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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

Multivariate linear regression of factor models

Many Python packages, such as SciPy, come with several variants of regression functions. In particular, the statsmodels package is a complement to SciPy with descriptive statistics and the estimation of statistical models. The official page for Statsmodels is https://www.statsmodels.org.

If Statsmodels is not yet installed in your Python environment, run the following command to do so:

$ pip install -U statsmodels
If you have an existing package installed, the -U switch tells pip to upgrade the selected package to the newest available version.

In this example, we will use the ols function of the statsmodels module to perform an ordinary least-squares regression and view its summary.

Let's assume that you have implemented an APT model with seven factors that return the values of Y. Consider the following set of data...

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