Engineering doctrine for systems that handle money.
Everything below was learned the only way that counts — operating our own trading systems in production. It's the doctrine we apply to client work because it's the doctrine our own capital already depends on.
Validate before you trust
Every strategy claim is treated as hostile until it survives purged walk-forward testing, deflated performance metrics, and regime-segmented analysis. The most valuable deliverable we produce is sometimes the word "no" — before the market says it more expensively.
Point-in-time or it didn't happen
Research data must reflect exactly what was knowable at decision time. As-of joins, corporate action adjustment, calendar correctness, and lineage from raw feed to feature — boring, decisive, non-negotiable.
Engineer for the bad day
Systems are designed around the volatility spike, the feed outage, and the 3am broker disconnect — not the demo. Health checks, reconciliation, circuit breakers, automated recovery, and alerts that reach a human when judgment is required.
AI-accelerated, human-judged
We use AI agents aggressively for code generation, test synthesis, and review — it's why a small practice ships at team speed. But every mathematical decision, every risk parameter, and every line that touches an order is verified by a human who understands the consequences. Tests prove correctness; the AI just gets us there faster.
Paper before production, always
No method graduates to real capital until it has run end-to-end under production conditions. We hold our own systems to that gate, and we'll hold yours to it too — even when it's tempting to rush.
Transfer everything
Code, infrastructure, documentation, runbooks, and a recorded walkthrough. Dependency is a consultancy business model; it isn't ours. The platform is how we scale — not lock-in.
The doctrine is live in The Lab.
Three production systems run under these rules every market day. Then bring us your stack and we'll apply the same standard.