Python配对交易策略代码

以下是使用Python实现配对交易策略的代码示例：

import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt

# 读取数据
stock1 = pd.read_csv('stock1.csv')
stock2 = pd.read_csv('stock2.csv')

# 合并数据
data = pd.concat([stock1['Price'], stock2['Price']], axis=1)
data.columns = ['stock1', 'stock2']

# 计算股票价格的对数收益率
data['log_ret1'] = np.log(data['stock1']) - np.log(data['stock1'].shift(1))
data['log_ret2'] = np.log(data['stock2']) - np.log(data['stock2'].shift(1))

# 计算股票价格的差值
data['diff'] = data['log_ret1'] - data['log_ret2']

# 计算股票价格的差值的移动平均线和标准差
data['mean'] = data['diff'].rolling(window=30).mean()
data['std'] = data['diff'].rolling(window=30).std()

# 计算z-score
data['zscore'] = (data['diff'] - data['mean']) / data['std']

# 计算交易信号
data['signal'] = np.where(data['zscore'] > 1.5, -1, np.nan)
data['signal'] = np.where(data['zscore'] < -1.5, 1, data['signal'])
data['signal'] = data['signal'].ffill()

# 计算交易策略的收益率
data['ret1'] = data['log_ret1'] * data['signal']
data['ret2'] = data['log_ret2'] * -data['signal']
data['ret'] = data['ret1'] + data['ret2']

# 计算累计收益率
data['cum_ret'] = data['ret'].cumsum()

# 绘制收益曲线图
data['cum_ret'].plot()
plt.show()

其中，`stock1.csv`和`stock2.csv`是两只股票的历史价格数据。代码实现了以下步骤：

1. 读取数据并合并成一个DataFrame。
2. 计算股票价格的对数收益率和差值。
3. 计算差值的移动平均线和标准差，并计算z-score。
4. 根据z-score计算交易信号。
5. 计算交易策略的收益率和累计收益率。
6. 绘制收益曲线图。