The Hidden Cost of a Good Idea - Where Quant Research Loses Time
From Research to Live Markets · Part 1
Why this series
DefconQ has always been about making difficult technical ideas easier to understand and, more importantly, useful. Much of that conversation has centred on KDB/Q. With From Research to Live Markets, I want to widen the lens to the investment decisions those systems support.
This recurring series will explore what happens between an interesting research question and a system that can act on the answer. We will look at market data, backtesting, real-time risk and the engineering choices that connect them. The aim is to give researchers and engineers a better understanding of each other’s problems, with examples they can question, reproduce and use.
Financial engineering spans pricing, risk, numerical methods and much more. Here, I am particularly interested in the point where financial reasoning meets implementation. A model’s assumptions have to survive the data pipeline, the portfolio constraints and the trading environment.
The right tools make that work easier. The right architecture makes it repeatable. Getting either wrong can consume expensive research time, produce misleading evidence or expose capital to decisions nobody intended to make.
For the first article, let’s start with a deceptively simple question: where does the time actually go?
