Modelling gut-feeling/subconscious knowledge of stock market traders Some (stock market) traders have the ability to produce a high percentage of winning trades (80%+, positive return) over years. I had the chance to look into real money trades of two such traders and I also got trading instructions from them for research. 

Now the interesting part is that if you strictly follow their rules then you usually end up with more losers than winners on the long run. But after a while you get some kind of subconscious "feeling" for winners which also shows in the results. I assume that this "feeling" is a hidden function which can be modeled.

My question is: Is there work about how to model such "gut feeling" and subconscious knowledge by means of machine learning (especially with little training data sets)? Is there relevant literature about this topic?

Regards,
You could perhaps model gut feeling or subconscious bias as a prior in a Bayesian context, and then try to learn from the data how much to modify/moderate the bias in each individual case.

I think there is another issue with the problem you outlined.  We might expect it to be normal to see more losers than winners in the long run: trading is a zero-sum game where the house always takes its cut. The trick to being a successful trading algorithm seems to be to make the losers small (cut them early) and the winners big (let them run).