[aothman]

Looks impressive, particularly if those are real trading results.

One concern I immediately have is overfitting, particularly for claims about how various difficult values have been optimized to be the "best possible". It looks like the parameter space in use is truly enormous and so it would be very easy to come up with hypotheses that perform fantastically on your dataset but terribly in real life. This seems like it would be a first-order concern, while the ability to run tests in a single day seems second-order if those tests are producing garbage outputs.

[joshu]

I accidentally down voted but meant to up vote. I agree.

Also, this appears to have no risk-oriented portfolio construction. You are calculating alphas somewhere, right?

(not to belittle this or anything. I'm just not a big believer in technical analysis, which is what this feels like. You should apply this kind of focus to real stat arb.)

[dododo]

if you use some kind of regularization (e.g., if you think the parameters are sparse) it's possible to fit to such a large space without overfitting. this is common in machine learning and statistics (e.g., "N less than p" problems, where N is the number of data and p is the dimensionality of parameterization, common in genomics).

also provided the test data (i.e., the data it's bidding on) is not used in training, this should be a fair(ish) test; overfitting on the training data should lead to poor test performance.

however it's not clear to me that the data aren't being used twice, and what machine learning is actually going on... so in the best of worlds it could be ok, but given the scant details it could be all bunkum...