QuantGorilla — Agentic Market Intelligence and Trading Systems Research
Johnny's role: Product architect and agentic systems orchestrator—product requirements, trading-workflow design, risk-control architecture, agent coordination, operator experience, evidence standards, and readiness decisions.
Multi-agent trading operator for Hyperliquid with scanner pipelines, risk gates, mode separation (paper/shadow/soak/live), and an operator cockpit. Research platform — not a profit guarantee.
Recruiter summary
- What it is
- Agentic market-intelligence and trading systems research platform with operator cockpit and mode-separated execution paths.
- What I directed
- Product requirements, scanner funnel design, risk gates, paper/shadow/soak/live separation, operator dashboards, and soak validation workflows across HL desk lanes.
- What was hard
- Designing high-stakes event-driven systems that fail safely, log rejections as first-class data, and keep operators in control.
- Evidence
- QuantGorilla HL repo with agentic harness evaluators and mode-labeled API/UI paths — paper and replay evidence; live promotion gated.
Product problem
Operators need agentic market-intelligence tooling that rejects most setups, never mixes paper/replay with live capital, and surfaces gate decisions — not a black-box autopilot.
Product status
In development — paper, shadow, and soak paths active; live promotion requires explicit evidence gates and operator approval.
System designed
Event-driven scanner funnel with qualification gates, mode-separated execution lifecycle (paper/shadow/soak/live), operator cockpit for health and gate visibility, and multi-lane desk architecture (HL, RH, MSTR).
- →Scanner and setup evaluation funnel with rejection logging
- →Risk-control and position lifecycle management
- →Operator cockpit with market-data health and gate visibility
- →Soak validation and readiness reporting
- →Multi-lane architecture (HL, RH, MSTR desks)
AI-assisted implementation
Directed AI-assisted implementation of scanner evaluators, agent coordination harnesses, cockpit surfaces, and soak reporting workflows — with human review on risk controls and promotion criteria.
Safeguards and evaluation
- ✓Mode labels enforced at API and UI layers
- ✓Risk budgets, kill-switch patterns, and operator override paths
- ✓Rejection reasons logged as first-class operational data
- ✓Soak and readiness reports required before live promotion
- ✓No investment-performance or profit guarantees in product copy
Verified evidence
- ✓Mode-labeled API and UI paths in QuantGorilla HL codebase
- ✓Agentic harness evaluators and soak validation workflows in repo
- ✓Operator cockpit surfaces with gate and health readouts (marketing preview may use fixture data)
Known limitations
- •Not production-ready for unattended live trading
- •Marketing previews and demos may show paper, shadow, soak, or fixture data — not live PnL
- •RH and MSTR desk lanes at varying maturity vs HL focus
- •No verified live profitability or investor-grade performance claims
Technologies used
Next milestone
Complete soak evidence package for HL desk lane and document explicit live-promotion checklist with operator sign-off.
Related content
Interested in this kind of product work?
I'm open to roles where AI Product & Automation Systems Specialist skills apply — agentic systems, automation, and evidence-driven product delivery.