nft.wash_trades

Dataset for identifying and analyzing potential wash trading activities in NFT transactions across various marketplaces and blockchains

Table Description

The nft.wash_trades table on Dune is crucial for identifying and analyzing potential wash trading activities in Non-Fungible Token (NFT) transactions across various marketplaces. It provides insights into the authenticity of NFT trades and helps in understanding market manipulation within the NFT space.

Usage

The nft.wash_trades table serves as an essential tool for analyzing the integrity of NFT market activities. It offers a detailed view of each NFT transaction, including data points such as blockchain, project, buyer and seller addresses, and various filters to detect potential wash trades. This table allows analysts to identify suspicious trading patterns, assess the prevalence of wash trading in different NFT projects, and gain insights into the overall health and authenticity of the NFT market.

Coverage

The nft.wash_trades table is maintained by Dune and its community of contributors. We strive to ensure the data is as accurate and up-to-date as possible. However, Dune does not guarantee the accuracy or completeness of the data provided. The table covers various blockchains and NFT projects, with a focus on identifying potential wash trading activities.

Mechanism

The nft.wash_trades table employs a multi-faceted approach to identify potential wash trades:

1. Basic Trade Information: Each entry includes details such as blockchain, project, NFT contract address, token ID, buyer, seller, and transaction specifics.

2. Funding Source Analysis: The table tracks the first funding sources for both buyers and sellers (buyer_first_funded_by, seller_first_funded_by) to identify potential connections between trading parties.

3. Wash Trade Filters: Several filters are applied to flag suspicious activities:

   • filter_1_same_buyer_seller: Identifies trades where the buyer and seller are the same.
   • filter_2_back_and_forth_trade: Detects repetitive trading between the same parties.
   • filter_3_bought_or_sold_3x: Flags NFTs that have been traded frequently in a short period.
   • filter_4_first_funded_by_same_wallet: Identifies trades where both parties were initially funded by the same wallet.
   • filter_5_flashloan: Detects trades potentially facilitated by flash loans.

4. Wash Trade Classification: The is_wash_trade column provides a final verdict on whether a trade is considered a wash trade based on the applied filters.

This mechanism allows for a comprehensive analysis of NFT trades, helping to distinguish genuine market activity from potential manipulation.

Table Schema

Column	Type	Description
blockchain	VARCHAR	Blockchain on which the trade occurred
project	VARCHAR	NFT marketplace name
version	VARCHAR	Contract version
nft_contract_address	VARBINARY	NFT contract address
token_id	DOUBLE	NFT token ID
token_standard	VARCHAR	Token standard (ERC721, ERC1155)
trade_category	VARCHAR	How the NFT was traded
buyer	VARBINARY	Buyer wallet address
seller	VARBINARY	Seller wallet address
project_contract_address	VARBINARY	Marketplace contract address
aggregator_name	VARCHAR	Aggregator name if applicable
aggregator_address	VARBINARY	Aggregator contract address
tx_from	VARBINARY	Address that initiated the transaction
tx_to	VARBINARY	Address that received the transaction
block_time	TIMESTAMP	UTC event block time
block_date	DATE	UTC event block date
block_number	BIGINT	Block number
tx_hash	VARBINARY	Transaction hash
unique_trade_id	VARCHAR	Unique trade identifier
buyer_first_funded_by	VARBINARY	Wallet that first funded the buyer in ETH
seller_first_funded_by	VARBINARY	Wallet that first funded the seller in ETH
filter_1_same_buyer_seller	BOOLEAN	Whether buyer and seller are the same address
filter_2_back_and_forth_trade	BOOLEAN	Whether the NFT was traded back and forth between buyer and seller
filter_3_bought_or_sold_3x	BOOLEAN	Whether the same NFT was bought 3+ times (excluding ERC1155)
filter_4_first_funded_by_same_wallet	BOOLEAN	Whether the same wallet first funded both buyer and seller
filter_5_flashloan	BOOLEAN	Whether the transaction included a flashloan
is_wash_trade	BOOLEAN	Final verdict: true if any filter was triggered

Table Sample