Session trades & timeline

This is the entity reference for session trades and the session timeline. By the end you’ll know what each output captures and how to use them in a review loop.

What this is

This part of the trading cycle is where execution becomes reviewable. The most important outputs:

Session trades — every executed trade, live or virtual, with the context that led to it.
Session timeline — the readable history of what happened in each cycle.
Cycle decisions and action history — the AI’s reasoning trail and the actions taken.
Run and review history — start-to-stop runs, useful for comparing executions.

This is one of Cortiq’s strongest features because it makes the AI workflow auditable. You don’t only see the trade result — you see the path that led to it.

How it fits into Cortiq

The trades and timeline land in the local SQLite database and surface across the workspace:

Library → Journal shows the trade journal and session journal.
Library → Conversations shows the raw AI dialogue.
Library → Dashboard aggregates across sessions.
Library → Session Cohorts compares sessions side-by-side.

For analysis and review, see Journal & analytics.

How to use it

What session trades record

The instrument and action taken.
Entry, stop, and target details.
Whether the trade was live or virtual.
Trade-management changes over time.
The strategy or idea that led to the trade.
Screenshots, notes, and journal context where available.

What the timeline records

What the AI saw.
What it concluded.
Whether it traded, held, or managed a position.
What happened after the decision.

Reference

How to use the trade and timeline history

Use case	What to look for
Review whether playbooks are too loose or too strict	Patterns of low-conviction entries or repeatedly-passed setups.
Compare live and virtual behavior	Differences in trade frequency, sizing, or post-entry management.
Identify drift in the AI decision process	Reasoning that’s noticeably different than past cycles for the same setup.
Improve future sessions based on evidence	Specific cycles where the AI’s reasoning was strongest or weakest.