# Commodities Trading Model Model physical commodities trading with stable margin per tonne, working capital intensity, demand-driven borrowing base utilisation, and three-statement output across energy, metals, and agriculture segments. - Canonical: https://finamodel.com/templates/commodities-trading-model - Excel download: https://finamodel.com/templates/commodities-trading.xlsx - Category: Capital Markets - Model type: Portfolio - Difficulty: Advanced - Audiences: Investors & analysts, Credit & risk, Traders, Risk managers, Commodity merchants, Hedging teams - Tags: Trading, Hedging, Risk, Mark-to-market ## Overview This commodities trading model forecasts a physical trading house's revenue, working capital needs, and leverage for three commodity segments (energy, metals, agriculture) operating with thin margins per tonne. Revenue scales from trading volumes (tonnes) multiplied by spot prices and stable gross margin per tonne - not margin percentage - because commodity prices fluctuate widely while margin per tonne is the stable metric traders track. The model projects 50 million tonnes annually across three segments, with $14/tonne energy margin, $12/tonne metals margin, and $10/tonne agriculture margin, yielding ~2% gross margin percentage (stable across scenarios). The model includes a demand-driven borrowing base facility where trade finance is drawn only as needed to fund working capital (receivables plus inventory minus payables). Opening balances and complete Day 0 balance sheet reconciliation are critical to prevent multi-billion-dollar imbalances. Capex and PP&E tracking includes existing assets ($1,200M gross with $400M accumulated depreciation) and new growth capex, with explicit opening gross PP&E and accumulated depreciation assumptions replacing magic-number scalars. The model separates RMI (ready-to-melt inventory) as a percentage of total inventory, using RMI only in eligible borrowing base and adjusted net debt calculations. This model serves trading houses and their lenders (syndicated warehouse providers) managing borrowing base facilities and covenant compliance. Institutional investors evaluating acquisition or growth capital opportunities use it to model working capital dynamics, leverage profiles, and scenario sensitivity to commodity price volatility and credit cycles. ## What's included - Physical and derivative position tracking by commodity - Margin-per-tonne revenue build (not margin percentage) - Working capital schedule for receivables, inventory, and payables - Borrowing base facility with eligible RMI inventory mechanics - Capex and PP&E with explicit opening gross and accumulated depreciation - Daily mark-to-market valuation with forward curves - Basis spreads and hedge ratios by term - VaR and Greeks calculation by portfolio segment - P&L attribution by price, carry, and hedging activity ## Commodities Trading Model: How the Template Works This commodities trading model helps evaluate liquidity and trade finance capacity for a physical trading house. It simulates three segments (energy, metals, agriculture) and produces integrated financial statements. The model is built around per-tonne margins, working capital cycles, and a demand-driven borrowing base. This overview explains its drivers, calculations, outputs, and practical use. ### Operating Drivers: Volumes, Per-Tonne Margins, and Working Capital The model's core driver is volume multiplied by gross margin per tonne for each commodity segment. This reflects how traders earn stable margins per tonne rather than volatile percentage margins. - Revenue derives from volume times price, while gross profit uses volume times margin per tonne, so percentage margins shrink as prices rise. Working capital is driven by days sales outstanding, days inventory outstanding, and days payable outstanding, determining receivables, inventory, and payables. - Inventory is split into readily marketable and non-RMI portions, which affects borrowing base eligibility and adjusted net debt. ### Calculation Flow: From Revenue to Borrowing Base Drawdown The calculation flow starts with segment volumes and margins, feeding into revenue, gross profit, and operating expenses. The bonus pool is based on pre-bonus operating profit, ensuring it reflects trading performance before financing costs. - Working capital balances are computed from revenue and COGS using days metrics. The borrowing base eligible amount combines receivables and eligible inventory at advance rates. - A pre-financing cash waterfall determines cash surplus or deficit, and trade finance drawdown fills only the deficit, capped by the eligible base. Interest on the facility uses the opening balance, avoiding circularity. ### Outputs: Integrated Statements and Covenant Monitoring The model produces a full set of integrated financial statements: income statement, balance sheet, and cash flow, all built from the drivers. The balance sheet includes a Day 0 opening column where share capital is derived to balance, preventing reconciliation errors. - Outputs also include a ratios and covenants sheet that monitors current ratio, adjusted net debt to EBITDA, interest coverage, and borrowing base utilisation. A checks sheet validates that the balance sheet balances in every year and that all covenants pass, providing a clear pass/fail signal. - These outputs help assess liquidity and compliance with lender requirements. ### Practical Use: Evaluating Liquidity and Capital Needs Practically, the model is used by lenders and traders to size borrowing base facilities and plan equity capital. It shows whether projected working capital needs can be funded within the available facility limit and whether covenant thresholds are expected to be met. - The demand-driven drawdown ensures the facility is not over-drawn, revealing headroom and commitment fees on undrawn amounts. Users can adjust assumptions for volume growth, price escalation, margin per tonne, and working capital days to test different scenarios. - This supports decisions about facility sizing, capital structure, and financial resilience. ## Built for trading-house economics Use this model when thin per-tonne margins, working capital intensity, and borrowing base covenants drive the financing decision. ## Margin per tonne, not percentage A useful commodities trading model anchors revenue on stable margin per tonne rather than margin percentage, which gets distorted by commodity price swings. ## Borrowing base aware This handles the demand-driven trade finance facility with eligible RMI inventory, OC adjustments, and covenant tracking that real trading houses live under. ## Built for trading-house economics Use this model when thin per-tonne margins, working capital intensity, and borrowing base covenants drive the financing decision. ## Margin per tonne, not percentage A useful commodities trading model anchors revenue on stable margin per tonne rather than margin percentage, which gets distorted by commodity price swings. ## Borrowing base aware This handles the demand-driven trade finance facility with eligible RMI inventory, OC adjustments, and covenant tracking that real trading houses live under. ## Features - **Basis management:** Model local vs. benchmark basis, storage costs, and optionality to optimize your physical hedge timing. - **Greeks and sensitivity:** Calculate delta, gamma, and vega by commodity and tenor so you can size hedges precisely. - **Daily reconciliation:** Automate P&L waterfall so traders see exactly what drove the daily result: price, carry, time decay, and gamma. ## Use cases - **Intraday risk monitoring:** Watch real-time exposures and trigger alerts when positions approach risk limits. - **Hedge effectiveness testing:** Backtest hedging strategies against historical curves to validate hedge ratios before deployment. - **P&L attribution and pricing:** Understand which trades and market factors drove performance, and feed pricing models. ## Frequently asked questions ### What is a commodities trading model? It is a three-statement model for a physical commodities trading house with stable per-tonne margin, working capital schedules, and borrowing base facility mechanics. ### Why margin per tonne instead of margin percentage? Commodity prices fluctuate widely while margin per tonne stays stable. Margin percentage is a misleading anchor when underlying prices move 20–40%. ### How does the borrowing base facility work? Trade finance is drawn only as needed to fund working capital (receivables plus eligible inventory minus payables), with RMI inventory treated separately for eligibility. ### Does it support stress scenarios? Yes. The model is built to flex commodity prices, working capital intensity, and covenant headroom across base, downside, and recovery scenarios. ### Is this useful for lenders? Yes. Syndicated warehouse providers use this structure to size facility limits, monitor covenants, and stress credit through commodity cycles. ## Related templates - [Commodity Hedging Model](https://finamodel.com/templates/commodity-hedging-model) - [FX Hedging and Portfolio Model](https://finamodel.com/templates/fx-portfolio-model) - [Bond Trading Model](https://finamodel.com/templates/bond-trading-model)