如何使用statsmodels库对时间序列数据运行ADFuller测试？

Question

I am completely new to programming languages and I have picked up Python for backtesting a trading strategy (because I heard it is relatively easy). I have made some progress in learning the basics, however I am currently stuck at performing an ADFuller tests on a timeseries dataframe.

This is how my Dataframe looks

Now I need to run ADF test on the columns - "A-Btd", "A- Ctd" and so on (I have 66 columns like these).I would like to get the test statistic/output for each of them.

I tired using lines such as cadfs = [ts.adfuller(df1)] . Since, I lack the expertise I am not able to adjust the code as per my dataframe.

I apologize in advance if I have missed out some important information I have to give out. Please leave a comment and I will provide it asap.

Thanks a lot in advance!

Answer 1

If you have to do it for so many, I would try putting the results in a dict, like this:

import statsmodels.tsa.stattools as tsa

df = ... #load your dataframe
adf_results = {}

for col in df.columns.values:  #or edit this for a subset of columns first
    adf_results[col] = tsa.adfuller(df[col])

Obviously specifying other settings as desired, eg tsa.adfuller(df[col], autolag='BIC') . Or if you don't want all the output and would rather just parse each column to find out if it's stationary or not, the test statistic is the first entry in the tuple returned by adfuller() , so you could just use tsa.adfuller(df[col])[0] and test it against your threshold to get a boolean result, then make that the value in your dict.