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Tags

Purpose​

Tags describe the context of your trades so TradeMonkey can apply rules correctly, separate different strategies, and surface meaningful patterns. They are not just search labels. Good tagging lets you answer questions like:

  • "Are my reversal setups actually worth trading?"
  • "Where do I break my own rules the most?"
  • "Which sessions or conditions cause my discipline to slip?"

What Tags Do​

Tags drive how TradeMonkey:

  • Decides which trades each rule applies to.
  • Groups trades in analytics (by strategy, setup, session, etc.).
  • Connects emotions and conviction to specific setups.
  • Lets AI highlight patterns across your behavior.

Without tags, all trades look the same. With tags, you can compare "scalps vs swings", "breakouts vs pullbacks", "London vs New York", and more.

System Tags vs Your Tags​

TradeMonkey applies two kinds of tags:

System Tags (automatic)​

TradeMonkey adds a baseline set of tags to every trade, based on objective data, for example:

  • Instrument type (FX, stock, crypto, etc.).
  • Session or time of day.
  • Trade direction (long/short).
  • Account type (demo, live, challenge, funded).
  • Whether a trade violated a rule.

You cannot edit or remove these. They ensure every trade has minimum structure for analysis.

Your Tags (manual)​

You add your own tags to describe how you think about the trade, such as:

  • Strategy – scalp, day-trade, swing, position
  • Setup – breakout, pullback, reversal, range-play
  • Conditions – high-volatility, low-liquidity, pre-news, post-news
  • Quality / conviction – A-setup, B-setup, C-setup, high-confidence, low-confidence
  • Context – earnings-week, fomc-day, recovery-mode, experimental

These tags are flexible. You can add, remove, or rename them as your playbook evolves.

How Tags Affect Rules and Analytics​

Tags are central to how TradeMonkey evaluates your rules and behavior:

  • Rule scoping
    Apply rules only to certain tags, e.g.

    • "Max 5 trades per day" → only for trades tagged scalp
    • "Only approved setups" → only allow breakout, pullback, trend-continuation

  • Allowed vs forbidden conditions

    • Allowed tags: "Only trade if tag is one of X"
    • Forbidden tags: "Never trade when tag is one of Y" (forbidden always wins if both are present)

  • Contextual adherence
    See how well you follow rules by tag, e.g. adherence for scalp vs swing vs reversal.

  • Pattern detection and AI
    Tags let analytics and AI compare performance, emotions, and execution quality across strategies, setups, sessions, and conditions.

If tags are inaccurate or inconsistent, your rule stats and patterns become unreliable.

How to Use Tags in Practice​

  1. Tag at entry, not at exit
    Add tags when you enter the trade, based on the setup and conditions you see right now.

  2. Cover multiple dimensions
    Aim for 2–4 tags per trade, for example:

    • scalp (strategy)
    • breakout (setup)
    • high-volatility (condition)
    • A-setup (quality)

  3. Keep a small, stable tag set

    • Start with 5–12 core tags you use all the time.
    • Avoid 30+ ultra-specific tags that only appear once or twice.

  4. Use the same criteria every time

    • Decide what qualifies as A-setup, high-volatility, reversal, etc.
    • Apply those definitions consistently, win or lose.

  5. Avoid outcome-based or emotional labels

    • Don't tag trades as winner, loser, bad-trade, stupid-entry.
    • Outcomes and emotions are tracked separately; tags should stay clinical and descriptive.

Tagging Best Practices​

  • Observable, not predictive
    Use tags that describe what is true now (breakout, pre-news, trending), not what you hope will happen (will-win, sure-thing).

  • Neutral, not judgmental
    reversal, high-volatility, after-loss are useful.
    greed-trade, easy-money, mistake are not.

  • Avoid redundancy
    Don't duplicate information TradeMonkey already knows:

    • No need for long / short, morning / afternoon, winner / loser tags.

  • Be careful with retroactive edits
    Fix obvious errors (wrong session, wrong setup), but avoid rewriting history to match how you feel about the outcome. That distorts time-based and AI-driven patterns.

Limitations and Expectations​

  • Tags do not make a trade good or bad; they just describe it.
  • Tags do not improve performance by themselves; they make it easier to see what needs to change.
  • Over-tagging or inconsistent tagging reduces the quality of your insights.
  • Tags work best when combined with complete data: conviction, emotions, rules, and notes.

Thoughtful, consistent tagging is one of the highest-leverage habits in TradeMonkey. The more clinically you describe your trades, the more precise your rules, analytics, and AI insights will be.