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Explore opportunities across the futures universe.
Access the key futures exchanges from one research workspace.
A quick view of the broader contract universe.
| Symbol | Contract | Category | Exchange | Months | Currency |
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Where recurring seasonal behavior is strongest.
Optimized spreads in screener
Top ranked pattern
Per one spread
Score 85 or higher
Browse structures and open their seasonal research.
Entries in the next two months · Calendar shown for 10 spreads; other structures per trade. Before costs.
Calendar spreads, butterflies and same-month wheat pairs from matched historical contract prices.
Loading historical data…
Price difference · cents per bushel
The dark line is the selected year's actual historical structure price. The blue line averages complete historical seasons on the same calendar dates, interpolates nontrading days, smooths five days and rebases to the selected year's first price. Incomplete seasons are excluded from the average. Neither line is a current quote or a forecast.
Hypothetical P&L for one structure in the selected window
Historical result for each matched season
Apply the same entry and exit dates to each historical season.
Entry uses the first matched trading day within 7 days; exit uses the last matched trading day within 7 days. Incomplete seasons are excluded. P&L = price move × $50 for one structure before costs.
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Explore inter-commodity and intermarket spreads: what the legs represent, why their relationship changes, how to read the direction, and how to calculate the result. Start with the explanation, then open the research available in FuturesSpread.
Intermarket is the broader family. Inter-commodity compares commodities or grades. Calendar spreads compare delivery months of the same market.
A price difference, a ratio and a processing margin are different measures. Match units and leg weights before reading them as a trade.
Long A−B means buy A and sell B. A widening favors the long spread; a narrowing favors the short. That convention depends on the stated formula.
The main relationships used in intermarket research. Each example below explains a mechanism; it is not a current trade recommendation or a live quote.
Different wheat grades trade against each other: KC hard red winter versus Chicago soft red winter, or Minneapolis hard red spring versus either winter benchmark. These are relationships between wheat classes, rather than two delivery months of one wheat contract.
Protein premiums, regional weather, export demand and milling requirements can move one class more than another. The same delivery month helps separate the grade relationship from the calendar relationship.
Both wheat prices rose, but KC rose more. If the difference fell to 40¢ instead, the same long pair would lose $500. A positive spread value is not itself a profit.
Wheat–corn, soybeans–corn and oats–corn compare different crops. They can reflect feed substitution, planting decisions and different crop cycles. Choosing two markets is a research starting point; it does not establish that every pair is a useful hedge.
Study which crop should outperform, then choose the leg direction. Related markets can both fall while a long spread still gains, or both rise while it loses.
Wheat lost 20¢, but corn lost 40¢. The short corn leg outweighed the wheat loss. Equal bushel contract sizes make this 1:1 example comparable; equal sizes do not guarantee equal market risk.
The crush compares the value of soybean meal and oil produced from soybeans with the cost of the beans. This is a three-leg processing relationship, not a direct subtraction of three differently quoted prices.
A long crush buys the product legs and sells the beans; it benefits when the weighted output value rises relative to the input. A short crush reverses those legs. Meal and oil demand can affect the margin differently.
This convention uses 44 lb of meal and 11 lb of oil per bushel. Physical yield ratios and tradable futures contract ratios differ. Our strategy results use each basket’s actual leg quantities and contract multipliers.
Oilshare asks a different question from crush: what percentage of the combined meal-and-oil revenue comes from oil? It measures the mix of output value, rather than revenue minus the cost of soybeans.
Biofuel demand and edible-oil markets can strengthen oil relative to meal. Oilshare can rise because oil rises, meal falls, or both move at different rates. The crush margin can change in the opposite direction.
An oilshare index, a futures basket and an oil-minus-meal price series are different instruments. A tradable position needs defined ratios; the $50-per-cent grain multiplier does not apply.
Brent–WTI compares two crude-oil benchmarks. Other energy relationships compare locations or grades of the same underlying commodity. The economic link is transport, quality, storage or access to different consumers.
A long Brent–WTI spread buys Brent and sells WTI. It is a view on Brent outperforming WTI, not necessarily on oil prices rising. Delivery conventions and settlement dates still need to be aligned.
