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import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
plt.style.use("fivethirtyeight")
%matplotlib inline

# For reading stock data from yahoo
from pandas_datareader.data import DataReader
import yfinance as yf
from pandas_datareader import data as pdr

yf.pdr_override()

# For time stamps
from datetime import datetime


# The tech stocks we'll use for this analysis
tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'XRP-EUR']

# Set up End and Start times for data grab
tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'XRP-EUR']

end = datetime.now()
start = datetime(end.year - 1, end.month, end.day)

for stock in tech_list:
    globals()[stock] = yf.download(stock, start, end)

company_list = [AAPL, GOOG, MSFT, AMZN, XRP]
company_name = ["APPLE", "GOOGLE", "MICROSOFT", "AMAZON", 'XRP']

for company, com_name in zip(company_list, company_name):
    company["company_name"] = com_name
    
df = pd.concat(company_list, axis=0)
df.tail(10)

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stocks = { name : yf.download(name, start, end) for name in tech_list }[code]