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Journal and Analytics

Why Review Matters

Cortiq is not only about opening trades. It is also about understanding what happened, why it happened, and whether the strategy is improving.

That is where journals and analytics become part of the product value.

Where To Find This In The App

This part of the platform spans multiple screens:

Library -> Journal for AI Journal and Trade Journal review
Library -> Dashboard for trading performance and risk monitoring
Library -> Session Cohorts for side-by-side comparison of sessions
Library -> Conversations for raw AI conversation review across sessions

Journal Types

Journal Type	Scope	Typical Use
Trade journal	Single trade	Review entry, management, exit, and lessons learned
Session journal	Entire session	Evaluate the behavior of a strategy run over a broader window
Run journal	One start-to-stop run	Compare one execution run against another

What The Analytics Layer Covers

Cortiq computes operational metrics such as:

Daily P/L
Weekly P/L
Total P/L
Win rate
Best and worst day
Best and worst trade
Average win and average loss
Total trade count

These are the metrics that help users distinguish a repeatable process from a temporary streak.

What This Can Do For You

Review features help the user:

understand whether a session is improving or drifting
separate lucky outcomes from disciplined execution
compare virtual results against live behavior
find out whether changes to playbooks or data packages actually helped
review the reason behind trades instead of only the result

Commercial Value Of This Feature

For a serious product, execution alone is not enough. Users need review loops.

Journals and analytics help with:

Validating whether a playbook is behaving as intended
Identifying drift in an AI-driven workflow
Comparing virtual and live performance patterns
Deciding what to refine next

Best Practice

After every meaningful strategy change:

Run the updated logic in a controlled scope.
Review the session journal, not only the trade list.
Check whether the win rate and P/L changed for the right reasons.
Adjust playbooks or risk settings before scaling again.