Position Sizing And Risk Calculator
Translate conviction, stop distance, and portfolio heat into a disciplined position size with explicit risk math.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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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