Traders buy NFTs either via the Blur aggregator or Blur's own marketplace from creators & and other users. When trading via Blur's own marketplace, traders pay royalty fees to the creators (supply-side fees).

Blur is a non-custodial NFT marketplace and a marketplace aggregator protocol built on Ethereum. Blur was founded by a pseudonymous team and launched in 2022.

BLUR tokenholders own and govern the protocol. They currently do not take a cut of the royalty fees paid by traders (revenue). Blur's investors include Paradigm, eGirl Capital, etc.

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Blur

# Blur Active users (daily) Methodology

## 1. Overview

Blur user daily active users (DAU) measures the number of unique wallet addresses that participated in NFT trading activity on the Blur platform each day. This metric captures both NFT buyers and sellers across all of Blur's trading mechanisms, providing a comprehensive view of platform engagement. The calculation counts each unique address once per day, regardless of how many trades they executed, and segments users by their role (buyer vs seller) in the trading activity.

## 2. Data sources

- **Blur v1 exchange events**: Parsed `OrdersMatched` events from the original Blur exchange contract containing trade executions, buyer/seller addresses, and trade details
- **Blur v2 exchange events**: Multiple execution events from the upgraded exchange including packed execution events (`Execution721TakerFeePacked`, `Execution721MakerFeePacked`, `Execution721Packed`) and standard execution events
- **Seaport order fulfillment events**: `OrderFulfilled` events from Seaport protocol versions 1.1 through 1.6, filtered to include only trades associated with Blur's zone owner ID
- **Blend lending protocol transactions**: Function call data from Blend's collateral trading functions including `buyLockedETH`, `buyLocked`, `buyToBorrowLocked`, and `buyToBorrowLockedETH` for NFTs used as loan collateral
- **Ethereum transaction records**: Base transaction data providing sender addresses and transaction context for proxy contract resolution
- **Seaport matched orders data**: Detection of matched order transactions to properly handle complex Seaport trade scenarios

## 3. Step-by-step calculation

### Component A: Blur v1 exchange trades
- **Purpose**: Extract user addresses from original Blur exchange trades
- **Logic**: Process `OrdersMatched` events to identify trade participants. Extract seller and buyer addresses from the matched orders, handling cases where the Blur swap contract acts as an intermediary. When the buyer address is the Blur swap contract, resolve the actual buyer from the transaction sender. Convert null token addresses to WETH for standardization. Calculate royalty amounts using fee rates and trade prices.
- **Key fields**: `trader` address from sell orders, transaction `from_address` for buyer resolution, `collection` and `tokenId` for NFT identification, `price` for trade volume

### Component B: Blur v2 exchange trades  
- **Purpose**: Extract user addresses from upgraded Blur exchange with packed data structures
- **Logic**: Process multiple v2 execution event types that use bit-packed data for efficiency. Use JavaScript functions to unpack compressed data containing token IDs, trader addresses, order types, prices, and collection addresses. Determine buyer/seller roles based on order type (0 for sell orders, 1 for buy orders). Extract fee recipient addresses and calculate fee amounts using rate tiers. Join with transaction data to resolve actual user addresses when needed.
- **Key fields**: `tokenIdListingIndexTrader` (packed), `collectionPriceSide` (packed), `orderType` for role determination, fee recipient and rate information

### Component C: Seaport-based Blur trades
- **Purpose**: Extract user addresses from Seaport trades executed through Blur
- **Logic**: Process `OrderFulfilled` events from multiple Seaport versions, filtering specifically for trades with Blur's zone owner ID. Handle complex offer/consideration logic to determine NFT vs currency items and identify buyer/seller roles. Distinguish between regular fulfillments and matched orders (which require different processing logic). Calculate volumes and fees from consideration amounts, handling cases where multiple considerations represent fees vs actual trade value. Convert null addresses to WETH for price conversion.
- **Key fields**: `offerer` and `recipient` addresses, `offer` and `consideration` arrays with token types and amounts, `zone` owner ID for Blur filtering

### Component D: Blend collateral NFT trades
- **Purpose**: Extract user addresses from NFT trades within Blend's lending protocol
- **Logic**: Process function calls for buying NFTs locked as collateral in Blend loans. Extract borrower addresses as sellers and function callers as buyers. Handle different function types for various collateral trading scenarios including direct purchases and borrow-to-buy operations. Set token address to WETH since these trades are conducted in ETH. Extract lien information to identify the specific NFTs being traded.
- **Key fields**: `borrower` from lien data as seller, `from_address` as buyer, `collection` and `tokenId` from lien, `price` from offer data

### Final aggregation
- **Combining components**: Union all four trade sources into a unified trade dataset with standardized fields for buyer address, seller address, timestamp, and trade metadata
- **User role separation**: Create separate records for each trade participant - one record for the NFT seller and one record for the NFT buyer, each labeled with the appropriate `user_type`
- **Time aggregation**: Truncate all timestamps to day boundaries using day-level timestamp truncation to enable daily aggregation
- **Daily distinct calculation**: Apply distinct operation on the combination of date, user address, user type, chain, and metadata fields to count unique users per day while preserving segmentation dimensions
- **Final calculation**: The DAU value represents the count of unique wallet addresses that participated in Blur trading activity on each day, segmented by user type (NFT seller vs NFT buyer) and including relevant business metadata for analysis

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a fixed 24-hour window.

BlurBLUR

Active users (daily)

Active users (daily)

Competitive landscape

Chain breakdown