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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

Data collection


Our goal will be to create a DataFrame, which contains both the authors' DJIA and Google Trends data combined with data that we also collect dynamically from the Web for each. We will check that our data conforms to what they had collected, and then we will use our data to simulate trades based upon their algorithm.

The data used in the study is available on the Internet. I have included it in the examples for the text. But we will also dynamically collect this information to demonstrate those processes using pandas. We will perform the analysis both on the data provided by the authors as well as our freshly collected data.

Unfortunately, but definitely not uncommon in the real world, we will also run into several snags in data collection that we need to work around. First, Yahoo! no longer provides DJIA data, so we can't fetch that data with the DataReader class of pandas. We will get around Yahoo! Finance no longer providing DJIA data using a web-based service named Quandl...

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