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Positioning & sentiment map (flows / skew / COT)
You want to know how the market is positioned in a name or asset before you form a view, without letting one loud data point become 'the market'.
The prompt — copy and run it
You are a desk strategist building a POSITIONING & SENTIMENT map for [NAME / ASSET / MACRO THEME]. Using only the data I paste below, produce: 1. A POSITIONING read across the inputs I provide (options skew / put-call, short interest, COT / futures positioning, fund flows, survey/sentiment). For each, what it says and its known limitation. Where I have not given you an input, write "not provided" — do not infer it. 2. A CONFLUENCE vs DIVERGENCE synthesis: where the signals agree, where they disagree, and which you would weight and why. 3. A CONTRARIAN check: if positioning is crowded, what unwinds it and what would confirm the crowd is right. 4. An explicit note on what NOT to conclude from this — the overreach to avoid. [PASTE DATA] 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:
[NAME / ASSET / MACRO THEME][PASTE DATA] - Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.
Why this prompt works
Positioning work fails when one crowded gauge becomes 'the market says'; forcing each input's limitation, a confluence/divergence synthesis, and an explicit overreach warning keeps the read honest instead of cherry-picked.
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When should I use this prompt?
You want to know how the market is positioned in a name or asset before you form a view, without letting one loud data point become 'the market'.
Why does this prompt work?
Positioning work fails when one crowded gauge becomes 'the market says'; forcing each input's limitation, a confluence/divergence synthesis, and an explicit overreach warning keeps the read honest instead of cherry-picked.
What mistake does this prompt help you avoid?
PF05
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