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Python for Finance

You're reading from   Python for Finance Apply powerful finance models and quantitative analysis with Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
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Author (1):
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Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (17) Chapters Close

Preface 1. Python Basics FREE CHAPTER 2. Introduction to Python Modules 3. Time Value of Money 4. Sources of Data 5. Bond and Stock Valuation 6. Capital Asset Pricing Model 7. Multifactor Models and Performance Measures 8. Time-Series Analysis 9. Portfolio Theory 10. Options and Futures 11. Value at Risk 12. Monte Carlo Simulation 13. Credit Risk Analysis 14. Exotic Options 15. Volatility, Implied Volatility, ARCH, and GARCH Index

Module dependency

At the very beginning of this book, we argued that one of the advantages of using Python is that it is a rich source of hundreds of special packages called modules.

To avoid duplicated efforts and to save time in developing new modules, later modules choose to use functions developed on early modules; that is, they depend on early modules.

The advantage is obvious because developers can save lots of time and effort when building and testing a new module. However, one disadvantage is that installation becomes difficult.

There are two competing approaches:

  • The first approach is to bundle everything together and make sure that all parts play together nicely, thus avoiding the pain of installing n packages independently. This is wonderful, assuming that it works. A potential issue is that the updating of individual modules might not be reflected in the super package.
  • The second approach is to use minimal dependencies. It causes fewer headaches for the package maintainer, but for...
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