Logarithmic returns in pandas dataframe

Question

Python pandas has a pct change function which I use to calculate the returns for stock prices in a dataframe   ndf  Return    ndf  TypicalPrice   pct change     I am using the following code to get logarithmic returns  but it gives the exact same values as the pct change   function   ndf  retlog   np log ndf  TypicalPrice   astype  float64   ndf  TypicalPrice   astype  float64   shift 1    np is for numpy

User · Answer

The results might seem similar  but that is just because of the Taylor expansion for the logarithm  Since log 1   x    x  the results can be similar    However       I am using the following code to get logarithmic returns  but it gives the exact same values as the pct change   function    is not quite correct   import pandas as pd  df   pd DataFrame   p   range 10     df  pct change     df pct change   df  log stuff           np log df  p   astype  float64   df  p   astype  float64   shift 1   df   pct change    log stuff    plot

User · Answer

Single line  and only calculating logs once  First convert to log-space  then take the 1-period diff       np diff np log df price     In earlier versions of numpy       np log df price   diff

User · Answer

Log returns are simply the natural log of 1 plus the arithmetic return   So how about this  df  pct change     df price pct change   df  log return     np log 1   df pct change   Even more concise  utilizing Ximix s suggestion  df  log return     np log1p df price pct change

User · Answer

Here is one way to calculate log return using  shift    And the result is similar to but not the same as the gross return calculated by pct change    Can you upload a copy of your sample data  dropbox share link  to reproduce the inconsistency you saw   import pandas as pd import numpy as np  np random seed 0  df   pd DataFrame 100   np random randn 100  cumsum    columns   price    df  pct change     df price pct change   df  log ret     np log df price  - np log df price shift 1    Out 56           price  pct change  log ret 0   101 7641         NaN      NaN 1   102 1642      0 0039   0 0039 2   103 1429      0 0096   0 0095 3   105 3838      0 0217   0 0215 4   107 2514      0 0177   0 0176 5   106 2741     -0 0091  -0 0092 6   107 2242      0 0089   0 0089 7   107 0729     -0 0014  -0 0014                                   92  101 6160      0 0021   0 0021 93  102 5926      0 0096   0 0096 94  102 9490      0 0035   0 0035 95  103 6555      0 0069   0 0068 96  103 6660      0 0001   0 0001 97  105 4519      0 0172   0 0171 98  105 5788      0 0012   0 0012 99  105 9808      0 0038   0 0038   100 rows x 3 columns

User · Answer

poulter7  I cannot comment on the other answers  so I post it as new answer  be careful with   np log df price  diff      as this will fail for indices which can become negative as well as risk factors e g  negative interest rates  In these cases   np log df price df price shift 1   dropna     is preferred and based on my experience generally the safer approach  It also evaluates the logarithm only once   Whether you use  1 or -1 depends on the ordering of your time series  Use -1 for descending and  1 for ascending dates - in both cases the shift provides the preceding date s value

[python] Logarithmic returns in pandas dataframe

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