Twenty years in physical commodities and LME futures trading — from trade administration through to Director — built a deep operational understanding of high-stakes, time-sensitive, consequence-heavy environments where traceability, precision, and speed of decision are not optional.
In parallel: a self-taught builder. A simple trade logging platform built in 2004 to automate daily contract processing was an early instinct that tooling could remove operational friction — one that has compounded into two serious software projects now in active development.
Currently building Pea, a governed agent runtime designed for enterprise environments that require auditable, memory-aware, and controllable AI infrastructure — and Decentre Studio, a generative creative suite with a patent-pending novel brush stroke synthesis engine. Both are self-funded, solo-built, and architecturally non-trivial.
The thread connecting twenty years of trading, two decades of self-directed learning, and two ambitious software builds is the same: a contrarian instinct for finding where existing systems are suboptimal and the persistence to build something better.
Building two concurrent software products from zero, solo, self-funded:
Pending USPTO patent application covering the generative stroke synthesis process and stroke tokenisation method.
Current trading platform side project: rules-based signal system backed by a multi-LM consensus machine — not autonomous trading but signal generation with human execution.