One continuous loop, from market feed to managed capital.
Every stage runs as coordinated agents under a central orchestrator. Outputs flow forward through deterministic gates, and open positions feed back into research as the fund is managed over time.
Three coordinated swarms.
Specialized agent groups, each owning a stage of the fund's operation and reporting through the orchestrator.
Research Swarm
Enterprise-grade analyst agents working across historical data, market trends, and behavioral sentiment — grounded in established quantitative methodologies and technical indicators, not guesswork.
Execution & Orchestration Swarm
Capital-deployment strategies for the selected equities, driven by the central orchestrator that sequences agents and routes every proposal through the risk and direction gates.
Fund Management Engine
Portfolio-manager agents that keep ledger records and make state-based decisions — continuously rebalancing from the current as-is book toward the intended to-be allocation for profitability.
The model proposes. Deterministic code disposes.
What makes LocusQuant different from a chatbot pointed at a brokerage: the guardrails are code, not prompts — and every decision is on the record.
The edge isn't a magic signal — it's the layer that scores every forecast against reality, sizes down when it's uncertain, and proves its own skill on the record.
Deterministic code gates
Risk boundaries and house-view consistency are enforced by plain Python gates, not by prompt guidance a model can talk its way around. A non-compliant proposal is rejected before it can execute.
Append-only audit trail
Every agent output and gate decision streams to an append-only store, so each decision is traceable back to the inputs that produced it — no silent overrides.
Honest-by-default UI
Degraded desks, missing data, and gate interventions are surfaced, not hidden. The interface shows what actually happened, including when the system overrode the model.
A working prototype, today.
Not a mockup — a running multi-agent system you can watch deliberate, backtest, and audit.
Orchestrated multi-agent graphs over Groq-served Llama models, with FinBERT sentiment and grounded market & macro data from SEC EDGAR and FRED.
$ we automate research → prediction → deliberation → execution → monitoring — humans keep four irreducible seats: exception resolution, compliance judgment, capital allocation, and legal sign-off.
What ships next.
The concrete near-term releases from the pipeline — each one still paper-traded and routed through the same deterministic gates.
$ view the full pipeline → /whats-next.html
A foundational model layered over the operating system — learning from the OS's own operational history to iteratively improve its strategies over time, so the fund gets sharper the longer it runs: the v1.0 “fund-in-a-box” destination.
Who's building it.
Data science background across credit-bureau and fintech systems — the quantitative and modeling core of LocusQuant.
Enterprise integration background — wiring the operating system into robust, production-grade infrastructure.
$ investors and partners welcome — reach the team about investment opportunities in LocusQuant through the .