Founded by an institutional finance professional with 14+ years across market risk, global equities, and systematic strategies.

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 pedigree: Deep experience across developed and emerging markets in market risk management, big data analytics, and system architecture.
• Systems at scale: Core member of the team that engineered VAR systems processing billions in daily exposure, building autonomous risk frameworks for institutional capital.
• Quant engineering: Independently designed and built GanIQ's ML-native systematic infrastructure - ML signal engine, cross-sectional ranking, regime-filtered allocation, and walk-forward validation across 13+ global equity markets.
• Trader's lens: Years of active trading with deep expertise in market risk, microstructure, and institutional execution, understanding how systematic strategies behave under real capital, not just in backtests.

Through 2008, the Flash Crash, Eurozone crisis, Brexit, and 2023. These events shaped how I think about risk, regime change, and every design decision in GanIQ's architecture today.

The majority of systematic funds still rely on linear factor frameworks from prior decades. I built what comes next: a ground-up ML-native engine trained on 40M+ rows of cross-sectional global equity data, continuously validated and regime-aware.

GanIQ is functioning infrastructure with live signal generation, institutional-grade backtesting, and a focus on reliable, repeatable outcomes for global allocators outside India.