(
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xaxis_title='日期',
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yaxis_title='回撤'
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)
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fig.show()
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except Exception as e:
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print(f"回撤分析绘制失败: {e}")
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# 5. 月度收益热力图
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print("\n绘制月度收益热力图...")
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try:
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monthly_returns = portfolio.returns().vbt.to_monthly()
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# 如果是多列，取平均值
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if hasattr(monthly_returns, 'columns') and len(monthly_returns.columns) > 1:
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monthly_returns = monthly_returns.mean(axis=1)
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fig = monthly_returns.vbt.heatmap(
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title='配对交易月度收益热力图'
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)
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fig.update_layout(
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xaxis_title='年份',
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yaxis_title='月份'
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)
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fig.show()
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except Exception as e:
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print(f"月度收益热力图绘制失败: {e}")
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# 6. 交易分析 - 分别分析每只股票
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print("\n绘制交易分析...")
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try:
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# 中芯国际交易分析
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trades_smic = portfolio['00981'].trades
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if len(trades_smic) > 0:
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fig = trades_smic.plot_pnl()
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fig.update_layout(title='中芯国际交易盈亏分布')
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fig.show()
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fig = trades_smic.plot_duration()
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fig.update_layout(title='中芯国际交易持续时间分布')
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fig.show()
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# 华虹半导体交易分析
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trades_hhic = portfolio['01347'].trades
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if len(trades_hhic) > 0:
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fig = trades_hhic.plot_pnl()
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fig.update_layout(title='华虹半导体交易盈亏分布')
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fig.show()
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fig = trades_hhic.plot_duration()
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fig.update_layout(title='华虹半导体交易持续时间分布')
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fig.show()
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except Exception as e:
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print(f"交易分析绘制失败: {e}")
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# 7. 订单流分析 - 分别分析每只股票
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print("\n绘制订单流分析...")
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try:
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# 中芯国际订单
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fig1 = portfolio['00981'].orders.plot()
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fig1.update_layout(title='中芯国际订单流分析')
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fig1.show()
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# 华虹半导体订单
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fig2 = portfolio['01347'].orders.plot()
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fig2.update_layout(title='华虹半导体订单流分析')
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fig2.show()
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except Exception as e:
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print(f"订单流分析绘制失败: {e}")
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# 8. 持仓分析 - 分别分析每只股票
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print("\n绘制持仓分析...")
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try:
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# 中芯国际持仓
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holdings_smic = portfolio['00981'].holdings
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fig1 = holdings_smic.vbt.plot(
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title='中芯国际持仓变化'
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)
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fig1.update_layout(
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xaxis_title='日期',
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yaxis_title='持仓价值'
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)
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fig1.show()
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# 华虹半导体持仓
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holdings_hhic = portfolio['01347'].holdings
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fig2 = holdings_hhic.vbt.plot(
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title='华虹半导体持仓变化'
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)
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fig2.update_layout(
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xaxis_title='日期',
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yaxis_title='持仓价值'
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)
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fig2.show()
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except Exception as e:
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print(f"持仓分析绘制失败: {e}")
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# 9. 现金变化 - 使用组合总现金
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print("\n绘制现金变化...")
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try:
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cash = portfolio.cash
|
||
# 如果是多列，取第一列
|
||
if hasattr(cash, 'columns') and len(cash.columns) > 1:
|
||
cash = cash.iloc[:, 0]
|
||
fig = cash.vbt.plot(
|
||
title='配对交易现金余额变化'
|
||
)
|
||
fig.update_layout(
|
||
xaxis_title='日期',
|
||
yaxis_title='现金余额'
|
||
)
|
||
fig.show()
|
||
except Exception as e:
|
||
print(f"现金变化绘制失败: {e}")
|
||
|
||
# 10. 资产配置比例
|
||
print("\n绘制资产配置比例...")
