Technology & systems
Engineering for dependable research.
Our technology philosophy favors simple, reproducible, and reliable systems — built around Python and a disciplined approach to data and automation.
Technology stack
The research stack
How the layers of the stack relate to one another, from raw data at the base to the interfaces on top.
- 01DataData Pipelines
- 02Research environmentResearch InfrastructurePython
- 03SystemsAlgorithmic SystemsBacktestingAutomated Workflows
- 04InterfacesAPIsBroker Connectivity
Disciplines
Tools and systems
The technologies and system disciplines that shape how we work. We describe our approach honestly and do not imply that any specific system is operational beyond what is stated.
- Python
- The primary language for research, modelling, and analysis across our work.
- Data Pipelines
- Automated flows that ingest, clean, and structure data for downstream use.
- Research Infrastructure
- Reproducible environments, versioning, and tooling for systematic research.
- Algorithmic Systems
- Systems that encode rules and logic into consistent, repeatable processes.
- Backtesting
- Frameworks for evaluating ideas against history with discipline and care.
- APIs
- Clean interfaces for integrating data, models, and services.
- Broker Connectivity
- Integration points for order routing and execution where relevant.
- Automated Workflows
- Scheduled, monitored processes that reduce manual effort and error.
Engineering principles
How we build
Reproducibility
Every result should be traceable to its data, code, and parameters.
Reliability
Systems should fail safely and behave predictably under load.
Simplicity
Prefer the simplest design that meets the requirement.
Security
Handle data and credentials with care by default.
Start a conversation
Let’s put data to work on your decisions.
Whether your interest is research, analytics, technology, or education, we’d welcome a conversation.