Quantitative

Quantitative Strategies

Q: How are quantitative strategies different from traditional investing?

Quantitative strategies rely on systematic models rather than discretionary judgment. Rules are codified, tested historically, and executed with discipline, reducing emotional decision-making and enabling scale across thousands of positions.

Q: What data do your models use?

We integrate market data, fundamental datasets, alternative signals such as sentiment and supply-chain indicators, and proprietary analytics. All data is validated for quality and relevance before entering production.

Q: How do you manage model risk?

We maintain diverse signal families, continuous out-of-sample monitoring, strict drawdown limits, and human oversight for regime changes. No single model dominates portfolio construction.

Q: What is the minimum investment?

Minimums range from €250,000 for factor overlays to €1,000,000 for higher-frequency statistical arbitrage strategies.

Systematic edge from data, discipline, and dynamic risk control.

Systematic investment strategies powered by data science, machine learning, and rigorous risk models.

180+

Signals in Production

15 Years

Average Model Backtest

120+

Data Sources Integrated

<0.35

Strategy Correlation

Overview

Why this. Why now.

Quantitative strategies form the backbone of our technology-driven investment approach. By combining alternative data, statistical inference, and machine learning, we build systems that identify persistent market inefficiencies while maintaining disciplined risk control. Our research process begins with hypothesis generation, drawing on academic finance, market microstructure, and behavioral patterns. Each signal is tested across decades of historical data, validated on out-of-sample periods, and stress-tested against extreme market conditions. We deploy capital across multiple strategy buckets: statistical arbitrage, trend following, factor investing, and machine-learning overlays. Diversification across independent signals reduces reliance on any single source of returns and helps deliver more consistent outcomes. Risk management is embedded at every stage. Position sizing, correlation monitoring, and drawdown controls are automated, enabling rapid response when market regimes shift. Transparency into model behavior and attribution is central to our client reporting.

Who it's for

Built around three client profiles.

Investors seeking uncorrelated, rules-based returns

Allocators comfortable with technology-driven execution

Portfolios needing diversification beyond traditional assets

Our edge

What makes our approach different.

Signal diversity

180+ independent signals across statistical arbitrage, trend, factor, and machine-learning families.

Rigorous validation

Every model is backtested over 15+ years and monitored on live out-of-sample data.

Automated risk control

Position sizing, correlation limits, and drawdown triggers operate in real time.

Process

From mandate to monitoring.

Research

Hypothesis generation from finance, microstructure, and behavioral datasets.

Validation

Historical testing, out-of-sample verification, and stress scenarios.

Deployment

Live execution with gradual capital scaling and performance attribution.

Monitoring

Continuous signal health checks and regime-change alerts.

Representative allocation

How capital is deployed.

Illustrative weights for a typical mandate. Actual allocations are tailored to each client's objectives and constraints.

Statistical arbitrage30%

Trend & macro25%

Factor overlays25%

Machine learning20%

Strategies

Available mandates.

Strategy	Risk Level	Target Return	Min. Investment	Liquidity
Multi-Signal Quantitative	Moderate-High	10-16%	€500,000	Monthly
Statistical Arbitrage	High	12-20%	€1,000,000	Monthly
Factor Overlay	Moderate	7-11%	€250,000	Quarterly

Risk considerations

What could go wrong.

Model Risk

Historical patterns may break down during unprecedented market regimes.

Crowding Risk

Widespread adoption of similar strategies can reduce alpha and increase liquidation risk.

Technology Risk

Execution and data infrastructure failures can disrupt systematic strategies.

Leverage

Certain quantitative approaches employ leverage, which amplifies both gains and losses.

Frequently asked

Common questions.

How are quantitative strategies different from traditional investing?+

Quantitative strategies rely on systematic models rather than discretionary judgment. Rules are codified, tested historically, and executed with discipline, reducing emotional decision-making and enabling scale across thousands of positions.

What data do your models use?+

We integrate market data, fundamental datasets, alternative signals such as sentiment and supply-chain indicators, and proprietary analytics. All data is validated for quality and relevance before entering production.

How do you manage model risk?+

We maintain diverse signal families, continuous out-of-sample monitoring, strict drawdown limits, and human oversight for regime changes. No single model dominates portfolio construction.

What is the minimum investment?+

Minimums range from €250,000 for factor overlays to €1,000,000 for higher-frequency statistical arbitrage strategies.

Quantitative

Discuss a quantitative mandate with our team.

A short conversation is the fastest way to see how this strategy would fit your objectives and risk budget.

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