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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

Introduction to machine learning

Before machine learning algorithms became mature, many software application decisions were rule-based, consisting of a bunch of if and else statements to generate the appropriate response in exchange to some input data. A commonly cited example is a spam filter function in email inboxes. A mailbox may contain blacklisted words defined by a mail server administrator or owner. Incoming emails have their contents scanned against blacklisted words, and should the blacklist condition hold true, the mail is marked as spammed and sent to the Junk folder. As the nature of unwanted emails continues to evolve to avoid detection, spam filter mechanisms must also continuously update themselves to keep up with doing a better job. However, with machine learning, spam filters can automatically learn from past email data and, given an incoming email, calculate...

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