Hence, I’ve elected to create this tutorial using Yahoo Finance’s Historical data download function. The only downside is if an API is deprecated, your code breaks. Python has a great library called pandas_datareader that allows you to pull in historical information right into a pandas dataframe. A solution to this is to pull monthly rates as the adjusted stock price for each month will be a better indicator of correlation. This causes problems when trying to figure out the correlation between stocks. ![]() ![]() However, if you start pulling data from different markets, daily historical rates won’t make sense as different markets are closed on different days. I mentioned in a previous post on how to get (nearly) live stock data from Google Finance.
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