trade-journal

name

trade-journal

description

Capture the psychological side of a trade - emotion, conviction source, confidence, and plan adherence - at entry and exit, then surface which felt-states actually lose money. Use when the user says "journal this trade", "log my entry", "I just bought/sold X", "reconcile my trade", "trade journal", "why do I keep losing", "what's my real psychological pattern", or wants emotional/behavioral review of their trading. Pairs with pre-trade-check (entry) and weekly-mirror (review).

Trade Journal (Psychological Layer)

Goal

Most retail losses are emotional, not analytical. decision_memory records the facts of a trade (price, conviction label, thesis) and shadow_account derives biases from price/date. Neither captures what the trader felt at the moment of entry. This skill does - emotion, conviction source, self-rated confidence, plan adherence - then get_journal_insights cross-tabs felt-state against realized outcomes to answer the one question that beats technical analysis: what emotional state precedes my losing trades?

This is a capture + mirror skill, not a recommender. It does not approve or reject trades (that's pre-trade-check).

When to invoke

• ENTRY: user just placed (or is about to place) a trade and wants it journaled. Natural follow-on from pre-trade-check.
• EXIT: user closed a position - reconcile the open journal entry.
• REVIEW: "why do I keep losing", "what's my pattern", "trade journal insights".

How to run

Step 0 - Detect phase (REQUIRED): entry, exit, or review. If ambiguous, ask.

ENTRY phase

Step 1 - Capture felt-state (ask only what the user has not already said):

1. Ticker?
2. Emotion right now? → one of: calm | fomo | fear | revenge | conviction | bored | uncertain
3. Where did the conviction come from? → thesis | chart | tip | social | news | gut
4. Confidence 1-5? (1 = a flyer, 5 = highest conviction)
5. What is the plan? (entry / stop / exit stated BEFORE the trade - one sentence)
6. Did pre-trade-check pass? (yes/no - if they skipped it, note that, it is itself a signal)

Step 2 - Persist:

Call mcp__aifolimizer__log_trade_journal with:
  ticker=<TICKER>
  emotion=<emotion>
  conviction_source=<source>
  confidence_1to5=<1-5>
  plan_intended=<one-sentence plan>
  felt_note=<free text, optional>
  pre_trade_check_passed=<true/false>

Step 3 - Mirror back one line. If emotion ∈ {fomo, revenge, fear} OR conviction_source ∈ {tip, social, gut} OR pre_trade_check_passed=false, flag it plainly: "Logged. Note: this is a {emotion}/{source} entry - historically your weakest setup (check insights)." Do not moralize beyond one line.

EXIT phase

Step 1 - Reconcile (ask):

1. Ticker?
2. Did you follow the plan you wrote at entry? (yes/no - be honest)
3. Emotion at exit? (same enum)
4. Was the outcome a surprise? → expected | mild_surprise | shock
5. One-line lesson?

Step 2 - Persist:

Call mcp__aifolimizer__log_trade_journal_exit with:
  ticker=<TICKER>
  plan_followed=<true/false>
  exit_emotion=<emotion>
  outcome_surprise=<level>
  lesson=<one line>

If it returns reconciled=false, there was no open entry - tell the user the entry was never journaled; offer to log a retroactive entry.

REVIEW phase

Step 1 - Mark outcomes current, then pull insights (in order):

1. mcp__aifolimizer__resolve_trade_outcomes (marks decisions to market so wins/losses are fresh)
2. mcp__aifolimizer__get_journal_insights

Step 2 - Present the mirror (use the output template). Lead with the single worst felt-state by win-rate. State the confidence-calibration verdict bluntly: if avg_confidence_losses >= avg_confidence_wins, the user's confidence is inverted - they feel most sure exactly when they are most wrong.

Output template (REVIEW)

TRADE JOURNAL - PSYCHOLOGICAL MIRROR
Scored trades: <scored_entries> of <total_entries>  (<open_unreconciled> not yet closed)

WIN-RATE BY ENTRY EMOTION
  conviction   <n>  <win_rate_pct>%
  calm         <n>  <win_rate_pct>%
  fomo         <n>  <win_rate_pct>%   <-- worst
  ...

WIN-RATE BY CONVICTION SOURCE
  thesis       <n>  <win_rate_pct>%
  social       <n>  <win_rate_pct>%   <-- worst
  ...

CONFIDENCE CALIBRATION
  avg confidence on WINS:   <avg_confidence_wins>
  avg confidence on LOSSES: <avg_confidence_losses>
  verdict: <calibrated | INVERTED - you are most sure when most wrong>

THE PATTERN: <one sentence naming the emotion+source that loses money>

Rules

• Capture only - never approve/reject a trade (route to pre-trade-check for that).
• Never invent felt-state. If the user did not state an emotion, ask; do not guess it from price action.
• Enums are fixed - map the user's words to the nearest enum value; if none fits, ask.
• Keep entry/exit interactions to <120 words. The review is the only long output.
• No PII. Journal stores ticker + felt-state only; no balances, no account IDs.

Gotchas

• get_journal_insights can only score entries whose ticker has a resolved decision (target_hit/stop_hit). If scored_entries is 0 but total_entries > 0, tell the user outcomes aren't resolved yet - run resolve_trade_outcomes or wait for trades to hit target/stop.
• Insights need a decision logged too (via pre-trade-check / log_trade_decision). A journal entry with no matching decision is captured but unscoreable. Encourage logging both.
• Small-sample caveat: with <5 scored trades per bucket, win-rates are noise. Say so.
• This skill feeds weekly-mirror (lessons) - do not duplicate the performance-math output; defer P&L/R-multiple to weekly-mirror.