You can find a commentary on that book here on Reddit.
I hope this book is better, but I will withhold judgement until someone comes along who's read it and is able to give an informed opinion.
I would probably call the selection of topics "finance basics that no one in industry uses any more".
It may be suitable as a beginner's guide if the quality of the exposition is good enough, but my suspicion is that you would be better off getting a copy of "Python for Data Analysis" and a decent quant finance textbook.
Fama and French are still industry leaders, and they use the three factor model as the foundation of all of their portfolio management at Dimensional Fund Advisors, hands down the most successful mutual fund in existence. http://us.dimensional.com/process/multifactor.aspx
They continue to out-perform active fund managers, and they power a boat load of the investments for many American Corporations. And those American Corporations love to watch their money grow.
What makes you think these models don't still power successful financial products today?
The concepts of the three factor model are important to learn but implementing one in practice is rarely done. These core factors are too crowded these days as all the quant funds are looking at the same factors.
The very example you gave to support your point actually detracts from it.
The goal of this book is clearly not going to be to teach you finance but rather to provide you with enough of the basics and beyond so you basically have all the tools needed to move forward.
Was very pleasantly surprised to see some of the stuff in the table of contents (ie. Pastor and Stambaugh's liquidity measure).
Will order this as I think it'll be a great reference.
But there are attempts at it. Most notable are:
- "An Introduction to the Mathematics of Financial Derivatives", by Neftci
- Wilmott books aren't bad.
- Brigo's "Interest Rate Models" is... flaky. It is a lot of material and seems to be quite rigorous, until some point most crucial for understanding, which gets skipped over. The interviews with traders at the end are good.
- Only buy Choudhry books, if you want to talk good about finance.
I'm amazed that GT let him teach that course. It's full of errors, omissions and bad habits. I vaguely followed along, and the most noticeable thing was how much the students complained about the low quality of the course.
The only result of someone implementing the advice in that course is that they'd lose a lot of money quite quickly.
Happy to go into more details by email.
The book mainly covers chapter by chapter "all you need" to do Finance with Python. From data structures, performance Python, (Bayesian) statistics, stochastics to Excel integration and Web technologies.
It also provides -- in addition to many smaller examples and use cases -- a larger case study about a complete, integrated derivatives pricing library.
Here the table of contents as it stands now (work still in progress! Early Release covers chapters 4-7, 1-3 and 8 will be added soon):
Note, I am Continuum Analytics alumni, of which is Yves J. Hilpisch is the European director and whose CEO was the lead contributer to NumPy for many years.
So my endorsement is not against Packt's Python for Finance but rather for O'Reillys Python for Finance based on previous work and experience of the Author.
In addition to the many, many libraries included with Anaconda, it installs ipython (and ipython notebook). I believe using Anaconda is among the easiest ways to get ipython on Windows, and you get all the other libs too.
: http://www.gpo.gov/fdsys/pkg/GPO-CONAN-2013/pdf/GPO-CONAN-20... -- http://www.gpo.gov/fdsys/pkg/GPO-CONAN-2013/content-detail.h...
PDFOPT='-x 0 -y 160 -W 556 -H 553 -nopgbrk -layout'
pdftotext $PDFOPT pdfs/GPO-CONAN-2013.pdf out.txt
Once I have the converted it to markdown I am going to use pandoc for output formatting.
Are you familiar with CONAN? It is an amazing work of scholarship, I don't know how to express my appreciation without resorting to hyperbole. It is a shame that it is distributed in a format that does not allow it to shine like it could. There are some neat legal citation extraction scripts in node.js that could lead to a really useful web version. I look forward to the day I can add it to my kindle and read it from start to finish.
Packt says that their ebooks are DRM free in the "Pactk Facts" section here: https://www.packtpub.com/about
Like other tech publishers, they frequently have discount codes on their ebooks, though $20 doesn't seem expensive if the information is useful.
They have very light DRM: they print your name (or whatever you give them as your name) in the book. There are no copy prevention measures, they're just straight pdf or other format.
It's actually a nice compromise.
I have a coy of Haskell Financial Data Modelling, which someone else had asked about.
I don't want to be too negative, but I didn't get too much out of this book. It moves pretty slow when explaining both Haskell and the financial content. To be fair, this might be considered an advantage if you are new to both.
It also doesn't really give you alot of "actionable code" that you can drop into an existing system. Each chapter is an introduction to large subjects, so I guess by definition the author can't dive very deeply.
In contrast the posted book seems to cover a bit more ground and seems to include on graphing and practical applications of black scholes, which is nice.
I'll post a review once I've read it.
I don't know the book, but anyone interested in financial modeling in Python should check out https://datanitro.com/ and their Excel API https://voyager.datanitro.com/. Definitely a big improvement over Visual Basic macros.
I show how to use Python, free software, as a financial calculator to estimate
PV, FV, PV of annuity, to estimate effective rates, beta and more.
It's not yet really functional, but i've been working on it for a while, and it scrapes yql and some morningstar pretty well.
It is in a similar vein: python + finance. Based on the depth of the blog posts, I expect a lot of good content.