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Python for Finance Cookbook – Second Edition

You're reading from   Python for Finance Cookbook – Second Edition Over 80 powerful recipes for effective financial data analysis

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Length 740 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Toc

Table of Contents (18) Chapters Close

Preface 1. Acquiring Financial Data 2. Data Preprocessing FREE CHAPTER 3. Visualizing Financial Time Series 4. Exploring Financial Time Series Data 5. Technical Analysis and Building Interactive Dashboards 6. Time Series Analysis and Forecasting 7. Machine Learning-Based Approaches to Time Series Forecasting 8. Multi-Factor Models 9. Modeling Volatility with GARCH Class Models 10. Monte Carlo Simulations in Finance 11. Asset Allocation 12. Backtesting Trading Strategies 13. Applied Machine Learning: Identifying Credit Default 14. Advanced Concepts for Machine Learning Projects 15. Deep Learning in Finance 16. Other Books You May Enjoy
17. Index

Evaluating an equally-weighted portfolio’s performance

We begin with inspecting the most basic asset allocation strategy: the equally-weighted (1/n) portfolio. The idea is to assign equal weights to all the considered assets, thus diversifying the portfolio. As simple as that might sound, DeMiguel, Garlappi, and Uppal (2007) show that it can be difficult to beat the performance of the 1/n portfolio by using more advanced asset allocation strategies.

The goal of the recipe is to show how to create a 1/n portfolio of the FAANG companies (Facebook/Meta, Amazon, Apple, Netflix, and Google/Alphabet), calculate its returns, and then use the quantstats library to quickly obtain all relevant portfolio evaluation metrics in the form of a tear sheet. Historically, a tear sheet is a concise (usually one-page) document summarizing important information about public companies.

How to do it...

Execute the following steps to create and evaluate the 1/n portfolio:

  1. Import...
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