I haven't yet sat down and decided on exactly what kind of structure I want to train
I took a rather lazy approach to this problem. I simply generated random nets with a fixed number of inputs and outputs. I fed market data into the inputs and then conducted virtual trades based on their outputs. The fitness of any given net was determined by how much profit they made in the virtual trading. My hope was that I would be able to evolve a net with quite good trading skills. That is why I used a different training data set for each generation, so that they could learn to pick up on a range of different trading patterns and not just one pattern from a single set of training data.
I'd recommend you pick up some light reading on theory, for any incredibly complex system like the bitcoin market its
possible to forcast market conditions with only 1 network, however holy crap is it unadvised to use only 1 just because of the absolute mountain of neurons + training data you'll have to sift through to get any meaningful forcasts, which means that each iteration of your network is going to take an obscenely large amount of time for little gain.
Also your bot being able to pick out trends, is it autoassociative and unsupervised? Those are the best net designs for trend spotting.