Python for Finance Cookbook

How to do it...

Execute the following steps to test the given time series for stationarity.

1. Import the libraries:

import pandas as pdfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacffrom statsmodels.tsa.stattools import adfuller, kpss

2. Define a function for running the ADF test:

def adf_test(x):        indices = ['Test Statistic', 'p-value',               '# of Lags Used', '# of Observations Used']    adf_test = adfuller(x, autolag='AIC')    results = pd.Series(adf_test[0:4], index=indices)    for key, value in adf_test[4].items():        results[f'Critical Value ({key})'] = value    return results

Now, we can run the test:

adf_test(df.price)

The code generates the following summary:

The null hypothesis of the ADF test states that the time series is not stationary. ...