Arbitrage analytics for crude oil

Crude Arbitrage offers calculated arbitrage values, landed values, and theoretical refinery margins for crude grades across regions, based on price benchmarks, freight, and logistics cost assumptions. It also provides supporting historical trade flow context (e.g., backward-looking flow views and Sankey visualisations) plus analyst Insights to guide interpretation. Cumulative fixtures are offered to monitor live activity before the flows happen.

Crude arbitrage decisions depend on consistent methodology and comparable economics across routes. Without specialised calculations that incorporate freight and logistics adjustments, traders can misread route competitiveness, underestimate delivered costs, or miss changes in relative value. Structured arb analytics improves speed, consistency, and confidence in opportunity screening.

Users typically start by scanning arb values and landed values to identify attractive routes, then use refinery margin comparisons to evaluate demand-side pull. They test assumptions using the Scenario Builder, validate plausibility against historical flow patterns, and use Insights to contextualise shifts. The output is used to support trade ideas, risk discussions, and route selection. The information is also commonly used to model forward import/export out of a specific region for supply & demand.

Trade flows provide the real-world grounding behind theoretical economics. Even when arb values look attractive, constraints like operational patterns, buyer behaviour, and market structure can limit execution. Flow views help users understand which routes have historically cleared, how trade has shifted, and whether “paper economics” aligns with market behaviour.

Kpler combines arb economics, landed value modelling, and refinery margin outputs in one workflow, and incorporates additional logistics cost adjustments (e.g., port fees, demurrage and other route-related costs) to give a more complete delivered-cost picture. It also pairs forward-looking economics with backward-looking flow context and published Insights, helping users move from calculation to interpretation.

• 10+ years of proprietary data versus competitors' more limited historical datasets
• Global AIS network with 13,000+ receivers across 190 countries (industry-leading coverage)
• Unique combination of satellite monitoring, customs data, and real-time market signal

The flow views and Sankey charts are historical, showing how trade has occurred over the previous periods. The arb values, landed values, and refinery margins are theoretical and forward-looking, reflecting current assumptions and inputs (prices, freight, costs). Users combine both: theory to screen opportunities, history to validate plausibility.

Key criteria include:

• Methodology transparency (Look for a provider that clearly documents)
  • Which costs are included (freight, port costs, insurance, demurrage, etc.) and which are excluded
  • How prices are constructed (benchmarks used, differentials, timing conventions, assessment windows)
  • Assumptions and defaults (standard voyage durations, load/discharge ports, vessel class assumptions)
  • How calculations change by route (e.g., STS, canal transits, specific port fees). Why? Transparency matters because small changes in assumptions can materially change economics and influence trade decisions
• Route and crude grade coverage
  • The routes you actually trade, including both “core” and less-liquid/seasonal routes
  • A wide list of spot-relevant crude grades, not just benchmarks
  • Flexibility to handle grade substitutions and comparisons across regions
  • The ability to view results by origin, destination, and trade lane. Why? Coverage gaps often lead to manual workarounds and inconsistent analysis - especially when desks trade niche grades or regional routes.
• Ability to compare arb value vs landed value vs refinery margins
  • Arb value: the relative opportunity between markets (where is the best destination?)
  • Landed value: the delivered cost into a destination (what does it really cost to put the barrel there?)
  • Refinery margins: the demand-side pull (does the receiving region have refining incentive to take the crude?). Why? Seeing these together is critical because the “best arb” route can differ from the route that best aligns with refining economics, market constraints, or likely buying behavior.
• Key criteria include:
  • Easy “what-if” testing of key variables: prices, freight rates, port fees, demurrage, delays
  • Rapid comparison across multiple routes and destinations
  • The ability to save and share scenarios (e.g., “high freight,”  “tight crude diffs”). Why? This is essential because arb signals can flip quickly in volatile markets — scenario testing lets teams stress decisions and avoid acting on fragile economics.
• Integration of flows and market context (a robust tool should not only calculate theoretical economics but also provide the following items)
  • Historical flow context (where barrels actually went and how patterns changed)
  • Visual tools like route distributions and Sankey diagrams
  • Context on constraints such as structural trade relationships, recurring buyer behavior, or sustained route closures This helps distinguish between:
  • opportunities that are theoretically profitable, and
  • opportunities that are actually tradeable.
• Clear auditability for internal discussions and risk review (strong arb analytics should be easy to defend internally by providing the following)
  • A clear breakdown of every input (prices, freight, costs) and the resulting economics
  • The ability to export results for reporting and risk committees
  • Consistent methodology across time (so teams can track what changed and why)
  • Version control / time-stamping of assumptions where possible Auditability is especially important when arb models are used to support supply decisions, hedging strategy, or risk sign-off.