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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

Chapter 7. Algorithmic Trading

In this chapter, we will examine how to use pandas and a library known as Zipline to develop automated trading algorithms. Zipline (http://www.zipline.io/) is a Python-based algorithmic trading library. It provides event-driven approximations of live-trading systems. It is currently used in production as the trading engine that powers Quantopian (https://www.quantopian.com/), a free, community-centered platform for collaborating on the development of trading algorithms with a web browser.

We previously simulated trading based on a historical review of social and stock data, but these examples were naive in that they glossed over many facets of real trading, such as transaction fees, commissions, and slippage, among many others. Zipline provides robust capabilities to include these factors in the trading model.

Zipline also provides a facility referred to as backtesting. Backtesting is the ability to run an algorithm on historical data to determine...

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