Frequently asked questions

What does TRONORIGIN actually do?
It analyzes the transaction history of a TRON wallet and produces a ranked list of the addresses most likely responsible for that wallet's origin and control. Each candidate gets a confidence-weighted score, a confidence band (Low / Medium / High), and a plain-language explanation of why it ranked where it did.
What does “origin” mean — what is the difference between activation, funding history, and current control?
Activation is who created or first funded the wallet. Funding history is who has been consistently involved over the wallet’s life — recurring transfers, resource delegation, and fee provision all count. Current control is who appears to be operating the wallet right now. These three phases are scored separately and combined, with current control carrying the most weight because it answers the question most investigators care about.
How does the confidence score work?
TRONORIGIN uses a three-phase weighted scoring model. Each phase evaluates different signals (transaction patterns, resource delegation, permission keys, fee provision, and more), applies bonuses and penalties, and produces a normalized score. The final confidence band reflects both the strength of the top candidate’s signals and the gap between the top candidate and the next-closest alternative. A 70% score with a large gap means something different than a 70% score with three candidates clustered within a few points of each other.
Where does TRONORIGIN get its data?
Analysis draws on public TRON blockchain data via TRONscan and TronGrid APIs. Token price lookups use CoinGecko. Security flags (blacklist status, fraud history) come from TRONscan’s security APIs. All data sources are public; TRONORIGIN does not have access to any private or off-chain information.
What is takeover detection?
Takeover detection fires when the address that dominated the wallet’s early history is different from the address that dominates its recent activity, and the divergence is significant enough to be unlikely by chance. When this happens, TRONORIGIN flags the result and classifies the likely type of transition — for example, a sale, a wallet compromise, or a secondary wallet coming into regular use. It does not mean something malicious happened; it means the data suggests control changed hands.
Can it identify a specific person?
No. TRONORIGIN attributes addresses to known clusters, entities, or labeled addresses (exchanges, contracts, faucets, and so on) where the on-chain evidence supports that attribution. It does not attempt to link blockchain addresses to real-world identities. If a candidate address is already labeled in the registry — for example, as a Binance hot wallet — that label will appear; otherwise, you see a raw address.
How accurate is it?
Accuracy depends on the wallet’s history. Wallets with rich, consistent activity and clear delegation relationships tend to produce reliable results. Wallets with very few transactions, heavy mixer involvement, or deliberately obfuscated histories will show lower confidence and may not produce actionable attribution. The confidence band and data quality indicator (Insufficient / Limited / Adequate / Rich) are there to tell you how much weight to put on any given result.
Is it free?
Yes, the tool is free to use. Running a standard or deep-scan analysis costs nothing. If usage patterns change in the future, pricing information will be posted here.
Is anything sent to a server when I analyze an address?
When you submit an address, the request goes to a Cloudflare Worker, which fans out to public TRON data sources (TRONscan, TronGrid, CoinGecko) to fetch transaction history and account data. The AI summary feature sends the structured analysis result to Google Gemini to generate the natural-language explanation. No query history is stored on TRONORIGIN’s infrastructure; each analysis is stateless. Be aware that requests to third-party APIs (TRONscan, CoinGecko, Gemini) are subject to those providers’ own data handling practices.
Why does the AI summary sometimes hedge?
The AI summary is generated from the structured analysis result — it reflects whatever confidence level the heuristic produced. When the underlying data is ambiguous, limited, or contradicted by competing signals, the summary will say so rather than projecting false certainty. That hedging is intentional: the goal is to help investigators make informed decisions, not to give them a confident-sounding answer that the data doesn’t support.
I found a wrong attribution — what should I do?
Use the feedback button in the analysis view to report it. Include the address you analyzed and a brief description of what you believe the correct attribution is. Attribution errors are the most valuable feedback the project receives and directly inform scoring improvements.