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I expect this one will be better:


Looks interesting. Can you elaborate on why you think it will be good? Glad to see they're using Pandas.

I am the author of the O'Reilly book.

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):


Looks very interesting. Is the table of contents final? I didn't see any mention of fixed income. Will this book serve as a good intro for a programmer from the equities world (no specialized math or finance training)?

No, the TOC is not final, but mainly. It is true, the examples are more from the equities world. However, the approach is not to show Python for equities, fixed income, commodities, trading, risk, etc. It is to show Python (technical) topic-by-topic (data, viz, IO, Excel, Web, etc.).

Adding to this: yes, the book should serve as a good intro to Python for people with an equities background. But it should also useful for people with a different financial background.

Thanks, I actually meant how useful this book will be for professional programmers who want to understand financial math? (btw, I actually bought your book already :) )

Is there a list I can subscribe to, to be notified when the book is finished?

Best to follow me on Twitter -- @dyjh

This book is written by Yves J. Hilpisch a premier figure in the big data python world and great teacher. He has another more advanced book, which I loved, called Derivative Analytics with Python[0] and has given many well known and received talks.

Note, I am Continuum Analytics[1] 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.

[0] http://www.python-for-finance.com/ [1] http://continuum.io/

The author is pretty well known in the (very narrow I suppose) circle of quants using Python. He has a company working in the field[0] and also works for Continuum Analytics[1] which are the guys doing very interesting things in PyData community.

[0] http://www.derivatives-analytics.com/ [1] http://continuum.io/our-team

Continuum make a free alternative python distribution, Anaconda, which is listed on python.org as an alternate. They also sell other python tools on top of Anaconda.

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.

For windows, I have been using python(x,y)--it's huge, but it works :-)

By the same author, "Derivatives Analytics with Python":


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