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

Two general formulae for many functions


This section is optional since it is quite complex in terms of mathematical expression. Skipping this section would not have any impact on the understanding of the other chapters. Thus, this section is for advanced learners. Up to now in this chapter, we have learnt the usage of several functions, such as pv(), fv(), nper(), pmt(), and rate() included in the SciPy module or numpy.lib.financial submodule. The first general formula is related to the present value:

On the right-hand side of the preceding equation, the first one is the present value of one future cash flow, while the second part is the present value of annuity. The variable type takes a value of zero (default value); it is the present value of a normal annuity, while it is an annuity due if type takes a value of 1. The negative sign is for the sign convention. If using the same notation as that used for the functions contained in SciPy and numpy.lib.financial, we have the following formula...