Statistical Modelling
Time-series analysis, feature research and hypothesis testing on market data.
Research
Research before deployment.
Every strategy begins as a hypothesis. We build the evidence for it — or we discard it. These are the directions our research follows; the parameters behind them stay in-house.
Domains
Where we look.
Six areas of continuous work, each feeding the others.
Market Microstructure
Order book dynamics, liquidity and order flow behaviour.
Arbitrage & Relative Value
Cross-venue, triangular, spatial and relative-value research.
Volatility & Regimes
Volatility modelling, regime detection and dynamic adaptation.
Machine Intelligence
Machine learning applied to structure in complex market data.
Execution Research
Routing, order types, latency and execution quality.
In depth
What each area covers.
Time-Series Research
Analysis of financial time series: feature construction, dependence structure, signal research and robustness testing across periods and regimes.
- Feature engineering and selection
- Dependence and stationarity analysis
- Out-of-sample and robustness testing
Market Microstructure
How markets actually behave at the level of the order book: liquidity, spread dynamics, slippage, execution quality and market-data freshness.
- Order book and bid/ask dynamics
- Liquidity and slippage
- Market-data quality and latency
Arbitrage & Relative Value
Research into price relationships across venues and instruments — cross-venue, triangular, spatial, statistical and relative-value approaches.
- Cross-venue and spatial dislocations
- Triangular relationships
- Statistical and relative value
Basis & Funding
Research on basis, funding and the relationships between spot and derivative markets where relevant to the strategies under study.
- Spot / derivative relationships
- Basis dynamics
- Funding behaviour
Volatility & Regimes
Realised and implied volatility, regime identification, and how systematic strategies should adapt as conditions change.
- Realised and implied volatility
- Regime detection
- Dynamic adaptation
Machine Intelligence
Machine learning and deep learning applied to representations of financial time series, including regime classification and structure discovery.
- Representation learning
- Regime classification
- Model validation and model risk
Execution Research
The last mile: maker and taker behaviour, order types, routing decisions, latency and the measurable quality of a fill.
- Maker / taker and order types
- Routing and venue selection
- Latency and execution analytics
We publish the shape of our research, never its contents. Model parameters, thresholds, signal rules and position logic are not disclosed on this website or in conversation with third parties.
Research roles
Come and test your own hypotheses.
We are hiring quantitative researchers and engineers who want to work on real market data.