Features Overview
Features are curated, transformed datasets created from raw fundamental inputs, designed as reliable building blocks for quantitative research.
What is a Feature?
A Feature is a derived dataset that applies consistent definitions and transformations to raw data, producing stable, research-ready inputs. Features are designed to:
- Reduce data preparation overhead
- Ensure consistent definitions across analyses
- Maintain point-in-time integrity for backtesting
Feature Structure
Each feature includes:
Column names vary by product and pillar. Taking
gbsi_us.fea_storage_inventory as the example:
| Field | Description |
|---|---|
effective_date | The business date the row describes |
storage_bcf | The measured value for that date |
storage_5y_avg | Five-year average for the same period |
storage_vs_5y_pct | Deviation from that average, as a percentage |
storage_zscore | Standardised deviation, comparable across series |
str_effective_at | When the value became publicly knowable |
str_observed_at | When Snowtrail could first have held it |
str_ingested_at | When this row was written |
Every feature table carries the three PIT timestamps. The domain columns differ by pillar; see each product's Features page for the full list.
Feature Categories
Features are organised by data domain:
- Supply features - Production, imports, capacity
- Demand features - Consumption, exports, usage patterns
- Storage features - Inventory levels, injection/withdrawal
- Infrastructure features - Capacity, utilisation, constraints
- Weather features - Temperature, degree days, anomalies