Data Dictionary
Complete reference of data fields and types used across Snowtrail products.
Point-in-Time Columns
Every Snowtrail table, signals, features, events, and backtests, includes two point-in-time columns that support reproducible research and audit.
| Column | Type | Description |
|---|---|---|
str_effective_at | date | The date this row became knowable: the business date it describes, plus the source's publication lag where one applies. This is the column the as_of parameter filters on. |
str_ingested_at | timestamp | The UTC timestamp of the pipeline run that last wrote this row. Rewritten on every rebuild, not a first-publication date, and not used by as_of. |
as_of filters on str_effective_at, so a query replayed at a historical date returns only what the market could actually have seen by then. This eliminates look-ahead bias arising from publication lag. It does not reconstruct values that a source has since restated, see Point-in-Time Integrity for what as_of does and does not do.
Common API Response Fields
When querying the Snowtrail API, responses include standard envelope fields.
| Field | Type | Description |
|---|---|---|
product_id | string | Identifier for the product (e.g. gbsi_us, glmi) |
count | integer | Number of records returned in this response |
has_more | boolean | Whether additional pages of results exist |
next_cursor | string | Cursor token for retrieving the next page of results |
Signal Columns
Signal schemas are product and dataset-specific. Below is an example using GBSI-US system stress.
Example: GBSI-US System Stress
| Column | Type | Description |
|---|---|---|
week_ending | date | The week-ending date for this observation |
stress_regime | integer (1--5) | Numeric stress regime classification, where 1 is lowest stress and 5 is highest |
stress_regime_label | string | Human-readable label for the stress regime |
confidence_score | float | Confidence in the regime classification |
directional_bias | string | Directional context associated with the current regime |
str_effective_at | date | Business date for this row |
str_ingested_at | timestamp | When this row was processed by Snowtrail |
Other signal datasets (e.g. balance momentum, price context, grid stress) follow similar patterns but with columns specific to their domain.
Feature Columns
Feature schemas vary by product and dataset. Features represent the derived, research-ready inputs that underpin signals and events. Every feature table includes:
| Column | Type | Description |
|---|---|---|
str_effective_at | date | Business date for this row |
str_ingested_at | timestamp | When this row was processed by Snowtrail |
Additional columns are specific to the product and feature dataset. For example, GBSI-US storage features include columns for storage levels, injections, withdrawals, and year-over-year comparisons.
Event Columns
Event schemas vary by product and dataset. Events represent discrete developments that materially affect market conditions. Every event table includes:
| Column | Type | Description |
|---|---|---|
str_effective_at | date | Business date for this row |
str_ingested_at | timestamp | When this row was processed by Snowtrail |
Additional columns describe the event type, severity, status, and other product-specific attributes.
Schema Discovery
Actual column names and types are product and dataset-specific. To explore the schema for a particular dataset:
- Use the Python SDK to fetch a sample and inspect the resulting DataFrame columns
- Consult the API Reference for endpoint-specific documentation
- Contact support@snowtrail.ai for detailed schema documentation