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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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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Getting Started with Python
2. Overview of Financial Analysis with Python FREE CHAPTER 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

SciPy implementations in root-finding

Before starting to write your root-finding algorithm to solve nonlinear or even linear problems, take a look at the documentation of the scipy.optimize methods. SciPy contains a collection of scientific computing functions as an extension of Python. Chances are that these open source algorithms might fit into your applications off the shelf.

Root-finding scalar functions

Some root-finding functions that can be found in the scipy.optimize modules include bisect, newton, brentq, and ridder. Let's set up the examples that we have discussed in the Incremental search section using the implementations by SciPy:

In [ ]:
"""
Documentation at
http://docs.scipy.org...
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