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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

Selecting m stocks randomly from n given stocks

Based on the preceding program, we could easily choose 20 stocks from 500 available securities. This is an important step if we intend to investigate the impact of the number of randomly selected stocks on the portfolio volatility, as shown in the following code:

import scipy as sp 
n_stocks_available=500 
n_stocks=20 
sp.random.seed(123345) 
x=sp.random.uniform(low=1,high=n_stocks_available,size=n_stocks)
y=[] 
for i in range(n_stocks): 
    y.append(int(x[i])) 
#print y 
final=sp.unique(y) 
print(final) 
print(len(final))
[  8  31  61  99 124 148 155 172 185 205 226 275 301 334 356 360 374 379
 401 449]
20

In the preceding program, we select 20 numbers from 500 numbers. Since we have to choose integers, we might end up with less than 20 values, that is, some integers appear more than once after we convert real numbers into integers. One solution is to pick more than we need. Then choose the first 20 integers. An alternative is to use the randrange...

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