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Mastering Pandas for Finance

You're reading from   Mastering Pandas for Finance Master pandas, an open source Python Data Analysis Library, for financial data analysis

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Product type Paperback
Published in May 2015
Publisher Packt
ISBN-13 9781783985104
Length 298 pages
Edition 1st Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with pandas Using Wakari.io FREE CHAPTER 2. Introducing the Series and DataFrame 3. Reshaping, Reorganizing, and Aggregating 4. Time-series 5. Time-series Stock Data 6. Trading Using Google Trends 7. Algorithmic Trading 8. Working with Options 9. Portfolios and Risk Index

Constructing an efficient portfolio


At the beginning of the chapter, we briefly covered the formulas to calculate the estimated return and variance of a portfolio. We will now dive into implementations of those calculations along with selecting portfolios that are on the efficient frontier.

To do this, we will need to cover the following concepts:

  • Gathering of historical returns on the assets in the portfolio

  • Formulation of portfolio risk based on historical returns

  • Determining the Sharpe ratio for a portfolio

  • Selecting optimal portfolios based upon Sharpe ratios

Gathering historical returns for a portfolio

In our examples, we will use data retrieved from Yahoo! Finance to create historical returns for the stocks in the portfolio. The calculations we will perform will utilize annualized returns. Yahoo! Finance data represents daily prices for the stocks, so we will need to convert those prices into annualized returns.

We can start this process using the following function, which will retrieve the...

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