Kpler Arbitrage is designed to help oil market professionals make faster, data-driven decisions about physical oil trading. It synthesizes real-time data from multiple sources to calculate landed values, refining margins, and arbitrage signals. By eliminating manual spreadsheet work and disconnected models, Kpler Arbitrage delivers a timely structured view of trade opportunities. Within seconds, users can assess if moving a cargo from Point A to Point B is profitable, and to what extent that opportunity is being acted on.

Landed values represent the total value of a parcel of oil at its destination, factoring in all associated logistics and market adjustments. This includes freight charges, the impact of time structure, as well as operational costs like inspection fees, demurrage, and financing.

Additional factors include the type of vessel used, the selected shipping route (such as via the Suez Canal or the Cape of Good Hope), and the timing of the loading or discharge window. We assume voyage duration to be distance at 12.5 knots + 3 days for load/discharge + 1 day for Suez transit (if applicable).

The formal calculation is:

Landed Value = FOB Differential +Time Structure + Freight + Costs + Benchmark Spread (if benchmarks differ) to Destination Benchmark

This framework creates a normalized basis for evaluating cargo economics across origins, destinations, and delivery periods.

The computation relies on a range of multiple data and news sources, covering spot and forward pricing.

For crude oil arbitrage, each grade of crude is linked to a specific loading port (the origin), and each destination is tied to a single representative port (see Figure 1) that serves as a pricing center due to liquidity and data availability.

For refined product arbitrage, landed values are computed on a port-to-port basis. This reflects the greater importance of localized product specifications and port-specific pricing within refined product markets.

FOB differentials reflect the physical premium or discount to an underlying benchmark price. These differentials are a key component of landed value calculations and are used to normalize cargo pricing across different loading periods, origins, and benchmark structures.

In physical oil markets, cargo pricing can vary materially depending on the timing of the loading window.

A simplified methodology would treat a cargo loading on the 1st day of the month identically to one loading on the 30th day of the same month against the same benchmark tenor. However, this does not fully reflect physical market behavior, particularly during periods of strong backwardation or contango.

In backwardated markets, earlier-loading cargoes typically command a premium relative to later-loading cargoes, as prompt barrels carry greater value. Conversely, in contango, later-loading cargoes may become relatively more attractive.

To account for these dynamics, Kpler adjusts FOB differentials according to prevailing market structure and the relative timing of the cargo loading window. This methodology is applied across both crude oil and refined products markets.

The timing adjustment methodology is generally not applied to crude grades sold through Official Selling Prices (OSPs) published by National Oil Companies (NOCs).

OSP-linked crude pricing is typically assessed as a fixed premium or discount against a benchmark average for the entire loading month (for example Brent Futures, Dated Brent, or Oman/Dubai averages). Because the benchmark reference is averaged across the month rather than linked to specific loading dates, there is generally no material pricing distinction between early-month and late-month cargo loadings.

As a result, intra-month timing adjustments are not incorporated into OSP-priced crude differentials.

For certain refined product markets, particularly gasoline, the origin value depends not only on its outright benchmark relationship, but also on the blending economics required to achieve destination specifications.

In these cases, the outright FOB value and differential are obtained through a computed blending process.

As mentioned in the previous section, a blending tool is required to derive the FOB Price, or origin value. Unlike many crude oil grades or finished refined products, gasoline cargoes are composed of multiple blending components, each contributing different physical and chemical properties to the final specification. As a result potential arbitrage economics depend on the underlying blends economics associated with blending a particular grade of gasoline.

Certain gasoline blending properties behave linearly (such as sulphur and aromatics among others) meaning the resulting property of the blended gasoline can be estimated as a weighted average of its components. Other key properties however (such as Reid vapor pressure (RVP), octane and distillation) exhibit non-linear blending behavior, where interactions between blend components cause the final specification outcome to deviate from a simple proportional relationship.

To address this, Kpler utilizes a non-linear optimization model to compute feasible blend compositions capable of meeting destination specifications. The optimization process evaluates available blend components, their quality specifications, market price and finished grade specification constraints & escalation. The model ultimately seeks to identify blend compositions that satisfy all required quality specifications while minimizing the cost of producing a specification-compliant gasoline blend.

Time structure plays a crucial role in reflecting the value of oil over time and aligning prices of the voyage periods.

The difference between the loading-period value and discharge-period value represents the intermonth time structure adjustment. An intramonth adjustment is also applied to account for voyages that do not align precisely with monthly pricing periods.