|
||
try:
|
||
# 计算各资产权重
|
||
asset_value = portfolio.asset_value()
|
||
total_value = portfolio.value()
|
||
weights = asset_value.div(total_value, axis=0).fillna(0)
|
||
|
||
fig = weights.vbt.areaplot(
|
||
title='资产配置比例变化'
|
||
)
|
||
fig.update_layout(
|
||
xaxis_title='日期',
|
||
yaxis_title='权重'
|
||
)
|
||
fig.show()
|
||
except Exception as e:
|
||
print(f"资产配置比例绘制失败: {e}")
|
||
|
||
# 显示交易记录
|
||
non_zero_size = size[(size != 0).any(axis=1)]
|
||
print(f"\n非零交易数量: {len(non_zero_size)}")
|
||
if len(non_zero_size) > 0:
|
||
print("交易记录示例:")
|
||
print(non_zero_size.head(20))
|
||
|
||
# 打印详细统计
|
||
print("\n=== 详细统计 ===")
|
||
try:
|
||
# 分别获取每只股票的统计
|
||
stats_smic = portfolio['00981'].stats()
|
||
stats_hhic = portfolio['01347'].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("\n中芯国际统计:")
|
||
print(f"总收益率: {safe_get_stat(stats_smic, 'Total Return [%]', 'N/A')}%")
|
||
print(f"年化收益率: {safe_get_stat(stats_smic, 'Annual Return [%]', 'N/A')}%")
|
||
print(f"夏普比率: {safe_get_stat(stats_smic, 'Sharpe Ratio', 'N/A')}")
|
||
print(f"最大回撤: {safe_get_stat(stats_smic, 'Max Drawdown [%]', 'N/A')}%")
|
||
|
||
print("\n华虹半导体统计:")
|
||
print(f"总收益率: {safe_get_stat(stats_hhic, 'Total Return [%]', 'N/A')}%")
|
||
print(f"年化收益率: {safe_get_stat(stats_hhic, 'Annual Return [%]', 'N/A')}%")
|
||
print(f"夏普比率: {safe_get_stat(stats_hhic, 'Sharpe Ratio', 'N/A')}")
|
||
print(f"最大回撤: {safe_get_stat(stats_hhic, 'Max Drawdown [%]', '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_smic = portfolio['00981'].trades.records_readable
|
||
trades_hhic = portfolio['01347'].trades.records_readable
|
||
|
||
if len(trades_smic) > 0:
|
||
print(f"\n中芯国际交易分析:")
|
||
print(f"总交易次数: {len(trades_smic)}")
|
||
if 'Duration' in trades_smic.columns:
|
||
print(f"平均持仓时间: {trades_smic['Duration'].mean():.1f} 天")
|
||
|
||
if len(trades_hhic) > 0:
|
||
print(f"\n华虹半导体交易分析:")
|
||
print(f"总交易次数: {len(trades_hhic)}")
|
||
if 'Duration' in trades_hhic.columns:
|
||
print(f"平均持仓时间: {trades_hhic['Duration'].mean():.1f} 天")
|
||
|
||
except Exception as e:
|
||
print(f"分析交易时出错: {e}")
|
||
|
||
except Exception as e:
|
||
print(f"创建投资组合时出错: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
# 显示原始价格走势对比
|
||
print("\n绘制原始价格走势...")
|
||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10))
|
||
|
||
# 价格走势
|
||
ax1.plot(close_smic.index, close_smic, label='中芯国际', linewidth=1)
|
||
ax1.plot(close_hhic.index, close_hhic, label='华虹半导体', linewidth=1)
|
||
ax1.set_title('股票价格走势')
|
||
ax1.set_ylabel('价格')
|
||
ax1.legend()
|
||
ax1.grid(True, alpha=0.3)
|
||
|
||
# 价格比率
|
||
ax2.plot(ratio.index, ratio, label='价格比率', linewidth=1, color='purple')
|
||
ax2.axhline(y=ratio.mean(), color='red', linestyle='--', alpha=0.7, label='平均比率')
|
||
ax2.set_title('价格比率走势')
|
||
ax2.set_ylabel('比率')
|
||
ax2.set_xlabel('日期')
|
||
ax2.legend()
|
||
ax2.grid(True, alpha=0.3)
|
||
|
||
plt.tight_layout()
|
||
plt.show()
|
||
|
||
print("程序执行完成!") |