An other idea came to mind.
instead of use ML for parameters, what about using the optimistation results and group the first 10 rows with parameters.
Then ML can take out of those 10 row which set of parameters will be the best. Especially on a short timeframe (1s-10s), a few days later again optimisation can be made and the best set of parameters added to the first group, so you buildup data for ML which covers more market scenarios.
Baloney or interesting?
