This documentation explains TradeOS at the product, architecture, safety, and builder-integration level. It is written for users, builders, reviewers, and partners evaluating the public intelligence layer.
Welcome to TRADEOS.tech
TRADEOS.tech is the crypto-market vertical of a broader source-grounded Data Intelligence OS strategy. It is built around a source-grounded intelligence product layer for research, token-risk review, public thesis intelligence, agent workflows, validation, Data Intel Credits, and admin review. It turns market, on-chain, token, risk, narrative, document, and workflow data into structured evidence packs, alerts, thesis drafts, review tasks, agent context, and outcome memory.
The motivation is simple: crypto should be easier to explore without getting pulled into copycat tokens, thin liquidity, founder hype, or narratives that have no evidence behind them. TradeOS is designed to help researchers, long-term investors, builders, communities, and curious people ask better questions before they risk attention, trust, or capital.
The cost of missing TradeOS-like data is that agents and users make decisions from stale, under-sourced, symbol-confused, or unreviewable context. See Problem Space for the practical cost of missing identity, freshness, source evidence, risk context, feedback, and audit trails, including magnitude anchors that range from analyst-hour drag to six- and seven-figure exposure mistakes in serious workflows.
The public product is read-only market intelligence. Private or legacy admin systems are separate from the public app and do not change the public rule: evidence and feedback come first; execution, custody, paid APIs, exports, x402 resources, and automation stay outside the DTI credit surface.
For the business framing, see TradeOS Business Thesis. TradeOS is one crypto-market vertical, not the entire Source Intelligence Network strategy.
Public thesis intelligence is a separate review workflow. It explains what the system is watching and why; it is not an execution signal, price target, or recommendation.
The mission
TradeOS is not a signal room and not just a document assistant. The market services, token-risk services, public-intelligence workflows, agent layer, replay system, audit trail, Data Intel Credit loop, and safety gates are foundation pieces that work together to support one mission: make crypto research harder to fake and easier to review.
What makes TRADEOS.tech different
Most crypto systems split the work across separate bots, dashboards, token scanners, research notes, and publishing tools. That creates a gap between what the system sees, what it can prove, what it tells a human, and what it is allowed to do.
TRADEOS.tech is built around five public principles:
1. Evidence comes first
Human-facing research should be written from structured evidence, not unsupported model output or hype. If there is no source, there should be no claim.
2. Token identity matters
Crypto symbols are not unique. Public thesis outputs should include chain and contract identity where available so readers can distinguish legitimate assets from lookalikes or scams.
3. Validation is separate from action
Replay, outcome labeling, and review tasks help TradeOS learn from market behavior without turning research into an instruction.
4. Risk controls are independent
Signals do not approve themselves. Source checks, risk context, policy, safety controls, and admin boundaries are separate responsibilities.
5. Public intelligence is accountable
Thesis drafts should explain what looks constructive, what still needs proof, what is uncertain, and what would change the view. Follow-ups and material-change alerts keep the public record honest.
6. Feedback improves the next cycle
Corrections, rejected drafts, stale-evidence flags, material-change alerts, replay findings, and outcome labels should not disappear after review. They become feedback that helps future retrieval, ranking, routing, confidence, and policy decisions.
7. Data Intel Credits reward useful review
Users can earn scoped human DTI credits by submitting useful feedback on specific evidence, forecast, bias, fusion, token-risk, digest, and thesis-review surfaces. Human DTI unlocks temporary public dashboard depth, public Ask packs, or read-only Review Lab access; it does not convert into API scale and does not unlock execution, custody, paid APIs, exports, x402 resources, or automation.
8. Alpha supports the intelligence layer
Signals, market regimes, order flow, forecasts, and outcomes are not separate gadgets. They are evidence-producing services that help TradeOS understand whether a thesis, alert, or risk view is actually supported by market behavior.
9. Intelligence access stays legible
Every product surface should make clear what the user can consume, what they can save or review, what they can validate with outcomes, and where builder earning paths or paid data access start. Free intelligence, credit-unlocked depth, private passes, app keys, API/data rights, and external distribution paths are related but separate access modes.
Platform at a glance
| Layer | Public role |
|---|---|
| Evidence ingestion | Collects approved market, on-chain, token, risk, and narrative inputs. |
| Evidence + identity | Normalizes claims with source, timestamp, confidence, chain, and contract context where available. |
| Market validation | Converts observations into scored evidence, replay context, and outcome memory. |
| Risk and policy review | Tracks source quality, identity, freshness, uncertainty, and safety boundaries. |
| Public intelligence | Produces thesis watchlists, candidates, material-change alerts, token-risk digests, and narrative reviews. |
| Language and agent layer | Uses evidence-backed context to support clearer summaries, agent explanations, and chat without becoming the source of truth. |
| Intelligence access | Separates free intelligence, DTI-credit-unlocked depth, private passes, builder keys, API/data rights, and external distribution paths. |
| Data Intel Credits | Rewards useful user labels with public dashboard depth, public Ask packs, or Review Lab access after quality review. |
| Feedback memory | Records review decisions, corrections, outcome labels, provenance, replay findings, and material changes for future improvement. |
| Admin interface | Supports review, monitoring, pause controls, label ops, and bounded workflows for authorized users. |
Where to go next
- Architecture Overview — understand the public architecture boundaries
- TradeOS Business Thesis — understand what business TradeOS is in, who it serves, and how it stays separate from execution
- Problem Space — understand the cost of missing source-grounded market intelligence
- How It Works — see the full intelligence loop
- Public Intelligence — see how source-backed thesis review works
- Data Intel Credit Loop — understand scoped product credits and builder feedback provenance
- Builder Kit — see public API quota, app reputation DTI, and paid-boundary rules for builders
- Evidence + Token Identity — understand why chain and contract context matter
- Risk Management — see the safety philosophy
- FAQ — review common questions and public-doc scope