Last month I joined a panel at Commodity Trading Week Americas 2026 titled “Data-Driven Trading: Strengthening Your Data Management Framework.”
Throughout the discussion, one point kept resurfacing: data quality isn’t just about the commodity itself, and this is one of the key differentiators between commodity and financial trading. Data for commodity trading needs to observe all the components of the supply chain: extraction, transportation, and delivery.
Weather and geopolitical instabilities came up repeatedly as examples of risk factors that must be built into commodity trading pipelines and integrated into the data management system. My fellow panelists and I agreed on the same point: strengthening a data management framework is foundational: better data collection, a real understanding of the features that explain a given problem, and integrating all of it into a structured, systematic record. Without this foundation, machine learning models and AI agents cannot unleash their full potential.
The Fundamentals of Data Management
During both the conference and the panel, three threads stood out to me: backtesting, reliability, and uncertainty.
On backtesting, can you honestly reconstruct what you knew at a given point in time, and replicate those exact conditions later, rather than unintentionally benefiting from hindsight? Data abundance makes this harder than it sounds: the same piece of information can exist in multiple places, with different values recorded at different times.
Unless your deduplication process is systematic, consistent, and reproducible, a backtest can quietly pick a version that is not available at that specific time — a subtle form of hindsight bias hiding inside routine data cleaning. When it comes to data reliability, data that feeds a trading decision or asset allocation must be verified, reproducible, and systematic, so that a prediction made today and rerun tomorrow remains identical.
Finally, a solid trading strategy requires uncertainty to be properly quantified against current market conditions, with volatility systematically propagated through the entire estimation process. The strategy should show an estimate if conditions unfold as expected, as well as show how an estimate would shift if the external scenario changed.
The Unique Uncertainties of Commodity Trading
The third point has many implications, especially in the commodity trading space. Commodities move, spoil, and get stuck in a canal. A single, seemingly small event, like the Panama Canal drought, cascades into traffic congestion, freight rate disruptions, and higher canal fees, and could ultimately result in significant demurrage costs.
When vessel transits through the Strait of Hormuz slowed sharply after hostilities between the US and Iran broke out in late February, close to 20% of global oil supply came off the water. That kind of disruption to one of the world’s major trading arteries pushed congestion onto alternative routes as vessels rerouted. Because bunker fuel is refined from oil, less crude moving meant less bunker supply and higher bunker costs.
How can we be prepared for similar future market upheaval? Traders are aware of the risks on certain routes and associated with certain trades. But do they have the right tools to quantify that uncertainty? Can they properly model scenarios with a quantitative, systematic, data-driven approach?
Veson’s new Bunker Insights is a direct answer to these questions.
Point-in-time structure, for real backtesting
During another Commodity Trading Week session, “Building Smarter Trades with Quant Models,” experts discussed the importance of proper data management to produce realiable backtesting results. Without precise time labeling on your data, today’s knowledge silently leaks into yesterday’s decision, and the backtest ends up looking more optimistic than it really is. Veson’s Bunker Insights is built to close that gap.
Bunker Insights is built within the Veson Platform and provides an independent bunker price benchmark that gives users global, systematic, point-in-time estimates of marine fuel markets across more than 1,300 ports ports. At the core of that probabilistic approach is a deep learning model trained on hundreds of thousands of aggregated and anonymized bunker transactions. Every estimate carries three separate timestamps: the date the estimate refers to, the date it was generated, and the timestamp when it became available to a user.
That distinction is at the core of quantitative trading and is key to defining solid backtesting strategies. If you can’t reconstruct exactly what information existed at a given moment, there is a high risk of leaking future data into your trading models that could skew their accuracy and end in lost revenue.
Behind that structure is Veson’s data science team, applying cutting-edge algorithms with the same scientific rigor and cross-disciplinary thinking used across the hard sciences, to uncover the complex patterns underlying maritime trade.
Quantified uncertainty
In Bunker Insights, users can hold a port, a fuel type, a quantity, and a risk tolerance fixed, and simulate different crude oil settlement prices to test how procurement economics would respond:
Users can also analyze the effect of port arbitrage by fixing all input variables and changing the port of procurement to determine the impact if the vessel bought bunker at a different location.
This engine enables a digital replica of the market conditions that drive bunker procurement. What happens to my exposure if oil price spikes 30% tomorrow? Is the deal still worth doing? Should I change my procurement terms before the market moves?
My closing advice on the panel was simple: trace the data products your company depends on every day back to its source, and make sure you have visibility into provenance, accuracy, and uncertainty at every step.
This framework is embedded in Bunker Insights, giving clients a complete view of bunker prices with quantified uncertainty, built on an independently validated, continuously improved dataset that’s available wherever they work.
