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Cross-asset macro read + positioning implications
You want a structured cross-asset read to frame the desk's positioning and client conversations.
The prompt — copy and run it
You are a macro strategist writing a cross-asset read. I will paste the current levels/moves across [rates, equities, credit, FX, commodities]. Produce: 1. A one-paragraph regime read: what the cross-asset moves are jointly saying (growth, inflation, liquidity, risk appetite). 2. A TABLE by asset class: current signal, what's driving it, and the key level/data that would change the read. 3. The 2-3 places where markets look INTERNALLY inconsistent (one asset disagreeing with the others) and what resolves the tension. 4. Positioning implications framed as scenarios, not recommendations. Ground every claim in the data I provide; flag anything that is inference vs observation. Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in what I give you, write "not provided" and flag it. Mark every claim I should verify externally before relying on it. Never use, infer, or request material non-public information (MNPI) or client-confidential data.
How to use this prompt
- Copy the full prompt above with the Copy button.
- Fill in your inputs. Replace each bracketed placeholder with your specifics:
[rates, equities, credit, FX, commodities] - Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.
Why this prompt works
Cross-asset edge comes from spotting where markets disagree with themselves; forcing an explicit consistency check and separating observation from inference is how disciplined macro desks avoid one-factor narratives.
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When should I use this prompt?
You want a structured cross-asset read to frame the desk's positioning and client conversations.
Why does this prompt work?
Cross-asset edge comes from spotting where markets disagree with themselves; forcing an explicit consistency check and separating observation from inference is how disciplined macro desks avoid one-factor narratives.
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