Learn how to build Python applications for quantitative finance and financial engineering using Packt's new book and eBook

June 2014 | Open Source

Packt is pleased to announce the release of Python for Finance, a hands-on guide with easy-to-follow examples to help readers learn about option theory, quantitative finance, financial modeling, and time series using Python. The print book is 408 pages long and is priced at $44.99. It is also available in all popular eBook formats, such as Kindle and selected library formats, for $22.94.

About the author: 

Yuxing Yan graduated from McGill University with a PhD in Finance. He has taught various finance courses, such as Financial Modeling, Options and Futures, Portfolio Theory, Quantitative Financial Analysis, Corporate Finance, and Introduction to Financial Databases to undergraduate and graduate students at seven universities: two in Canada, one in Singapore, and four in the USA. Dr. Yan has actively done research for several publications including 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. For example, his latest publication, co-authored with Shaojun Zhang, will appear in the Journal of Banking and Finance in 2014. His research areas include investment, market microstructure, and open source finance. 

Python is a widely used general-purpose, high-level programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C. The language provides constructs intended to enable clear programs on both a small and large scale.

Python for Finance explores the basics of programming in Python. It is a step-by-step tutorial that will show readers how to run various statistical tests with the help of concise, practical programs. This book introduces the basic concepts and operations related to Python. Readers will also learn how to estimate illiquidity, the liquidity measure, roll spread, spread based on high-frequency data, and beta (rolling beta), as well as how to draw volatility smiles and skewness and construct a binomial tree to price American options.

The following chapters are covered in this book:

Chapter 1: Introduction and Installation of Python
Chapter 2: Using Python as an Ordinary Calculator
Chapter 3: Using Python as a Financial Calculator
Chapter 4: 13 Lines of Python to Price a Call Option
Chapter 5: Introduction to Modules
Chapter 6: Introduction to NumPy and SciPy
Chapter 7: Visual Finance via Matplotlib
Chapter 8: Statistical Analysis of Time Series
Chapter 9: The Black-Scholes-Merton Option Model
Chapter 10: Python Loops and Implied Volatility
Chapter 11: Monte Carlo Simulation and Options
Chapter 12: Volatility Measures and GARCH 

This book is targeted at graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs.

Python for Finance
Build real-life Python applications for quantitative finance and financial engineering

For more information, please visit book page

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