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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

The Greeks for free

In the binomial tree pricing models that we have covered so far, we traversed up and down the tree at each point in time to determine the node values. From the information at each node, we can reuse these computed values easily. One such use is the computation of Greeks.

The Greeks measure the sensitivities of the price of derivatives, such as options with respect to changes in the parameters of the underlying asset, often represented by Greek letters. In mathematical finance, the common names associated with Greeks include alpha, beta, delta, gamma, vega, theta, and rho.

Two particularly useful Greeks for options are delta and gamma. Delta measures the sensitivity of the option price with respect to the underlying asset price. Gamma measures the rate of change in delta with respect to the underlying price.

As shown in the following diagram, an additional layer...

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