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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

Technical analysis techniques


We will now cover two categories of technical analysis techniques, which utilize moving averages in different ways to be able to determine trends in market movements and hence give us the information needed to make potentially profitable transactions. We will examine how this works in this section, and in the upcoming section on Zipline, we will see how to implement these strategies in pandas and Zipline.

Crossovers

A crossover is the most basic type of signal for trading. The simplest form of a crossover is when the price of an asset moves from one side of a moving average to the other. This crossover represents a change in momentum and can be used as a point of making the decision to enter or exit the market.

The following command exemplifies several crossovers in the Microsoft data:

In [8]:
   msft['2002-1':'2002-9'][['Adj Close', 
                            'MA30']].plot(figsize=(12,8)); 

As an example, the cross occurring on July 09, 2002, is a signal of...

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