For contracts that expire during a calendar month, Kpler incorporates the benchmark roll within the calculation rather than applying a single monthly structure across the entire voyage. This effectively results in a midpoint-to-midpoint assessment of value across the relevant pricing periods and provides a more representative measure of the time value associated with the cargo.

The underlying benchmark and forward curve used for the calculation vary according to the commodity, benchmark, and destination market being evaluated.

Certain refined product markets experience material seasonal specification changes. Gasoline is a notable example, where summer and winter grades display different pricing structures.

Where a cargo is expected to move between seasonal pricing periods, Kpler aligns the time structure calculation with the appropriate seasonal market.

This methodology ensures that the time structure adjustment reflects the economic value of carrying a cargo through time while maintaining consistency between comparable product specifications.

Users can adjust the underlying pricing basis by selecting a different value from the dropdown in the top right of the matrix.

Freight is a key cost component and is handled using a blend of observed and modeled data. Where available, Kpler uses direct spot market rates for relevant routes and vessel classes. When this data is missing or unavailable, Kpler imputes values by adjusting freight rates from similar trade routes.

For voyages where STS operations are required, such as movements in and out of the USGC on a VLCC, we account for the additional time and costs required as part of the freight costs. Extra charges for port or canal costs are also taken into consideration.

To ensure freight rates are aligned to the precise voyage schedule, Kpler uses FFA curves and interpolates these to create a daily rate series. This enables the integration of freight costs into the broader landed value calculation on a day-by-day basis.

In addition to freight and market structure adjustments, Kpler incorporates a range of operational and financing costs into the landed value calculation. These costs are intended to reflect the expenses associated with transporting a cargo from origin to destination and are applied consistently across crude oil and refined product markets.

Demurrage fee per barrel = (Demurrage Rate * Number of Days) / Loaded Quantity.

Working Capital cost per barrel = Borrowing Rate * Price per Barrel * Voyage Duration / 365.

A fixed fee applied on a $/tonne basis.

For certain cargoes, additional logistics-related charges may be incorporated into the FOB differential where appropriate. Examples include pipeline transportation, dock fees, terminal handling costs, or other origin-specific charges.

In many trading scenarios, the benchmark used to price a cargo at origin may differ from the benchmark used to assess value at destination.

To ensure cargoes can be compared on a consistent basis, Kpler incorporates a benchmark spread adjustment whenever the origin and destination pricing references differ. This adjustment normalizes cargo values onto a common benchmark basis and allows arbitrage economics to be assessed consistently across markets.

While landed values represent the price of crude at a destination, crude margin analysis reveals value. Because delivered crude pricing is often opaque and illiquid, refining margins serve as a proxy for assessing a refiner's willingness to process a given crude.

Kpler computes three types of margins to reflect different refinery configurations:

Different crudes affect margin calculations in different ways. The yields are based on the specific assays(quality characteristics) of each crude. We also account for regional variations in typical refinery output (for example, maximizing gasoline output in the US), as well as sulfur-related differences in product yield, since higher-sulfur crudes result in some mass loss through desulfurization and typically yield a less finished product slate on a mass basis.

Our refinery margin calculations are intended to represent the value of a marginal barrel of a given crude within an already optimized regional refinery setup. In this framework, refinery units are assumed to remain efficiently configured, so the calculation reflects how an incremental barrel would perform within that system. This provides a simplified but useful way to compare the economics of different crude grades across regions and refinery configurations.

Margins are generated using forward product prices, region-specific yield assumptions, and detailed crude assays. This enables traders and refiners to compare how different grades would perform across various configurations and locations.

For each trading region, we have developed forward curves for each refined product. This allows us to compute a margin for each delivery month. We do not use finer granularity than monthly, given the constraints refiners face in selling refined product cargoes. For clarity, the refining margin calculations used in the Arbitrage model follow the same methodology as the refining margins of the Insights team.

An arbitrage can be viewed through two lenses on the Kpler Arbitrage tool; landed values and refinery margins. Both methods allow users to compare an imported crude grade against a reference grade for the region. Both arbitrage views take a profit-based view to allow users to quickly identify how an imported grade performs against a regional reference.

Kpler assigns a regional sweet and source reference crude to each destination region. These crudes are carefully selected, based on the following criteria:

We therefore identify reference crudes for each trading region that are part of the typical refinery slate and are locally produced (if possible, i.e., not already arbitraged out of the region). We also recognize that sweet (low sulfur) and sour (high sulfur) crude markets are distinct. Thus, we establish one reference crude per region for each sulfur content category. We have also made it possible for users to customize the reference grade.

