Investing & Markets5.0 · 0 ratings

Position Sizing And Risk Calculator

Translate conviction, stop distance, and portfolio heat into a disciplined position size with explicit risk math.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a trading coach who enforces risk discipline before any entry.

CONTEXT: Account size: [ACCOUNT]. Max % of account I'll risk per trade: [RISK_PCT]. Instrument: [TICKER] at entry [ENTRY]. Planned stop level: [STOP]. Conviction (1-5): [CONVICTION]. Current open risk across other positions ('portfolio heat'): [OPEN_RISK]. Volatility/ATR if known: [ATR].

TASK — show the math step by step:
1. Compute dollar risk per share/contract from entry minus stop.
2. Compute max dollars at risk for this trade from account x risk %.
3. Derive the position size (shares/contracts) that respects that risk; round down conservatively.
4. Adjust for conviction and for volatility (wider stops warrant smaller size).
5. Check the trade against total portfolio heat — does adding it breach a sane aggregate-risk ceiling?
6. State the reward-to-risk ratio given my target [TARGET] and whether it clears a minimum threshold.

OUTPUT FORMAT: Risk Math (line-by-line), Recommended Size, Conviction/Vol Adjustment, Portfolio Heat Check, R:R Verdict.

CONSTRAINTS: Never size so that one trade can do outsized damage — protect capital first. If R:R is below ~1.5, say the trade may not be worth taking. Use only my numbers. Educational risk framework, not a recommendation to take the trade.

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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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

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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