QuantSentinel.
Systematic options & futures trading platform for Indian retail markets
Investor deck · Closed beta · 2026
90% of Indian retail F&O traders lose money.
The losses are structural — not a knowledge gap, not a will-power gap. The same four traps catch nearly every retail participant:
- Cost drag consumes the median strategy's edge
- Behavioural bias defeats systems without enforcement
- Asymmetric information vs. institutional desks
- Complexity surface beyond individual cognitive bandwidth
The market that this happens in.
Institutional-grade systematic infrastructure — sized for retail capital.
One user-facing dial controls the entire aggression axis. The platform handles signal generation, risk gating, structure selection, and execution as a coherent system.
- Risk-first design — 7 walls + 12 kill switches before any trade
- 17 specialised ML systems across 5 capability groups; 6 closed-loop learners
- 5 strategy families with intelligent regime-aware selection
- Indian-market-native cost engine, calendar, regulatory awareness
- MLOps stack: MLflow + custom additions for production safety
Architecture at a glance.
One dial. The entire aggression axis.
Capital protected by seven concentric walls.
Seventeen learning systems, retrained on their own cadences.
The right tool for the regime.
Premium selling
Iron condor, iron fly, narrow IC, jade lizard, short strangle, final-hour condor — 5 structures gated by regime + ML strike selector.
Directional
Long futures + intelligent option hedge (mandatory at MODERATE conviction). Debit spreads for defined-risk directional.
Expiry day
Four time-window strategies for Indian weekly expiry (morning theta, mid-day range, final hour, high-IV cross-window).
Permanent tail layer
Always-on 5-delta puts, ~0.5% of capital/month, absorbs catastrophic moves.
Conviction → action.
Multi-tenant from the first commit.
Where the platform is today.
Validated
- All 7 risk gates fire correctly
- All 12 kill switches activate at thresholds
- All 17 ML systems train successfully + versioned
- MLflow + custom safety layers validated
- Multi-tenant isolation (Postgres + WS + audit)
- Live signal end-to-end < 200ms
In paper-test
- 9 tenants in closed beta
- Paper trading on real-time market data
- Cost engine + slippage model running live
- Real cohort numbers replace illustrative figures when statistically meaningful
Three viable monetisation paths.
Subscription
₹5,000–25,000 / month flat, tiered by capital.
Performance-linked
10–20% of gains above benchmark hurdle.
At 1,000 active users: ₹6-50 crore annual revenue. At 10,000: ₹60-500 crore.
Where this goes next.
Near-term (6 months)
- Extended paper-test validation across more market regimes
- Live trading for closed-beta cohort
- US market extension (architecture supports it)
- Mobile app for daily check-ins
Medium-term (12-24 months)
- Bank Nifty + stock options expansion
- Cross-asset combined strategies
- Institutional PMS-style offering
- White-label partnerships
- API access for sophisticated users
Three converging trends.
- Technology bar has risen. SEBI's algo-trading framework + peak margin rules push the industry toward institutional-grade infrastructure. Platforms without proprietary systematic capability are increasingly exposed.
- Retail capital wants quality. The 2021-2026 cohort is more educated, more analytical, willing to pay for systematic infrastructure.
- No dominant player. Algo marketplaces and analytical dashboards exist, but the integrated systematic platform category is unfilled.
We are open to the conversation that turns 18 months of focused work into a decade of capital deployed well.
- Strategic acquirers evaluating systematic-trading capability
- Fintech / financial-services / investment firms
- Institutional partners for white-label or licensing
founder [at] quantsentinel.fastwheel.ai · Full architecture →