Log-Return using Dataframe Python

Question

I am using data from quandl and would like to calculate the log-return as shown below using a dataframe:

Date        YAHOO/ACN - Close   YAHOO/AAPL - Close  YAHOO/ACN - Log-Return  YAHOO/AAPL - Log-Return
02.01.2002  26.209999           23.299999       
03.01.2002  25.389999           23.580001          -0.031785623              0.01194562
04.01.2002  27.700001           23.69               0.087077093              0.004654081
07.01.2002  26.450001           22.9               -0.046176253             -0.033916108
08.01.2002  27.280001           22.61               0.030897674             -0.012744624
09.01.2002  27.57               21.65               0.010574355             -0.043386832
10.01.2002  27.82              21.23                0.009026961             -0.019590179

The main problem is, that the headers for the closprices is using the reference together with the ticker. How can I calculate the log return? My code is shown below:

import quandl

token = quandl.ApiConfig.api_key = 'xxx' 

ticker = ['YAHOO/ACN.4', 'YAHOO/AAPL.4']

DataLevels = quandl.get(ticker,start_date='2002-1-1', end_date='2002-2-11',authtoken=token, collapse='dayly', returns='pandas')

DataLevels['log_return'] = np.log(DataLevels / DataLevels.shift(1))

print(DataLevels)

jezrael · Accepted Answer · 2017-05-26 14:27:10Z

1

It seems you can use map for replace column names with concat:

ticker = ['YAHOO/ACN.4', 'YAHOO/AAPL.4']
#simplier df
DataLevels = quandl.get(ticker, collapse='dayly', returns='pandas')

print (DataLevels.head())
            YAHOO/ACN - Close  YAHOO/AAPL - Close
Date                                             
1980-12-12                NaN              28.750
1980-12-15                NaN              27.250
1980-12-16                NaN              25.250
1980-12-17                NaN              25.875
1980-12-18                NaN              26.625

df1 = np.log(DataLevels / DataLevels.shift(1))
df1.columns = df1.columns.map(lambda x: x.replace('Close','Log-Return'))
df = pd.concat([DataLevels, df1], axis=1)
print (df.head())
            YAHOO/ACN - Close  YAHOO/AAPL - Close  YAHOO/ACN - Log-Return  \
Date                                                                        
1980-12-12                NaN              28.750                     NaN   
1980-12-15                NaN              27.250                     NaN   
1980-12-16                NaN              25.250                     NaN   
1980-12-17                NaN              25.875                     NaN   
1980-12-18                NaN              26.625                     NaN   

            YAHOO/AAPL - Log-Return  
Date                                 
1980-12-12                      NaN  
1980-12-15                -0.053584  
1980-12-16                -0.076227  
1980-12-17                 0.024451  
1980-12-18                 0.028573

answered May 26, 2017 at 14:27

jezrael

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2 Comments

MCM Over a year ago

Thanks a lot that is what I was looking for!

jezrael Over a year ago

Glad can help, nice weekend!

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