Building agentic trading workflows that connect market research, independent review and rules-based execution.
The goal: bring AI agents and automated algorithms into one observable process, from the first hypothesis to the post-trade review.
Market evidence supports a candidate thesis.
Critic checks the evidence. Portfolio reviews exposure.
Code checks signal freshness, sizing and protection.
A reviewed thesis still needs to pass every execution check. Approval alone does not place an order.
Market data, news, strategy experiments and execution often live in separate tools. The reasoning behind a trade gets lost between them.
We’re building a workspace for systematic traders and small research teams to investigate a thesis, challenge its assumptions, check exposure and trace what happened next.
Agents interpret evidence. Deterministic code controls position sizing, execution checks and order submission.
Existing repositories provide the engineering foundation. Integrating and validating them as one product is the next step.
A coordinator brings specialist research, critique, portfolio review and operations together through an MCP-based workflow.
A trading backend and dashboard for developing strategies, scanning markets, monitoring positions and reviewing executions.
Both foundations are in active development. Deployment validation, strategy evaluation and product integration are ongoing.
The next milestone is a repeatable, measurable workflow with clear evidence for what works and what doesn’t.
Strengthen baselines, account for fees and slippage, and keep a record of rejected hypotheses alongside promising results.
Compare evidence synthesis, structured tool use and independent critique on held-out cases. Measure quality, latency and inference cost.
Run shadow and demo evaluations, test outage recovery, and improve monitoring before expanding symbols, strategies or users.
We plan to research Claude for market-context synthesis, strategy critique and tool-driven coordination. Current prototypes use other model providers; Claude evaluation is a next step.
I’m developing ThesisGrid and looking to deepen the research, evaluate agent models and scale the infrastructure as the evidence matures.