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Python for Finance Cookbook

You're reading from  Python for Finance Cookbook

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781789618518
Pages 432 pages
Edition 1st Edition
Languages
Author (1):
Eryk Lewinson Eryk Lewinson
Profile icon Eryk Lewinson

Table of Contents (12) Chapters

Preface Financial Data and Preprocessing Technical Analysis in Python Time Series Modeling Multi-Factor Models Modeling Volatility with GARCH Class Models Monte Carlo Simulations in Finance Asset Allocation in Python Identifying Credit Default with Machine Learning Advanced Machine Learning Models in Finance Deep Learning in Finance Other Books You May Enjoy

Evaluating the performance of a basic 1/n portfolio

We begin with inspecting the most basic asset allocation strategy: the 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, calculate its returns, and then use a Python library called pyfolio 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...

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