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Mastering Python for Finance

You're reading from   Mastering Python for Finance Understand, design, and implement state-of-the-art mathematical and statistical applications used in finance with Python

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781784394516
Length 340 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (17) Chapters Close

Mastering Python for Finance
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Python for Financial Applications FREE CHAPTER 2. The Importance of Linearity in Finance 3. Nonlinearity in Finance 4. Numerical Procedures 5. Interest Rates and Derivatives 6. Interactive Financial Analytics with Python and VSTOXX 7. Big Data with Python 8. Algorithmic Trading 9. Backtesting 10. Excel with Python Index

Chapter 9. Backtesting

A backtest is a simulation of a model-driven investment strategy's response to historical data. While working on designing and developing a backtest, it would be helpful to think in terms of the concept of creating video games.

In this chapter, we will design and implement an event-driven backtesting system using object-oriented design. We can then plot our resulting profits and losses onto a graph to help us visualize the performance of our trading strategy. Is this sufficient to deduce a good model?

There are many concerns to be addressed in backtesting, for example, the effects of transaction costs, execution latency of orders, access to detailed transactions, and quality of historical data. Notwithstanding these factors, the primary goal of creating a backtesting system is to test a model as accurately as possible.

Backtesting involves a lot of research that merits its own literature. We will briefly cover some thoughts that you might want to consider when implementing...

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