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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 (12) Chapters Close

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

Summary

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 and to compute various factors related to either risk or return. The factors are typically used together to find a combination that is predictive of return.

While working on designing and developing a backtester, to achieve functionalities, such as simulated market pricing, ordering environment, order matching engine, order book management, as well as account and position updates, we can explore the concept of an event-driven backtesting system.

In this chapter, we designed and implemented an event-driven backtesting system using the TickData class, the MarketDataSource class, the Order class, the Position class, the Strategy class, the MeanRevertingStrategy class, and the Backtester class. We plotted our resulting profits and losses onto a graph to help us visualize the performance...

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