Refinery margins provide a profit-based view of a crude grade at a given destination. While the landed value of an imported crude may be negative or its refining margin positive, if a refiner has a local alternative that produces a preferable value, the incentive to import a crude from further afield would be smaller.

Arbitrage using refinery margins sees the reference grade subtracted from the target (imported) grade. This computation is inverted – with the target grade subtracted from the reference grade.

To assess whether an import arbitrage is open, Kpler compares the imported crude’s margin to a relevant local reference crude (typically a regional sweet or sour benchmark).

Margin arbitrage differential = margin (imported crude) = margin (reference crude)

Signal definition:

Arbitrage open if margin (imported crude) > margin (reference crude)

While landed values provide a price-based view of a crude grade at a given destination, in the arbitrage view of landed values, the logic is inverted to show a profit-based view, highlighting the incentive of an imported grade versus a reference grade.

Landed value arbitrage differential = -(landed value (imported crude) − landed value (reference crude))

Signal definition: Arbitrage open if landed value (imported crude) < landed value (reference crude)

Unlike crude oil markets, refined product arbitrage calculations do not compare imported cargoes against a reference feedstock or refinery margin framework.

Instead, arbitrage is assessed by comparing the delivered value of a cargo against the prevailing market value for the same product specification in the destination region.

This reflects the commercial decision faced by physical traders and marketers, where the profitability of a trade is determined by whether a cargo can be sourced, transported, and delivered into a destination market at a value below the prevailing local market price.

Computation: arbitrage differential = destination sales price – landed value

Signal definition: arbitrage open if landed value < destination sales price. Signal will be positive if open.

What criteria or market realities determine which combinations of grade, origin, destination, and vessel class are available?

What filters or constraints (e.g., data quality, market liquidity, strategic importance) are applied to limit these combinations?

The user interface is structured around a matrix and supporting modules that allow for deep exploration of arbitrage signals.

While the overall workflow is consistent across commodities, the available filters differ between crude oil and refined products arbitrage.

Common Filters

Destination

This filter allows users to specify the destination market for which arbitrage signals are calculated.

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Matrix Granularity

This filter controls the level of temporal granularity used within the matrix. The date used for the computation of the period is the midpoint of the selected window. There are three levels of granularity:

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Vessel Classification

Allows users to select one or more vessel classes. Available vessel classes include:

Crude Oil Filters

Refined Products Filters

In addition to the filters shown in the filter bar are a set of filters in the Matrix.

Arbitrage matrix: The arbitrage matrix presents computed data, showing positive or negative signals using red-green coloring or a grayscale. Rows represent commodities, grades, products, or delivery regions, while columns reflect time windows. Cells update every 30 minutes to reflect the latest data inputs.

How is missing data handled in the matrix display?

How are values sorted?

How frequently is matrix data updated, and what latency should users expect?

The comparison icon in the top right corner of the Arbitrage workspace opens a split-screen view containing two matrices. This allows users to compare how economics have evolved against any date since 3 January 2024. The historical comparison is based on the latest available snapshot (22:30 UTC) on the selected date.

This module provides a detailed breakdown, numerically and graphically, of an individual cell selected in the Matrix, showing key information and the constituent elements of the arbitrage calculation.

The accompanying trend lines help users identify which particular component(s) may be driving or detracting from the arbitrage economics.

Users can expand individual components where an arrow appears on the left-hand side, to view in further detail the composition and assumptions of each element.

Scenario builder

The Scenario Builder allows the user to edit values of the landed value calculation, which are also reflected in Refinery Margin and Arbitrage value types.

Comparison

In the comparison tab users can select up to four additional crude grade and vessel combinations to compare against the combination selected in the matrix.

Tonnage supply

This tab allows the user to overlay historical tonnage availability for a given number of days forward (Load Date – Today), together with the computed freight for the voyage. In the top right corner of the widget is a slider that allows the user to set the ETA to 15 days, rather than matching the load date.

It makes it easier to judge whether an arb is actually executable. When tonnage is tight, freight can move fast and wipe out the edge, and this helps users spot those situations earlier for planning and execution.

Crude yields (crude only)

This tab provides transparency to the user, allowing them to see the underlying yield assumptions which underpins the computed arbitrage. Here we see each refined product that a given crude produces for the selected trading region and refinery type.

