Risk appetite ≠ Risk tolerance.
One sets our direction. The other triggers our controls.
GanIQ delivers disciplined, rules-based alpha across 13+ global equity markets, built for institutional partners who value reliability, rigour, and repeatable outcomes.
Many systematic strategies still rely on linear factor models and mean-reversion frameworks designed decades ago. Market structure has evolved. Systematic infrastructure should too.
GanIQ was designed from the ground up as ML-native systematic infrastructure: a disciplined, data-driven signal engine trained on 40M+ rows of cross-sectional global equity data, continuously validated and regime-aware.
ML-powered cross-sectional ranking across 6,000+ tickers spanning 13+ global equity markets. Normalized price/volume ratios, cross-sectional rank features, volatility-scaled return targets, and macro regime flags.
Dynamic allocation between equities and cash based on regime signals, preserving capital in adverse conditions and deploying when edge is highest. No leverage. No derivatives. Rules-based, signal-driven allocation.
Every position sized by signal confidence, cross-sectional rank, and portfolio context. Proprietary stock exclusion classifier and portfolio-level what-if analysis. Risk is embedded in the signal, not layered on after the fact.
Signals validated on real market data across multiple market cycles - bull, bear, sideways, and crisis. What does not survive costs and regime changes in testing does not go live.
Sustainable alpha increasingly accrues to firms that process signal systematically, adapt to regime change, and size positions with precision.
GanIQ is built for that mandate: institutional-grade infrastructure, global market coverage, and a focus on reliable, repeatable outcomes across market cycles.
The most rigorous way to evaluate a systematic strategy is to run it. We work with partners willing to allocate test capital under a clearly defined performance framework, with full transparency into signals, sizing, and risk management.
For investors backing a functioning ML-native quant firm: live signal generation, 40M+ market observations supporting research & validation, and institutional-grade backtesting. Seed capital to accelerate model development, infrastructure scale, and global market expansion.
Suren, Founder, GanIQ · ex-Lehman Brothers · ex-Nomura · NISM Research Analyst (Cert. No. NISM-202600099788)
Deep experience across institutional market risk, derivatives, global equities, and big data analytics. The perspective to understand what allocators require, and the technical depth to build it.
GanIQ brings together allocator experience and ML-native systematic infrastructure.
Institutional-quality systematic alpha requires the right architecture, the right data, and the discipline to let validated models drive decisions.
If you are a hedge fund, AMC, or investor evaluating systematic global equity exposure, we welcome a conversation.
Email Suren directly: suren@ganiq.io
NISM Research Analyst (Cert. No. NISM-202600099788)