Calculate expected optimization memory usage

[CooleRnax]

Hello.

I'm having troubles with calculating the expected optimization memory usage.
I need it to split big parameters dict during optimization into chunks if memory usage is too high.

I came up with this function.
But it doesn't work always.
Please help!

inputs:
params - dict | with strategy parameters for the optimization
start, end - datetime | start and end of the ticker data
freq - str | ticker data freq (15Min, 1H, etc)

def memory_gen(params, start, end, freq, threads=8, log=False):
    period_len = len(pd.PeriodIndex(freq=freq, start=start, end=end))
    params_len = 1
    for param in params:
        if type(params[param]) == list:
            if not params_len == 1:
                params_len *= len(params[param])
            elif params_len == 1 and len(params[param]) > 1:
                params_len = len(params[param])

        n = 20.0

    memory = round((period_len * params_len) * n / 1000000 + 0.200 * threads, 2)
    if log:
        logger.info(freq, memory)
    return memory