Commodity Futures Storage And Carry Cost Modeling logo

Commodity Futures Storage And Carry Cost Modeling

Community
HimanshuJ16
commodity-futures-storage-and-carry-cost-modeling

Use when pricing physical commodity futures or designing roll strategies, linking spot to futures through financing, storage and insurance, extracting implied convenience yield and classifying contango against backwardation.

Overview

PublisherHimanshuJ16
RepositoryAlgo-Trading-Skills
Skill namecommodity-futures-storage-and-carry-cost-modeling
Stars
50
Forks
7
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by HimanshuJ16 on GitHub. Read the source before you install it.

Installation

Install the Commodity Futures Storage And Carry Cost Modeling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HimanshuJ16/Algo-Trading-Skills/tree/main/skills/commodity-futures-storage-and-carry-cost-modeling
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HimanshuJ16/Algo-Trading-Skills.git /tmp/Algo-Trading-Skills
mkdir -p .claude/skills
cp -r /tmp/Algo-Trading-Skills/skills/commodity-futures-storage-and-carry-cost-modeling .claude/skills/commodity-futures-storage-and-carry-cost-modeling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Commodity Futures Storage And Carry Cost Modeling in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Commodity Futures Storage And Carry Cost Modeling on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Commodity Futures Storage And Carry Cost Modeling is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

When to Use

Use this skill when pricing physical commodity futures (Crude Oil CL, Natural Gas NG, Gold GC, Agriculture ZC) or designing term-structure roll strategies. The Cost of Carry model links spot prices ($S_0$) to futures prices ($F_T$) using financing costs ($r$), physical storage/insurance costs ($c$ proportional and/or $U$ per unit), and implied convenience yield ($y$). High convenience yield causes Backwardation ($F_T < S_0$), signaling physical inventory scarcity, whereas high storage costs relative to convenience yield lead to Contango ($F_T > S_0$).

When NOT to Use

  • As an arbitrage signal generator on the cheap side of the curve. For a consumption commodity the cost-of-carry relation is an inequality, $F_0 \le (S_0 + U)e^{(r+c)T}$, not an equality. Only the rich side is enforceable. A futures price below your fair-value estimate is a view about convenience yield, not a riskless trade, because you cannot generally borrow and sell short a physical commodity.
  • For non-storable commodities (electricity, most weather and freight underlyings). Storage arbitrage is the entire basis of this model; without storability the futures price is a risk-neutral expectation, not a carry relation. See weather-derivatives-and-niche-instrument-handling.
  • When spot and futures are not the same deliverable. A refiner's local crude assessment is not the contract-grade deliverable at the delivery point. A basis difference between grades or locations shows up here as a spurious convenience yield or a spurious arbitrage.
  • At sub-daily maturities. The implied-yield inversion divides by $T$, so within a day or two of expiry ordinary quote noise annualises into implausible yields.

Prerequisites

  • Spot price $S_0$ and futures contract price $F_{market}$ sampled at the same timestamp, for the contract-deliverable grade and delivery point.
  • Time to maturity $T$ in years, on a stated day-count basis.
  • A continuously compounded annual financing rate $r$ on that same day-count basis. Money-market quotes (e.g. SOFR, ACT/360, simple) must be converted before use.
  • Storage cost as a proportional annual rate $c$, a fixed currency amount per unit per year $U$, or both.

Workflow

  1. Full-Carry Price (the no-arbitrage bound):
    • $F_{full} = (S_0 + U_{PV}) \cdot e^{(r + c) T}$, where $U_{PV}$ is the present value of the fixed per-unit storage charge accruing over $[0, T]$.
    • This is the upper bound on the futures price, and equals the price at $y = 0$.
  2. Theoretical Futures Price at an Assumed Yield:
    • $F_{theoretical} = F_{full} \cdot e^{-yT}$. Because $y$ is unobservable, this is a view, not a fair value that arbitrage enforces.
  3. Implied Convenience Yield Extraction:
    • $y = \frac{1}{T}\ln\left(\frac{F_{full}}{F_{market}}\right)$.
    • If $y < 0$ the bound is violated. Before treating it as profit, re-check timestamp synchronisation, grade/location deliverability, and whether storage and financing costs are understated — those explain the great majority of apparent violations.
  4. Regime Identification:
    • $F_{market} > S_0$ is CONTANGO; $F_{market} < S_0$ is BACKWARDATION; equality is FLAT. Do not fold the equality case into either regime — a flat curve is a distinct, informative state.
  5. Arbitrage Audit (Cash-and-Carry only):
    • Raise a CASH_AND_CARRY signal only when $F_{market} > F_{full} \cdot (1 + \text{round-trip costs})$: buy spot, pay financing and storage, sell futures, deliver. This leg is executable by anyone with capital and storage capacity.
    • Do not raise an arbitrage on the other side. A cheap futures price is surfaced separately as a reverse-carry candidate, actionable only by an existing inventory holder (who is really monetising their own convenience yield) or in a commodity with a genuine lease/borrow market such as gold.

