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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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Toc

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

Generating random numbers from a uniform distribution

When randomly choosing m stocks from n available stocks, we can draw a set of random numbers from a uniform distribution. To generate 10 random numbers between 1 and 100 from a uniform distribution, we have the following code. To guarantee for the same set of numbers, the seed() function is used:

>>>import scipy as sp 
>>>sp.random.seed(123345) 
>>>x=sp.random.uniform(low=1,high=100,size=10) 

Again, low, high, and size are the three input names. The first one specifies the minimum, the second one specifies the high end, while the size gives the number of the random numbers we intend to generate. The first five numbers are shown as follows:

>>>print(x[0:5])
[ 30.32749021 20.58006409 2.43703988 76.15661293 75.06929084]
>>>

Next program randomly roll a dice with a value from 1, 2, and up to 6:

import random
def rollDice():
    roll = random.randint(1,6)
    return roll
i =1
n=10
result=[]
random.seed...
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