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Most trading journals are built around free-text notes — you write a paragraph about each trade describing what happened, what you were thinking, and what you’d do differently. It feels productive. But it has a fundamental problem: you can’t analyze text at scale.

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The problem with notes

After your first 50 trades, free-text notes feel useful. You can scroll through them and spot some patterns. After 500 trades? You have pages of text that nobody — including you — is going to re-read. And even if you did, you couldn’t answer basic questions like:
  • How many times did I chase an entry this month?
  • What’s my win rate when I trade during news events?
  • How much money has revenge trading actually cost me?
These questions require structured data, not paragraphs.

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How tags solve this

A tag is a short, structured label — “chased entry,” “revenge trade,” “trending market.” When you tag a trade instead of writing about it, that observation becomes a data point you can filter, count, and measure. After 100 trades tagged with “chased entry,” TurtleMetrics can tell you:
  • You chased entries 23 times
  • Your win rate on those trades was 28%
  • They cost you $1,450 in total P&L
  • Your average loss on chased entries is 3x your average loss on patient entries
That’s not a feeling or a vague memory — it’s a specific, actionable number that makes the case for changing your behavior far more convincingly than any journal entry could.

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Tags don’t replace reflection

Tags aren’t about removing thought from your review process. You still need to think about each trade and decide which tags apply. The difference is that your observations get stored as structured data instead of unstructured text — so they compound into insights over time rather than disappearing into a scroll of paragraphs.

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The compounding effect

Tags become more powerful the longer you use them:
Trades TaggedWhat you can learn
10–20Not much yet — too small a sample
30–50Early patterns start emerging. You begin to see which tags correlate with wins vs. losses
100+Statistically meaningful data. You can make confident decisions about what to keep doing and what to stop
500+Deep behavioral insights. You can slice data by tag combinations, time periods, and setups to find highly specific patterns

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Getting started

If you’re used to writing notes, the switch to tags can feel restrictive at first. Start here:
  1. Think about the 5–10 observations you write most often in your notes
  2. Turn each one into a tag (e.g., “took profits too early” → “Early exit”)
  3. Organize them into categories
  4. Start tagging consistently from your daily calendar review
Within a few weeks, you’ll have enough data to see your first real insights in Tag Analysis.

Tags & Categories

How to set up and manage your tagging system.

Tag Analysis

See what your tags reveal about your trading.