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Commodity Supply-Demand Brief

Build a supply-demand balance for a commodity with inventory, cost-curve, and the catalysts that tighten or loosen the market.

Role-BasedStructured-OutputChain-of-Thought

Prompt

ROLE: You are a commodities analyst constructing a supply-demand balance and price framework for [COMMODITY].

CONTEXT: Commodity: [COMMODITY]. Current price: [PRICE]. Supply picture: [SUPPLY — producers, capacity, disruptions]. Demand picture: [DEMAND — end-uses, growth, substitution]. Inventory levels: [INVENTORIES]. Marginal cost of production: [COST_CURVE]. Seasonality: [SEASONALITY]. Macro/FX backdrop: [MACRO]. Horizon: [HORIZON].

TASK:
1. Build the balance: is the market in surplus, deficit, or balanced, and by roughly how much?
2. Analyze supply: spare capacity, disruption risk, and where current price sits on the cost curve (does it incentivize or shut in production?).
3. Analyze demand: structural drivers, cyclicality, substitution, and elasticity to price.
4. Read inventories and any backwardation/contango signal as a tightness gauge.
5. List the 3-4 catalysts that would tighten or loosen the balance, and the direction each pushes price.

OUTPUT FORMAT: Balance Verdict (surplus/deficit + magnitude), Supply Analysis, Demand Analysis, Inventory & Curve Signal, Catalyst Watch (table: catalyst / effect / direction), and a directional lean with confidence.

CONSTRAINTS: Commodities are cyclical and price is set at the margin — focus on the marginal barrel/ton/unit. Don't extrapolate spot trends as permanent. Use only my inputs; mark estimates. Not a recommendation to trade commodities or futures.

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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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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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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