Chris Conlan

Data Scientist

Pulling All Sorts of Financial Data in Python [Updated for 2021]

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Contributed so kindly by Joe Wojniak

It may seem obvious, but financial research requires data — and a lot of it. If financial research isn’t your day job, it can be surprisingly difficult to come by. Here are some suggestions for acquiring data to use in your financial research project.

Data Sources

Every project has to start somewhere. In development, you need a data that is representative of the production data. This can be a historical or a static data set. In development, you keep a local copy and run your testing on it. In production, you update it and re-run your analysis on it regularly. Here are some great places to get this data.

Simulated End-of-day and Alternative Data

Financial research tends to produce unique efforts. Rarely does a researcher ask that their research be reproduced (and hence to be validated by being reproduced.) Chris Conlan has done something incredible here. Chris has provided Python trading algorithms and the data to reproduce the results. In a world where it seems that the zero-sum game rules, Chris has provided the methods and the data so that you can improve your trading chops. Check out the repo and accompanying book https://github.com/chrisconlan/algorithmic-trading-with-python.

Stooq for Multiple Asset Classes

See this site https://stooq.com/.

Stooq is a great website because it provides data for free. It’s a little too good to believe, but get your free data while it’s available for:

  • Indices
  • Currencies
  • Stocks
  • Bonds
  • Historical data – want to know what the Nasdaq was back in 1938? Stooq has it!. Just make sure you have plenty of bandwidth for this download. Daily, Hourly, and 5 minute data is available for all major exchanges. See this link https://stooq.com/db/h/

Yahoo Finance Still Exists

The Yahoo Finance API isn’t as easy to access as it used to be. But there is a work around. The Quantstat Github repo written by Ran Aroussi provides a Python library that connects to the Yahoo Finance API. The Quantstat library also provides a ready-to-go tearsheet with plots.

See the repo: https://github.com/ranaroussi/quantstats.

Google Finance

This is another popular API that is harder to access now. The Google Finance API is now only available through Google Sheets. Type the following into a cell: =GOOGLEFINANCE({SYMBOL}). Replace {SYMBOL} with a ticker symbol to get the current price of a market traded asset.

Stooq Pandas Datareader Interface

Surprised? Stooq has an API via a Pandas datareader interface that you can use to access daily, weekly, monthly, quarterly and yearly data. See https://pandas-datareader.readthedocs.io/en/latest/readers/stooq.html.

Other Pandas Datareaders

Need to refresh intraday? Check out the Pandas datareader list. The data sources here can be accessed programmatically using the Pandas datareader matching the data source listed. There is example code given here https://pandas-datareader.readthedocs.io/en/latest/remote_data.html.

Here are some Pandas datareader interfaces.

The Ever Changing World of Data

The world of financial data is constantly changing. Quantopian provided an online community where quants could practice and improve their skills. Then, as of November 2020, Quantopian is no more and many of the people behind Quantopian are now working at Robinhood! Pyfolio and Zipline continue to be available on Github but they don’t seem to be actively supported anymore. So, why do I mention this? The data sources listed above may work and they may not work depending upon when you’re reading this (I am writing this in December 2020, so this is my effort at future proofing this article!).

Code Sample for Stooq

By installing pandas and the pandas_datareader packages from Python’s pip, you can easily get data from Stooq using the following code snippet.

# requires ...
# pip install pandas pandas-datareader
import pandas as pd
import pandas_datareader.data as web
df = web.DataReader('AAPL', 'stooq')

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