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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!

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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.

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