The barrel exposure must match the actual contracts being traded. A location spread can remain wide because moving the commodity is costly; a large difference does not ensure convergence.
A crack spread compares refined products with crude oil. The common 3:2:1 model uses three crude-oil contracts against two gasoline contracts and one distillate contract. It represents a theoretical gross refining margin.
Long the modeled margin means buying the product basket and selling crude. Short the margin means buying crude and selling products. Refinery outages and product demand can alter the relationship.
This formula assumes product quotes in dollars per gallon; convert cents first if needed. Forty-two gallons equal one barrel. Gross margin excludes actual refining costs, so it is not net refinery profit.
A spark spread compares electricity revenue with the gas required to generate it. A dark spread uses coal instead of gas. A clean spread also deducts the cost of carbon emissions.
These relationships depend on plant efficiency, fuel prices and power demand. A plant can sell expensive electricity yet have a weak margin if its fuel cost rises faster. Power delivery periods and fuel units must match.
Seven is a teaching assumption, not a universal plant heat rate. Carbon costs, efficiency and regional conventions affect the calculation. Dollar P&L also requires the chosen power and fuel contract quantities.
Gold–silver is often studied as a ratio, while gold–platinum can be expressed as a price difference. A ratio compares relative prices; it is not itself the dollar value of a two-contract position.
Precious-metal investment flows and industrial demand can diverge. A long gold–silver relationship buys gold and sells an appropriately sized silver exposure. The hedge ratio must be specified.
Standard GC represents 100 troy oz; standard SI represents 5,000 troy oz. One of each is not an equal-ounce or equal-dollar hedge. Futures P&L must be calculated separately for both legs.
Live cattle versus feeder cattle compares finished and younger animals. A cattle feeding relationship can also include corn as a feed-cost leg. This is a weighted production-cost basket, not simply one price minus another.
Finished-cattle demand, feeder supply and feed costs all matter. A modeled producer margin buys finished-cattle value and sells input-cost exposures. Actual animal weights, feeding time and feed consumption determine the economic ratios.
These are simplified production values, not live quotes. A futures hedge requires separate contract sizes, quantities and delivery periods. Other production costs are excluded.
Intermarket research also compares financial futures: two Treasury maturities, two equity indexes, or related currencies. These are relative-value trades across contracts, while a calendar spread uses different months of the same contract.
Treasury trades are commonly sized by interest-rate sensitivity (DV01). Equity pairs may use dollar exposure or beta. Currency pairs require consistent quote directions and currency conversion. There is no universal 1:1 hedge.
Yield changes are not futures price changes; a yield-curve steepener cannot be defined from the words “buy the spread” alone. State the actual instruments, buy/sell directions and hedge quantities.
The actual line uses matched observed dates from both grain contracts. Its level is the price difference, not the profit earned since entry.
The blue normalized seasonal curve illustrates the average historical shape. Its displayed height is rescaled; trade results use raw historical prices.
Drag over the chart to set entry and exit, choose long or short, and inspect each season’s P&L, drawdown and sample size. Optimization describes the historical sample.
Historical results are before commissions, slippage and other costs. Correlation can change, and losses on the two legs can accumulate. Margin is collateral, not the purchase price or maximum possible loss; offsets depend on the broker and clearing rules.
Available here: grain pairs across Chicago wheat, KC wheat, corn, oats and soybeans (subject to overlapping contract prices), soybean crush baskets, and historical spring-wheat research. Other families on this guide are educational; their analyzers and price feeds are not connected here.
CME · Soybean crush conventions ↗CME · Oilshare ↗CME · Crack spread units ↗CME · Gold–silver ratio ↗ICE · Spark spreads ↗AVAILABLE WHEAT WORKSPACE · KC HRW / CHICAGO SRW
One contract of the first market minus one of the second, for the same delivery month.
For a 1:1 long spread, a widening to 55¢ is a 5¢ move, or $250 before costs; a narrowing to 45¢ is a $250 loss. These prices are illustrative. The green line below is the actual matched spread; the blue line is a normalized seasonal pattern, not a price forecast.
KC HRW price − Chicago SRW price = the spread, in cents per bushel.
Drag across the chart to select a window.
Each historical season is normalized before averaging. The line is scaled for comparison and is not a price forecast.
