Quant trading from research to production.
We build research platforms, data pipelines, execution tooling, and controls around repeatable quantitative work.
Teams we have worked with
What we do
Research platforms
Reproducible datasets, experiments, parameters, artefacts, and comparisons from notebook to review.
Market and alternative data
Ingest, normalise, timestamp, validate, and version the data behind every result.
Execution systems
Observable order and strategy services with explicit limits, recovery, and reconciliation.
How we do it
- 1
Freeze the evidence
Version the data, code, assumptions, and market knowledge available to the test.
- 2
Challenge the result
Test leakage, costs, stability, sensitivity, and alternative explanations.
- 3
Run in shadow
Compare live intended behaviour with the backtest and execution reality.
- 4
Scale behind limits
Increase exposure only as performance, operations, and controls agree.
We treat reproducibility and operations as part of the model.
Point-in-time truth
The training and backtest view contains only what would have been known at the decision time.
Evaluation before promotion
Leakage, costs, stability, regimes, and operational assumptions tested before a strategy advances.
Limits before autonomy
Position, exposure, venue, loss, and kill controls live outside the strategy that they constrain.
What you'll achieve
Research assistance
Generate and revise analysis inside the firm's libraries, conventions, tests, and review process.
Unstructured signals
Turn filings, transcripts, news, and specialist sources into timestamped, attributable features.
Operational investigation
Join logs, orders, fills, limits, and deployments so incidents can be reconstructed quickly.
Case studies
We have done this before.
Common questions
Do you build low-latency systems?
We can build performance-sensitive data and execution services, with the latency target set by the strategy and venue rather than assumed upfront.
Can you improve an existing research environment?
Yes. Reproducibility, data lineage, experiment tracking, test harnesses, and promotion workflows can be added without replacing every researcher tool.
Where do language models fit in quantitative research?
They are useful for coding assistance, document extraction, retrieval, and hypothesis preparation. Results still have to be reproduced and tested by deterministic systems.
Services for quantitative trading teams
Tell us what you need built.
A 20 minute call is enough to work out whether a pod fits. The first week of work is defined on that call.