My plan wasn't to train just one net and then use it to trade for me. My plan was to train multiple nets and then use them to create a committee of machines and average the outputs from all of them. Associative neural networks seem to be an extension of that basic concept but I'm not really sure of the exact details. Autoassociative neural networks seem to a whole other thing and I haven't read much about how they work either. Apparently the Hopfield network is one example of an autoassociative neural network but I'd have to read more about it to understand how it works. However I did read that one of the main uses of the Hopfield network is to pick out patterns from noisy data so I can see why you'd think that was a good choice.

I described exactly how my nets are structured when I described how the DNA is formatted. The design is not autoassociative as far as I know but the nets are obviously trained using unsupervised learning because they use a genetic algorithm for training and nothing else. All I do is give them some training data and then let them evolve by themselves based on how well they perform at virtual trading (which is linked to their ability to predict future trends in the data). If I had of ranked them based purely on their ability to predict what data will come next in their training data then I would still have to build my own trading algorithms based on the predictions they make. I wanted them to evolve their own trading strategies by themselves so I ranked them by how well they performed at virtual trading.