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Python for Finance
Download Python for Finance
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About the Author
Yuxing Yan 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 Based on lecture notes, he has the first draft of an unpublished manuscript titled Financial Modeling using R. In addition, he is an expert on financial data.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 yany@canisius.edu.
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Product details
Paperback: 408 pages
Publisher: Packt Publishing - ebooks Account (April 25, 2014)
Language: English
ISBN-10: 1783284374
ISBN-13: 978-1783284375
Product Dimensions:
7.5 x 0.9 x 9.2 inches
Shipping Weight: 1.9 pounds (View shipping rates and policies)
Average Customer Review:
3.2 out of 5 stars
25 customer reviews
Amazon Best Sellers Rank:
#2,172,833 in Books (See Top 100 in Books)
Although the author seems quite knowledgeable about his subject, there are abrupt changes in context which do not lend themselves well to the learning process. There are some outright mistakes , such as in the "def pv_f(pv, r, n) definition statement on pg. 44). At times, I had the feeling that the author had handed the proofreading task to a less-than-interested grad student, whose lack of attention to detail took a toll on the content and continuity. If the reader was hoping to be spoon-fed from this book, he/she will be disappointed. Nevertheless, I am working through the book at my own pace as time allows. In spite of its shortcomings, I could give the book a cautiously lukewarm recommendation.
First of all this is not an in-depth python book or an in-depth finance book. So if you are looking for something with a strong technical bent on python or finance this is not it.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.
An OK introduction to the subject of using Python for financial programming.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.Pros:Fun introduction to the subjectCons:Some of the information seems to be repeated
This book may be ok for the author's course, but to me it does a poor job with both finance and Python. The finance is a mishmash of concepts and tools. The Python part covers lots of stuff, in some cases way more than we need, but to no clear purpose. I've been teaching myself Python -- with lots of help from local experts -- and I thought C. Wen got it right: save your money, use the Python docs (most of them are pretty good) and Stackoverflow. Or check out the Sargent-Stachurski Quant Econ website. It's a little terse, but the content is great. We may do a similar site shortly focused on data and graphics: pandas, Matplotlib, etc.
Very good book. I appreciate it's timeliness. There are many typos and context errors, in this first printing. However it's a small price to pay for it being written on the most current version of Python. Simple and well done.
Amazing book with many interesting examples. Completely worth it
Completely lacking in structure. This is not a book so much as a set of post-it notes assembled in completely random order with sections repeated often and practically verbatim. How many times throughout the book can you repeat the process for downloading stock data from Yahoo Finance (for example)? I was really anticipating receiving this book since there is nothing really out there that's specifically targeted at Python for finance people (that I know of). The closest is "Python for Data Analysis" which is a very good book. I'm so disappointed. Author, please look at that book as an idea as to how to structure and organize a subject and then redo this.The reason I did not give this one star is that there are some good pieces of code in here to be found if you laboriously leaf through the book. I'm "Fighting Tribeca Irish"'s significant other (shared account).
The book doesn't follow a logical step by step process needed for someone who wants to learn a new language.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 InformationWrote in a logical step by step way to learn to program Python,
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