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 (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who executed at least one trade on VerseDX within any 7-day period. This metric uses a rolling 7-day window approach, where each day's WAU represents the count of distinct users who were active on that day or any of the 6 preceding days. The calculation leverages HyperLogLog data structures for memory-efficient processing of large user sets and provides breakdowns across multiple dimensions including chain, user type, and product.

## 2. Data sources

The WAU calculation relies on the following raw blockchain data sources:

- **VerseDX v1 swap events**: Parsed `Swap` events from VerseDX v1 smart contracts on Ethereum, containing trade execution data including token amounts, user addresses, and transaction details
- **Ethereum transaction records**: Raw Ethereum transaction data providing the `from_address` field to identify the user who initiated each trade transaction
- **Token and chain metadata**: Configuration data for chain availability filtering and business classification metadata used for dimensional breakdowns

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Extract and classify all VerseDX trading activity from blockchain event logs
- **Logic**: Parse VerseDX v1 `Swap` events from Ethereum smart contract logs, filtering for events occurring on or after June 14, 2022 (likely the protocol launch date). Join these swap events with corresponding Ethereum transaction records to extract the `from_address` field, which identifies the user who initiated each trade. Calculate token amounts by taking the difference between `amount0In` minus `amount0Out` and `amount1In` minus `amount1Out` for each swap. Classify all activity with business metadata including chain as 'ethereum', business line as 'Exchange AMM', market sector as 'exchanges', and business segment as 'dex'. Remove duplicate events using distinct selection.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` and `log_index` for unique identification, `from_address` as `user_address` for user identification, calculated `token0_amount` and `token1_amount` for trade volumes

### Component B: Daily active user calculation
- **Purpose**: Aggregate trading activity to identify unique users active each day
- **Logic**: Group the trade events by day using `timestamp_trunc(block_timestamp, day)` to create daily buckets. Count distinct `user_address` values within each day to identify unique users who executed at least one trade. Classify all users with `user_type` as 'Trader' since all activity represents trading behavior. Apply chain availability filtering to ensure data quality and completeness. Preserve dimensional attributes including chain, product identifier, and business segment information for downstream breakdowns.
- **Key fields**: `timestamp` truncated to day granularity, `chain` for blockchain identification, `user_type` set to 'Trader', `user_dau` containing the unique user address

### Component C: HyperLogLog conversion and windowing
- **Purpose**: Convert daily user sets into efficient data structures and create 7-day rolling windows
- **Logic**: Transform daily user sets into HyperLogLog data structures using `hll_count.init()` with 15-bit precision for memory-efficient set operations. Generate a continuous date spine covering the full analysis period to ensure no missing timestamps. For each day in the spine, create a 7-day window by generating timestamps for that day plus the 6 preceding days using `generate_timestamp_array()`. Join the windowing data with the HyperLogLog daily user sets to prepare for set merging operations.
- **Key fields**: `timestamp` for window anchor dates, `joinday` for the 7-day lookback period, `hll` containing the HyperLogLog user set representation

### Component D: Multi-dimensional WAU aggregation  
- **Purpose**: Calculate weekly active users across different dimensional breakdowns
- **Logic**: Merge HyperLogLog sets within each 7-day window using `hll_count.merge_partial()` to combine daily user sets, then extract the final unique user count using `hll_count.extract()`. Create separate aggregations for four different dimensional breakdowns: overall project level (project_dbt_tag), chain level, user type level, and product level. For each breakdown, group by the relevant dimensions while setting non-relevant dimensions to null. This approach provides WAU metrics that can be analyzed at different levels of granularity.
- **Key fields**: `timestamp` for the calculation date, `dimensions` indicating the breakdown type, `chain_id`, `product_id`, `user_type` as dimensional attributes, `user_wau` as the final unique user count

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project_dbt_tag, chain, user_type, product_id) into a single result set, maintaining the dimensional hierarchy structure
- **Time aggregation**: Use daily boundaries with 7-day rolling windows, where each day's WAU represents unique users active in that day's 7-day lookback period
- **Final calculation**: The metric value represents the count of unique user addresses who executed at least one trade within the 7-day rolling window, calculated using HyperLogLog merge operations for computational efficiency

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

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

VerseVERSE

Active users (weekly)

Active users (weekly)

Competitive landscape