Python for Finance: Analyze Big Financial Data 1st Edition

by Yves Hilpisch (Author)

58 ratings

ISBN-13: 978-1491945285

ISBN-10: 9781491945285

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Python for Finance: Mastering Data-Driven Finance
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The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:

Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

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Editorial Reviews

About the Author

Yves Hilpisch is the founder and managing partner of The Python Quants, an analytics software provider and financial engineering group. The Python Quants offer, among others, the Python Quant Platform (http://quant-platform.com) and DX Analytics (http://dx-analytics.com). Yves also lectures on mathematical finance and organizes meetups and conferences about Python for Quantitative Finance in New York and London.

Product details

ASIN ‏ : ‎ 1491945281
Publisher ‏ : ‎ O'Reilly Media; 1st edition (January 6, 2015)
Language ‏ : ‎ English
Paperback ‏ : ‎ 606 pages
ISBN-10 ‏ : ‎ 9781491945285
ISBN-13 ‏ : ‎ 978-1491945285
Item Weight ‏ : ‎ 2.27 pounds
Dimensions ‏ : ‎ 7.01 x 1.22 x 9.17 inches

Best Sellers Rank: #747,821 in Books (See Top 100 in Books)
- #504 in Data Modeling & Design (Books)
- #683 in Business Finance
- #898 in Python Programming

Customer Reviews:
58 ratings

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1 star		7%

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J.T.

Code examples have become dated

Reviewed in the United States on April 6, 2017

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Code examples are quickly becoming outdated. You will spend a decent amount of time just trying to figure out why the code isn't working then searching for current code to replace the dated stuff.

33 people found this helpful

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Jim

Not a good book. Find another one

Reviewed in the United States on June 11, 2018

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This book will drive you crazy. So much of the code has variables that have not been created prior to being referenced.
Then you have to figure out what they are and what their value and type should be. Also it looks like most of the code is entered using a terminal instead of creating a module with something like Spyder. A real pain.

3 people found this helpful

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William P RossTop Contributor: Architecture

Suitable Financial Resource But Needs Work On Python Parts

Reviewed in the United States on June 3, 2016

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This book addresses a niche audience who is experienced in financial analysis but has little programming experience. In the first few chapters we are introduced to Python and why it has become popular in the financial field. Some sample code is shown for how you would setup an options simulation using Monte Carlo methods.

The middle chapters cover different Python libraries which are useful in finance such as Numpy and Pandas. The later chapters cover financial simulations again. I did not really understand this ordering of the chapters. It felt like the book dived too fast into simulations, then took a bunch of steps back to cover Python, and then switched back to financial discussion.

Python 2.7 is the language used in this book along with the IPython interactive prompt. I did not understand this decision at all either as Python 3 and files would have been a lot better to stay up to date with current programming practices.

Some of the programming practices mentioned were just plainly inaccurate in certain cases. For example there is the reduce function mentioned on page 92 where the author says: "reduce helps when we want to apply a function to all elements of a list object that returns a single value only." Reduce is not available in Python 3 because it is now deprecated. I went on StackOverflow to learn more about reduce and the top rated answers said that you should not use reduce anymore, and that in 99% of cases it is better to write out a loop for this functionality instead.

I found the author to recommend the non-Pythonic way in a number of different places. In chapter 13 on object orientated programming the author briefly describes how you can make variables private in Python by adding an underscore. He says (p.385): "It might be helpful to have (class/object) private attributes". I thought this was a poorly worded description that does not describe the Pythonic way at all.

Some sections use repetitive wording often. For example, in the first couple chapters the author uses the expression "On one hand... On the other hand" at least 5 times. I think the author needed to tell a better story overall and work on sentence structure.

The financial code sections seemed like they could be really useful to someone in the industry. I do have enough previous experience with the options models to say how accurate these parts are. The book does cover both the European and American options models. Sections about Monte Carlo simulations were helpful.

Overall, this book could be really useful to someone in finance that has not programmed much in Python. I think this book would have worked a lot better to have assumed that the reader is familiar with Python, and then go more in depth on practical applications.

48 people found this helpful

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Robert Bates

good intro into using Python + Pandas to analyze financial ...

Reviewed in the United States on August 7, 2017

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Required reading for a quant trading course, good intro into using Python + Pandas to analyze financial data.

