Signs you need this
- Strategies look great in backtests and disappoint live.
- Risk limits are checked in spreadsheets after the fact.
- Credit or trading decisions must happen in milliseconds, not minutes.
Overview
Quantitative finance combines everything we do: time-series modelling, low-latency engineering, statistical rigour and risk controls. It is also where sloppy engineering costs real money.
We build research and backtesting frameworks, signal-generation and portfolio-optimisation models, risk management tooling and execution systems, for funds, prop desks, fintechs and lenders.
How this differs from Data Science Solutions: Quantitative Finance adds market-data handling, backtesting rigour, risk controls and low-latency execution to general modelling.
What we deliver
- Backtesting frameworksEvent-driven engines with realistic costs, slippage and walk-forward validation.
- Signal & alpha researchFeature pipelines and models for price, volatility and flow prediction.
- Portfolio optimisationMean-variance, risk-parity and constraint-aware allocation with rebalancing logic.
- Risk managementExposure, VaR and drawdown monitoring with limits and alerts.
- Execution systemsOrder management, smart routing and low-latency market-data handling.
- Credit & lending modelsUnderwriting ensembles and decisioning services for lenders.
Where it fits
Systematic strategiesFrom research notebook to a monitored live strategy with risk limits.
Market microstructure analyticsOrder-book and flow analysis for execution improvement.
Digital lendingSub-100 ms credit decisions with ensemble models and an event-sourced ledger.
Crypto and multi-assetData pipelines and strategies across venues and asset classes.
Technology we use
Chosen per project. We are vendor-neutral and will recommend what fits your constraints.
PythonNumPyPandasStatsmodelsPyTorchTimescaleDBRedisKafkaFastAPIC++ (latency-critical paths)Cassandra
How we work
Mandate & constraints
Universe, horizon, risk budget, latency and regulatory boundaries.
Research infrastructure
Data, backtester and evaluation conventions before strategy work begins.
Strategy & risk build
Signals, portfolio construction and risk controls developed together.
Paper to live
Simulated trading, then staged capital with monitoring and kill switches.
Typical first engagement
A research-infrastructure sprint: data pipeline, event-driven backtester and evaluation conventions your strategies will share.
Common questions
Do you build for retail or institutional clients?
Primarily funds, prop desks, fintechs and lenders. We build systems and tooling; we do not provide investment advice.
How do you avoid overfitting?
Walk-forward validation, out-of-sample holdouts, realistic transaction costs and scepticism toward results that look too good.
Can you work with our existing data vendors?
Yes. We integrate with common market-data and broker APIs and build normalised pipelines over them.
