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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

Exercises

  1. Do we have to install NumPy independently if our Python was installed via Anaconda?
  2. What are the advantages of using a super package to install many modules simultaneously?
  3. How do you find all the functions contained in NumPy or SciPy?
  4. How many ways are there to import a specific function contained in SciPy?
  5. What is wrong with the following operation?
    >>>x=[1,2,3]
    >>>x.sum()
  6. How can we print all the data items for a given array?
  7. What is wrong with the following lines of code?
    >>>import np
    >>>x=np.array([True,false,true,false],bool)
  8. Find out the meaning of skewtest included in the stats submodule (SciPy), and give an example of using this function.
  9. What is the difference between an arithmetic mean and a geometric mean?
  10. Debug the following lines of code, which are used to estimate a geometric mean for a given set of returns:
    >>>import scipy as sp
    >>>ret=np.array([0.05,0.11,-0.03])
    >>>pow(np.prod(ret+1),1/len(ret))-1
  11. Write a Python...
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