How many Hidden Layers and Neurons should I use in an RNN? I am very new to neural networks and machine learning and I have been making a Bitcoin price predictor to learn it. I was wondering about the number of hidden layers I'd need in a recurrent neural net using LSTM cells. 

I have 60 inputs for 30 previous days' close prices in 12-hour intervals and require 1 output for the future 12 hours. 

I am doing this with Keras in python 3.6.
Any help would be awesome!
Number of layers is a hyperparameter. It should be optimized based on train-test split. You can also start with the number of layers from a popular network. Look at kaggle.com and see how many layers do they use in competitions.