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In [17]:

    
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import pandas_datareader.data as web
import datetime



In [20]:

    
prices = web.DataReader(['CVX','XOM','BP'], 'yahoo', start=datetime.datetime(2010,1,1),
                            end=datetime.datetime(2013,1,1))['Adj Close']

prices.head()



In [21]:

    
volume = web.DataReader(['CVX','XOM','BP'], 'yahoo', start=datetime.datetime(2010,1,1),
                                end=datetime.datetime(2013,1,1))['Volume']

volume.head()



In [22]:

    
returns = prices.pct_change()

returns.head()



In [33]:

    
corr = returns.corr



In [34]:

    
%matplotlib inline
prices.plot()









    Out[34]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fd0245b2bd0>



In [35]:

    
# plotting correlation
import seaborn as sns
import matplotlib.pyplot as plt



In [36]:

    
sns.corrplot(returns, annot=False, diag_names=False)









    Out[36]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fd0246f1c90>

	BP	CVX	XOM
Date
2010-01-04	42.257896	61.192517	56.700952
2010-01-05	42.557951	61.625962	56.922342
2010-01-06	42.779422	61.633698	57.414323
2010-01-07	42.765133	61.401501	57.233934
2010-01-08	42.865151	61.509861	57.004337

	BP	CVX	XOM
Date
2010-01-04	3956100.0	10173800.0	27809100.0
2010-01-05	4109600.0	10593700.0	30174700.0
2010-01-06	6227900.0	11014600.0	35044700.0
2010-01-07	4431300.0	9626900.0	27192100.0
2010-01-08	3786100.0	5624300.0	24891800.0

	BP	CVX	XOM
Date
2010-01-04	NaN	NaN	NaN
2010-01-05	0.007101	0.007083	0.003905
2010-01-06	0.005204	0.000126	0.008643
2010-01-07	-0.000334	-0.003767	-0.003142
2010-01-08	0.002339	0.001765	-0.004012