Trading Journal
How to Tag Your Trades for Better Analysis
July 2026
6 min read
Journaling
A spreadsheet with entry price, exit price, and P&L for two hundred trades looks like data. It isn't — not in any useful sense. Without tags describing what kind of trade each one was, there's no way to ask "which setups actually work" or "what's costing me money" — just a column of numbers with no structure to query.
Analysis requires grouping — comparing trades of one type against trades of another type. A win rate of 52% across two hundred trades is one number, but that number is an average that hides everything useful: maybe the London session setups win at 68% and the New York session ones lose at 38%. Without a session tag, that split is invisible, buried inside a single misleading average.
The averaging problem
Every untagged trade log suffers from the same issue: aggregate statistics average away the exact patterns a trader needs to see. Tags are what let you break the aggregate back apart into the groups where the real signal lives.
Setup type
The specific pattern that triggered entry — e.g. liquidity sweep, FVG retest, breakout. This is the single most valuable tag for finding your actual edge.
Trading session
London, New York, Asian, or overlap. Performance often varies dramatically by session, and this tag is what surfaces that.
Emotional state at entry
Calm, confident, anxious, revenge, bored. Emotional state is one of the strongest predictors of trade quality and is invisible without a tag.
Mistake type (if applicable)
Oversized, early entry, ignored stop, chased price. Tagging mistakes specifically, not just "bad trade", is what turns a loss into an actionable lesson.
These four categories aren't arbitrary — they cover the dimensions that most commonly explain why a trade won or lost. A tagging system built around fewer categories misses real patterns; one built around many more becomes too tedious to maintain consistently.
An Example Tagged Trade
Instrument
GER40
Setup type
Liquidity sweep + CHoCH
Session
London open
Emotional state
Calm, followed plan
Mistake type
None
Result
+1.8R
A single trade like this means little on its own. But once fifty trades share the "liquidity sweep + CHoCH" and "London open" tags, you can isolate their combined win rate and expectancy — and that's the number that tells you whether this is your actual edge or just a setup you like the look of.
How Many Tags Is Too Many
- 4-6 categories, a handful of options each. More granularity sounds appealing but fragments your sample size until no group has enough trades to be statistically meaningful.
- Consistency beats completeness. A simple system you actually fill out on every trade beats a detailed one you abandon after two weeks — inconsistent tagging produces misleading analysis, which is worse than no tagging at all.
- Tag immediately, not at day's end. Emotional state and reasoning are only accurate if captured right after the trade closes — by evening, memory has already been reshaped by the outcome.
Tag Every Trade in Seconds
Logify's fast tagging workflow lets you log setup, session, emotional state, and mistakes right after every trade — no extra spreadsheet needed.
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Frequently Asked Questions
What tags should I use for my trading journal?
The most useful tags cover setup type, trading session, emotional state at entry, and mistake type when applicable. These four categories cover the dimensions that most commonly explain why a trade won or lost, which is what makes tagged data analyzable rather than just a chronological list.
How many tags is too many?
A workable tagging system usually has 4-6 categories with a handful of options each, since a system with too many tags becomes tedious to fill out consistently, and inconsistent tagging is worse than no tagging because it produces misleading analysis.
Should I tag trades immediately or at the end of the day?
Tag immediately after closing each trade while the context — emotional state, reasoning, any deviation from plan — is still fresh. End-of-day tagging relies on memory that's already distorted by the outcome, which produces less accurate and less useful tags.
Can tags reveal a losing pattern I don't already know about?
Yes — this is often where tagging is most valuable. A mistake-type tag applied consistently across weeks frequently reveals that a large share of losses trace back to one specific, correctable behavior, something that's nearly impossible to see from reviewing individual trades one at a time.