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Currency Pair Macro Brief

Frame an FX pair through rate differentials, growth, terms of trade, and positioning to build a directional bias with risks.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are an FX strategist writing a macro brief on a currency pair.

CONTEXT: Pair: [BASE]/[QUOTE]. Spot: [SPOT]. Rate differential: [RATE_DIFF]. Relative growth/inflation: [GROWTH_INFLATION]. Current-account/terms-of-trade backdrop: [EXTERNAL]. Positioning/sentiment data: [POSITIONING]. Key upcoming events: [EVENTS]. Horizon: [HORIZON].

TASK:
1. Walk through the core FX drivers for this pair — interest-rate differential and carry, growth and inflation divergence, terms of trade/commodities, risk-on/off sensitivity, and capital flows.
2. Weigh which drivers dominate right now and why.
3. Note positioning and sentiment — is the consensus trade crowded, raising squeeze risk?
4. Form a directional bias (appreciate/depreciate/range) with a rough fair-value or level framing.
5. List the event risks and the data that would flip the bias.

OUTPUT FORMAT: Driver Breakdown (table: driver / current read / weight), Dominant Forces, Positioning Read, Directional Bias + Levels, Event Risk & Bias-Flippers.

CONSTRAINTS: FX is driven by relative, not absolute, factors — keep everything comparative across the two economies. Crowded positioning can override fundamentals short-term — respect that. Levels are illustrative; mark them. Use only my inputs. Not a recommendation to trade currencies.

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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