Products
Clean, derived datasets designed as reliable building blocks for quantitative research and analysis.
Features are curated, transformed datasets created from raw fundamental inputs such as supply, demand, flows, weather, and infrastructure data.
They are designed to be directly usable in quantitative research, screening, and modelling workflows.
Rather than exposing raw data alone, Snowtrail Features apply consistent definitions and transformations to produce stable, research-ready inputs.
Snowtrail Features provide structured, point-in-time safe data derived from complex market inputs, enabling research teams to focus on modelling and insight rather than data preparation.
{
"feature": "storage_inventory",
"product": "gbsi_us",
"week_ending": "2026-01-24",
"data": {
"region": "East",
"working_gas_bcf": 892,
"net_change_bcf": -156,
"vs_five_year_avg": "Below Normal",
"days_of_supply": 42
},
"metadata": {
"published_at": "2026-01-24T16:00:00Z",
"version": "1.2.0"
}
}Illustrative output. Fields and labels shown for demonstration purposes.
Storage inventory features track regional working gas levels, weekly changes, and context against historical norms, providing a structured view of US natural gas supply.
These normalized metrics enable consistent regional comparison and feed directly into balance models and supply analysis workflows.
Research teams often spend a disproportionate amount of time cleaning, aligning, and validating data before meaningful analysis can begin.
Common challenges include:
Inconsistent transformations applied across datasets
Revisions that break historical analysis
Unclear feature definitions across sources
High overhead for feature maintenance pipelines
Snowtrail Features reduce this burden by providing well-defined, reusable building blocks that can be trusted over time.
Snowtrail Features support a wide range of quantitative workflows.
Using derived inputs as components in custom models.
Comparing markets, regions, or assets on a consistent basis.
Running historical analysis with point-in-time safe data.
Allowing teams to move quickly from idea to test without rebuilding pipelines.
Features are designed to integrate naturally into existing research stacks.
Snowtrail Features are built with research integrity as a priority.
Features reflect what was known at the time, supporting robust backtesting.
Feature logic is consistent and documented, reducing hidden changes.
Common frameworks are applied across markets, enabling comparability.
Features are built for modelling and analysis, not just visualisation.
If you're interested in accelerating research and reducing data engineering overhead, we'd be happy to show how Snowtrail Features can fit into your workflow.
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