用data.DataReader读取股价数据并分析--python学习笔记17

#加载相应的包
import numpy as np
import pandas as pd
from pandas_datareader import data,wb
 #从互联网读入数据
goog=data.DataReader('GOOG',data_source='yahoo')
goog.head()
goog.tail()
 #实现对波动率的计算
goog['log_ret']=np.log(goog['Close']/goog['Close'].shift(1))
goog['volatility']=pd.rolling_std(goog['log_ret'],window=252)*np.sqrt(252)
 #绘制图形
%matplotlib inline
goog[['Close','volatility']].plot(subplots=True,color='Red',figsize=(8,6))

  #似乎中间有除权的行为影响了判断。我们用除权后的价格进行计算
goog['log_ret']=np.log(goog['Adj Close']/goog['Adj Close'].shift(1))
goog['volatility']=pd.rolling_std(goog['log_ret'],window=252)*np.sqrt(252)
 #绘制新图形
%matplotlib inline
goog[['Adj Close','volatility']].plot(subplots=True,color='Red',figsize=(8,6))  

 #用除权后的数据避免了断崖式的缺口，但是，规律仍旧不是非常明显，我们在用收益率试一试
goog['log_ret']=np.log(goog['Adj Close']/goog['Adj Close'].shift(1))
goog['volatility']=pd.rolling_std(goog['log_ret'],window=252)*np.sqrt(252)
 #绘制新图形
%matplotlib inline
goog[['log_ret','volatility']].plot(subplots=True,color='Red',figsize=(12,6))