Out of sample predictions with LSTM
There are two main ways to create train/validation set for a time series situation: 

splitting your samples ( taking for example 80 % of the time series for training and 20 % for validation) 
splitting your time series ( training your model on the first n-k values of the time series and validation on the k other values)
This is a general question about making real future predictions with an LSTM model using keras & tensorflow in Python (optional R). 
For example stock prices. I know there is a train/test split to measure the accuracy/performance of the model comparing my results with the test prices. But I want to make real future predictions/out of sample predictions. Does anyone has an idea & would like to share some thoughts on it?
Me only came to mind to use a rolling window but that didn't work at all. So I'm glad about every tip you guys have.