A floating-storage signal for a crude trading desk
A commodity trading house (anonymized)
Illustrative case study. Client identity is anonymized and figures represent the class of outcome MarineAware targets, not a specific contracted result.
Challenge
The desk already bought high-grade AIS and cargo data but could not turn it into a proprietary edge. Floating storage, ship-to-ship transfers and port queues were visible in the raw feed but arrived as noise, not signal — and always later than the desk wanted.
Approach
We engineered features directly from AIS: floating-storage tonnage from laden tankers idling beyond normal dwell, STS transfer detection, port-congestion queues and draft-derived utilisation. We fused these with UN Comtrade trade-flow context, then backtested the resulting indicator with explicit handling of alpha decay and data latency before wiring it into the desk's existing research model.
Outcome
The desk gained a proprietary floating-storage indicator with a documented lead time over the printed market, delivered as a clean signal into models it already trusted — with no conflict of interest across competing desks.
This engagement is representative of MarineAware’s market-intelligence work. Figures are illustrative of the class of outcome we target and are anonymized to protect client confidentiality.
Raw data is not a signal
Every serious desk can buy the same AIS and cargo feeds. The edge is never the feed — it is the transformation. A laden tanker idling beyond its normal dwell is a data point; the change in aggregate idle tonnage across a region, cleaned of coverage artefacts and seasonality, is a signal.
We sit between the vendor and your book
We are not another data vendor and we do not run a competing desk. Our job is the layer in the middle: the feature engineering, the fusion and the backtesting discipline that turns a purchased stream into defensible, conflict-free alpha inside the models you already run.