Infrastructure that makes autonomous quant agents credible.
TrustyGenius is not a chat layer bolted onto a broker API. The agents sit on top of a research and execution substrate: market data, graph computation, validation, portfolio state, audit trails, and database-enforced access controls.
From market event to reviewed decision
The platform is built around the full research lifecycle. Each step leaves state behind, so the team can inspect why a strategy was proposed, which evidence supported it, and when it should be paused.
- 01
Ingest
Market data, broker state, reference data, filings, macro series, and custom feeds land in a shared data plane.
- 02
Compute
Graph and time-series extensions propagate derived objects, rolling contracts, indicators, risk views, and scenario overlays.
- 03
Research
Geniuses generate hypotheses, run Python, call approved tools, and write evidence into the journal instead of leaving it in a chat transcript.
- 04
Validate
Backtests, walk-forward checks, Monte Carlo paths, and paper trading create a record of what survived and what failed.
- 05
Monitor
Live strategies are watched for regime drift, alpha decay, correlation creep, reconciliation breaks, and risk-limit violations.
Postgres-native by design
Trading systems fail when intelligence, data, and permissions are split across too many loosely connected services. TrustyGenius keeps the important state close to Postgres and uses focused extensions where the default database is not enough.
tg_graph
A live DAG for market objects, derived series, strategy dependencies, and price propagation.
tg_timeseries
Columnar time-series storage and Arrow IPC paths for fast research reads.
tg_indicators / tg_pricing
Technical indicators, options pricing, validation helpers, and compute routines near the data.
tg_auth
JWT validation and row-level security at the database boundary, not just in application code.
.NET API
Application services, broker integration, scheduled jobs, SignalR updates, and operational workflows.
React workbench
Research, dashboards, strategy configuration, paper trading, and admin views for the team.
Your alpha should not become someone else's training data.
Runs inside your boundary
Deploy in your cloud, your VPC, or on-prem infrastructure. Data sources and broker credentials stay under your control.
Database-enforced isolation
Row-level security and database-side JWT validation reduce reliance on app-layer filtering.
Auditable agent work
Insights, strategy candidates, tool calls, validation results, and operator actions can be inspected after the fact.
Provider flexibility
Use the model providers your governance allows. The platform is built to route work through approved keys and tools.
Built for a real operations path
TrustyGenius can start small for evaluation and graduate into a managed production footprint. The default path is Docker-based, with room to adapt to the container platform and controls your team already uses.
Evaluate
Seed data, paper account, and a focused instrument universe.
Operate
Scheduled Geniuses, dashboards, reconciliation, alerts, and audit logs.
Extend
Custom feeds, brokers, models, tools, asset classes, and Geniuses.
The point of the architecture
Low latency matters, but the larger value is discipline. The system gives autonomous agents enough context to be useful, enough controls to be reviewable, and enough memory to improve from the team's feedback instead of starting over every morning.