Autonomous quant platform
TrustyGenius
Specialist AI agents search for trade ideas, test them against real market data, and monitor the ones that survive. The platform runs inside your VPC, connected to your data, broker, and risk process.
TrustyGenius gives a quant team the loop they actually need: idea, evidence, paper execution, and ongoing risk review. The agents are the visible layer. Underneath is purpose-built infrastructure: live market data, a research graph, Arrow-backed time series, portfolio state, broker integration, audit logs, and validation gates every strategy must clear before it graduates.
A general-purpose AI assistant can explain a chart or call an API. TrustyGenius is the operating system around the work. It remembers what was tested, why it passed or failed, what is live, and when the assumptions start to break.
The interesting part is not the signal. It is the process that decides whether the signal deserves attention.
TrustyGenius keeps research close to execution. A Genius can write an insight, attach evidence, run a backtest, simulate outcomes, and move a candidate into paper trading without losing the trail of decisions.
Autonomous agents
Alpha Discovery scans for hypotheses, Commodity Cycle reads spreads and term structure, Macro & Risk watches the regime, Portfolio Risk Sentinel sizes positions and defends drawdowns, and Quant Research Lab searches for features. They run on schedule and leave an audit trail.
Learn more →Postgres-native architecture
Custom Rust extensions handle graph propagation, time-series storage, indicators, pricing, and validation close to the data. RLS and JWT validation live at the database layer.
Learn more →Two ways to engage
License & Deploy gets the platform running on your infrastructure. Co-Build extends it around a specific edge: new asset class, alt-data feed, broker, or custom Genius.
Learn more →Built by Joe — a career in energy trading and quant analytics consulting, building TrustyGenius to be the platform he wished his clients had.
Read the full story →See how it handles your edge.
A short conversation on your stack, your data, and your current research bottleneck. The useful question is not whether AI can trade. It is where your team loses time between idea and evidence.