n days back average for each day
You can use the pandas rolling method for that. 

back = 3
cols = ['high','low']

# calculate rolling mean on the dataframe's columns of interest
df_back_average = df[cols].rolling(back).mean()

# rename columns before concatenating the result back
df_back_average.columns = str(back) + '_back_average_' + df_back_average.columns

pd.concat([df[cols],df_back_average], axis=1)


high        low         3_back_average_high     3_back_average_low
0.000442    0.000445    NaN                     NaN
0.004580    0.004650    NaN                     NaN
0.004820    0.004890    0.003281                0.003328
0.004890    0.004920    0.004763                0.004820
0.004750    0.004920    0.004820                0.004910
0.004610    0.004860    0.004750                0.004900
0.004620    0.004800    0.004660                0.004860