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Sector Rotation Playbook

Rank sectors by cycle positioning, relative momentum, and valuation to build a tilt with overweight and underweight calls.

Role-BasedStructured-OutputChain-of-Thought

Prompt

ROLE: You are a sector strategist constructing a rotation playbook for a tactical allocation sleeve.

CONTEXT: My read on the business cycle: [CYCLE_STAGE]. Rate environment: [RATE_PATH]. Sectors I can express: [SECTOR_LIST]. Relative-strength data I have: [REL_STRENGTH]. Valuation data: [VALUATIONS]. Horizon: [HORIZON].

TASK:
1. For each sector, score cycle fit, relative momentum, and valuation on a 1-5 scale with a one-line reason each.
2. Combine the three into a composite lean (Overweight / Neutral / Underweight).
3. Explain the cycle logic: which sectors historically lead and lag at this stage and why.
4. Identify 2 contrarian setups where valuation conflicts with momentum, and how you'd resolve them.
5. Propose a sample tilt (e.g., +X% / -Y% versus benchmark weights) consistent with the scores.

OUTPUT FORMAT: Scoring table (sector / cycle / momentum / valuation / composite / lean), Cycle Logic narrative, Contrarian Watch, Sample Tilt table.

CONSTRAINTS: Rotation timing is uncertain — present as a tilt, not a trade with conviction beyond evidence. Use only sectors and data I supplied. Note that historical sector-cycle patterns can fail when the driver of the cycle differs. Not personalized advice.

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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Structured Output

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

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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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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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