Use Data Analytics to Build Your Own Stock Fund and Beat the S&P 500

person icon David Clinton

Use Data Analytics to Build Your Own Stock Fund and Beat the S&P 500

person icon David Clinton

ebook icon Bootstrap IT

language icon Language - English

updated on icon Updated on Oct, 2022

architecture icon Data Analytics,Python,Investing


This eBook includes

Formats : PDF (Read Only)

Pages : 46

ISBN : 978-1777721077

Language : English

About the Book

Book description

The short book is an introduction to the use of data analytics for identifying undervalued stocks which can be combined into customized investment funds. The methodology relies on price histories and business fundamentals made public through mandated financial filings.

Of course, the stocks that were undervalued as this book was written won't necessarily remain that way for long. The idea is to apply the methodology you'll see here to build your own funds. You can use the same Python code within Jupyter Lab notebooks (available for free from the associated GitLab repository) to apply the system. Not a Python fan? It'll work just as well no matter what data tools you prefer.

The system

Given the resources of the modern internet, you can quickly and easily access detailed fundamentals data describing all 3,200 stocks traded on the major exchanges. With that, using any of the many mainstream analytics tools available, you can find just the companies that rate the highest among their peers using specific metrics.

In other words, let's say you suspect that the share prices of companies with extremely low "Liabilities to Equity Ratios" are likely to rise over the next few years, then you might select just the 20 companies rated highest for that metric. You can just as easily filter for a combination of multiple metrics and go with the 20 companies who best match all of those standards. You could also further diversify your portfolio by choosing companies from a range of industry categories.

When you're done, you'll have built yourself a sophisticated and highly customized and (hopefully) high-performing fund portfolio.

How do you know it will work?

Well that's the beauty of having lots of historical data, as it makes it possible to test our assumptions.

Here's how that'll go. As you'll see in the chapter "Testing Your Model", you can use historical fundamentals data from previous years, create hypothetical stock portfolios based on any metrics you like, and then pull their actual market prices. The portfolio profiles that have consistently performed well in the real world in the past, are likely (although, of course, not guaranteed) to do so in the future.

How should we define "performed well"? Well I'd say that, over the past century, the blue ribbon marker for stock market success has been the S&P 500 index. If our funds can outperform the S&P, then we're in pretty good shape.


Build yourself your own sophisticated and highly customized and (hopefully) high-performing fund portfolio using publicly available data and Python code.

Use Data Analytics to Build Your Own Stock Fund and Beat the S&P 500

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Author Details

David Clinton

David Clinton

Linux System Administrator, AWS Solutions Architect, and Technology Content Provider: technology books and courseware

David Clinton is an AWS Solutions Architect and a Linux server administrator. He has written about and published training material (including books with Wiley/Sybex, Bootstrap IT, and Manning, and online courses on Pluralsight and Udemy) for many cutting edge technology subjects including Linux systems, Cloud Computing (in particular Amazon's AWS), container technologies like containers, data analytics, and DevOps tools. 

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