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Execution Patterns

Purpose​

Execution patterns are recurring behaviors in your trading that show up across many trades, not just one. They connect your psychology, rule adherence, and outcomes to answer:

"What do I tend to do over and over, in specific conditions, and what does it usually lead to?"

They do not tell you what to do next. They simply mirror what your data shows.

What This Feature Does​

Execution patterns combine multiple parts of your data:

  • Psychology – emotions, conviction scores, plan adherence.
  • Compliance – which rules you follow or break.
  • Outcomes – win rate, P&L, risk-reward.
  • Timing – time of day, session, day of week.
  • Context – tags (setups, strategies, accounts, etc.).

From this, TradeMonkey surfaces stable, recurring tendencies, for example:

  • "After an opening loss, you often exceed your daily trade limit."
  • "FOMO-tagged trades have much lower win rate than your baseline."
  • "Afternoon trades show lower conviction and lower adherence than morning trades."

Patterns are descriptive, not rules. Rules are things you set ("Max 3 trades per day"). Patterns are things the system observes ("On days with a first-trade loss, you frequently trade more than 3 times").

Where You'll See Execution Patterns​

Execution patterns power several parts of the app:

  • AI Diagnostics
    Uses your data to highlight the most important patterns in a single report (for example, emotional cost, conviction decay, time-of-day effects).

  • AI Deep Dive Explorer
    Starts from a known pattern ("conviction drops during drawdowns") and lets you ask follow-up questions to see when, where, and under what conditions it's strongest.

  • Rules vs Reality Dashboard
    Shows rule-driven patterns (for example, which rules you break after a loss, or which sessions have more violations).

  • Time-Scoped Analytics
    Compares patterns across time windows (for example, "Last 30 days" vs "All time") to show whether behaviors are improving, stable, or drifting.

  • Execution Recap
    Lets you visually confirm patterns across multiple trades (screenshots, emotions, conviction, exits).

You won't always see a "Patterns" tab by name; instead, patterns appear as clinical statements embedded in these views.

How to Use It​

  1. Log complete data

    • Record emotions, conviction, plan adherence, and tags along with each trade.
    • The more complete your data, the more reliable the patterns.

  2. Run your usual reviews

    • Use Time-Scoped Analytics, Rules vs Reality, and AI Diagnostics for weekly or monthly reviews.
    • Let the system surface patterns instead of hunting manually.

  3. Read pattern statements carefully

    • Focus on what the pattern is, when it shows up, and how it differs from your baseline.
    • Prioritize patterns with clear impact (large P&L differences, strong win-rate gaps, frequent rule violations).

  4. Drill deeper when needed

    • Use AI Deep Dive Explorer when a pattern looks important (for example, FOMO, afternoon performance, post-loss behavior).
    • Ask follow-up questions like "When is this strongest?" or "What conditions usually surround this?"

  5. Decide what it means for you

    • TradeMonkey reflects what happened; it does not tell you what to change.
    • Use patterns to inform adjustments to your rules, schedule, sizing, or strategies in other parts of the app.

Example Pattern Statements​

You might see statements like:

  • "Trades entered after 2 PM show lower plan adherence and worse average P&L than morning trades."
  • "FOMO-tagged entries have significantly lower win rate and worse risk-reward than your overall average."
  • "On days where your first trade is a loss, your daily trade-count rule is broken far more often than on winning-start days."
  • "High-conviction entries correlate with better outcomes and higher adherence across setups."

All of these are mirrors: they describe what your data is doing, without telling you what you should do next.

Important Notes and Limits​

  • Needs enough data
    Patterns only appear once there are enough trades and repeated situations. Very small samples may be hidden or clearly marked as "limited data."

  • Data quality matters
    Missing emotions, conviction, or tags reduce what can be detected. Incomplete fields lead to weaker or fewer patterns.

  • Descriptive, not prescriptive
    Patterns say "what tends to happen," not "what you must do." Any changes you make are your own decisions.

  • Correlation ≠ causation
    If FOMO trades underperform, that's a correlation. The system cannot know whether emotion itself, market conditions, or something else is driving the result.

  • Past ≠ guaranteed future
    Patterns are based on past trades. They show tendencies, not guarantees about upcoming trades.

Support​

If a pattern looks wrong or confusing, you can always cross-check it with your raw analytics. For help understanding what you're seeing, contact hello@trademonkey.app.