# Copyright 2023, MetaQuotes Ltd.
# https://www.mql5.com

import numpy as np
from statsmodels.tsa.stattools import adfuller

#initialize array with 100 elements
x = np.array([0.97841555,0.31931195,0.68205832,0.56256707,0.05741117,0.30310286,
              0.13354023,0.61382247,0.20699517,0.61969826,0.55718307,0.90422809,
              0.24220947,0.08719106,0.26714434,0.39439596,0.93919107,0.07756139,
              0.53188798,0.5074042,0.40468052,0.41235659,0.79233157,0.58948591,
              0.22049794,0.68278894,0.09500558,0.40421058,0.9971231,0.29665678,
              0.08254796,0.8089725,0.61434576,0.97610604,0.84084868,0.8034953,
              0.765576,0.25014613,0.16268394,0.34259495,0.40085009,0.8416158,
              0.6321962,0.45165205,0.12209775,0.40556958,0.96253644,0.30619429,
              0.70573114,0.51574979,0.90168104,0.80757639,0.94321618,0.58849563,
              0.38905617,0.04574506,0.63134219,0.89198262,0.24102367,0.45749333,
              0.76804682,0.50868223,0.91132151,0.7372344,0.32551467,0.27799709,
              0.04059095,0.86024797,0.74600612,0.01264258,0.89364963,0.99373472,
              0.36177673,0.47173929,0.15124127,0.77354455,0.45131917,0.27258213,
              0.69618127,0.35105122,0.1261404,0.21705172,0.88979093,0.97598448,
              0.03787156,0.54034132,0.58336702,0.61701685,0.11673483,0.99940389,
              0.99371688,0.04428256,0.00239077,0.34609507,0.57588045,0.20222325,
              0.20684364,0.29630613,0.65178447,0.86559185])
   
#perform ADF test on array 
result = adfuller(x)

#print ADF statistic and p-value
print(f"ADF statistic: {result[0]}, p-value:{result[1]}")

#print critical values
print(f"Critical values:{result[4]}")