snowtrail 0.3.0

Python SDK for Snowtrail Research API - commodities intelligence for systematic trading.

Snowtrail Python SDK

Python SDK for the Snowtrail Research API - commodities intelligence for systematic trading.

Installation

pip install snowtrail

Verify your setup:

python -m snowtrail.check_setup

Quick Start

from snowtrail import Snowtrail

# Initialize the client (uses SNOWTRAIL_API_KEY env var if set)
client = Snowtrail(api_key="your-api-key")

# Get latest GBSI-US system stress signal
df = client.gbsi_us.system_stress()
print(df)

# Get historical data
df = client.gbsi_us.system_stress(
    date_from="2024-01-01",
    date_to="2024-12-31",
    limit=500
)

All endpoints return a pandas DataFrame by default.

Client Overview

The SDK is a thin wrapper around the REST API with no business logic:

• Typed accessors for each product (client.gbsi_us, client.pemi, etc.)
• DataFrame responses for easy analysis and backtesting
• Automatic retries with exponential backoff for transient errors
• Environment-based auth via SNOWTRAIL_API_KEY

The client maps directly to API endpoints - what you see in the API docs is what you get.

Authentication

Set your API key via environment variable (recommended):

export SNOWTRAIL_API_KEY="your-api-key"

from snowtrail import Snowtrail

# Automatically uses SNOWTRAIL_API_KEY
client = Snowtrail()
df = client.gbsi_us.system_stress()

Or pass it directly:

client = Snowtrail(api_key="your-api-key")

Products

Product	Description	Primary Signal	Frequency
gbsi_us	US Natural Gas Balance Stress Index	system_stress()	Weekly
gbsi_eu	EU Natural Gas Balance Stress Index	system_stress()	Daily
pemi	Power Event Market Intelligence	grid_stress()	Event-driven
glmi	Global LNG Market Intelligence	marginality()	Monthly
wrsi	Weather Risk Signal Intelligence	forecast_stress()	4x daily
wssi_us	Weather Storage Shock Index	demand_shock()	4x daily

Return Types

All data endpoints return a pandas DataFrame by default:

df = client.gbsi_us.system_stress()
# Returns: pandas.DataFrame with columns like week_ending, stress_regime, etc.

For raw JSON responses (including metadata), use _get_raw():

response = client.gbsi_us._get_raw("system_stress", latest=True)
# Returns: {"product_id": "gbsi_us", "table": "...", "data": {...}, "metadata": {...}}

Usage Examples

GBSI-US (US Natural Gas)

# Signals
df = client.gbsi_us.system_stress()

# Features
df = client.gbsi_us.balance_momentum()
df = client.gbsi_us.storage_inventory()
df = client.gbsi_us.supply_elasticity()
df = client.gbsi_us.features()

# Events
df = client.gbsi_us.storage_surprise()
df = client.gbsi_us.regime_shift()

GBSI-EU (EU Natural Gas)

# Filter by country
df = client.gbsi_eu.system_stress(country="DE")
df = client.gbsi_eu.composite(country="NL")
df = client.gbsi_eu.dispersion()

WRSI (Weather Risk)

# Filter by geography
df = client.wrsi.forecast_stress(geography="US")
df = client.wrsi.forecast_dynamics(region_type="state")

Historical Data

All endpoints support date range queries:

# Get history instead of latest
df = client.gbsi_us.system_stress(
    date_from="2023-01-01",
    date_to="2024-01-01",
    limit=1000
)

Point-in-Time Queries (Backtest-Safe)

Use as_of to query data as it was known at a specific date:

# What did the system stress signal look like as of June 2025?
df = client.gbsi_us.system_stress(
    date_from="2025-01-01",
    date_to="2025-06-01",
    as_of="2025-06-01"
)

Pagination

For large result sets, use cursor-based pagination:

response = client.gbsi_us._get_raw("system_stress", latest=False, date_from="2020-01-01")
while response.get("has_more"):
    next_page = client.gbsi_us._get_raw(
        "system_stress",
        latest=False,
        date_from="2020-01-01",
        cursor=response["next_cursor"]
    )
    response = next_page

Retries and Timeouts

The SDK handles transient failures automatically:

• Retried errors: 429 (rate limit), 500, 502, 503, 504
• Retry attempts: 3 with exponential backoff (1s, 2s, 4s)
• Default timeout: 30 seconds per request
• Respects Retry-After header when present

# Custom timeout
client = Snowtrail(api_key="...", timeout=60)

Error Handling

from snowtrail import Snowtrail, AuthenticationError, RateLimitError, NotFoundError, APIError

client = Snowtrail(api_key="your-api-key")

try:
    df = client.gbsi_us.system_stress()
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded after retries")
except NotFoundError:
    print("Endpoint not found")
except APIError as e:
    print(f"API error: {e}")

Low-Level Access

For direct API access, use the underlying HTTP client:

# Direct GET request (returns raw dict)
response = client._client.get("/gbsi_us/system_stress", params={"latest": True})

# Health check
client.health()  # {"status": "ok"}

# List all products
client.products()  # [{"id": "gbsi_us", "name": "GBSI-US", ...}, ...]

The client includes a User-Agent header (snowtrail-python/{version}) for debugging.

Examples

See the examples/ directory for Jupyter notebooks:

• 01_quickstart.ipynb - Basic SDK usage and product exploration
• 02_signal_analysis.ipynb - Signal analysis and visualization workflows

API Reference

• Full API documentation: https://api.snowtrail.ai/docs
• Product documentation: https://docs.snowtrail.ai

Support

• Email: [e-mail]
• Documentation: https://docs.snowtrail.ai