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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

An introduction to backtesting


A backtest is a simulation of a model-driven investment strategy's response to historical data. The purpose of performing experiments with backtests is to make discoveries about a process or system. Using historical data, you can save time in testing an investment strategy for the period forward. It helps you test an investment theory based on the movements of the tested period. It is also used to both evaluate and calibrate an investment model.

Creating a model is only the first step. The investment strategy will typically employ the model to help you drive simulated trading decisions and compute various factors related to either risk or return. These factors are typically used together to find a combination that is predictive of return.

Concerns in backtesting

However, there are many concerns to be addressed in backtesting. A backtest can never exactly replicate the performance of an investment strategy in an actual trading environment. The quality of the historical...

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