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Mastering Python for Finance

You're reading from   Mastering Python for Finance Understand, design, and implement state-of-the-art mathematical and statistical applications used in finance with Python

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781784394516
Length 340 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (17) Chapters Close

Mastering Python for Finance
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Python for Financial Applications FREE CHAPTER 2. The Importance of Linearity in Finance 3. Nonlinearity in Finance 4. Numerical Procedures 5. Interest Rates and Derivatives 6. Interactive Financial Analytics with Python and VSTOXX 7. Big Data with Python 8. Algorithmic Trading 9. Backtesting 10. Excel with Python Index

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 capital market line to determine the optimal portfolio and the market portfolio. Then, we solved for the security market line using regression that 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 regression to help us determine the coefficients of these 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 defined a maximization or minimization objective function, and added inequality constraints to our problems to solve for unknown...

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