Very simple example of working with python dataframes and matplotlib to observe some useful info.

qqq_vs_spy_plot.py

import matplotlib.pyplot as plt
import pandas, os, re
import fix_yahoo_finance as yf                    # Might need to "> pip install fix_yahoo_finance"

stocks=["SPY","QQQ"]                              # Array of stocks to plot
bgn_date='2014-06-12'                             # Set the date range
end_date='2018-09-28'

fig,ax = plt.subplots()

for stock in stocks:
  file="./%s_%s_%s.json"%( stock, re.sub('\W+','_',bgn_date), re.sub('\W+','_',end_date) )
  if os.path.exists(file):
    df=pandas.read_json(file)                     # Read in local copy
  else:
    df=yf.download(stock,bgn_date,end_date)       # If never downloaded do it now
    df.to_json(file)                              # and save a local copy

  pcnt_chg=df.Close/df['Close'].iloc[0]           # create a % change from first day closing ('Close') value
  pcnt_chg.plot(ax=ax)                            # Add each stock % change to the plot (dataframes have instance of pyplot in them already)

ax.grid()                                         # Turn on a grid
plt.show()                                        # show the final plot