Python for Finance (Anglais) Broché – 25 avril 2014
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Description du produit
Présentation de l'éditeur
About This Book
- Estimate market risk, form various portfolios, and estimate their variance-covariance matrixes using real-world data
- Explains many financial concepts and trading strategies with the help of graphs
- A step-by-step tutorial with many Python programs that will help you learn how to apply Python to finance
Who This Book Is For
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 programming knowledge is helpful, but not 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 n-stock portfolio and estimate its variance-covariance matrix
- Estimate VaR (Value at Risk) for a stock or portfolio
- Run CAPM (Capital Asset Pricing Model) and the Fama-French 3-factor model
- Learn how to optimize a portfolio and draw an efficient frontier
- Conduct various statistic tests such as T-tests, F-tests, and normality tests
Python is a free and powerful tool that can be used to build a financial calculator and price options, and can also explain many trading strategies and test various hypotheses. This book details the steps needed to retrieve time series data from different public data sources.
Python for Finance explores the basics of programming in Python. It is a step-by-step tutorial that will teach you, with the help of concise, practical programs, how to run various statistic tests. This book introduces you to the basic concepts and operations related to Python. You will also learn how to estimate illiquidity, Amihud (2002), liquidity measure, Pastor and Stambaugh (2003), Roll spread (1984), spread based on high-frequency data, beta (rolling beta), draw volatility smile and skewness, and construct a binomial tree to price American options.
This book is a hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python.
Biographie de l'auteur
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 with several publications in 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, coauthored 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. He is proficient at several computer languages such as SAS, R, MATLAB, C, and Python. From 2003 to 2010, he worked as a technical director at Wharton Research Data Services (WRDS), where he debugged several hundred computer programs related to research for WRDS users. After that, he returned to teaching in 2010 and introduced R into several quantitative courses at two universities. Based on lecture notes, he has the first draft of an unpublished manuscript titled Financial Modeling using R. In addition, he is an expert on financial data. While teaching at NTU in Singapore, he offered a course called Introduction to Financial Databases to doctoral students. While working at WRDS, he answered numerous questions related to financial databases and helped update CRSP, Compustat, IBES, and TAQ (NYSE highfrequency database). In 2007, Dr. Yan and S.W. Zhu (his coauthor) published a book titled Financial Databases, Shiwu Zhu and Yuxing Yan, Tsinghua University Press. Currently, he spends considerable time and effort on public financial data. If you have any queries, you can always contact him at firstname.lastname@example.org.
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Commentaires client les plus utiles sur Amazon.com
It is, however, a great read if you are familiar with doing a lot of finance in another language like R and want to transition to python. With bloomberg providing a python API and C++ still being a real pain the rear this is a good way for more "analyst" types to become much more fluent and competent using a vastly more flexible language. It is not mega detailed - basically a "crash through" approach to doing a bit of python and doing it quickly. This is by no means a standalone solution to anything.
The best use of this book is in conjunction with something more rigorous for finance and python. Aside from that it could be put to good use in an undergrad finance class so that instead of messing around in excel people actually learn a bit of code that they can build on later.
I picked up this book on a whim after reading about Quant Finance and figured it would be fun to play around with some of the basic tools of the trade.
I would say this book is suitable for Masters students in Finance, Financial Engineering or similar. It covers some interesting subjects such as Monte Carlo simulators and options.
The book could have done with a bit better editing. Some of the topics are repeated unnecessarily. I don't blame the author here, but more poor editing.
Fun introduction to the subject
Some of the information seems to be repeated
If you are trying to learn a new programming language you expect the examples in the book are well coded so you just copy and paste and see them work, however the book is plagued with too many errors just visit their web page [...]
Third it doesn't cover it deep Python nor Finances,
If you are new to Python then buy this book Python for Informatics: Exploring Information
Wrote in a logical step by step way to learn to program Python,
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