TRAXR · Data Model

TRAXR Data Model

XRPL-native entities and deterministic structures used for pool analysis and scoring.

Data Model Overview

TRAXR operates on a strictly read-only data model derived from XRPL ledger state. All entities represent either direct on-ledger structures or deterministic transformations of those structures.

Core XRPL Entities

AMM Pool
Liquidity reserves, fee parameters, pool state, and swap sensitivity derived from XRPL AMM objects.
Issuer
Issuing account flags, history, and structural trust dependencies within the ledger.
Trustline
Supply limits, balances, and issuer relationships that define asset circulation and exposure.
Ledger Snapshot
A point-in-time view of XRPL state used as a deterministic scoring baseline.

Deterministic Data Model

All TRAXR scores are derived from a single XRPL ledger snapshot. No predictions, no heuristics — only deterministic state.

Snapshot-based data model · deterministic inputs · reproducible metrics

Derived Metrics

From the core entities, TRAXR derives normalized metrics such as liquidity depth, relative impact sensitivity, temporal stability, and issuer-based trust signals. These metrics are composable, auditable, and reproducible.

Determinism & Reproducibility

Given the same ledger snapshot, the TRAXR data model will always produce identical derived metrics and scores. No probabilistic or non-deterministic inputs are introduced at any stage.

Current MVP Data Model

Snapshot-based XRPL data ingestion
Third-party XRPL data sources for bootstrapping
Manual refresh cycle (~24h) for validation
Deterministic scoring over cached state

Evolution to Native Infrastructure

The target TRAXR data model operates on continuous XRPL indexing with automated ingestion, historical replay, and real-time metric updates — without altering the core entity definitions.

ℹ️ Data structures are intentionally minimal and XRPL-native to preserve long-term compatibility and auditability.

Know the data. Know the risk.