Interesting! I do like your format of the connection system, I'm currently using a significantly more unwieldly series of if statements to generate the number of weights and to designate the design and layout of the network (I'm making assumptions that aren't really what should be done for a foundation library), what's your current method of error reduction? Are you using back propegation? Genetics?

I'm currently using genetics with an extensive RNG factor for essentially everything including gene splicing. I haven't yet begun work on transforming my weight values into grey code (http://en.wikipedia.org/wiki/Gray_code if you want a refresher) to minimise the generation cycle length just yet. I'm also strongly considering transforming my currently 100% genetic algorithm into a hybrid with a single backpropegation step after the creation of each child.

I haven't yet sat down and decided on exactly what kind of structure I want to train, but I'm strongly considering the large heirarchical multi network specialist strategy when it comes to trading. I plan on having 1 autoassoicative network to decide what the trend is, then for each kind of trend that the network decides is unique, a new specialist recurrent network designed to work in that specific regime. The only problem I can see with that are the boundary states between each "trend", but I think thats an issue that can be solved with a strong autoassociative network training regime for the top tier network.

With regards to open source, I'm personally not against open source in the slightest; I haven't used any open source code  (besides the boost:: library) myself but I can definitely imagine it being helpful and I would love to help others, but right now I have a 76 year old unemployed father who I'm taking care of and I'd like to make enough money so I don't have to choose between myself and him. Hopefully when this project is complete I won't have to worry ahah, sorry for spilling out all my personal life in a tech thread