Why are optimization results from cartesian products not reproducible by default?

[binarymason]

If optimization is doing an exhaustive search through all of the combinations of the cartesian product of parameters, why do I need to set random_state to get reproducible results? I would think an exhaustive search would give me reproducible results no matter what.

Thanks!

backtesting.py/backtesting/backtesting.py

Line 1254 in e3cccdf

backtest.optimize(sma1=[5, 10, 15], sma2=[10, 20, 40],

[kernc]

random_state= comes in use when max_tries= is set.

backtesting.py/backtesting/backtesting.py

Lines 1216 to 1222 in e3cccdf

	`max_tries` is the maximal number of strategy runs to perform.
	If `method="grid"`, this results in randomized grid search.
	If `max_tries` is a floating value between (0, 1], this sets the
	number of runs to approximately that fraction of full grid space.
	Alternatively, if integer, it denotes the absolute maximum number
	of evaluations. If unspecified (default), grid search is exhaustive,
	whereas for `method="skopt"`, `max_tries` is set to 200.