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

Exercises


  1. http://finance.yahoo.com), download the last five years of price data for a few companies, such as IBM, WMT, and C (City Group). Test whether their daily returns follow a normal distribution.

  2. Write a Python program to use the scipy.permutation() function to select 12 monthly returns randomly from the past five-year data without replacement. To test the program, you can use Citigroup and the time period from January 2, 2012 to December 31, 2016 from Yahoo! Finance.

  3. Write a Python program to run bootstrapping with n given returns. For each time, we select m returns where m>n.

  4. To convert random numbers from a uniform distribution to a normal distribution, we have the following formula:

    Based on the formula, generate 5,000 normally distributed random numbers; estimate their mean, standard deviation, and test it.

  5. Assume that the current stock price is $10.25, the mean value in the past five years is $9.35, and the standard deviation is 4.24. Write a Python program to generate 1,000 future...