Mastering Python for Finance - Second Edition

Detrending

The process of removing a trend line from a non-stationary data is known as detrending. This involves a transformation step that normalizes large values into smaller ones. Examples could be a logarithmic function, a square root function, or even a cube root. A further step is to subtract the transformation from the moving average.

Let's perform detrending on the same dataset, df_settle, with logarithmic transformation and subtracting from the moving average of two periods, as given in the following Python code:

In [ ]:    import numpy as np    df_log = np.log(df_settle)In [ ]:    df_log_ma= df_log.rolling(2).mean()    df_detrend = df_log - df_log_ma    df_detrend.dropna(inplace=True)    # Mean and standard deviation of detrended data df_detrend_rolling ...