In internal benchmarking provided by the Helios product team, our cash-price forecasting models outperformed common baselines (seasonality/naïve/OLS), averaging ~108% more accurate than those standards, with the best model showing a ~565% improvement and overall accuracy up to ~5× better than traditional industry approaches. Those gains come from combining climate-risk indicators, regional production context, and real-time market signals rather than relying on seasonality alone.

Past performance testing aligns with that story. In a documented multi-year backtest, simple, rules-based strategies keyed to Helios climate-risk signals achieved ~65–71% directional accuracy across soy, corn, spring wheat, and cocoa futures prices, with $283k profit over the same time period —evidence that our signals are predictive, not merely descriptive. The paper lists strategy rules, trade counts, and per-crop results so the methodology can be reviewed in detail.

Separately, a scaled scenario shared by the team shows that a retail user following CommodiTrack™ signals with a simple long-only approach could have achieved ~$2.8M in simulated profits over four years. (Helios internal scenario shared here; use-case dependent.)

Lastly, the meteorological backbone we consume is also credible. Our providers expose leading national and global NWP models—ECMWF IFS/AIFS, NOAA GFS/HRRR, DWD ICON, and others—selected by region and cadence; Open-Meteo’s docs explicitly detail these model sources and update frequencies. ECMWF itself publishes annual verification showing continued improvements and high skill in near-surface variables after recent IFS upgrades, underscoring the underlying weather guidance quality we build on.

Our climate and market datasets are built from globally recognized, reputable sources such as the FAO, USDA, WITS, NOAA, ECMWF, Deutscher Wetterdienst, and other national meteorological and trade databases. These are publicly documented, so you can independently verify the inputs we use.

While some raw inputs are public or licensed, everything we publish is transformed into proprietary intelligence. We clean, normalize, and standardize pricing data into a consistent, easy-to-read format that’s unique to Helios. Our proprietary algorithms—such as WA%R (weighted average percent of risk), climate risk signals, seasonal risk ratings, and price/yield forecasts—are built on over 20 years of historical climate data and pinpoint coverage of 90% of the world’s export-oriented crop regions, down to the lat/long.

We don’t just trust the data because our models say it’s right—we validate it with real-world expertise:

Advisory Board – We’re guided by leaders with decades of industry experience:

Every dataset we publish undergoes multi-stage quality control, including automated anomaly detection and manual expert review. We normalize time zones, align reporting schedules, and weight climate station data for local accuracy—ensuring our signals reflect real-world conditions, not statistical noise.

All data is stored and processed on Google Cloud Platform, benefiting from their encryption, compliance certifications, and security architecture. We never train our models on your proprietary or confidential data, and our systems are secured end-to-end with TLS, role-based access controls, and continuous monitoring.

Our insights have helped commodity traders capture alpha from climate-driven futures swings, global retailers secure alternative sourcing, and financial institutions scale climate risk and pricing forecasts. Time and again, clients have used Helios to make decisions that save money, reduce risk, and improve timing in volatile markets.