Quantsentinel — Executive Brief

Systematic options & futures trading platform · Indian markets · closed beta

What it is

A systematic options-and-futures trading platform built for Indian retail markets — currently in closed beta with 9 tenants. Eighteen months of focused development, ~12,000 production lines of code, 13 microservices, 17 specialised ML systems across five capability groups, MLOps stack built on MLflow with custom production additions. Designed around the structural reasons 90% of retail F&O traders lose money: cost drag, behavioural bias, asymmetric information, and complexity beyond what any individual can manage manually.

The architecture in one paragraph

A user moves a single 0-100 knob to express their risk appetite. The knob drives a 4-layer alpha engine (Alpha Discovery → Signal Generation → Strategy Selection → Execution & Hedging) gated by a 7-wall risk castle, twelve hard kill switches, and a permanent tail-hedge layer costing ~0.5% of capital monthly. Seventeen ML systems retrain on cadences appropriate to their data sources. Multi-tenant from the first commit: every signal, position, and audit row is tenant-scoped, with Postgres-level isolation enforced by triggers.

What's been validated

All 7 risk gates fire correctly on synthetic stress tests
All 12 kill switches activate at their thresholds
All 17 ML systems train successfully and produce versioned artifacts
MLflow integration validated with custom additions (concurrent-training locks, atomic promotions, validation contracts)
Hot-swap of new model versions into running singletons (no container restart)
Multi-tenant isolation (Postgres + WebSocket + audit-trail)
Live signal generation end-to-end < 200ms per signal
Paper testing on real-time market data — closed-beta phase ongoing

Market opportunity

Daily Nifty options turnover

₹500+ crore

Active F&O traders

1 crore+

Annual aggregate retail losses

₹50,000+ crore

Loss rate (SEBI data)

~90%

Annual growth (active traders)

30-40%

Target user capital allocation

₹10-50 lakh

Revenue model

Subscription tiers (₹5k-25k/month flat fee scaled by capital), performance-linked overlay (10-20% of gains over a benchmark hurdle), or hybrid. At 1,000 active users, revenue range is ₹6-50 crore annually. At 10,000 users, ₹60-500 crore.

Why now: SEBI's algo-trading framework and peak margin rules have raised the technology bar; existing retail platforms without proprietary systematic capability are increasingly exposed. The integrated-systematic-platform category has no dominant player.

Current status

Closed beta with 9 tenants. Paper testing ongoing through the dry-run window. Live trading deployment planned for the existing beta cohort once paper-testing benchmarks are sustained for an additional 90 days. The architecture supports US/EU market extension; mobile app and enhanced analytics dashboard are on the near-term roadmap.

What we are not claiming

Guaranteed profitability or risk-free returns
Past performance indicating future results
That the platform has been live-tested through a true black-swan day

The conversation we are open to

Strategic acquirers evaluating systematic-trading capability. Fintech investors, financial-services platforms, and PE funds. Institutional partners interested in white-label deployment or technology licensing. Sophisticated retail traders with ₹10 lakh+ allocations interested in beta access.

Full architecture and capability documentation: blog.quantsentinel.fastwheel.ai/platform

Technical deep-dive: /platform-technical

Investor deck: /deck