Python for Finance and over 2 million other books are available for Amazon Kindle . Learn more

Buy New

or
Sign in to turn on 1-Click ordering.
Buy Used
Used - Very Good See details
Price: £22.98

or
 
   
Trade in Yours
For a £8.09 Gift Card
Trade in
More Buying Choices
Have one to sell? Sell yours here
Sorry, this item is not available in
Image not available for
Colour:
Image not available

 
Start reading Python for Finance on your Kindle in under a minute.

Don't have a Kindle? Get your Kindle here, or download a FREE Kindle Reading App.

Python for Finance [Paperback]

Yuxing Yan
3.5 out of 5 stars  See all reviews (2 customer reviews)
Price: £27.99 & FREE Delivery in the UK. Details
o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o
In stock.
Dispatched from and sold by Amazon. Gift-wrap available.
Want it tomorrow, 23 Sep.? Choose Express delivery at checkout. Details

Formats

Amazon Price New from Used from
Kindle Edition £10.50  
Paperback £27.99  
Trade In this Item for up to £8.09
Trade in Python for Finance for an Amazon Gift Card of up to £8.09, which you can then spend on millions of items across the site. Trade-in values may vary (terms apply). Learn more

Book Description

25 April 2014 1783284374 978-1783284375

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.

Overview

  • 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

In Detail

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.

What you will learn from this book

  • 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

Approach

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.



Product details

  • Paperback: 408 pages
  • Publisher: Packt Publishing (25 April 2014)
  • Language: English
  • ISBN-10: 1783284374
  • ISBN-13: 978-1783284375
  • Product Dimensions: 23.5 x 19 x 2.1 cm
  • Average Customer Review: 3.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 478,088 in Books (See Top 100 in Books)

Product Description

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, 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.

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 high-frequency database). In 2007, Dr. Yan and S.W. Zhu (his co-author) 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.


Inside This Book (Learn More)
Browse Sample Pages
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Search inside this book:

What Other Items Do Customers Buy After Viewing This Item?


Customer Reviews

5 star
0
2 star
0
1 star
0
3.5 out of 5 stars
3.5 out of 5 stars
Most Helpful Customer Reviews
1 of 1 people found the following review helpful
3.0 out of 5 stars somewhat superficial 8 Jun 2014
Format:Paperback
This book might not serve well anyone seriously trying to get into the finance world by programming applications in Python. On a more optimistic note, the book might be better directed at an undergraduate who has read about topics like Black Scholes pricing of options. And who wants to quickly code some simple test programs. The back of the book says it is useful for practitioners. Nonsense. Practitioners in finance already have scads of far more complex programs for modelling.

Each chapter of the book basically is a simple rendition of code for the chapter topic. Somewhat superficial. As another reviewer perhaps accurately observed, it is as though the author just went through Wikipedia and dragged out several finance topics and the starting equations. And then coded those into chapters.

One good aspect is that the text describes how to use two standard Python modules, NumPy and SciPy. For numerical scientific and financial contexts. If you do end up using this book, you should absolutely learn how to use the routines in those modules. First, the modules have been extensively debugged and are being freely maintained. Second, you add more value for yourself by starting there rather than wasting your time recoding their routines. Though granted, an exception could be where you explicitly want to recode some of their routines as a learning experience for yourself.
Comment | 
Was this review helpful to you?
Format:Paperback|Verified Purchase
Quite thorough though.
Easy to follow.
Comment | 
Was this review helpful to you?
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com: 4.0 out of 5 stars  13 reviews
15 of 18 people found the following review helpful
2.0 out of 5 stars Save your money 2 May 2014
By C. Wen - Published on Amazon.com
Format:Kindle Edition
Really not very good. I was intrigued by the table of contents and the topics covered, but the depth of any one of those topics is so shallow that I imagine the author put this book together in a weekend using google and wikipedia.

