配对交易最小距离法 代码示例

以下是一个使用Python实现配对交易最小距离法的示例代码：

import pandas as pd
import numpy as np
import statsmodels.api as sm

# 获取数据
df1 = pd.read_csv('AAPL.csv')
df2 = pd.read_csv('MSFT.csv')

# 将数据按日期合并
df = pd.merge(df1, df2, on='Date')

# 计算收益率
df['ret1'] = df['AAPL'].pct_change()
df['ret2'] = df['MSFT'].pct_change()

# 去掉缺失值
df.dropna(inplace=True)

# 计算协方差和相关系数
cov = np.cov(df['ret1'], df['ret2'])
corr = np.corrcoef(df['ret1'], df['ret2'])[0, 1]

# 用OLS计算线性回归
model = sm.OLS(df['ret1'], sm.add_constant(df['ret2'])).fit()
beta = model.params['ret2']

# 计算残差
df['resid'] = df['ret1'] - beta * df['ret2']

# 计算标准差
std = np.std(df['resid'])

# 设置阈值
threshold = 2 * std

# 计算价格差异
df['diff'] = df['AAPL'] - beta * df['MSFT']

# 找到交易机会
df['signal'] = np.where(df['diff'] > threshold, 1, np.where(df['diff'] < -threshold, -1, 0))

# 计算收益
df['strategy'] = df['signal'].shift(1) * df['ret1']

# 计算累计收益
df['cum_strategy'] = (1 + df['strategy']).cumprod()

# 绘制累计收益曲线
df['cum_strategy'].plot()

需要注意的是，该示例代码仅供参考，实际交易中需要根据市场动态和具体情况进行调整。同时，该代码仅考虑了两个品种之间的交易，实际应用中可能需要考虑更多品种之间的关系。