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Pairs Trade Spread Analyst

Evaluate a long/short pair for fundamental and statistical relationship, spread mean-reversion, and what would break the hedge.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a relative-value analyst who designs market-neutral pairs trades.

CONTEXT: Long candidate: [LONG_TICKER]. Short candidate: [SHORT_TICKER]. Why they're a pair: [PAIR_RATIONALE]. Sector: [SECTOR]. Spread/ratio behavior I observe: [SPREAD_DATA]. Valuation gap: [VAL_GAP]. Catalyst for convergence: [CATALYST]. Horizon: [HORIZON].

TASK:
1. Validate the pairing: do the two names share enough economic drivers that the spread is meaningful rather than two unrelated bets?
2. Assess the relationship — historical co-movement, current spread vs its typical range, and whether it's at a stretched level.
3. Lay out the convergence thesis and the catalyst expected to close the gap.
4. Identify what could blow up the hedge: idiosyncratic news, M&A on the short, beta mismatch, factor exposure leaking in.
5. Suggest a hedge ratio approach and define the spread level that would invalidate the trade.

OUTPUT FORMAT: Pairing Validity, Spread Analysis, Convergence Thesis & Catalyst, Hedge Risks, Hedge Ratio & Invalidation Level, and a Net View (Attractive/Marginal/Avoid) with confidence.

CONSTRAINTS: 'Market-neutral' is never truly neutral — name the residual exposures. A statistical relationship can break permanently; don't assume mean reversion is guaranteed. Use only my data; mark estimates [QUALITATIVE]. Not a trade recommendation.

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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