I skimmed through it and it appears each topic gets about 1 page of coverage with a little bit of code and not much of an explanation on how it works. Additionally, there are no real algorithms here, and instead you get shown how the author would use some open source library (without any notes about the nuances of those libraries.)

If you've ever done anything in finance with python, this book is a waste of paper for you. If you are new to python perhaps there is some utility in seeing how python could be utilized. But the superficial coverage of the topics means you really aren't going to be able to just take the code and do anything meaningful yourself.

Save your money and just use stackexchange/google. I got the kindle edition and returned the book within 5 minutes.
6 of 7 people found the following review helpful
2.0 out of 5 stars Pasted together like a disparate set of post-it-notes 24 May 2014
By Fighting Tribeca Irish - Published on Amazon.com
Format:Paperback|Verified Purchase
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).
1 of 1 people found the following review helpful
4.0 out of 5 stars Short but Solid 26 May 2014
By Walter Reade - Published on Amazon.com
Format:Paperback
I jumped on the opportunity to receive a review copy of Python for Finance. I've been programming in Python for the last 2 years, and I'm particularly interested in technical computing.

Python for Finance is targeted for graduate students in finance, although I believe it is also appropriate for senior-level computer science majors who are interested using Python for financial computing.

Let me start with a few minor quibbles. First, I've never cared for how Pakt formats their books; it's clunky and seems dated. Second, there are a number of places in the book where the English is awkward and could have benefited from some decent editing.

Because Python for Finance is fairly short (about 350 pages of content), don't expect it to be the only book you'll need if you want to really learn Python. It will, though, get you started and up to speed with the language. About a third of the way through, the author introduces the NumPy and SciPy modules, which are essential for technical computing. Because of the scope of the book, these are given limited coverage, but enough to provide readers with an understanding of how powerful these tools are.

Python for Finance then moves into plotting, analysis of time series, and then jumps into option modeling, which is where the book really starts to tie things together, including various tools to pull financial information from the web. A chapter on the ins and outs of Python loops precedes what was my favorite chapter - Monte Carlo Simulations and Options. This chapter really highlights the power of Python for technical computing.

Overall, I enjoyed Python for Finance. I learned new tools and techniques in Python as well as a significant amount about financial computing.
1 of 1 people found the following review helpful
5.0 out of 5 stars Not God's Gift to Finance or Python But Useful Nonetheless 10 Aug 2014
By AlexT - Published on Amazon.com
Format:Kindle Edition|Verified Purchase
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.
1 of 1 people found the following review helpful
3.0 out of 5 stars somewhat superficial 8 Jun 2014
By W Boudville - Published on Amazon.com
Format:Paperback
This book might not serve well anyone seriously trying to get into the finance world by programming applications in Python. On a more optimistic note, the book might be better directed at an undergraduate who has read about topics like Black Scholes pricing of options. And who wants to quickly code some simple test programs. The back of the book says it is useful for practitioners. Nonsense. Practitioners in finance already have scads of far more complex programs for modelling.

Each chapter of the book basically is a simple rendition of code for the chapter topic. Somewhat superficial. As another reviewer perhaps accurately observed, it is as though the author just went through Wikipedia and dragged out several finance topics and the starting equations. And then coded those into chapters.

One good aspect is that the text describes how to use two standard Python modules, NumPy and SciPy. For numerical scientific and financial contexts. If you do end up using this book, you should absolutely learn how to use the routines in those modules. First, the modules have been extensively debugged and are being freely maintained. Second, you add more value for yourself by starting there rather than wasting your time recoding their routines. Though granted, an exception could be where you explicitly want to recode some of their routines as a learning experience for yourself.
Were these reviews helpful?   Let us know
Search Customer Reviews
Only search this product's reviews

Customer Discussions

This product's forum
Discussion Replies Latest Post
No discussions yet

Ask questions, Share opinions, Gain insight
Start a new discussion
Topic:
First post:
Prompts for sign-in
 

Search Customer Discussions
Search all Amazon discussions
   


Look for similar items by category


Feedback