Hypothetical P&L from repeated historical trades
Cents per bushel for the selected position
Loading historical windows…
—Drag across the chart or select Optimize.
| Year | Spread position | Entry date | Entry spread | Exit date | Exit spread | Move | P&L (1 spread) | Best move ⓘ | Worst move ⓘ | Max DD ⓘ | Result |
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Ranked seasonal opportunities across available markets using the same optimization and historical engine as the analysis dashboard.
Historical calendar spread results across available markets · average P&L per spread before costs.
| Rank | Spread | Position | Optimized window | Consistency | Avg. move | Avg. P&L | Score | |
|---|---|---|---|---|---|---|---|---|
Ranking spreads… | ||||||||
See how active Corn futures are priced across delivery months and identify contango, backwardation and mixed curve structures.
Last Price by delivery contract. An upward slope indicates contango; a downward slope indicates backwardation.
Price difference versus the previous listed delivery month.
| Contract | Delivery | Last price | vs previous | Structure |
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Analyze 1:-2:1 futures butterflies using the same historical seasonality and optimization engine as calendar spreads.
Each row is calculated from Bloomberg historical contract prices and optimized over Sep 18 – Nov 30.
| Rank | Formula | Position | Optimized window | Consistency | Avg. move | Avg. P&L | Score | |
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Calculating butterflies… | ||||||||
Daily butterfly value compared with the selected historical seasonal average.
The structure is calculated as Leg 1 − 2×Leg 2 + Leg 3.
Daily 1:-2:1 structure value (cents per bushel)
Hypothetical P&L from recurring trades in the selected seasonal window
Selected seasonal window
Selected Butterfly window
| Year | Position | Entry Date | Entry Value | Exit Date | Exit Value | Move | P&L | Result |
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Leg 1 − 2×Leg 2 + Leg 3
Explore one contract, compare its current path with historical seasonality and review long-term performance.
Current price uses the left axis. The historical pattern uses a separate 0–100 shape index on the right.
Weekly observations from the active contract of the selected delivery month.
Change from the seasonal starting point to the last available price.
| Year | Start | Last | Change | Return | Range |
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The actual line shows the selected dated contract. The seasonal line compares the same delivery month across historical years. Profit and loss use actual entry and exit price changes, adjusted for LONG or SHORT and multiplied by the contract point value.
Daily spread price (cents per bushel)
Hypothetical P&L from recurring trades in the seasonal window
Spread movement during Sep 18 → Nov 30
Selected seasonal window · 15 historical windows · 15Y history · BUY December Corn / SELL May Corn
Calculated from every daily spread value inside the selected seasonal window.
| Year | Spread Position | Entry Date | Entry Spread | Exit Date | Exit Spread | Move | P&L (1 Spread) | Best Move | Worst Move | Max DD | Result |
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Explore the futures universe available inside FuturesSpread — from grains and energy to metals, rates, crypto and global indexes.
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FuturesSpread is being built around dated futures contracts rather than opaque continuous series. That makes it possible to analyze calendar spreads, seasonal relationships and curve structure while keeping the underlying contracts visible.
We built FuturesSpread to turn dated futures contracts, seasonal relationships and curve structure into research that traders can actually inspect — not black-box scores they are expected to trust.
Seasonality can look convincing in a single average line. But averages can hide weak years, unstable regimes and extreme outliers. FuturesSpread is designed so every headline statistic can be traced back to the contracts and historical seasons behind it.
We preserve the identity of the actual delivery months instead of hiding the research behind an opaque continuous series.
Average, median, win rate, drawdown, individual years and selected-window P&L are visible together.
Seasonality, contango, backwardation and curve structure are research context — not automatic predictions.
Discovery, validation, curve context and historical evidence belong in one coherent workspace.
Our historical foundation is built around Bloomberg data and contract-level mappings. The goal is simple: when a spread says July minus December, the history should represent that same economic relationship year after year.
We want the speed and polish of a modern SaaS product without sacrificing the ability to inspect the raw historical logic behind the result.
Open the product and test a spread yourself.
Open FuturesSpread →The black line is the live calendar spread: December corn minus May corn. The blue curve averages the same spread structure across the selected historical window. Historical performance is shown in cents and dollars per one spread.