I think what will happen is your selected NNs will be trained to pick up on outliers, i.e., those trades that make tons of profit and happen to coincide with whatever inputs you have. So you will be fitting to those very rare, coincidental trades that made money, i.e., so rare that they will likely be singletons in history. It really depends on how tight your selection criteria is, I guess. Essentially what you are doing is clustering your trades based on inputs and profit/loss. The tigher your clustering criteria, the more likely you will get singletons, and hence they will not be generalizable. I think you would need to decide on your cluster distance criteria based on some kind of relative information content of your clusters for them to be useful.
Providers watch out the big Z's watching you