Data engineering
Pipelines for historical and live market data with validation, versioning and gap detection.
Algorithmic trading R&D
Quantitative research with realistic assumptions
We help traders, teams and businesses turn trading ideas into testable hypotheses and robust research infrastructure — from clean data and reproducible experiments to simulation and execution engineering.
Research pipeline
From hypothesis to controlled deployment
Each stage has a purpose: to find out, as cheaply and early as possible, whether an idea deserves to go further.
Hypothesis
State what market behaviour you expect and why — before touching the data.
Data
Source, clean and validate data; check for gaps, survivorship bias and look-ahead leakage.
Rules & features
Define decision rules and features with as few free parameters as possible.
Backtest
Event-driven simulation including trading costs, slippage and realistic fills.
Validate
Out-of-sample, walk-forward and parameter-stability tests; stress tests across volatility regimes.
Forward test
Paper or demo trading to compare live behaviour with simulated expectations.
Controlled deployment
Small-scale, monitored roll-out with predefined stop conditions.
Capabilities
Research and engineering capabilities
Backtesting infrastructure
Event-driven engines that model costs and fills, with reproducible configurations.
Simulation & stress testing
Monte Carlo resampling, regime analysis and scenario tests to probe robustness.
Execution engineering
Order routing, position management and latency-aware design matched to the strategy's horizon.
Risk modelling
Exposure, drawdown and position-sizing frameworks with explicit, documented limits.
AI-assisted research
Machine learning for feature discovery, regime classification and anomaly detection — validated with the same rigour as any model, under human oversight.
Honest limitations
What a backtest can't tell you
Backtests are simulations built on historical data and assumptions. They cannot capture every real-world condition, and they are vulnerable to overfitting — tuning a model to past noise rather than durable behaviour.
We treat a backtest as one piece of evidence, never as a forecast. Results are reported with their assumptions, data periods, costs and known limitations so they can be interpreted responsibly.
- Past performance does not predict future results
- Small parameter changes can change results dramatically
- Live execution differs from simulated fills
- Market regimes change; models can stop working
- Data errors can create false confidence
Automation architecture
Infrastructure designed for reliability
Event-driven design
Components react to market and order events, keeping logic deterministic and testable.
Observability
Structured logs, metrics and alerts for every decision the system makes.
Security
Secrets kept out of code, least-privilege API keys and isolated runtime environments.
Configuration control
Risk parameters versioned separately from code, with change history.
FAQ
Research questions
Do you share strategies or performance results publicly?
No. Client work is confidential and we do not publish performance claims. Results shared with a client are labelled as historical or simulated, with assumptions stated.
Can AI find a profitable strategy?
AI and machine learning can help analyse data and test ideas, but they cannot guarantee a profitable strategy. Models can overfit and market behaviour changes. We apply strict validation and keep people responsible for decisions.
What do I own at the end of a project?
Deliverables and intellectual-property terms are agreed in writing before work begins.
Turn a trading idea into a testable hypothesis
Tell us what you want to investigate. We'll suggest a research plan with realistic scope and honest checkpoints.