Book Image

Python for Finance - Second Edition

By : Yuxing Yan
5 (1)
Book Image

Python for Finance - Second Edition

5 (1)
By: Yuxing Yan

Overview of this book

This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.
Table of Contents (23 chapters)
Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
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...