67Quant
Autonomous strategies, with the math you can audit.
Agent-driven trading platform. Strategies are designed, backtested, and executed by agents — with full reasoning trails for every signal.
Most retail trading agents are black boxes — you can't see why a trade was made, only that it was. Most quant platforms expect you to write Python from scratch. The middle is empty: agent-driven strategies you can actually audit.
Agents design, backtest, and execute. Every signal carries its full reasoning trace — what the agent saw, what it considered, what it chose, why. The math is auditable; the execution is real. Backtest before deploy, eval before each session, kill-switch always one step away.
- 01Agent-designed strategies with reasoning trace per signal
- 02Backtest harness as a deploy gate
- 03Eval suite re-run before every live session
- 04Live execution with hard kill-switches
67Quant runs on 4of the studio’s patterns.
These are the standardized agents and automations nested inside this product. The same pieces show up — with the same contract — across every app the studio builds.
- Trace Logger
Reasoning trail for every trading signal
Every decision an agent makes is auditable.
- Drift Watcher
Watches signal calibration + execution slip
Catch the moment an agent stops performing.
- Scheduler
Fires signal generation on market-data ticks
Agents run on triggers, not vibes.
- Eval Runner
Gates strategy + signal-generator deploys
Every agent ships with a test set it has to pass.
Bring the question.
The Automation Studio mode builds patterns like the ones above into your product, then keeps them tuned.
The rest of
the body of work.
One flagship live, more in build. Every product runs the same standardized stitch underneath.