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Data Infrastructure

TRADEOS.tech uses layered data infrastructure so evidence can be collected, validated, reviewed, measured, and improved without mixing unrelated concerns. The data model is designed around source separation, evidence normalization, outcome labeling, feedback memory, bounded access, and auditability.

Public Data Principles​

Source Separation​

Market data, on-chain observations, token metadata, risk inputs, public-intelligence evidence, and execution outcomes are treated as separate data classes. This makes it easier to reason about where a claim came from and whether it is appropriate for research, validation, or execution review.

Evidence Normalization​

Raw observations are normalized before they are used by downstream workflows. A public-intelligence claim should carry enough context to answer:

  • what was observed;
  • where it came from;
  • when it was observed;
  • which token identity it refers to, where applicable;
  • how fresh and reliable the evidence appears to be;
  • whether the evidence supports, weakens, or changes a thesis.

Outcome Labeling​

Signals and thesis candidates are not just generated and forgotten. TradeOS records outcomes so later reviews can ask whether the original evidence held up. This is the foundation for replay, post-outcome analysis, material-change alerts, and public thesis accountability.

Feedback Memory​

Review decisions, user corrections, stale-evidence flags, rejected drafts, replay findings, and material-change notes are treated as operational memory. They can improve future retrieval, ranking, routing, claim framing, and policy checks without turning public docs into a promise of unrestricted model training on private data.

Bounded Access​

Public-intelligence workflows receive bounded evidence through controlled interfaces. This boundary keeps research and review surfaces focused on the evidence needed to support user-facing claims.

Data Retention Posture​

Retention is configured by deployment and data class. The public policy is:

  • short-lived operational state should expire when it is no longer useful;
  • durable audit and outcome records should remain reviewable;
  • public-intelligence evidence should retain enough history to support corrections, follow-ups, and material-change analysis;
  • feedback and outcome memory should retain enough context to explain why a future view changed;
  • private credentials and secrets should never be stored in public-intelligence records;
  • logs and review artifacts should avoid unnecessary sensitive payloads.

Retention policy is designed to keep evidence and audit material reviewable while limiting unnecessary sensitive payloads.

Observability Without Overexposure​

TradeOS exposes health and review status to authorized operators. External readers should understand the operating principles:

  • stale inputs should be detected;
  • policy failures should block unsafe output;
  • risk events should be visible to authorized operators;
  • publication workflows should be pausable;
  • evidence and outcomes should be auditable.