Hard to say GraHal,
I would optimise hyperparameters, i.e. the parameters that control the learning algorithm like ETA… more that optimise is to find the correct one. For the data I choose as input ETA is around 0.1.
Another parameter to be optimised is the threshold for decision take, the output in order to predict like 0.6 or 0.7 (output is always values from 0 to 1 )
The use of pending orders is highly recommended.
You can also test different kind of inputs like Volume.
Values for the classifier are irrelevant just set one combination and run.
Test 100K should be taking ages or ?
