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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
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

Predicting trends with classification-based machine learning

Classification-based machine learning is a supervised machine learning approach in which a model learns from given input data and classifies it according to new observations. Classification may be bi-class, such as identifying whether an option should be exercised or not, or multi-class, such as the direction of a price change, which can be either up, down, or unchanging.

In this section, we will look again at creating cross-asset momentum models by having the prices of four diversified assets predict the daily trend of JPM on a daily basis for the year of 2018. The prior 1-month and 3-month lagged returns of the S&P 500 stock index, the 10-year treasury bond index, the US dollar index, and gold prices will be used to fit the model for prediction. Our target variables consist of Boolean indicators, where a True value...

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