Investment Technology & Model Development
Quantmade develops AI-supported investment technology, systematic model infrastructure and research-to-production workflows — together with disciplined model development and validation processes — for institutional-grade quantitative investment processes.
From Design to Governance
Quantmade applies disciplined model development and validation processes — from design and feature engineering through to robustness testing, deployment and ongoing governance.
Four Key Quant Strategy Frameworks
Quantmade develops systematic strategies across four time horizons – from intraday long-short trading to long-term quant-driven equity and ETF portfolios – each engineered for different market opportunities and return objectives.
Long-Short Quants
Equity Indices · Commodities · Forex · AI-Driven
Our intraday models trade systematic long-short strategies across equity indices, commodities, and forex. Predictive AI algorithms and real-time market regime detection identify short-term directional price patterns – on both the long and short side. All signals are executed rules-based and fully automatically, with strict intraday risk management and defined loss limits.
- Long-short strategies across equity indices, commodities & forex
- Predictive AI & real-time regime detection for precise timing
- Fully automated, rules-based execution free of emotional bias
- Strict intraday risk management with defined loss limits
Short-Term Swing Trading Quants (Stocks)
Equity Baskets · US & Europe Universes · CAGR 10–21%
Our swing trading models pursue systematic long-only strategies on equity baskets across the US and European stock universes. Machine-learning-based signal generation is combined with adaptive market risk modelling that continuously adjusts to changing market conditions. The strategy range spans from defensive to offensive – with calculated CAGR values between 10% and 21%. Dynamic position sizing consistently controls drawdowns.
- Long-only strategies on US & European equity baskets
- ML-based signal generation with adaptive risk model
- Strategy spectrum from defensive to offensive – CAGR: ca. 6%–30%
- Dynamic position sizing for drawdown control
Long-Term Portfolio Quants (Stocks)
Equity Portfolios · Quant-Driven · Systematic
Our long-term quant strategies build systematic equity portfolios on a purely quantitative basis. Rules-based selection, weighting, and rebalancing processes leverage factor and regime signals to achieve risk-adjusted portfolio returns across different market phases – ideal for institutional mandates and long-term oriented asset managers.
- Quant-driven equity portfolio construction
- Rules-based selection, weighting & rebalancing
- Factor & regime signals for risk-adjusted returns
- Optimized for institutional mandates & long-term investors
Long-Term Portfolio & Rebalance Quants (ETFs)
ETF Portfolios · AI-Driven · Multi-Asset
Our AI-driven ETF portfolio strategies combine machine learning with dynamic multi-asset allocation. At the core is the SupraQuant allocation system, which continuously monitors correlation structures, market regime signals, and risk budgets to dynamically adjust allocations. The ETF-based implementation ensures maximum scalability and optimized risk-adjusted returns across all market phases.
- AI-driven ETF portfolio strategies (multi-asset)
- SupraQuant system for dynamic allocation
- Continuous monitoring of correlation & market regime
- ETF-based implementation for maximum scalability
The Quant Factory is the Source for ready to use Quant Models
The measured library behind the strategy approaches above. Select a segment panel to screen its configurations on the return/risk maps — CAGR against annualized volatility and maximum drawdown.
Short-term Swing Trading: 137 of 137 configs. Return-/Risk-maps: better is up and left.
CAGR vs Vol
Return per unit of annualized volatility. Dashed line is CAGR = Vol.
CAGR vs Drawdown
Return per unit of peak-to-trough loss. Dashed line is CAGR = Max DD.
Illustrative visualisation of the internal Quant Factory screening view. Figures shown are library research statistics, not client account performance, and are not an indication of future results.
Model Governance
Every model in development is accompanied by a research document covering objectives, data sources, feature definitions, validation methodology and known limitations.
All production-candidate models undergo out-of-sample testing on data not used during development. Results are compared against pre-specified benchmarks and acceptance criteria.
Backtests run on point-in-time universes that include delisted, merged and failed instruments, so historical results reflect the investable opportunity set as it existed at each date rather than only surviving names.
Model code and configuration are managed under version control. Changes to production models are documented, reviewed and logged with full traceability.
Deployed models are monitored continuously for performance consistency, prediction accuracy stability and data integrity. Deterioration triggers a formal review process.
Technology Philosophy
Quantmade builds investment technology on the premise that rigorous engineering, transparent architecture and disciplined validation are essential for institutional credibility. Every system component is designed for reproducibility, auditability and operational robustness.
AI Integration
Artificial intelligence methods are integrated throughout the research and model development process — from data processing and feature engineering to signal generation and model monitoring. AI is applied as a structured, validated tool within disciplined quantitative frameworks, not as a substitute for rigorous research methodology.
Institutional-Grade Standards
Technology and infrastructure are developed to institutional standards, including version control, testing frameworks, model governance documentation and operational monitoring. These standards are designed to support the requirements of professional and institutional counterparts.