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Python for Finance

You're reading from Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

Product type Paperback

Published in Apr 2014

Publisher

ISBN-13 9781783284375

Length 408 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Financial Technology

Author (1):

Yuxing Yan

View More author details

Table of Contents (14) Chapters

Preface

1. Introduction and Installation of Python FREE CHAPTER

2. Using Python as an Ordinary Calculator

3. Using Python as a Financial Calculator

4. 13 Lines of Python to Price a Call Option

5. Introduction to Modules

6. Introduction to NumPy and SciPy

7. Visual Finance via Matplotlib

8. Statistical Analysis of Time Series

9. The Black-Scholes-Merton Option Model

10. Python Loops and Implied Volatility

11. Monte Carlo Simulation and Options

12. Volatility Measures and GARCH

Index

Chapter 7. Visual Finance via Matplotlib

Graphs and other visual representations have become more important in explaining many complex financial concepts, trading strategies, and formulae. In this chapter, we discuss the module matplotlib, which is used to create various types of graphs. In addition, the module will be used intensively in Chapter 9, The Black-Scholes-Merton Option Model, when we discuss the famous Black-Scholes-Merton option model and various trading strategies. The matplotlib module is designed to produce publication-quality figures and graphs. The matplotlib module depends on NumPy and SciPy which were discussed in Chapter 6, Introduction to NumPy and SciPy. There are several output formats, such as PDF, Postscript, SVG, and PNG.

In particular, we will cover the following:

Several ways to install matplotlib
Simple examples of using matplotlib
Net Present Value (NPV) profile, DuPont identity, stock returns, and histogram
Total risk, market risk (beta), and firm-specific...

The rest of the chapter is locked

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Authors (1)

Yuxing Yan

Yuxing Yan

Yuxing Yan graduated from McGill University with a PhD in finance. Over the years, he has been teaching various finance courses at eight universities: McGill University and Wilfrid Laurier University (in Canada), Nanyang Technological University (in Singapore), Loyola University of Maryland, UMUC, Hofstra University, University at Buffalo, and Canisius College (in the US). His research and teaching areas include: market microstructure, open-source finance and financial data analytics. He has 22 publications including papers published in the Journal of Accounting and Finance, Journal of Banking and Finance, Journal of Empirical Finance, Real Estate Review, Pacific Basin Finance Journal, Applied Financial Economics, and Annals of Operations Research. He is good at several computer languages, such as SAS, R, Python, Matlab, and C. His four books are related to applying two pieces of open-source software to finance: Python for Finance (2014), Python for Finance (2nd ed., expected 2017), Python for Finance (Chinese version, expected 2017), and Financial Modeling Using R (2016). In addition, he is an expert on data, especially on financial databases. From 2003 to 2010, he worked at Wharton School as a consultant, helping researchers with their programs and data issues. In 2007, he published a book titled Financial Databases (with S.W. Zhu). This book is written in Chinese. Currently, he is writing a new book called Financial Modeling Using Excel — in an R-Assisted Learning Environment. The phrase "R-Assisted" distinguishes it from other similar books related to Excel and financial modeling. New features include using a huge amount of public data related to economics, finance, and accounting; an efficient way to retrieve data: 3 seconds for each time series; a free financial calculator, showing 50 financial formulas instantly, 300 websites, 100 YouTube videos, 80 references, paperless for homework, midterms, and final exams; easy to extend for instructors; and especially, no need to learn R.

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