In [1]:

    

%matplotlib inline


    

In [2]:

    

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)


    

In [3]:

    

print(data.head())


    

    




         Date   Value
0  2015-05-01  233.43
1  2015-04-30  235.13
2  2015-04-29  225.69
3  2015-04-28  222.66
4  2015-04-27  222.59

In [4]:

    

csvfile='bitcoin.csv'
data.to_csv(csvfile)
df = pd.read_csv(csvfile, index_col='Date', parse_dates=True)
print(df.head())


    

    




            Unnamed: 0   Value
Date                          
2015-05-01           0  233.43
2015-04-30           1  235.13
2015-04-29           2  225.69
2015-04-28           3  222.66
2015-04-27           4  222.59

In [7]:

    

df['7MA'] = pd.rolling_mean(df['Value'], 7)
df['30MA'] = pd.rolling_mean(df['Value'], 30)
df['Difference'] = df['Value'].diff()
print(df[100:110])


    

    




            Unnamed: 0   Value         7MA        30MA  Difference
Date                                                              
2015-01-21         100  215.20  244.482857  233.761667      -18.70
2015-01-20         101  212.99  236.905714  232.808333       -2.21
2015-01-19         102  212.39  228.465714  232.059000       -0.60
2015-01-18         103  211.18  223.332857  230.965000       -1.21
2015-01-17         104  197.12  216.544286  229.673667      -14.06
2015-01-16         105  205.35  212.590000  228.385667        8.23
2015-01-15         106  218.11  210.334286  227.479000       12.76
2015-01-14         107  176.50  204.805714  225.364333      -41.61
2015-01-13         108  230.89  207.362857  225.666333       54.39
2015-01-12         109  270.00  215.592857  227.266667       39.11

In [8]:

    

import matplotlib.pyplot as plt
df[['Value','7MA','30MA']].plot()
plt.show()


    

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