# How to determine my pacf and acf in ARIMA with daily time series data?

I have daily time series data for revenue sales ranging from 2018-04-01 to 2021-02-19. I want to implement an ARIMA model and the plot of the data looks like this:

Based on my augmented-dickey fuller test, the original data seems stationary

To determine my p and q values, I plotted my pacf and acf plots but the first value to go above the confidence band is around ~92 for both. I know this has a quarterly trend (3 months) so it makes sense that I see this pattern, but it's taking far too long to run in my model and I wanted to know if I was analyzing the plots properly.

Here is the code I'm using to implement the model:

model = sm.tsa.statespace.SARIMAX(train,
order = (92,0,92),
enforce_stationarity = False,
enforce_invertibility = False)
model_aic = model.fit()
print(model_aic.summary().tables[1])