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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

In this chapter, we looked at the use of the Component Object Model (COM) to allow the reuse of objects across different software and hardware environments to interface with each other, without the knowledge of its internal implementation.

To build the server component of the COM interface, we used the pythoncom module to create a Black-Scholes pricing COM server with the three magic variables: _public_methods_, _reg_progid_, and _reg_clsid_. Using topics in Chapter 4, Numerical Procedures, we created COM server components using the binomial tree by the CRR model and trinomial lattice model. We learned how to register and unregister these COM server components with the Windows registry.

In Microsoft Excel, we can input a number of parameters for a particular option and numerically compute the theoretical option prices using the COM server components we built. These functions are made available in the formula cells using Visual Basic. We created the Black-Scholes model, binomial tree...

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