We design, build, and operate the machine learning, execution, and data infrastructure behind small quantitative trading and analytics teams. The same stack we run at Chemily, available to a handful of clients each year.
From signal research to live execution, we cover the full quantitative stack. The same engineers write the model, ship the pipeline, and watch the dashboards at 4am.
Custom ML pipelines for forecasting, classification, and regime detection. Feature engineering through model serving, all reproducible, all in version control.
Execution systems with walk-forward backtests, paper trading, and live deployment. Risk controls and circuit breakers are part of the design, not the cleanup.
Real-time pipelines and warehouses for market, alternative, and operational data. Clean inputs are why our models behave; we spend most of our time here.
Architecture review and migration support across AWS, GCP, and Azure. We help teams ship faster without paying for unused capacity or accidental complexity.
Our execution stack runs on cloud infrastructure with native AI inference. Every signal passes through ingestion, feature computation, model scoring, risk checks, and routing, observable end to end.
Backtests, latencies, error rates. The list below describes what we design for and how we operate, not a marketing P&L.
Quantro Analytics is the quantitative trading arm of the Chemily group. We were spun out so the trading work has its own legal entity and risk surface, but we share engineering practice and tooling with chemily.ca, which is run by the same team.
That shared practice covers machine learning models, automated execution systems, data pipelines, and cloud architecture. It is the same stack we have been operating in production at Chemily, now offered to clients who need a small, senior team to build similar systems for them.
We are based in Markham, Ontario, in the Greater Toronto Area. All engagements are run directly by the founders and engineers, not through a sales layer.
Most of what we ship is judged by numbers, not opinions: backtests, latencies, error rates. The code that runs in production is the same code we wrote during research, plus version control, observability, and tests. When trade-offs come up we usually pick risk control over a flashier number, because the cost of a bad day compounds for a year.
We take on a small number of new client engagements each year. If your team is exploring quantitative strategies, building a data pipeline, or rethinking your cloud architecture, we would like to hear about it.