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#%%
import random
import numpy as np
from pymoo.core.problem            import ElementwiseProblem
from pymoo.algorithms.moo.nsga2    import NSGA2
from pymoo.optimize                import minimize

from pymoo.operators.sampling.rnd  import FloatRandomSampling
from pymoo.operators.crossover.sbx import SBX
from pymoo.operators.mutation.pm   import PM

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# -*- coding: utf-8 -*-
import array
import random
import json

import numpy as np
import matplotlib.pyplot as plt

from math import sqrt

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#%%
import random
import numpy as np

#%%


#%%
def calculate_fitness(individual_chromosome):

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#%%
import warnings
warnings.filterwarnings("ignore")
def action_with_warnings():
    warnings.warn("should not appear")
with warnings.catch_warnings(record=True):
    action_with_warnings()
import sys
sys.path.insert(0, '.')
import os

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#%%
# References:
#   - https://github.com/sammchardy/python-binance/issues/868
#

#%%
from __future__ import annotations
from abc        import ABC, abstractmethod
from socket import timeout
from typing     import List

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import numpy as np
from numba import jit
from numba import float64
from numba import int64

@jit((float64[:], int64), nopython=True, nogil=True)
def _ewma(arr_in, window):
    r"""Exponentialy weighted moving average specified by a decay ``window``
    to provide better adjustments for small windows via:

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import numpy as np
import pandas as pd
import datetime as dt

from sklearn.datasets import make_classification

def create_price_data(start_price: float = 1000.00, mu: float = .0, var: float = 1.0, n_samples: int = 1000000):
    
    i = np.random.normal(mu, var, n_samples)
    df0 = pd.date_range(periods=n_samples, freq=pd.tseries.offsets.Minute(), end=dt.datetime.today())

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Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

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#https://pastebin.com/jgr4ZBVK
#https://www.reddit.com/r/algotrading/comments/p4flbp/can_i_make_tick_volume_and_dollar_bars_from_ohlc/

class VolumeBarAggregator():

    class NumpyWrapper():
        def __init__(self, length):
            self.arr = np.array(dtype=float, )

    def __init__(self):