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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

Summary

In this chapter, we took a brief look at the use of the CAPM model and APT model in finance. In the CAPM model, we visited the efficient frontier with the CML to determine the optimal portfolio and the market portfolio. Then, we solved for the SML using regression, which helped us to determine whether an asset is undervalued or overvalued. In the APT model, we explored how various factors affect security returns other than using the mean-variance framework. We performed a multivariate linear regression to help us determine the coefficients of the factors that led to the valuation of our security price.

In portfolio allocation, portfolio managers are typically mandated by investors to achieve a set of objectives while following certain constraints. We can model this problem using linear programming. Using the Pulp Python package, we can define a minimization or maximization...

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