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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NFT marketplaces

X2Y2

# X2Y2 Active users (monthly) Methodology

## 1. Overview

Monthly active users (MAU) for X2Y2 measures the distinct count of unique addresses that participated in NFT trading activity on the X2Y2 marketplace within a 30-day rolling window. This metric answers the business question: "How many unique users are actively trading NFTs on X2Y2 each month?" The calculation uses HyperLogLog sketches for efficient distinct counting across large datasets and provides dimensional breakdowns to analyze user activity patterns by different segments.

## 2. Data sources

- **X2Y2 smart contract events**: Parsed `EvInventory` events from the X2Y2 Exchange contract on Ethereum mainnet containing trading activity data
- **Ethereum transaction logs**: Raw blockchain event logs that capture all X2Y2 marketplace transactions
- **Smart contract event data**: Structured event data including maker/taker addresses, NFT details, prices, and transaction metadata from X2Y2 trading activities

## 3. Step-by-step calculation

### Component A: Trade event processing
- **Purpose**: Extract and structure NFT trading events from X2Y2 smart contract logs
- **Logic**: Parse `EvInventory` events from the X2Y2 Exchange contract to extract trading data. Use JavaScript UDF to convert hexadecimal values to readable numbers for token IDs and addresses. Determine buyer and seller roles by analyzing the `intent` field - when intent equals '1', the taker is the buyer and maker is the seller; when intent equals '0', the maker is the buyer and taker is the seller. Extract NFT contract addresses, token IDs, trade prices, and fee information. Handle currency conversion by replacing null addresses with WETH address for consistency.
- **Key fields**: `block_timestamp`, `transaction_hash`, `maker`, `taker`, `intent`, `item.data`, `currency`, `detail.price`

### Component B: Daily active user calculation  
- **Purpose**: Identify unique users who participated in NFT trading on each day
- **Logic**: Extract user addresses from processed trades by unioning NFT seller addresses and NFT buyer addresses into separate records. Classify each address with appropriate user type labels ('NFT seller' or 'NFT buyer'). Truncate all timestamps to daily boundaries using `timestamp_trunc` with day granularity. Remove duplicate combinations of timestamp, user type, and user address to ensure each user is counted only once per day per role. Include metadata such as chain identifier, market sector ('nfts'), and business segment ('marketplace').
- **Key fields**: `timestamp`, `user_type`, `chain`, `user_address`, `market_sector_id`, `business_segment_id`

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets into efficient data structures for distinct counting across time windows
- **Logic**: Transform daily active user data into HyperLogLog sketches using `hll_count.init` function with precision parameter of 15. Group data by timestamp, timestamp grain, chain, user type, and product ID. Create sketches that can be efficiently merged across different time periods while maintaining accuracy for distinct counting operations.
- **Key fields**: `timestamp`, `timestamp_grain`, `chain_id`, `user_type`, `product_id`, `hll`

### Component D: Rolling window generation
- **Purpose**: Create 30-day rolling windows for MAU calculation
- **Logic**: Generate a complete datespine covering all days from the earliest trade date to the current date using `generate_timestamp_array` with daily intervals. For each target date in the datespine, create a 30-day lookback window by generating an array of timestamps from 29 days prior to the target date. Join the datespine with these rolling window arrays to create combinations of target dates and their corresponding lookback periods.
- **Key fields**: `timestamp` (target date), `joinday` (days within 30-day window)

### Component E: Window-based HLL aggregation
- **Purpose**: Combine HyperLogLog sketches across 30-day windows
- **Logic**: Join the rolling window structure with daily HLL sketches on matching dates. For each target timestamp, collect all HLL sketches from the corresponding 30-day window. Group by target timestamp and dimensional attributes (timestamp grain, chain, user type, product ID). Use `hll_count.merge_partial` to combine multiple HLL sketches from the 30-day period into a single merged sketch representing all unique users in that window.
- **Key fields**: `timestamp` (target date), dimensional grouping fields, merged `hll` sketches

### Final aggregation
- **Combining components**: Extract final distinct user counts from merged HLL sketches using `hll_count.extract` function. Create separate dimensional breakdowns for project-level (all dimensions combined), chain-level, user type-level, and product ID-level MAU metrics. Union all dimensional breakdowns using `UNION ALL` operations to create a unified output with different granularities.
- **Currency conversion**: Not applicable for user counting metrics
- **Time aggregation**: Use 30-day rolling windows calculated for each daily timestamp, providing continuous MAU values rather than calendar month boundaries
- **Final calculation**: The MAU value represents the distinct count of unique user addresses that participated in any NFT trading activity (as buyer or seller) within the 30-day period ending on each target date, efficiently calculated using HyperLogLog probabilistic counting techniques

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

Unique revenue-generating users over a rolling 30-day window.

X2Y2X2Y2

Active users (monthly)

Active users (monthly)

Competitive landscape