I feel Quants and others around them don’t always point out the real difference between Traders, Coders and Quants aka strategists. In this context we have to.
Once secDB core is built, SecDB is “programmed” in each desk by regular software developers, not secDB core team. Business end-users are risk managers and traders, majority of whom are probably not trained for or fascinated by Slang or python.
In terms of formal training, regular software developers are trained in engineering; quants in math; traders in investment. (Think of prop trading, to keep things simple.)
However, some quants qualify as software developers. (Actually, most c++ analytic libraries are owned by these part-time developers:-). Some sharp minds in the camp eventually realize 1) the inherent dependency graph in this world, and then rediscover 2) OODB. Put the customized quantitative dependency-graph-aware objects into an OODB and SecDB is born. Grossly oversimplified, but at this juncture we have to swallow that in order to wrap our mind around a monstrous concept, step back and see the big picture. (signal/noise ratio and focus badly needed.)
Another article said secDB “allows Goldman’s sales force and traders to model, value and book a transaction”, confirming that model and valuation are among the first goals of secDB.
Quants create this platform for themselves (ie for quantitative research, back testing, model tuning…), and then persuade developers to adopt. In such a world, quants are the law-makers. Power shifts from traders and IT to quants. In the traditional world, IT resembles the powerful equipment manufacturers of the dotcom era.
Now I feel the creators of SecDB may not be the typical desk quant, risk quant or strategist. He or she might be a quant developer
I guess Quants’ job is to analyze 1) financial instruments and 2) markets, and try to uncover the quantitative “invisible hands”. (In http://bigblog.tanbin.com/2011/11/more-information-you-have-less.html, I question the value of this endeavor.) They _numerically_ analyze real time market data, positions, recent market data (all fast changing), historical data, product data…. Too much data to be digestible by business, so they condense them into mathematical “models” that attempt to explain how these numbers change in relation to each other. Classic example is the term structure and the BS diffusion model. A proven model acquires a life of its own —
– become part of a trading strategy
– sell-side often offer these models as value-added services to hedge funds. I guess they can send trading signals or digested, derived data to hedge funds.
– risk mgr are often the #1 user of these models. Risk mgr and quants validate models by back testing.