Trading-model research platforms, end to end. Not just models — the framework, the schedulers, the registries, and the dashboards that turn a quant's hypothesis into a monitored production strategy.
$ rb run alpha.xs_momentum --universe HK ▸ resolving features ok 42 cached, 3 built ▸ training ok walk-forward · 12 folds ▸ evaluation ok sharpe 1.83 mdd -7.4% ▸ registry ok model xsmom@2026.05.15-r3 ▸ canary deploy ok 5% paper, monitor 7d $ ▌
Research platform & alpha-model framework
Feature engineering & data pipelines
Training, registry, deployment, monitoring
Cloud, containers, observability, CI/CD
Quant + trader feedback loops
The framework you spend most of your day in. Declarative model definitions, walk-forward CV, leakage-safe feature evaluation, and a benchmark grid that lets a quant compare three weeks of model variants over coffee.
Treat features like a build system: declarative dependencies, point-in-time correctness baked in, content-addressed caching, and the same graph at training time and serving time.
The production half of the loop: the same feature graph that ran in research, now consuming a market-data stream and emitting signals into the trading stack — with point-in-time guarantees, lineage, and a kill switch.
A self-hosted model registry that's the source of truth for what trains, what ships, and what's running. Each model carries its full lineage — code commit, feature graph hash, data window, owner, evaluation report — and the production platform refuses to load anything it didn't register.
A small library and a sidecar that let a researcher prototype freely in a notebook, then promote the cell that worked into a versioned, reproducible pipeline — without rewriting it. The notebook is the IDE; the platform is the runtime.