Setup Tags
Track strategy intent with labels such as breakout, pullback, mean reversion, and range play.
sellTrade Labeling
Trade Labeling That Helps You Find Repeating Mistakes
With Trarity trade labeling, you can label your trades and tag your trades with organised trade setup tags and mistake tags so your journal becomes easier to analyse. This trade labeling workflow helps you review decisions clearly inside your trading journal and connect execution quality to outcomes.
labelTrade Tagging Basics
What Is Trade Labeling?
Trade labeling describes the process of attaching structured data to each position so your decisions are searchable later. Strong trade tagging uses consistent trading journal tags for setup type, mistake categories, psychology tags, and session context, making every review more objective.
When this data is organised, your trading analytics stops being guesswork and starts showing exactly where your edge appears and where your process breaks down.
insightsPerformance Clarity
Why Traders Should Tag Every Trade
If your journal has no structure, repeating behaviour hides in plain sight. Consistent trade labels make it simple to spot what to keep and what to remove from your process.
- check_circleFind repeating patterns across instruments and sessions
- check_circleTrack setup performance with objective trade tags
- check_circleIdentify entry mistakes before they become habits
- check_circleHighlight exit mistakes that cap strong trades
- check_circleAudit risk management mistakes with real evidence
grid_viewTag Framework
Best Trade Tags to Use
Start with a simple structure and keep names consistent. These four groups make your journal easier to filter, compare, and review over time.
monitoringStructured Tracking
Track Setups, Mistakes and Psychology
Trarity works as a practical trading setup tracker and trading mistake tracker in one place. If you are learning how to tag trades or how to label trades consistently, templates make the process fast while still letting you personalise your categories.
- check_circleCompare setup performance across market conditions
- check_circleReview mistake categories by frequency and cost
- check_circleTrack psychology tags beside P&L and expectancy
- check_circleBuild personalised tag templates for faster journalling
manage_searchJournal Filters
Find Repeating Patterns in Your Journal
Trade labels improve your trade review process because you can filter, sort, and cross-reference each decision quickly. By combining trading tags, trade tags, and setup performance data, your journal shows exactly which behaviour repeats in winning and losing periods.
- check_circleFilter by setup, session, and psychology in seconds
- check_circleSort by outcome to isolate high-impact habits
- check_circleCross-reference tags to validate or reject assumptions
- check_circleUse a repeatable trade review process every week
smart_toyAI + Labels
Combine Trade Labels with AI Feedback
Labels make coaching sharper. When your journal is structured, the AI trading coach can surface patterns from your tags automatically and produce feedback that is specific to your setups, mistakes, and routines.
- check_circleAI highlights recurring mistakes from your label history
- check_circleTag context makes coaching more personalised
- check_circleClear action points tied to real trades, not theory
helpFAQ
Frequently Asked Questions
Trade labeling (or trade labeling) means tagging trades with structured metadata so your journal is easy to analyse. Instead of only storing P&L, you attach setup, mistake, psychology, and session context to every trade.
The best starting structure includes setup tags, mistake tags, psychology tags, and session tags. This gives you clean filters for performance analysis without overcomplicating your workflow.
Import your trades, apply a tag template, and then customise any categories to match your strategy. Trarity keeps the process quick, so consistent labeling becomes part of your normal review routine.
Yes. Mistake tags make it easy to track entry mistakes, exit mistakes, revenge trading, overtrading, FOMO trading, and risk management mistakes over time.
Yes. Tag data makes AI feedback more specific because the coach can compare outcomes by setup, behaviour, and session. Learn more on the AI coaching page.
You get full access for 30 days with no credit card required, so you can build your labeling process and review meaningful data before committing.
Start Labeling Your Trades Today
Use structured tags to improve every trade review and build a data-backed process in your journal.