Calculate features on stationary time-series data

I am trying to create a deep learning model that predicts the future price of crypto currencies based on past data. I downloaded the Open, High, Low, Close and Volume (OHLCV) data from yahoo finance and made it stationary by differencing it.

Now I also want to calculate some technical indicators from the OHLCV data. For example the simple or exponential moving average. I'm guessing that the calculated features also need to be stationary. Is that correct? So do I calculate the features and then make it all stationary or do i calculate the features based on the stationary OHLCV data?

My english is not the best but I hope you can understand my question.