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 :) )
