Getting stock market data

Start by importing the packages. We will need pandas and the pandas_datareader.

# Import modules
import pandas as pd
from pandas_datareader import data

Datareader allows you to import data from the internet. I have found that Quandl and robinhood works the best as a source for stockmarket data.

Note that if you want an other type of data (e.g. GDP, inflation, etc.) other sources exist.

#import stock from robinhood
aapl_robinhood = data.DataReader('AAPL', 'robinhood', '1980-01-01')
aapl_robinhood.head()
close_price high_price interpolated low_price open_price session volume
symbol begins_at
AAPL 2017-08-04 153.996200 154.990700 False 153.306900 153.681100 reg 20559852
2017-08-07 156.379100 156.487400 False 154.272000 154.655900 reg 21870321
2017-08-08 157.629700 159.352900 False 155.847400 156.172300 reg 36205896
2017-08-09 158.594700 158.801500 False 156.674500 156.822200 reg 26131530
2017-08-10 153.543100 158.169600 False 152.861000 158.070700 reg 40804273
#import stock from quandl
aapl_quandl = data.DataReader('AAPL', 'quandl', '1980-01-01')
aapl_quandl.head()
Open High Low Close Volume ExDividend SplitRatio AdjOpen AdjHigh AdjLow AdjClose AdjVolume
Date
2018-03-27 173.68 175.15 166.92 168.340 38962839.0 0.0 1.0 173.68 175.15 166.92 168.340 38962839.0
2018-03-26 168.07 173.10 166.44 172.770 36272617.0 0.0 1.0 168.07 173.10 166.44 172.770 36272617.0
2018-03-23 168.39 169.92 164.94 164.940 40248954.0 0.0 1.0 168.39 169.92 164.94 164.940 40248954.0
2018-03-22 170.00 172.68 168.60 168.845 41051076.0 0.0 1.0 170.00 172.68 168.60 168.845 41051076.0
2018-03-21 175.04 175.09 171.26 171.270 35247358.0 0.0 1.0 175.04 175.09 171.26 171.270 35247358.0