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import csv
import time
import zipfile
from pprint import pprint

import pandas
import requests

contas_list = [
    'BPA', # Balanço Patrimonial de Ativos

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import yfinance
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as mplt

# Download dos dados
data = yfinance.download(
    tickers = 'PSSA3.SA ITUB4.SA',
    start="2020-05-01",
    end="2020-08-05",

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import numpy as np
import pandas as pd
import yfinance
import matplotlib.pyplot as mplt
from bs4 import BeautifulSoup
import dryscrape

iframe_url = 'http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-taxas-referenciais-bmf-ptBR.asp'
session = dryscrape.Session()
session.visit(iframe_url)

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import time
import locale
locale.setlocale(locale.LC_ALL, 'pt_BR.UTF-8')

import requests
import pandas as pd
from bs4 import BeautifulSoup

def parse_df(content):
    soup = BeautifulSoup(content, 'html.parser')

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import time
import yfinance
import dryscrape
from bs4 import BeautifulSoup
import pandas as pd

def get_b3_html():
    url = 'https://sistemaswebb3-listados.b3.com.br/indexPage/day/IBOV?language=pt-br'
    session = dryscrape.Session()
    session.visit(url)

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import time
import yfinance
import dryscrape
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

def get_market_data(tickers, start, end):

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.optimize as sco
from quotes import tickers

df = pd.read_pickle("quotes.pkl")

logrets = np.log(df / df.shift(1))
rmean = logrets.mean() * 365