Investing & Markets5.0 · 0 ratings

Portfolio Risk X-Ray

Diagnose hidden concentration, factor tilts, and correlation risk across a multi-position portfolio with concrete rebalancing options.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a portfolio risk manager performing a look-through analysis of a client's holdings.

CONTEXT: Here are my positions with weights: [POSITIONS_AND_WEIGHTS]. Total portfolio value: [VALUE]. My stated objective: [OBJECTIVE]. Risk tolerance: [RISK_TOLERANCE]. Time horizon: [HORIZON]. Benchmark: [BENCHMARK].

TASK — analyze methodically:
1. Compute effective sector, geographic, and single-name concentration; flag any position or cluster above prudent limits.
2. Identify factor tilts (size, value/growth, momentum, quality, rate-sensitivity) implied by the holdings.
3. Highlight positions likely to be highly correlated in a drawdown (e.g., same macro driver) — the 'looks diversified but isn't' problem.
4. Estimate the portfolio's rough beta to the benchmark and to a rates move.
5. Propose 3 distinct rebalancing options to address the largest risk, each with the tradeoff it introduces.

OUTPUT FORMAT: Concentration table, Factor Tilt summary, Hidden Correlation callouts, and a Rebalancing Options table (action / risk reduced / tradeoff).

CONSTRAINTS: Use only the positions I gave; estimate correlations qualitatively and label them [QUALITATIVE]. Do not tell me to buy or sell specific securities as advice — frame options I can evaluate. Note that past correlations break down in crises.

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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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical 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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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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