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 (daily) Methodology

## 1. Overview

Daily active users (DAU) for X2Y2 measures the count of unique users who participate in NFT trading activities on the X2Y2 marketplace within each day. This metric captures both NFT buyers and sellers to provide a comprehensive view of platform engagement. The calculation uses HyperLogLog aggregation to efficiently count distinct users across multiple dimensions including project-level totals, chain breakdowns, user type segmentation, and product categories.

## 2. Data sources

The metric relies on the following raw blockchain data sources:
- **X2Y2 Exchange events**: Parsed `EvInventory` events from the X2Y2 Exchange contract on Ethereum, containing trade execution details including maker/taker addresses, intent flags, item specifications, and pricing information
- **Ethereum transaction data**: Block timestamps and transaction metadata to establish daily time boundaries for user activity aggregation
- **Contract interaction logs**: Raw event logs from X2Y2 protocol contracts capturing NFT marketplace trading activity

## 3. Step-by-step calculation

### Component A: Trade extraction and parsing
- **Purpose**: Extract and standardize NFT trading data from raw X2Y2 Exchange events
- **Logic**: Process `EvInventory` events from the X2Y2 Exchange contract, parsing hexadecimal-encoded data fields to extract token IDs, NFT contract addresses, and trade details. Use JavaScript user-defined functions to convert hexadecimal values to decimal numbers. Apply intent-based logic to determine buyer and seller roles where intent flag '1' indicates the taker is the buyer, and intent flag '0' indicates the maker is the buyer
- **Key fields**: `transaction_hash`, `block_timestamp`, `maker` and `taker` addresses, `intent` flag, `token_id` extracted from hexadecimal data, `nft_buyer_address` and `nft_seller_address` derived from intent logic

### Component B: User identification and categorization  
- **Purpose**: Identify unique daily users and categorize them by participation type
- **Logic**: Extract user addresses from both buyer and seller roles in each trade, creating separate records for NFT buyers and NFT sellers. Truncate block timestamps to daily boundaries and assign user type labels ('NFT buyer' or 'NFT seller'). Include additional dimensions like chain, market sector, business segment, and product identifiers
- **Key fields**: `timestamp` truncated to day level, `user_type` categorization, `user_address` as the unique identifier, `chain`, `market_sector_id`, `business_segment_id`, `product_id`

### Component C: Daily distinct user counting
- **Purpose**: Count unique users within each day and dimension combination
- **Logic**: Group user addresses by date, user type, chain, and other dimensional attributes, then count distinct users within each group. Apply incremental processing logic to focus on recent data when running updates
- **Key fields**: `user_dau` as the distinct user address, grouped by `timestamp`, `timestamp_grain`, `chain`, `user_type`, `product_id`

### Component D: HyperLogLog aggregation across dimensions
- **Purpose**: Efficiently aggregate distinct user counts across multiple dimensional breakdowns
- **Logic**: Initialize HyperLogLog sketches for each user group, then merge and extract counts across different dimensional views including project-level totals (all users), chain-level breakdowns, user type segmentation (buyers vs sellers), and product-level analysis. Use BigQuery's HyperLogLog functions with 15-bit precision for approximate but highly accurate distinct counting
- **Key fields**: `hll` sketches initialized and merged, `user_dau` extracted counts, `dimensions` indicating breakdown type, `chain_id`, `user_type`, `product_id` as dimensional attributes

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project-level, chain-level, user type, product-level) into a single result set with standardized schema including dimension type indicators and nullable dimensional attributes
- **Time aggregation**: Daily boundaries established through timestamp truncation to day level, with `timestamp_grain` set to 'day' for all records
- **Data completeness**: Apply finalization logic to fill missing days with zero values and ensure consistent schema structure across all dimensional breakdowns
- **Final calculation**: The final `user_dau` value represents the count of distinct users who participated in X2Y2 NFT trading activity within each day, segmented by the specified dimensional breakdown (project total, chain, user type, or product)

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

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

X2Y2X2Y2

Active users (daily)

Active users (daily)

Competitive landscape