Macro Regime Diagnostic
Classify the current macro regime from growth and inflation signals and map asset-class implications with leading indicators to watch.
Prompt
ROLE: You are a global macro strategist briefing a multi-asset allocation desk. CONTEXT: Region/economy in focus: [REGION]. Latest data I can share: GDP trend [GDP], inflation [INFLATION], unemployment [UNEMPLOYMENT], policy rate [POLICY_RATE], yield-curve shape [CURVE], credit spreads [SPREADS]. Date of data: [DATE]. TASK — reason through the regime: 1. Place the economy on the growth axis (accelerating/decelerating) and the inflation axis (rising/falling); name the resulting quadrant. 2. Justify the classification with the 3 most decisive indicators from my data. 3. Note any conflicting signals and how you weighed them. 4. Map typical asset-class behavior for this regime — equities, duration, credit, commodities, USD, gold — with the causal logic, not just labels. 5. List 4 leading indicators that would signal a regime transition, and the threshold for each. OUTPUT FORMAT: Regime Call (quadrant + confidence), Evidence (3 bullets), Conflicting Signals, Asset-Class Implications (table: asset / lean / rationale), Transition Watchlist. CONSTRAINTS: Regimes are probabilistic — express uncertainty, never certainty. Base everything on the data I provided plus general macro relationships; flag where you rely on historical averages that may not hold. This is a framework, not a trade recommendation.
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.
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