As I stare college bills in the face and know that retirement awaits in the not-too-distant future, I’m working hard to improve my financial literacy. One way I’m trying to do this and work on my programming and data analysis techniques at the same time is to download financial data directly and do some direct analysis with tools like pandas. Right from the start, I’ve found two convenient ways to download the financial data you wish to examine.
Option 1: quandl
Quandl is a great source for datasets and they make accessing their data even easier with their API. One big drawback I’ve encountered with the API is that I have yet to get it to work behind my company’s firewall. The only other point to note is that if you intend on making over 50 calls in one day, you’ll need to get a free API key.
import quandl
df_amzn1 = quandl.get("WIKI/AMZN", start_date="2018-01-01", end_date="2019-01-01")
df_amzn1.head()
Option 2: pandas-datareader
Pandas-datareader wraps a lot of interesting APIs and hands the results back to you in the form of a pandas dataframe. In my example, I’m using pandas-datareader to call the Yahoo finance API to get Amazon stock price information. Apparently, the Yahoo API has changed too much/too frequently to the point where the pandas-datareader folks have said “enough, already” and deprecated their support of the API. Not content to let go just yet, others have offered up the aptly named fix-yahoo-finance package that can be used to plug the Yahoo hole in pandas-datareader. One other note: unlike quandl, I have successfully used pandas-datareader behind my company’s firewall. If you find yourself with SSL and timeout exceptions at work, you may want to give pandas-datareader a try.
from pandas_datareader import data as pdr
import fix_yahoo_finance as yf
yf.pdr_override()
df_amzn2 = pdr.get_data_yahoo("AMZN", start="2018-01-01", end="2019-01-01")
df_amzn2.head()
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