Liquidity providers deposit funds into Verse and receive trading fees from traders (supply-side fees).

Verse is an automated market maker-based decentralized exchange built on Ethereum. Verse was created by Bitcoin.com, and launched in 2022.

VERSE tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). Verse's investors include Blockchain.com, KuCoin Ventures and others.

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Exchanges (DEX)

Verse

# Verse Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who traded on VerseX DEX within a 30-day rolling window. This metric tracks trader engagement by counting distinct user addresses that executed at least one swap transaction during any 30-day period. The calculation uses HyperLogLog probabilistic data structures to efficiently compute unique user counts across multiple dimensional breakdowns including overall project-level, by blockchain, by user type, and by product.

## 2. Data sources

The MAU calculation draws from several core blockchain data sources:

- **Ethereum blockchain events**: Parsed VerseX DEX v1 `Swap` events from the `SwapsPair_Swap_event` logs on Ethereum, containing swap transaction details including token amounts and user interactions
- **Ethereum transaction data**: Complete Ethereum transaction records providing user addresses (`from_address` field) and transaction metadata for linking swap events to actual users
- **Blockchain metadata**: Chain identifiers and protocol classification data for dimensional analysis

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Identifies all trading activity on VerseX DEX to establish the foundation for user activity tracking
- **Logic**: Extracts swap events from VerseX DEX v1 smart contracts on Ethereum blockchain, filtering for transactions occurring on or after June 14, 2022. Joins swap event logs with corresponding Ethereum transaction data to obtain user addresses. Calculates net token amount changes for both tokens in each swap by computing the difference between input and output amounts (`amount0In` minus `amount0Out`, `amount1In` minus `amount1Out`). Enriches each trade with business metadata including chain identifier, business line classification as 'Exchange AMM', market sector as 'exchanges', and business segment as 'dex'
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address`, `token0_amount`, `token1_amount`, `chain`, `business_line`

### Component B: Daily active user calculation  
- **Purpose**: Aggregates trading activity to identify unique users active each day
- **Logic**: Groups all trade events by daily timestamp boundaries using timestamp truncation to day granularity. Extracts distinct user addresses from each day's trading activity and classifies all users as 'Trader' type since only trading activity is captured. Applies chain availability filtering to ensure data consistency across blockchain networks. Maintains dimensional metadata for downstream aggregation including chain, market sector, business segment, and product identifiers
- **Key fields**: `timestamp`, `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau`

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily unique user counts into efficient probabilistic data structures for rolling window calculations
- **Logic**: Transforms daily active user data into HyperLogLog sketches using 15-bit precision for memory-efficient unique counting. Groups by timestamp, chain, user type, and product dimensions while initializing HLL sketches for each combination. These sketches enable efficient merging across time windows without storing individual user addresses
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll`

### Component D: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for each day to calculate Monthly Active Users
- **Logic**: Generates a continuous date spine from the earliest DAU date to the current date to ensure no gaps in time series. For each date in the spine, creates a 30-day lookback window including the current day plus the previous 29 days using timestamp array generation. Cross-joins the date spine with these 30-day windows to create all day-window combinations. Joins this expanded timeframe with the HLL sketch data to associate user activity with appropriate time windows, applying incremental processing filters to optimize performance
- **Key fields**: `timestamp`, `joinday`, `hll`

### Component E: Multi-dimensional MAU aggregation
- **Purpose**: Calculates Monthly Active Users across different business dimensions for comprehensive analysis
- **Logic**: Processes the windowed data through four separate dimensional aggregations. First, creates project-level MAU by merging all HLL sketches within each 30-day window regardless of chain, user type, or product, setting dimension as 'project_dbt_tag'. Second, calculates chain-level MAU by grouping and merging HLL sketches by chain identifier within each window. Third, computes user type-level MAU by aggregating sketches by user type classification. Fourth, generates product-level MAU by grouping sketches by product identifier. Each aggregation uses HLL merge operations to combine probabilistic sketches and extract final unique user counts
- **Key fields**: `dimensions`, `chain_id`, `user_type`, `product_id`, `user_mau`

### Final aggregation
- **Combining components**: Unions all four dimensional MAU calculations into a single result set, with each row representing MAU for a specific dimension-date combination. Null values are used for irrelevant dimension fields in each breakdown
- **Currency conversion**: Not applicable for user count metrics, as MAU represents user quantities rather than monetary values
- **Time aggregation**: Uses 30-day rolling windows calculated daily, where each date's MAU includes unique users from that date plus the previous 29 days. Monthly partitioning is applied for efficient incremental processing
- **Final calculation**: Applies the `finalize_metric_user_mau` macro to standardize the output schema, fill missing dates with zero values using the 'fill_zeros' strategy, and format the final metric according to Token Terminal's standard user metric structure

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

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

VerseVERSE

Active users (monthly)

Active users (monthly)

Competitive landscape