Initial foray into Data Science with Python

Professionally I have undertaken a major segue into Python from the usual Excel VBA development environment.

However this has remained very tightly linked to the quant library and specific business requirement solving as before.   So Iface the intresteing situation of nominally becoming a “Python Developer” without having the necessary interview answers or expected jobskills.

What I mean is that with Excel VBA work within Finance, and especially supporting the desks of Wholesale banks certain things are expected.   Working with Front Office designed sheets and integrating those and also the desk requirements into solutions using the IT designated strategic frameworks and technology.

So while you are expected to be very experienced / capable in Excel VBA itself it is understood that this will be back up with a large amount of esoteric and niche software across the “tech stack”.   Mainly IT developed XLAs / sheet utils and other “best practice” while using a normally mature (and highly guarded IP) layer of Quant library in some other language such as C++ / Java.

So getting things done is a case of elegantly adding in the solution to the spaghetti code thatis already there, without reinventing the wheel and / or making it any worse than it already is.    A majority of the heavy lifting on the data manipulation, trade access, market retrieval and manipulation,  valuation functionality will already be implemented bythe quant / strats function.

However the open nature of the python distribution and usage paradigm would make it insane for a bank to re invent / ignore numpy, pandas, scikit etc.   In my opinion not using the basic python libs available in Anaconda is madness.

So this leaves me with the strange situation that I am using python coupled with the quantlibrary to solve complex business problems without actually really using much of the available libraries themselves.

In effect I will be a 2 year Python developer ( on top of 12 – 15 y of financial software development ) without really being able to back that up.  Unless discussing highly proprietary Variable Annuity cases in any future project / interview.

Anyone who knows large Investment / Wholesale banking IT will know that picking up and running with some new ( to the bank ) technology and thinking isnt the done thing.  It is a configuration nightmare and makes a lot of senior people in both Front Office and also IT quite nervous.

New is bad, things should creep in slowly.   Well… Python has crept in and our small teamhas a chance to utilise most of its power as we see fit.   So I plan to familiarise myself with the Data Science elements via extra curricular development and hope that keeps my CV sharp while also providing a chance to try something interesting.