2 people found this helpful

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andrew

Well written Python intro

Reviewed in the United States on September 16, 2015

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The intro, which is normally worthless in textbooks, was informative. I only wish some of the textbooks that I used in grad school had been so well laid out. I haven't found any glaring errors, which I usually find in most of my texts, but that may come at a later time when I'm going over something I've already read.

As someone who started using Matlab and R while getting my MBA in finance, I am finding this book to be very helpful in gaining a working knowledge of the other programming languages that I can use in my work and play.

8 people found this helpful

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Scott A. Guillaudeu

Clear explanations and great examples

Reviewed in the United States on October 22, 2016

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It's worth the price for Chapter 8: Performance Python alone. The whole book is easy to read and gives nice clear explanations for some very complicated topics.

6 people found this helpful

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Herbert

"Python for Finance" is a very useful reference and guide that merges the two subjects very ...

Reviewed in the United States on October 17, 2015

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About a year ago, I took an online intro to Python Programming course and got hooked on PYTHON. a few weeks later I took another course in Finance Engineering. This book, "Python for Finance" is a very useful reference and guide that merges the two subjects very well. Python is the open source tool that can be used to study many areas of finance. You will be pleasantly surprised how easy the python coding is. My Finance Engineering used "R-Programming" examples, but those same examples can be done in Python and some of those routines are in this book.

5 people found this helpful

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Was trying to learn more about how to better make use of our data sets

Reviewed in the United States on March 23, 2016

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Was trying to learn more about how to better make use of our data sets. This has been a pretty good review of best practices and we are making use of some of the ideas already. Thanks

2 people found this helpful

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Hari Rajapakshe

Just does what it says in the title.

Reviewed in the United Kingdom on April 17, 2015

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This book gives an excellent all-round and practical insight into what it says in the title, “Python for Finance”. It will not make you into an expert of Python nor quantitative finance. However, it provides some real gems of insight of how to do things efficiently and succinctly; Yves provides a holistic theme of using several Python frameworks in a consistent way, and illustrates ideas using progressively evolving examples.

Really great thing about this approach is that one can be productive learning concepts (if you’re new to quant finance), learn how to implement these concepts in python (if you’re new to Python), and make use of some great frameworks of Python (if you’re new to frameworks; eg: numpy, Pandas, IPython notebook…) via building practical examples that you can test real-world problems from the day one. I’ve been in quant finance for many years, but relatively new to Pandas and some of the Python frameworks; without a doubt, Yves’ book managed to save me many weeks of effort having to Google, read other analysis/blogs/opinions etc...

In summary, the book provides you tons of useful insights, and other references for you to read about, and to consolidate your knowledge of Python and quantitative methods for finance; whether you’re an expert in either areas, or neither areas…. If you’re an expert in both, the book will convince you of your know-how, and will still give you useful references to keep your knowledge up to date. A brilliant buy.

9 people found this helpful

Steve Bell D.Phil

Helpful book for Python learners

Reviewed in the United Kingdom on November 2, 2016

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If you know some quantitative finance and would like to switch from using Excel or VBA this is a very helpful book. It covers all of the essential topics such as data types, visualisation and numerical Python (NumPy). Unusually for a beginners book it covers Pandas very early on. Again, this is helpful as Pandas is such a powerful tool for financial time series analysis and puts Python on a level with R. Some of the explanations of Python syntax are a bit shallow but combined with online resources at the Python Software Foundation you'll get working with Python for finance quickly. I also found the layout of the book off-putting at times: it has pages of code in some parts with no explanation. Surely this could have been placed on a website? In the best part of the book Hilpisch shows the reader how to use the Pandas DataReader for downloading financial data from the web.

One person found this helpful

Amazon Customer

Great book, do make use of the online tools ...

Reviewed in the United Kingdom on January 18, 2017

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Great book, do make use of the online tools as well and work through the examples. A great way to pick up python, from a finance background and in a hurry. Good core reading.

Covers much of Numpy, Pandas, Scipy, Matplotlib.

One person found this helpful

Pedro Jimenez-Delgado

It is a good book for the price and kind of a standard ...

Reviewed in the United Kingdom on May 16, 2017

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It is a good book for the price and kind of a standard in the matter, good place to start, do not expect truly deep things though,from the finance point of view it is just the essentials

Robin G.

Five Stars

Reviewed in the United Kingdom on October 12, 2016

Verified Purchase

Amazing and hugely useful book. I use it as a ref all the time now.

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