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

Using simulation to estimate the pi value


It is a good exercise to estimate π value by simulation. Let's draw a square with 2R as its side. If putting the largest circle inside the square, its radius will be R, described by the following equation:

On the other hand, the square is the product of its sides:

Dividing Equation (4) by Equation (5), we have the following result:

Reorganize it; we end up with the following equation:

In other words, the value of π will be 4* Scircle/Square. When running the simulation, we generate n pairs of x and y from a uniform distribution with a range of zero and 0.5. Then we estimate a distance that is the square root of the summation of the squared x and y, that is, .

Obviously, when d is less than 0.5 (value of R), it will fall into the circle. We can imagine throwing a dart that falls into the circle. The value of the pi will take the following form:

The following graph illustrates these random points within a circle and within a square:

The Python program to...