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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

Excel for finance


The spreadsheet application in the Microsoft Office suite was designed for statistical, engineering, and financial data management. In finance, Microsoft Excel is used as a handy tool for bond traders and an integral part of banking operations to task automations using VBA. For example, built-in Excel functions, such as TBILLYIELD and DURATION, helps you calculate the yield of a T-bill and the Macaulay duration of a bond and displays these values onto a cell.

Excel supports the use of COM to extend the functionality for custom tasks. This is achieved with the use of COM add-ins as an in-process COM server. With VBA, a wrapper can be created for the COM add-in function so that the COM component can be used as a worksheet cell formula function.

In this chapter, we will take a look at building a COM server in Python. We will then use Microsoft Excel, as our source of data parameters, to perform numerical pricing with the COM object. Using this basic example, we can then extend...

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