Platform

An evidence system around the algorithm

Research, validation, deployment and telemetry share a coherent operating model so that every promoted change can be inspected and explained.

From hypothesis to observed operation

The Alpha Workbench is not a single strategy and does not turn raw optimisation output into deployable alpha. It coordinates the evidence and decisions required to move responsibly through the lifecycle.

01DiscoverOptuna and structured candidate surfacing

02ValidateWalk-forward, embargo and robustness checks

03ApproveRecorded promotion and human accountability

04DeployRepeatable shadow and live packaging

05ObserveTrade, heartbeat, drift and incident telemetry

High-level technology

Designed for production operation and portability.

The architecture favours replaceable components, auditable state and a controlled path from one live container to a resilient fleet.

Experience	Alpha Workbench, operational dashboards and daily evidence reporting
Application	Python analytics, Java 21 trading runtime and REST services
Research	Optuna, pandas, scikit-learn, clustering and statistical validation
Data	PostgreSQL 17, governed operational schemas and durable run/trade telemetry
Runtime	Docker Compose today, with a controlled Kubernetes migration path and CI/CD-ready packaging
Observability	Prometheus, Grafana Cloud, heartbeats, incident tracking and alerting
Infrastructure	Managed compute and database services, TLS ingress and restricted network access

Strategy code, parameters, credentials and detailed network topology are intentionally excluded.

Operational visibility

The system is designed to be supervised.

Live and shadow positions, optimisation progress, market-data health, fleet heartbeats, incidents and infrastructure alerts are surfaced through dedicated operational views.

Lucitech operational monitoring showing database health, Prometheus metrics and live currency feeds — Operational monitoring across managed database health, Prometheus collection and market-data feed availability.

Roadmap

Scale only after repeatability is demonstrated.

PROVE · 0–6 MONTHS

Operational evidence

Grow live-versus-shadow evidence, harden BAU maintenance, expand the micro-lot fleet and publish a governed case study.

PACKAGE · 6–12 MONTHS

Repeatable delivery

Standardise private deployments, formalise support and controls, and migrate selected workloads toward Kubernetes where resilience justifies it.

SCALE · 12–24 MONTHS

Transferable operation

Support a multi-client model, broader adapters, repeatable commercial channels and selective specialist capacity.