Creators & users sell NFTs on X2Y2. They receive royalties from secondary sales (supply-side fees).

X2Y2 is a non-custodial NFT marketplace protocol built on Ethereum and launched in 2022.

X2Y2 tokenholders own and govern the protocol. They currently take a cut of the total transaction fees paid by buyers (revenue).

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X2Y2

# X2Y2 Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for X2Y2 measures the number of unique users who participated in NFT trading activities on the X2Y2 marketplace within any 7-day period. This metric answers the business question: "How many distinct users are actively trading NFTs on X2Y2 over rolling weekly periods?" The calculation uses a 7-day rolling window approach with HyperLogLog data structures to efficiently count distinct users across multiple dimensions, ensuring accurate deduplication even when users appear multiple times within the window.

## 2. Data sources

- **Ethereum blockchain events**: Parsed `EvInventory` events from the X2Y2 Exchange contract on Ethereum, containing NFT trade execution data
- **Token metadata**: Information about NFT collections and token standards for proper identification
- **Chain availability data**: Configuration data defining which blockchain networks are supported and available for analysis
- **External reference data**: Protocol-specific addresses and constants including X2Y2 fee recipient addresses and null address mappings

## 3. Step-by-step calculation

### Component A: Trade data extraction
- **Purpose**: Extracts and structures individual NFT trades from raw X2Y2 Exchange contract events
- **Logic**: Processes `EvInventory` events from the X2Y2 Exchange contract, using a JavaScript User-Defined Function to convert hexadecimal token IDs to decimal format. Identifies NFT buyers and sellers by examining the `intent` flag in the event data - when intent is '1', the taker is the buyer and maker is the seller; when intent is '0', these roles are reversed. Extracts NFT contract addresses from hex-encoded data fields and handles currency address mapping, converting null addresses to WETH addresses for standardization. Each trade record includes `block_timestamp`, `transaction_hash`, buyer and seller addresses (`nft_buyer_address`, `nft_seller_address`), NFT collection address (`nft_address`), and token ID information.
- **Key fields**: `block_timestamp` for time-based analysis, `nft_buyer_address` and `nft_seller_address` for user identification, `chain` set to 'ethereum', `user_type` categories, and trade identification fields

### Component B: Daily active user calculation  
- **Purpose**: Identifies unique users who participated in trading activities each day, categorized by user type
- **Logic**: Takes the trade data and creates separate user records for buyers and sellers. For each trade, generates two user activity records: one for the NFT buyer with `user_type` set to 'NFT buyer' and one for the NFT seller with `user_type` set to 'NFT seller'. Truncates `block_timestamp` to day-level granularity to create daily boundaries. Applies distinct deduplication to ensure each user appears only once per day per user type. Includes dimensional attributes like `chain`, `market_sector_id`, `business_segment_id`, and `product_id` for later aggregation flexibility.
- **Key fields**: `timestamp` truncated to day, `user_type` ('NFT buyer' or 'NFT seller'), `user_address` (either buyer or seller address), `chain` identifier, and business classification fields

### Component C: HyperLogLog initialization for WAU windowing
- **Purpose**: Prepares daily user data for efficient distinct counting across 7-day rolling windows using probabilistic data structures
- **Logic**: Initializes HyperLogLog sketches for each day's user data, grouping by `timestamp`, `chain`, `user_type`, and `product_id`. Uses BigQuery's `hll_count.init()` function with precision parameter 15 to create HLL sketches that can efficiently estimate distinct counts. This probabilistic approach allows for accurate distinct user counting even when merging data across multiple days without the memory overhead of exact distinct counting.
- **Key fields**: `timestamp` for daily boundaries, `hll` containing the HyperLogLog sketch data, and dimensional grouping fields

### Component D: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for each date to enable WAU calculation across continuous time periods
- **Logic**: Generates a complete datespine covering all dates from the earliest available data to the current date minus one day, ensuring no missing dates in the time series. For each date in the datespine, creates a 7-day lookback window by generating an array of the 7 dates ending on that date (current date minus 6 days through current date). Joins this windowing structure with the daily HLL data to associate each day's user activity with all the 7-day windows it should contribute to. This approach ensures that every user who was active within any 7-day period is counted in the WAU for the end date of that period.
- **Key fields**: `timestamp` representing the end date of each 7-day window, `joinday` representing each individual day within the window, and the associated HLL sketch data

### Component E: Dimensional WAU aggregation
- **Purpose**: Calculates WAU across different business dimensions while maintaining accurate distinct user counting
- **Logic**: Aggregates the windowed HLL data across four different dimensional breakdowns. For project-level WAU, merges all HLL sketches across all dimensions using `hll_count.merge_partial()` and extracts the final distinct count with `hll_count.extract()`. For chain-level WAU, groups by `chain_id` while setting other dimensions to null. For user-type-level WAU, groups by `user_type` (NFT buyer vs NFT seller) while nullifying other dimensions. For product-level WAU, groups by `product_id` while nullifying other dimensions. Each aggregation level provides a different analytical perspective on user activity patterns.
- **Key fields**: `dimensions` indicating the aggregation level, dimension-specific fields (`chain_id`, `user_type`, `product_id`), and `user_wau` containing the final distinct user count

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set, with each row representing WAU for a specific date and dimensional combination
- **Currency conversion**: Not applicable for user count metrics - no currency conversion required
- **Time aggregation**: Uses 7-day rolling windows with daily boundary logic, where each date represents the end of a 7-day period
- **Final calculation**: The `finalize_metric_user_wau` macro standardizes the schema, applies the X2Y2 data identifier, and implements a fill-zeros strategy to populate missing periods with zero values rather than gaps in the time series. Incremental processing includes safety margins and lookback periods to ensure data consistency during updates.

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

Unique revenue-generating users over a rolling 7-day window.

X2Y2X2Y2

Active users (weekly)

Active users (weekly)

Competitive landscape