Full procedure: see references/workflows.md. Standards reference: see references/standards.md. Printable pre-flight checklist: see assets/checklist.md.

Common Pitfalls

  • Treating backwardation as a reverse cash-and-carry arbitrage: Crude oil routinely trades at implied convenience yields of tens of percent annualised during tight-inventory periods. A model that compares the market price to a fixed "baseline" convenience yield and flags every deviation will fire a false arbitrage on essentially every backwardated market, because the short-physical leg needed to capture it does not exist.
  • Ignoring Convenience Yield ($y$): Assuming futures prices are purely driven by $r + c$. In tight physical markets, convenience yield surges, causing deep backwardation that pure storage models fail to explain.
  • Fixed vs. Proportional Storage Costs: Exchanges regulate physical storage as a fixed charge per unit per day, not as a percentage of spot — CBOT caps grain storage in fractions of a cent per bushel per day and adjusts that cap through the Variable Storage Rate mechanism. Modelling a fixed charge as a percentage of spot silently makes storage cheap when the commodity is cheap, which is exactly backwards.
  • Day-Count / Compounding Misalignment: $T$ and $r$ must share a day-count basis, and $r$ must be continuously compounded. Pairing an ACT/360 simple money-market quote with an ACT/365 $T$ biases the implied yield by roughly 1.4% of the rate before any market signal is present.
  • Silent NaN propagation: nan <= 0 is False, so a naive positivity check passes NaN straight through to math.log and returns a NaN price alongside a confidently wrong regime string. Validate for finiteness, not just sign.
  • Non-synchronous quotes: A settlement-price futures quote against a live spot tick manufactures basis out of nothing. At short maturities the $1/T$ factor amplifies it into a headline-grade convenience yield.

Verification

  • Instantiate CommodityCarryCostModel(risk_free_rate=0.05, storage_cost_rate=0.02). With $S_0 = 100$, $T = 1.0$, $y = 0.01$, verify the theoretical price is $100 \cdot e^{0.06} \approx 106.1837$; with $y = 0.10$, verify $100 \cdot e^{-0.03} \approx 97.0446$. The full-carry price ($y = 0$) must be $100 \cdot e^{0.07} \approx 107.2508$.
  • Feed a WTI-like backwardated curve ($S_0 = 80$, $F = 76$, $T = 0.5$). Confirm the regime is BACKWARDATION, the implied convenience yield is roughly 17%, and is_arbitrage_opportunity is False — this is a normal market, not a trade.
  • Feed $F_{market} = 115$ against $S_0 = 100$, $T = 1.0$. Confirm CASH_AND_CARRY, a negative implied yield, and convenience_yield_bound_violated.
  • Feed $F_{market} = S_0$ and confirm the regime is FLAT, not BACKWARDATION.
  • Run python -m unittest discover -s skills/commodity-futures-storage-and-carry-cost-modeling/scripts and confirm 100% pass rate.

Related Skills

  • synthetic-continuous-futures-contract-construction
  • calendar-spread-and-multi-leg-order-atomicity
  • futures-contract-roll-automation
  • physical-vs-cash-settlement-handling

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Commodity Futures Storage And Carry Cost Modeling AI skill do?

Use when pricing physical commodity futures or designing roll strategies, linking spot to futures through financing, storage and insurance, extracting implied convenience yield and classifying contango against backwardation.

Why use Commodity Futures Storage And Carry Cost Modeling on TypingMind?

Because you install it once and use it with any model. Commodity Futures Storage And Carry Cost Modeling is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Commodity Futures Storage And Carry Cost Modeling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HimanshuJ16/Algo-Trading-Skills/tree/main/skills/commodity-futures-storage-and-carry-cost-modeling. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Commodity Futures Storage And Carry Cost Modeling?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Commodity Futures Storage And Carry Cost Modeling?

As many as you like. As long as a model supports skills, you can use Commodity Futures Storage And Carry Cost Modeling with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Commodity Futures Storage And Carry Cost Modeling AI skill free?

Yes. It is published on GitHub by HimanshuJ16 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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