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Mastering Python for Finance

You're reading from   Mastering Python for Finance Implement advanced state-of-the-art financial statistical applications using Python

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789346466
Length 426 pages
Edition 2nd Edition
Languages
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Author (1):
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James Ma Weiming James Ma Weiming
Author Profile Icon James Ma Weiming
James Ma Weiming
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started with Python FREE CHAPTER
2. Overview of Financial Analysis with Python 3. Section 2: Financial Concepts
4. The Importance of Linearity in Finance 5. Nonlinearity in Finance 6. Numerical Methods for Pricing Options 7. Modeling Interest Rates and Derivatives 8. Statistical Analysis of Time Series Data 9. Section 3: A Hands-On Approach
10. Interactive Financial Analytics with the VIX 11. Building an Algorithmic Trading Platform 12. Implementing a Backtesting System 13. Machine Learning for Finance 14. Deep Learning for Finance 15. Other Books You May Enjoy

Root-finding algorithms

In the preceding section, we discussed some nonlinear models commonly used for studying economics and financial time series. From the model data given in continuous time, the intention is therefore to search for the extrema that could possibly infer valuable information. The use of numerical methods, such as root-finding algorithms, can help us find the roots of a continuous function, f, such that f(x)=0, which can either be the maxima or the minima of the function. In general, an equation may either contain a number of roots or none at all.

One example of the use of root-finding methods on nonlinear models is the Black-Scholes implied volatility modeling discussed earlier, in The implied volatility model section. An option trader would be interested in deriving implied prices based on the Black-Scholes model and comparing them with market prices. In the...

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