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.
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.
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.
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.
$ reach the team through the .