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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
Languages
Tools
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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

Modeling a portfolio with pandas


A basic portfolio model consists of a specification of one or more investments and their quantities. A portfolio can be modeled in pandas using a DataFrame with one column representing the particular instrument (such as a stock symbol) and the other representing the quantity of the item held.

The following command will create a DataFrame representing a portfolio:

In [2]:
   def create_portfolio(tickers, weights=None):
       if (weights is None): 
           shares = np.ones(len(tickers))/len(tickers)
       portfolio = pd.DataFrame({'Tickers': tickers, 
                                 'Weights': weights}, 
                                index=tickers)
       return portfolio

Using this, we can create a portfolio of two instruments, Stock A and Stock B. The amount of shares for each is initialized to 1. This would represent an equally weighted portfolio as the number of shares of each stock is the same:

In [3]:
   portfolio = create_portfolio(['Stock A'...
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