AI Trade Review runs a structured analysis on a single closed trade — reading its risk quality, execution, psychology, plan adherence, and mistake tags — then produces a key takeaway, up to four supporting evidence points, and a next-action recommendation. One click. Cached permanently.
Every AI Trade Review produces the same three-part structure — so you know exactly what to expect. No paragraphs of generic advice. No vague encouragement. Direct observations grounded in the trade's numbers.
One sentence that identifies the most important observation about this specific trade — what worked, what didn't, or what the numbers reveal about the execution. Direct and specific — always references the trade's actual data.
Up to four supporting observations displayed separately so they are easy to scan. Each evidence box references specific numbers: the R-multiple, risk percentage, emotional state logged, session timing, or mistake tag — not generalizations.
A concrete recommendation for your next session or review — something to try, change, or study based on what this trade's data suggests. A behavioral or process adjustment. Not a trading signal.
AI Trade Review does not just read P&L. It reads every field on the trade record alongside 30 days of journal context so the review can reference patterns, not just isolated numbers.
Risk percentage (planned vs actual), stop loss placement, take profit target, position size, and whether risk stayed within your account rules. The review flags if sizing was inconsistent with what you committed to.
Entry price, exit price, R-multiple achieved vs maximum possible R, trade duration, fees, and slippage. The review identifies early exits, late entries, and execution drift relative to the setup plan.
Emotional state at trade time (calm, greedy, anxious, frustrated, fearful, euphoric, confident), confidence level (1–10), and whether the process was marked as followed. The review correlates these with the outcome.
Whether the followed-plan flag was set, which mistake tags were logged (late entry, oversized, moved stop, revenge trade, FOMO, early exit), and whether execution matched the Text Journal session plan for that day.
Post-trade notes written at logging time, custom trade tags, and strategy name. The review references your own written observations as part of the analysis — not just the numeric fields.
Your last 40 trades, day readiness for that session, and journal plans from the same period are included as context. The review can reference patterns specific to this instrument, session, or emotional state across recent history.
AI Trade Review is triggered on demand — you choose which trades to analyze. It does not run automatically so you control when you spend credits.
Navigate to the trade detail page of any logged trade — closed, open, missed, or canceled. The review button appears at the top of the page, before the raw metrics, so it's the first thing you see.
The button shows the credit cost before you confirm. The review runs immediately — typically 3–8 seconds. A loading indicator shows "Analyzing trade…" while AI reads the trade data and journal context.
The three-part output appears: Key Takeaway at the top, Evidence boxes below, and the Next Action recommendation at the bottom. Each section is distinct so you can scan quickly or read deeply.
The review is saved to the trade record. Every future visit shows the cached review for free — no credit cost. Click "Regenerate" only if you want a fresh analysis after adding notes or correcting mistake tags.
Once a trade is reviewed, the insight is stored permanently. Every subsequent visit retrieves it at no cost. Credits are only charged when you generate or regenerate — not when you read.
AI Trade Reviews don't exist in isolation — every insight is stored on the trade and becomes part of the journal context that feeds Pulse, monthly reports, and behavioral analytics.
When you ask Pulse "what's my most common mistake this month?", it has access to the AI insight records from your trade reviews — not just raw trade data — so it can reference more nuanced behavioral patterns.
Weekly and monthly AI reports pull from trade insights to surface the most-repeated behavioral issues. The more trades you review, the more specific your automated reports become.
If the trade was logged via Snap Journal or has a pre-trade screenshot attached, the review can reference the chart image. The insight card appears alongside the screenshot on the trade detail page.
Missed and canceled trades can be reviewed. AI treats them as deliberate no-trade decisions — not breakeven results — so you can study the decision to skip or cancel without distorting your analytics.
If you used Text Journal that day, the review compares your planned bias, setup conditions, and invalidation plan against what actually happened in the logged trade — showing drift between plan and execution.
If you marked "process followed" but logged risk exceeded your day rules, the review treats the guardrail violation as authoritative — not your self-assessment — and flags the inconsistency directly.
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