QuantML scans the market every trading day to identify potential stock opportunities.
Every opportunity is tracked and benchmarked against the SPY, providing full transparency into performance over time.
QuantML V11 is currently available to a limited number of investors, advisers and wealth managers helping shape the future of AI-powered investment intelligence.
Applications are personally reviewed.
Full strategy history · Completed reporting period 1 Dec – 14 Jul 2026 · Top-5 equal-weight · Institutional sleeve · 7-day hold · settled days only
Based on 91 settled strategy days.
QuantML V11 is in development. The performance shown below represents the completed V10/V10.1 reporting period through 14 July 2026.
Simulated / hypothetical · $10,000 notional · gross of fees · not live capital · past performance does not guarantee future results.
Arithmetic Return Sum measures aggregate signal returns, while Compounded Return reflects actual sequential portfolio growth from reinvesting gains and losses.
Reproducibility Statement: All performance figures are derived from the QuantML signal ledger and can be reproduced from the underlying daily signal outputs.
Top-5 Long/Short Equity Signal
Buy & Hold
Financial markets are inherently volatile.
Most investment strategies rely on discretionary judgment or manual processes that do not scale, while many AI-driven models perform well in backtests but fail in live trading.
S&P 500 averages 10% yearly, but daily fluctuations make short-term investing risky. Traditional quant strategies no longer deliver consistent alpha.
Traders use separate tools for research, backtesting, predictions, and execution—leading to inconsistency and errors.
Most ML trading models overfit and fail live. Black box systems lose trader trust due to lack of transparency and poor risk management.
A unified pipeline from data acquisition to disciplined execution
Market data is structured into model-ready features using quantitative techniques. The system evaluates price behaviour, statistical patterns, and other market characteristics to identify potential opportunities.
Machine learning models produce probabilistic signal outputs. Signals are generated across the defined universe of equities and represent statistical insights rather than deterministic predictions.
Portfolio weights are generated to manage exposure and concentration risk. The framework produces suggested portfolio weights for each signal. These weights help manage risk while allowing the signal framework to express its directional view.
The platform presents:
All results are displayed through the QuantML website and QuantML App.
Markets are unpredictable. Long-term capital growth depends on disciplined exposure control.
QuantML is designed to:
Portfolio weights are capped and diversified to prevent overexposure to any single position.
Allocation sizing adapts dynamically, reducing exposure during elevated volatility periods.
Systematic rules govern every rebalance, removing emotional bias from position sizing.
Drawdown tracking and benchmark comparisons provide full transparency into portfolio exposure.
This is structured risk management — not predictive certainty.
Built to address the structural weaknesses that cause most ML trading systems to fail
Most systems optimise returns before enforcing risk discipline. QuantML reverses this sequence—capital preservation precedes performance optimisation.
No black boxes. Every signal includes confidence scores, probability differentials, and transparent signal classifications so users can make their own informed decisions.
Unlike static factor models, QuantML auto-relearns with the latest 5 years of data, keeping the system aligned with current market regimes.
Continuous monitoring of feature drift, prediction drift, and performance drift prevents invisible degradation and triggers alerts for anomalies.
Institutional-grade infrastructure designed for transparency, auditability, and regulatory readiness
Multiple specialised models trained per ticker and sector cluster, combining diverse predictors for robust signal generation.
Continuous retraining with rolling 5-year windows ensures models stay aligned with current market conditions.
Volatility-normalised stop-loss and take-profit levels adapt to each asset's behaviour, not arbitrary fixed percentages.
Real-time tracking of feature, prediction, and performance drift prevents invisible model degradation.
Complete trade-level logging with timestamps, signals, confidence scores, and execution details for full transparency.
Designed to support the audit and reporting requirements institutions face. Reporting suitable for institutional oversight.
QuantML integrates flexibly across institutional workflows, acting as an AI signal and risk layer.
Signal generation for discretionary or systematic strategies. Independent risk-aware validation layer alongside existing models.
White-labelled AI signals for retail or professional clients. Premium subscription products without in-house AI development.
Transparent governance, audit readiness, and compliance-friendly reporting for sophisticated allocators.
QuantML is designed for users who prioritise risk discipline and transparency over speculative trading.
Targeting a $120T+ global asset management industry with AI/ML finance growing at 10-12% CAGR to $40B by 2030
Massive addressable market seeking better returns
Growing at 10-12% CAGR, hot growth area
Retail platforms globally seeking AI edge
QuantML is opening discussions with select strategic partners with expertise in trading, risk management, and portfolio construction
We are seeking a partner with:
Experience navigating multiple market regimes
Institutional-level risk oversight
Strategic input on portfolio construction
Operational discipline in review and governance
Long-term alignment with platform evolution
Specific terms are intentionally flexible and co-defined with the right partner.
Direct access to the QuantML analytics platform
Visibility into model architecture, backtesting framework, and live signal generation
Transparency into risk controls and allocation logic
Governance, reporting, and review frameworks suitable for institutional oversight
Participation in product roadmap discussions
Strategic influence without access to the underlying training code
QuantML currently operates as a quantitative analytics and signal platform. Capital deployment remains external to the platform.
Strategic input into model design, capital discipline, and operational structure to accelerate:
Expansion across US, European, and Asian equities
Multi-strategy architecture development
Capital allocation and portfolio optimization enhancements
Institutional-grade reporting and oversight frameworks
B2B partnerships with funds, brokers, and platforms
QuantML is architected to scale without compromising risk control.
Current focus areas:
Multi-market expansion
Strategy sleeve development
Risk-first capital allocation refinement
SaaS and institutional deployment models
QuantML has completed full backtesting validation and is generating signals in a simulated, forward-tested environment with structured monitoring infrastructure.
If our philosophy, governance framework, and risk-first architecture align with your perspective, we welcome a discussion.
All enquiries are treated confidentially.
QuantML is a quantitative analytics platform providing AI-generated market signals and risk-first portfolio allocation insights for informational purposes only.
QuantML does not:
All outputs are general information made available equally to all users and are not tailored to individual financial circumstances.
Users remain solely responsible for all investment decisions and trade execution.
Investing involves risk, including the possible loss of capital. Past performance does not guarantee future results.
For full details, please review our: