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Snowtrail Documentation

website·Indexed 30 days ago

https://docs.snowtrail.ai

A modular intelligence platform for energy and commodity markets. Snowtrail provides structured data through a RESTful API and S3 delivery, organizing market intelligence into four core types: Signals, Features, Events, and Backtests. The platform includes specialized indices such as the Gas Balance Stress Index (GBSI), Weather Risk Stress Index (WRSI), Global LNG Marginality Index (GLMI), and Power Event Market Index (PEMI).

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What's inside Snowtrail

  1. Overview of Snowtrail Backtests

    Backtests in Snowtrail provide pre-computed, point-in-time correct performance analysis for signals. Their primary purpose is to determine how effectively a signal captures the market condition it was designed to detect. Crucially, backtests measure whether regime classifications correspond to meaningfully different market outcomes, rather than predicting price direction.

  2. Overview of GBSI-US Features

    GBSI-US Features are derived inputs that underpin both Signals and Events, providing research-ready representations of the US gas balance. These features are constructed using stable definitions and consistent transformations, derived from supply and demand components, storage dynamics, flow and infrastructure behavior, and weather-driven consumption influences. Typical use cases include custom signal development, quantitative modeling, backtesting balance-driven strategies, and cross-period/cross-regime comparisons.

  3. Overview of WRSI Features use cases

    WRSI Features provide derived weather metrics and regime classifications that serve as the foundation for signals. They are primarily designed for research and backtesting, regime-aware modelling, and performing cross-season comparisons.

  4. Overview of PEMI Events

    PEMI Events capture discrete physical developments derived from outage notifications (Nord Pool UMM and ENTSO-E) at a (bidding_zone, event_date) grain. They record announced generation and transmission unavailability, including infrastructure outages, maintenance events, logistical constraints, and operational disruptions. Events are time-bounded and asset-aware, firing only on the transition into an elevated state rather than every day the state persists.

  5. Overview of Snowtrail Data Model

    Snowtrail organizes market data into four core types to support institutional commodity trading:

    • Signals: Quantitative classifications derived from fundamental data used for position sizing and risk conditioning (not price prediction).
    • Features: Curated, transformed datasets created from raw inputs, serving as building blocks for quantitative analysis.
    • Events: Structured records of market developments such as outages, policy changes, and supply disruptions.
    • Backtests: Walk-forward validation results with bootstrap confidence intervals for evaluating signal quality.

  6. Overview of GBSI-EU Signals for European Gas Markets

    GBSI-EU Signals apply the Snowtrail Signals framework to the European gas market. They summarize balance conditions into interpretable indicators that reflect system tightness, flexibility, and relative stress. Common use cases include monitoring regional/system-wide balance conditions, assessing sensitivity to supply or infrastructure disruptions, providing context for hub pricing and spreads, and feeding balance indicators into systematic models.

  7. Overview of WRSI Events for weather monitoring

    WRSI Events provide discrete context for developments related to weather regime transitions, forecast uncertainty, and extreme temperature risk. Unlike WRSI Signals, which provide a continuous view, Events highlight specific moments of shift or unusual uncertainty. Typical use cases include monitoring regime transitions, identifying elevated forecast uncertainty, tracking directional instability in model outputs, and assessing tail risk from extreme temperature scenarios.

  8. Overview of GLMI Features

    GLMI Features provide the underlying components that drive marginality, enabling systematic analysis and scenario exploration. These features include supply availability, regional demand conditions, infrastructure constraints, and shipping and logistics dynamics.

  9. Overview of GBSI-EU Features

    GBSI-EU Features are derived inputs used to underpin signals and events. They provide structured representations of supply and demand components, storage behavior, and cross-border flows and infrastructure utilization. These features are intended for use in custom analysis and research workflows.

  10. Understand Snowtrail Core Data Types

    Snowtrail organizes its data model into four primary types, each serving a specific purpose in quantitative research and market analysis:

    • Signals: Quantitative classifications of market conditions derived from fundamental data. They describe the current market state and are used for position sizing and risk conditioning rather than price direction prediction.
    • Features: Curated and transformed datasets created from raw fundamental inputs, serving as reliable building blocks for research.
    • Events: Structured records of discrete market developments (e.g., outages, policy changes, supply disruptions) that may impact fundamentals or prices.
    • Backtests: Point-in-time correct performance analyses for each signal, used to determine if regime classifications correlate with different market outcomes.

  11. Understand GBSI-US Events for US gas balance analysis

    GBSI-US Events capture discrete developments that materially affect the US gas balance, highlighting periods where conditions deviate from normal behavior. These events are time-bounded, enabling analysis of when a development began, its duration, and subsequent evolution. They typically reflect abrupt changes in demand, supply disruptions or recoveries, structural shifts in balance dynamics, or breaches of stress thresholds. Common use cases include monitoring emerging stress conditions, performing post-event attribution, and linking market outcomes to underlying developments.

  12. Understand GLMI Events for LNG Marginality

    GLMI Events track developments that materially affect LNG marginality, providing discrete context around transitions in marginality regimes. Captured events typically include supply disruptions, infrastructure outages, shifts in shipping economics, and changes in regional demand patterns.