Key FeaturesBook DescriptionA hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.What you will learn- Build a financial calculator based on Python
- Learn how to price various types of options such as European, American, average, lookback, and barrier options
- Write Python programs to download data from Yahoo! Finance
- Estimate returns and convert daily returns into monthly or annual returns
- Form an nstock portfolio and estimate its variancecovariance matrix
- Estimate VaR (Value at Risk) for a stock or portfolio
- Run CAPM (Capital Asset Pricing Model) and the FamaFrench 3factor model
- Learn how to optimize a portfolio and draw an efficient frontier
- Conduct various statistic tests such as Ttests, Ftests, and normality tests
Who this book is for
Table of Contents:
Table of Contents- Python Installation and Introduction
- Using Python as an ordinary calculator
- Using Python as a financial calculator
- Python codes (13 lines) to price a call option
- Introduction to modules
- Introduction to NumPy and SciPy modules
- Visual Finance via Matplotlib
- Statistical Analysis of Time Series
- Python loops and implied volatility based on the Black-Scholes model
- Statistical analysis of time series data
- Monte Carlo Simulation for options
- Volatility Measures and GARCH
About the Author :
Yan Yuxing :
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.
