Python pandas calculate rolling stock beta using rolling apply to groupby object in vectorized fashion

Question

I have a large data frame, df, containing 4 columns:

             id           period  ret_1m   mkt_ret_1m
131146       CAN00WG0     199609 -0.1538    0.0471         


        

Answer 1

Try pd.rolling_cov() and pd.rolling.var() as follows:

import pandas as pd
import numpy as np
from StringIO import StringIO

    df = pd.read_csv(StringIO('''              id  period  ret_1m  mkt_ret_1m
    131146  CAN00WG0  199609 -0.1538    0.047104
    133530  CAN00WG0  199610 -0.0455   -0.014143
    135913  CAN00WG0  199611  0.0000    0.040926
    138334  CAN00WG0  199612  0.2952    0.008723
    140794  CAN00WG0  199701 -0.0257    0.039916
    143274  CAN00WG0  199702 -0.0038   -0.025442
    145754  CAN00WG0  199703 -0.2992   -0.049279
    148246  CAN00WG0  199704 -0.0919   -0.005948
    150774  CAN00WG0  199705  0.0595    0.122322
    153318  CAN00WG0  199706 -0.0337    0.045765
    160980  CAN00WH0  199709  0.0757    0.079293
    163569  CAN00WH0  199710 -0.0741   -0.044000
    166159  CAN00WH0  199711  0.1000   -0.014644
    168782  CAN00WH0  199712 -0.0909   -0.007072
    171399  CAN00WH0  199801 -0.0100    0.001381
    174022  CAN00WH0  199802  0.1919    0.081924
    176637  CAN00WH0  199803  0.0085    0.050415
    179255  CAN00WH0  199804 -0.0168    0.018393
    181880  CAN00WH0  199805  0.0427   -0.051279
    184516  CAN00WH0  199806 -0.0656   -0.011516
    143275  CAN00WO0  199702 -0.1176   -0.025442
    145755  CAN00WO0  199703 -0.0074   -0.049279
    148247  CAN00WO0  199704 -0.0075   -0.005948
    150775  CAN00WO0  199705  0.0451    0.122322'''), sep='\s+')

    df['beta'] = pd.rolling_cov(df['ret_1m'], df['mkt_ret_1m'], window=6) / pd.rolling_var(df['mkt_ret_1m'], window=6)

print df

Output:

              id  period  ret_1m  mkt_ret_1m      beta
131146  CAN00WG0  199609 -0.1538    0.047104       NaN
133530  CAN00WG0  199610 -0.0455   -0.014143       NaN
135913  CAN00WG0  199611  0.0000    0.040926       NaN
138334  CAN00WG0  199612  0.2952    0.008723       NaN
140794  CAN00WG0  199701 -0.0257    0.039916       NaN
143274  CAN00WG0  199702 -0.0038   -0.025442 -1.245908
145754  CAN00WG0  199703 -0.2992   -0.049279  2.574464
148246  CAN00WG0  199704 -0.0919   -0.005948  2.657887
150774  CAN00WG0  199705  0.0595    0.122322  1.371090
153318  CAN00WG0  199706 -0.0337    0.045765  1.494095
160980  CAN00WH0  199709  0.0757    0.079293  1.616520
163569  CAN00WH0  199710 -0.0741   -0.044000  1.630411
166159  CAN00WH0  199711  0.1000   -0.014644  0.651220
168782  CAN00WH0  199712 -0.0909   -0.007072  0.652148
171399  CAN00WH0  199801 -0.0100    0.001381  0.724120
174022  CAN00WH0  199802  0.1919    0.081924  1.542782
176637  CAN00WH0  199803  0.0085    0.050415  1.605407
179255  CAN00WH0  199804 -0.0168    0.018393  1.571015
181880  CAN00WH0  199805  0.0427   -0.051279  1.139972
184516  CAN00WH0  199806 -0.0656   -0.011516  1.101890
143275  CAN00WO0  199702 -0.1176   -0.025442  1.372437
145755  CAN00WO0  199703 -0.0074   -0.049279  0.031939
148247  CAN00WO0  199704 -0.0075   -0.005948 -0.535855
150775  CAN00WO0  199705  0.0451    0.122322  0.341747