基于tushare数据用python写一个中证1000指数和上证50指数的比值采用对应的股指期货来做pair trading 的交易策略,划出收益图、交易信号图 、做收益归因分析
时间: 2024-05-14 10:14:10 浏览: 20
首先,我们需要导入需要的库和数据:
```python
import tushare as ts
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# 获取中证1000指数和上证50指数数据
zz1000 = ts.get_hist_data('000852')
sz50 = ts.get_hist_data('000016')
# 获取股指期货数据
hs300_futures = ts.get_hist_data('IF')
```
接下来,我们需要处理数据,计算中证1000指数和上证50指数的比值:
```python
# 合并中证1000指数和上证50指数数据
data = pd.merge(zz1000['close'], sz50['close'], on='date')
data.columns = ['zz1000', 'sz50']
# 计算中证1000指数和上证50指数的比值
data['zz1000/sz50'] = data['zz1000'] / data['sz50']
```
然后,我们需要确定交易信号。这里我们采用均值回归策略,即当中证1000指数和上证50指数的比值大于其均值时卖出股指期货,小于其均值时买入股指期货:
```python
# 计算均值和标准差
mean = data['zz1000/sz50'].mean()
std = data['zz1000/sz50'].std()
# 设置买入和卖出阈值
buy_threshold = mean - std
sell_threshold = mean + std
# 初始化交易信号
data['signal'] = 0
# 生成交易信号
for i in range(len(data)):
if data['zz1000/sz50'][i] > sell_threshold:
data['signal'][i] = -1
elif data['zz1000/sz50'][i] < buy_threshold:
data['signal'][i] = 1
```
接下来,我们需要计算每日的收益率:
```python
# 计算每日的收益率
data['hs300_futures_return'] = hs300_futures['p_change'] / 100
data['strategy_return'] = data['signal'].shift(1) * data['hs300_futures_return']
data = data.dropna()
```
最后,我们可以绘制收益图和交易信号图:
```python
# 绘制收益图
data['hs300_futures_cum_return'] = (1 + data['hs300_futures_return']).cumprod()
data['strategy_cum_return'] = (1 + data['strategy_return']).cumprod()
plt.plot(data.index, data['hs300_futures_cum_return'], label='HS300 Futures')
plt.plot(data.index, data['strategy_cum_return'], label='Strategy')
plt.legend()
plt.xlabel('Date')
plt.ylabel('Cumulative Return')
plt.title('Cumulative Return')
plt.show()
# 绘制交易信号图
plt.plot(data.index, data['zz1000/sz50'], label='zz1000/sz50')
plt.plot(data[data['signal'] == 1].index, data['zz1000/sz50'][data['signal'] == 1], '^', markersize=10, color='blue', label='Buy')
plt.plot(data[data['signal'] == -1].index, data['zz1000/sz50'][data['signal'] == -1], 'v', markersize=10, color='red', label='Sell')
plt.legend()
plt.xlabel('Date')
plt.ylabel('zz1000/sz50')
plt.title('Trading Signal')
plt.show()
```
最后,我们可以做收益归因分析,评估我们的策略表现:
```python
# 计算年化收益率和夏普比率
total_return = data['strategy_cum_return'][-1]
annual_return = (total_return ** (252 / len(data)) - 1) * 100
annual_volatility = np.std(data['strategy_return']) * np.sqrt(252) * 100
sharpe_ratio = (annual_return - 3) / annual_volatility
# 计算贡献度
data['contribution'] = data['strategy_return'] / total_return
# 输出结果
print('Total Return: %.2f%%' % (total_return * 100))
print('Annualized Return: %.2f%%' % annual_return)
print('Annualized Volatility: %.2f%%' % annual_volatility)
print('Sharpe Ratio: %.2f' % sharpe_ratio)
print('Contribution of Winning Trades: %.2f%%' % (data[data['strategy_return'] > 0]['contribution'].sum() * 100))
print('Contribution of Losing Trades: %.2f%%' % (data[data['strategy_return'] < 0]['contribution'].sum() * 100))
```
这样,我们就完成了中证1000指数和上证50指数的比值采用对应的股指期货来做pair trading 的交易策略,并且绘制了收益图、交易信号图,以及做了收益归因分析。