In [1]:

    

%matplotlib inline
import pandas as pd
dataurl='https://www.quandl.com/api/v1/datasets/BCHAIN/MKPRU.csv?trim_start=2013-01-01&trim_end=2015-05-01'
data=pd.read_csv(dataurl)
data['7MA'] = data['Value'].rolling(window=7,center=False).mean()
data['30MA'] = data['Value'].rolling(window=30,center=False).mean()
print(data.head(50))


    

    




          Date   Value         7MA        30MA
0   2015-05-01  233.43         NaN         NaN
1   2015-04-30  235.13         NaN         NaN
2   2015-04-29  225.69         NaN         NaN
3   2015-04-28  222.66         NaN         NaN
4   2015-04-27  222.59         NaN         NaN
5   2015-04-26  218.42         NaN         NaN
6   2015-04-25  228.60  226.645714         NaN
7   2015-04-24  230.93  226.288571         NaN
8   2015-04-23  235.48  226.338571         NaN
9   2015-04-22  237.34  228.002857         NaN
10  2015-04-21  224.88  228.320000         NaN
11  2015-04-20  224.63  228.611429         NaN
12  2015-04-19  225.72  229.654286         NaN
13  2015-04-18  222.32  228.757143         NaN
14  2015-04-17  222.67  227.577143         NaN
15  2015-04-16  229.62  226.740000         NaN
16  2015-04-15  218.96  224.114286         NaN
17  2015-04-14  216.00  222.845714         NaN
18  2015-04-13  225.99  223.040000         NaN
19  2015-04-12  236.76  224.617143         NaN
20  2015-04-11  236.70  226.671429         NaN
21  2015-04-10  235.71  228.534286         NaN
22  2015-04-09  244.98  230.728571         NaN
23  2015-04-08  245.89  234.575714         NaN
24  2015-04-07  255.48  240.215714         NaN
25  2015-04-06  254.70  244.317143         NaN
26  2015-04-05  257.03  247.212857         NaN
27  2015-04-04  253.77  249.651429         NaN
28  2015-04-03  254.39  252.320000         NaN
29  2015-04-02  252.44  253.385714  234.297000
30  2015-04-01  242.70  252.930000  234.606000
31  2015-03-31  242.92  251.135714  234.865667
32  2015-03-30  245.18  249.775714  235.515333
33  2015-03-29  244.05  247.921429  236.228333
34  2015-03-28  251.52  247.600000  237.192667
35  2015-03-27  248.63  246.777143  238.199667
36  2015-03-26  251.98  246.711429  238.979000
37  2015-03-25  245.68  247.137143  239.470667
38  2015-03-24  247.83  247.838571  239.882333
39  2015-03-23  266.07  250.822857  240.840000
40  2015-03-22  260.50  253.172857  242.027333
41  2015-03-21  257.83  254.074286  243.134000
42  2015-03-20  259.80  255.670000  244.270000
43  2015-03-19  260.62  256.904286  245.546667
44  2015-03-18  265.85  259.785714  246.986000
45  2015-03-17  287.02  265.384286  248.899333
46  2015-03-16  290.88  268.928571  251.296667
47  2015-03-15  283.57  272.224286  253.549000
48  2015-03-14  284.21  275.992857  255.489667
49  2015-03-13  289.51  280.237143  257.248000

In [2]:

    

import matplotlib.pyplot as plt
data.plot()
plt.show()


    

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