Many of you will laugh at this idea and with good reason. Common sense dictates that this should be a bad idea, and everything you've ever read probably said do not do this. However, I am bored, and there could be some "merit" to this. The basic idea is you develop x number of neural networks. All of them independent of each other and trained on different types of inputs. I am sure many of us have already done something similar. Grab the ones that appear the most curve fit. Such as very few trades (However at least 10 trades), extremely high profit factors etc... Now you run each of the "cherry picked" neural networks on outside data (not from your training set). All of them that performs at least 1 trade and maintained exceptional metrics keep. Now you build a large strategy using all of these neural networks, so that if any of the neural networks fire off you take the trade. The idea behind this is YES you are curve fitting. But think about what curve fitting is. Put together 50... 100+ separate neural networks and you now have an overall strategy that trades often, but each individual component is only trained to trade when it sees a very specific pattern. The main down side is those specific patterns each neural network has seen are not statiscally significant. Meaning the results from each pattern could be the result of random chance.. How do you weed out those patterns that were just pure luck? Anyone try something like this before? How were the results?