import pandas as pd

import statsmodels.api as sm

import statistics

import matplotlib.pyplot as plt

from scipy.stats import zscore as zs

import statsmodels.tsa.stattools as ts

pathh = r'C:\Users\Joao\AppData\Roaming\MetaQuotes\Terminal\FB9A56D617EDDDFE29EE54EBEFFE96C1\MQL5\Files\daytrade_win\\'

nomeArq = 'Robo_win_acoes_PERIOD_M10_close_500Candles'

base = pd.read_csv(pathh + nomeArq + '.csv', sep=';')

atvA = 'CMIG4'

atvB = 'WINV20'

period = 350

_df, _adf = .1, '10%'

desvio = 2

yi, xi = 0, 0

for i in range(base.shape[1]-1):

yn = base.iloc[:,i].name

xn = base.iloc[:,i].name

if yn == atvA:

yi = i

if xn == atvB:

xi = i

period = period*-1

y = base.iloc[period:,yi].values

y = zs(y)

x = base.iloc[period:,xi].values

x = zs(x)

ynn = base.iloc[:,yi].name

xnn = base.iloc[:,xi].name

x = sm.add_constant(x)

model = sm.OLS(y, x).fit()

adf = ts.adfuller(model.resid, 1)

std = statistics.stdev(model.resid)

if model.resid[-1] > desvio*std or model.resid[-1] < desvio*std*-1:

print('Pode Operar!')

print(ynn, xnn)

if adf[1] < _df:

print('DF: Stationary')

if adf[0] < adf[4][_adf]:

print('ADF: Stationary')

print('df: {} / adf: {}' .format(_df, _adf))

plt.plot(model.resid)

plt.ylabel('Residual')

plt.axhline(0, color='black',label='mean')

plt.axhline(desvio*std, color='red', linestyle='--', linewidth=2)

plt.axhline(-desvio*std, color='green', linestyle='--', linewidth=2)

plt.xlabel('')

plt.margins(0.1)

plt.show()