Here is a suggestion to help you get better models. Pick something that you normally trade by some other method. Pick a few things that you normally use that you think help you decide when to buy and sell such as put-call ratio, bollinger bands, or whatever. Limit yourself to no more than 6-8 inputs. Choose a variable to predict, optimize your 6 inputs, and let 'er rip. Save some data for out-of-sample testing. If you have the CPU power, go for walk-forwards testing of at least 2-3 periods, but keep a sizable training data set. After 50 iterations, download the models and check them on the data you held back. Unless at least 9 of the top 10 are profitable out-of-sample, start over, otherwise, let it run to completion. Leave NGO on random refill for the first 50 iterations to make sure you have nice diverse models.
CATNN includes TDNN with floating time delays, and is thus TDNN is a subset of TDNN. BP includes no time delays and is thus a subset of TDNN (and CATNN). Since they are simpler there are less degrees of freedom and they require less data (or can handle more variables).
If these models are looking at the same things you usually do, they should be able to interpret them as well as you can, or better.
Hope this helps,
Carl
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