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Python for Finance. - Second Edition

You're reading from  Python for Finance. - Second Edition

Product type Book
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Pages 586 pages
Edition 2nd Edition
Languages

Table of Contents (23) Chapters

Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Python Basics Introduction to Python Modules Time Value of Money Sources of Data Bond and Stock Valuation Capital Asset Pricing Model Multifactor Models and Performance Measures Time-Series Analysis Portfolio Theory Options and Futures Value at Risk Monte Carlo Simulation Credit Risk Analysis Exotic Options 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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