Log-Return 使用数据框 Python

Question

我正在使用来自 quandl 的数据，并想使用数据框计算 log-return，如下所示：

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

主要问题是，closprices 的 headers 将参考与行情一起使用。如何计算日志return？我的代码如下所示：

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)

Answer 1

看来你可以使用 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

Log-Return 使用数据框 Python

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