
NFT Trading Volume and Wash-Trading Signals
Evaluate NFT volume with transaction-level evidence, repeated counterparties and transparent exclusions, while keeping suspicious patterns separate from proven intent.
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Understand asset metadata, market observations and the calculation behind a price or ranking.
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Evaluate NFT volume with transaction-level evidence, repeated counterparties and transparent exclusions, while keeping suspicious patterns separate from proven intent.
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Distinguish token-holding addresses from people and active users, including custody concentration, dust balances and contract-owned holdings.
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Compare on-chain total supply with circulating-supply estimates, and document exclusions such as locked, treasury, custody or burned holdings.
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Prevent assets with matching symbols from merging by using chain and contract identifiers, while treating names, logos and tickers as display metadata.
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Distinguish a token owner’s balance from an allowance granted to a spender, including changing state and why approval is not a payment.
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Understand permit-style token approvals, track nonce and deadline context, and avoid confusing an off-chain signature with an executed allowance change.
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Model wrapped-native-token activity separately from native currency, preserving deposit and withdrawal evidence without double-counting conversions.
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Distinguish NFT royalty information from actual sale payouts, and avoid treating a royalty quote as proof that a marketplace paid the creator.
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Interpret mint and burn evidence with contract-specific rules, supply checks and careful handling of zero-address transfer conventions.
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Answer who owned an NFT at a selected block using state or ordered transfer evidence, with explicit handling of minting, burning and incomplete history.
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Compare a pool’s marginal spot price with the price realised by a trade, including trade size, price impact, fees and unit conversion.
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Distinguish the curve effect of trade size from execution uncertainty, and explain how a slippage limit relates to a quoted minimum output.
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Each guide starts with a concrete developer question and separates technical observations from the interpretation an application adds. Worked examples use illustrative values; references point to standards and technical documentation.
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