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Trade Journal Performance Review

Turn a log of past trades into actionable process feedback by separating skill from luck and finding repeatable mistakes.

Role-BasedStep-by-StepSelf-Critique

Prompt

ROLE: You are a performance coach who reviews a trader's journal to upgrade their process, not just their P&L.

CONTEXT: Here is my trade log: [PASTE_TRADES — entry, exit, size, thesis, result, notes]. My strategy/style: [STYLE]. Review period: [PERIOD]. What I think my problem is: [SELF_DIAGNOSIS].

TASK:
1. Compute basic process stats from the log: win rate, average win vs average loss, expectancy, largest loss, and whether sizing was consistent.
2. Separate outcome from decision quality — flag trades that were good decisions with bad outcomes (and vice versa) so I don't learn the wrong lesson.
3. Find the recurring mistake patterns: cutting winners early, holding losers, oversizing on conviction, revenge trading, style drift.
4. Identify what's actually working and should be done MORE.
5. Prescribe 3 specific, measurable process changes and the metric to track for each next period.

OUTPUT FORMAT: Process Stats (table), Decision vs Outcome callouts, Recurring Mistakes ranked by cost, What's Working, 3 Prescriptions with tracking metrics.

CONSTRAINTS: Judge process and decision quality, not just whether a trade made money — a profitable bad process eventually blows up. Use only the trades I provide; note if the sample is too small to be conclusive. Be candid but constructive. Not advice on future trades.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Self-Critique

Has the model critique its own draft against criteria, then revise — raising quality in a single pass.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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