Data Schemas
RAX API responses follow consistent data schemas to ensure predictability and ease of integration.
Schemas are designed to balance clarity, extensibility, and backward compatibility.
General Response Structure
Most API responses follow a common structure.
Typical responses include:
A data object containing the requested payload
A metadata object describing freshness and context
Optional confidence indicators
This structure allows consumers to separate analytical content from system context.
Risk Score Schema
Risk-related responses typically include:
A normalized risk score value
A timestamp indicating when the score was computed
A confidence indicator
Optional supporting metrics
Risk scores are always relative and should be interpreted in context.
Exposure Schema
Exposure responses describe dependency and concentration.
Common fields may include:
Protocol identifiers
Chain identifiers
Exposure weights or percentages
Risk flags for elevated concentration
Exposure schemas emphasize structure over raw allocation.
Ranking Schema
Ranking responses return ordered lists of entities such as vaults or strategies.
Typical fields include:
Rank position
Entity identifier
Yield or performance metrics
Associated risk score
Liquidity context
Rankings are comparative snapshots rather than recommendations.
Portfolio Schema
Portfolio-related responses aggregate multiple signals.
They may include:
Portfolio risk score
Exposure breakdown
Aggregated metrics such as VaR
Time series references
Portfolio schemas are designed to support both dashboards and automated systems.
Time Series Schema
Time series responses provide historical data points.
Typical elements include:
Timestamps
Metric values
Sampling intervals
Data completeness indicators
Time series schemas support monitoring, trend analysis, and alerting.
Confidence and Metadata
Many responses include metadata fields such as:
Data freshness
Model confidence
Source identifiers
These fields help consumers evaluate reliability and relevance.
Schema Evolution
Schemas may evolve over time.
Backward compatibility is prioritized. When breaking changes are required, they are introduced through new API versions.
Summary
RAX data schemas provide a consistent and extensible foundation for consuming risk intelligence.
They are designed to support long-term integration across a wide range of applications and agents.
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