feature
Trade studies
A trade study asks questions about a cohort of your executed trades — win rate, average R, time of day, tag breakdown — starting from an AI search result or pinned collection.
Chartnaut is an agent-powered trading environment. You can author and run trade studies yourself. Agents can run them on the same trade sets.
What you get
A structured analysis over trades you already took — not over market setups in the abstract. Results help you review execution and outcomes for a specific slice of your journal.
Start from a trade set
Run an AI trade search or open a pinned collection
Choose Study or Study in chat
Describe what you want to measure — for example “win rate and average R by tag” or “how often I exited before target”
Review the study output in chat or the study panel
Keep the study
Save or pin the study when you want to return to it. Tie it to the same collection so the cohort stays defined.
What good looks like
You pin london-breakouts-q1, run a study on average R and tag frequency, and learn that one tag correlates with outsized winners — then adjust your playbook rules.
Important distinctions
Trade study = analysis over your trades (fills, P&L, tags, properties). Lives in the Trades world.
Research study = analysis over Definition events (where a setup appeared in history, what happened next). Lives under a Definition in Research — see Studies overview.
Same word “study,” different data:
Trade study | Research study | |
|---|---|---|
Data | Your journal | Emitted setup events |
Starts from | Search / collection | Definition + dataset |
On chart | No (journal analytics) | Yes — in-terminal pins |
Next
Pinned collections — durable cohorts for studies
AI trade search — find the cohort
Research studies overview — setup research, not journal research