Open Source in Finance Forum London 2026

Building quantitative analytics often requires rapid prototyping and frequent iteration. In practice, models are developed in isolated environments using inconsistent tools, creating inefficiencies, governance risks, and performance limitations that lead to costly rewrites when scaling to production. This session describes how we adopted a platform-engineering approach to model development and execution, enabling high delivery velocity without sacrificing architectural control.

Rather than treating each model as a standalone implementation, we built a standardised model execution platform with semantic data access, reusable runtime services, and specification-based model definitions. The platform supports scenario analysis, back-testing, and interchangeable data sets, with performance, reproducibility, and governance built in by design.

The talk shows how re-engineering the model lifecycle around a shared platform removed repeated rewrites and enforced consistent architecture across teams. Specifications are executable and runtime behaviour is standardised. Testing, performance, and data provisioning are built into the core, enabling scale and keeping engineering discipline.