Gasoline Blend Composition (gasoline only)

For gasoline arbitrage calculations, this tab provides transparency into the blend composition used to derive the FOB value of the selected gasoline grade.

Users can view the blend components that make up the optimized gasoline blend, together with their respective proportions and quality specifications. This enables users to understand how the final gasoline specification is achieved and which components contribute most significantly to the overall blend value.

The tab also displays the target specification constraints used within the optimization process.

When a cell is selected in the matrix, the flows vs rolling arbitrage is available to see in the right side panel, under the Flows tab.

What is this module, and what does it signify?

How are the flow values computed, and which data points feed into this module?

Without a specific cell selection in the Matrix:

When a cell is selected in the Matrix:

What is the time window used for the rolling arbitrage calculation, and why was this specific window chosen?

How are flow metrics correlated with the rolling arbitrage values?

The Sankey diagram illustrates the volume and direction of physical oil flows between regions, with color showing arbitrage status and bar width indicating relative magnitude. This module visually represents the significance of open and closed arbitrages, weighted by physical trade flows. The numerical values in the arbitrage matrix do not, by themselves, indicate the volume of physical flow that exists or will necessarily occur. For example, a specific arbitrage might appear very widely open, but due to physical or operational constraints, it may not be acted upon, meaning a large flow won't materialize.

What specific data points (origin, destination, volume, etc.) are used to generate the Sankey diagram?

How is the width of each flow link calculated (e.g., based on volume, fixture count, etc.)?

The cumulative fixtures chart provides a view of how loading programs are progressing over the course of a given loading period in terms of reported fixtures.

A faster accumulation of fixtures indicates higher booking activity on specific routes or markets, which may translate into an increase in observed flows as destinations become clearer. This is particularly useful for monitoring early signals of changing trade patterns.

This module reduces the need for manual tracking of fixture reports by providing a consolidated view of how booking activity is evolving relative to previous months.

What does the chart signify?

The module charts the cumulative volume of newly reported fixtures over time. It compares the current month's booking pace against both the previous month and the average pace over the past year.

Each time a new fixture is reported, the line for the corresponding month increases by the estimated cargo tonnage.

When the current month's trajectory exceeds historical patterns, this suggests stronger-than-usual program completion and may point to increased activity on specific routes. In cases where destination is not yet selected, this can serve as an early indicator of future flow realization.

For markets where loading programs tend to be relatively stable from month to month, deviations in booking pace may reflect timing differences, routing changes, or shifts in underlying demand rather than outright changes in total traded volumes.

What is the source of fixture data, and how reliable is it?

Fixture data is aggregated from shipping market sources. Kpler uses the initial reported date (the first time any shipbroker reported the fixture) as the reference date for each fixture.

How is the cumulative fixture value computed (e.g., simple aggregation vs. adjusted for cancellations/updates)?

What specific details are available in the pop-up for fixture information, and how are they prioritized?

How frequently is fixture data updated, and what latency should users expect?

Fixture data is updated as and when new information is received from Kpler's sources.

Why EUROPE & APAC when searching for NWE/MED/EAST/SE ASIA?

The nature of fixture reports means destination information is often reported at a broader regional level rather than a specific market. For example, loadings from WAF or USGC may be reported as "UKCM" (Europe) rather than specifying NWE or MED.

To ensure relevant fixture activity is still captured, broader destination groupings are used where appropriate. As a result, MED and NWE fixture searches may be aggregated under EUROPE, while EAST and SE ASIA may be grouped under APAC.

In the top banner, next to the Dashboard tab, is Breakdown. Here, users can compare different arbitrage combinations and the individual constituent elements in a single page view.

The data shown is the same as that displayed in the Dashboard but is presented in a tabular format rather than a matrix, allowing for easier comparison of arbitrage windows.

Each row represents a specific origin, destination, load date, and discharge date. Each column shows the value of the individual components that make up the computed arbitrage.

1 September 2025: First version of this methodology.

15 October 2025:

8 January 2026:

15 January 2026: Updated description of landed value in export view

3 February 2026:

25 February 2026:

24 March 2026: Update to description of cumulative fixtures section.

20 April 2026: Refined wording on refinery margin methodology

8 May 2026: Added section for Breakdown tab

1 June 2026: Added section for crude yields tab

10 July 2026: Added refined product arbs to methodology; overhauled entire document to separate general items, crude-specific items, and refined product specific items.

23 July 2026: Added details of new crude regions.