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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

What else can I do with COM?

On changing the values of N, we can see that the values from our custom-defined functions changes on the fly. This makes it possible for dynamic computations of securities, or even real-time numerical pricing, when connected to a market data feed, where values such as S0 or K are changing every second.

The COM server components are separated from each other. Using Python, we can change the implementation of the COM server using the Python modules, such as NumPy or SciPy, to achieve certain aspects of numerical pricing without relying too much on Excel's built-in functions. This also means that we can interchange and interface components that are not related to Excel. The COM model simply provides a transparent bridge between these components and Excel.

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