Rolling windows in Multi-index Pandas Dataframe

Question

I have a multi-index dataframe that looks like this:

                      LAST PRICE     HIGH      LOW  PX CLOSE 1D  
date                                                                           
2010-01-04 SPX Index      1132.99  1133.87  1116.56      1115.10       
           CL1 Comdty       81.51    81.79    79.66        79.36         
2010-01-05 SPX Index      1136.52  1136.63  1129.66      1132.99       
           CL1 Comdty       81.77    82.00    80.95        81.51         
2010-01-06 SPX Index      1137.14  1139.19  1133.95      1136.52       
           CL1 Comdty       83.18    83.52    80.85        81.77       
2010-01-07 SPX Index      1141.69  1142.46  1131.32      1137.14      
           CL1 Comdty       82.66    83.36    82.26        83.18       
2010-01-08 SPX Index      1144.98  1145.39  1136.22      1141.69        
           CL1 Comdty       82.75    83.47    81.80        82.66

I would like to add new columns with simple and exponential moving averages for 'LAST PRICE'. How do I do this so that the calculations are correct per underlying? I believe I need to use groupby but I can't get the syntax correct.

thanks in advance

piRSquared · Accepted Answer · 2017-01-03 15:31:35Z

2

please refer to ewm documentation for more information

Use unstack

df['PX CLOSE 1D'].unstack().ewm(halflife=1).mean()

            CL1 Comdty    SPX Index
date                               
2010-01-04   79.360000  1115.100000
2010-01-05   80.793333  1127.026667
2010-01-06   81.351429  1132.451429
2010-01-07   82.326667  1134.952000
2010-01-08   82.498710  1138.429677

answered Jan 3, 2017 at 15:31

piRSquared

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