Python moving average SMA and double moving average strategy

import pandas as pd
import numpy as np
import tushare as ts
import seaborn
import matplotlib.pyplot as plot
import matplotlib as mpl
mpl.rcParams['font.sans-serif'] = ['SimHei']  # 设置matplotlib可以显示汉语
mpl.rcParams['axes.unicode_minus'] = False

#pd.set_option()就是pycharm输出控制显示的设置
pd.set_option('expand_frame_repr', False)#True就是可以换行显示。设置成False的时候不允许换行
pd.set_option('display.max_columns', None)# 显示所有列
#pd.set_option('display.max_rows', None)# 显示所有行
pd.set_option('colheader_justify', 'centre')# 显示居中

pro = ts.pro_api('tushare的TOKEN')

data = pro.daily(ts_code='000001.SZ', start_date='20180101', end_date='20190101')
#print(data.head())
data.set_index('trade_date',inplace=True)  #设置索引覆盖原来的数据
print(data.head())
data['SMA_20'] = data['close'].rolling(20).mean()  #简单移动平均线计算
data['SMA_60'] = data['close'].rolling(60).mean()
data[['close', 'SMA_20', 'SMA_60']].plot(figsize=(10, 6))  #画图
plot.show()

#计算股票收益，有两种计算收益率方法：1.离散收益率  2.连续收益率
data['position'] = np.where(data['SMA_20'] > data['SMA_60'], 1, -1)  #当20日均线大于60日均线时记录1，反之记录-1
data['returns'] = np.log(data['close']/data['close'].shift(1))  #连续收益率计算
#data['returns_dis'] = data['close']/data['close'].shift(1) - 1  #离散收益率计算
data['strategy'] = data['position'].shift(1)*data['returns']
data['returns'].cumsum().apply(np.exp).plot(figsize=(10, 6))  #计算累计收益并输出图，np.exp方法返回x的指数
plot.show()

data['returns'].hist(bins=50)  #绘制直方图
plot.show()