I'm only using a fairly simple genetic algorithm to "evolve" the nets.

Probably a good idea. Training the nets using only a genetic algorithm seems be a very time consuming task. A hybrid method might speed it up significantly and avoid the limitations of backpropegation at the same time.

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.