We want a wide coverage and automation depth knowing that this has trade offs. We know that user interaction is indispensable in the constructor phase as well as progressive problem phase, where tasks work with real data. But to what extent?
It's now time to talk about the resolution of all that conflicts that may even turn into a crisis.
And in the end it's not so difficult.
We want extendability, technologies that help to fix what they broke and build skills from using the innovation.
This principle solutions represent our Ending Payoff:
- Provide ready to use solutions that are development systems in one
- Provide know how packages
ad 1.) Provide technologies that empower the extension of the algorithmic engines and the programming language in which you program your tasks.
UnRisk Engines extend the Wolfram Engines into the universe of derivatives and structured financial products and the UnRisk Financial Language is built atop the Wolfram Language to program cascades of valuation and risk management tasks. UnRisk services tie things together by scheduling the tasks and build communication layers for interactive front ends.
There's one detail that I find essential: all (point&click) interactions at UnRisk solutions are logged as a UnRisk Financial Language program that can be re-fed into the system to detect input errors or test input scenarios quickly.
Quant developers use UnRisk to extend it into their universe of investment and risk management...
ad 2.) Provide not only use training, but give full explanation on the theories, models, methods…and critical implementations…disclosing all assumptions, oversights…
For packaging know how, we've established the UnRisk Academy.
Is UnRisk so specific?
No, it's a reference - we've used the same principles for building an offline and online quality management system for the hot rolling of heavy steel plates. Computational engines wrapped by a domain specific, task-oriended language, services managing the engines and build communication layers for front-ends, APIs…
What technology you should build your innovation atop depends on the innovation type…"Wolfram" is just another reference…whatever technology stack you choose for transformative developments, it helps if it provides a symbolic, declarative, multi programming paradigm...language, a wide coverage of algorithms, mechanisms for hybrid (multi language) programming, fast engines that implement the language, data management, document formats, front-end builders…
Constructors-Progerssive Problems-Solutions the building blocks of the quant innovation determine the effectiveness, efficiency, completeness, consistency...of your innovation...It's vital that they avoid/minimize operational risk…they determine whether it does work or doesn't.
The very detail, the language design, the interaction patterns, the navigation…the result representation…determines whether it sells.
In the next post I'll present the simplified Quant Innovation Mesh 2.0.
And later I'll think more about how the insight gained from applying the scheme will help optimizing the Market Risk...