Quote from: bitfreak! on February 06, 2014, 04:06:58 AM

Quote from: BadBitcoin (James Sutton) on February 05, 2014, 02:40:05 PM

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

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.

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Also your bot being able to pick out trends, is it autoassociative and unsupervised?

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.

back again, it's been a crazy week. My foundation net is almost functioning perfectly but I think I need to tweak it for a few more hours to get the RMS per iteration to improve significantly.

I'm currently using a really interesting hybrid approach that I came up with, I order every individual 0 - popsize -1, and then based on that order put it into an expotential decaying function to determine if it's a parent or not. I then have an if statement in the reproduction function that takes the order of the individual as the parameter for another expotential decaying probability boolean, and then if it succeeds the genes of the parent are directly passed to the child (child = parent), I've worked it out so that theres only a probability of ~10% for the best of the generation but thats something that I'll be tweaking.

the rest of the reproductive system is all stoichastic, everything is probability based like parent selection, gene pairing, mutation, etc. I've done a few monte carlo simulations (IE I run a very large population iteration through and cout every if statement success). For a genetic algorithim I think this method is solid, but as a backup I also have a "shake" function, which does a single iteration of back propagation to every child before its tested for fitness. One thing that I'm worried about with my back propagation is that since my training regimen is "all in" (IE I run each individual through the entire training data and then average the RMS of the output errors), I run the risk of having a faulty backpropagation, since I'm averaging the values of my inputs with all of the possible values that input can have throughout the training data.

I think I might do a few tests without the back prop to see if that's the issue, and have a read in some of my textbooks to determine what the best way to incorporate back prop into a genetic algo is, I may have to change my training regimen to be stage based.

Also (I know your using built in libraries but you might know) how are you calculating normalized fitness? I'm currently taking the negative expotential of the average RMS over the training data times a lamda constant (IE fitness = e^(-lamda*avg_rms) for an individual and then dividing each individual fitness by the average fitness of the iteration, so I always have fitness values greater than 0, and their always "different" enough for the computer to recognize the differences.

I haven't gotten to the point yet of testing my network on real data, I'm still testing with an XOR table, but since I'm writing this from scratch I feel that I'll be able to reach the goal of a real bitcoin forcast/profit maker soon, once my foundation is finished everything should be (knock on wood) as easy as using a library.