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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Verse

# Verse Average trading volume (per user) Methodology

## 1. Overview

The VerseDEX trading volume average per user metric measures the average daily trading volume generated per daily active user on the VerseDEX decentralized exchange. This metric provides insight into user engagement and trading intensity by dividing the total daily trading volume in USD by the number of unique daily active users (traders). It helps assess the quality and activity level of the user base beyond just raw volume or user count metrics.

## 2. Data sources

- **VerseDEX swap events**: Parsed `Swap` events from VerseDEX v1 Ethereum smart contracts containing token amount changes for each trade
- **VerseDEX pair creation events**: Parsed `PairCreated` events from VerseDEX factory contracts defining trading pairs with token addresses
- **Ethereum transaction records**: Core transaction data providing user addresses and timestamps for each swap transaction  
- **End-of-day token price feeds**: Daily USD price data for tokens traded on VerseDEX
- **Token metadata**: Token symbols and decimal precision data for amount normalization and pair naming

## 3. Step-by-step calculation

### Component A: Trade event processing
- **Purpose**: Extract and process individual swap transactions from VerseDEX
- **Logic**: Extract `Swap` events from VerseDEX v1 Ethereum contracts starting from June 14, 2022. For each swap, calculate the net token amount changes by subtracting output amounts from input amounts (`amount0In` - `amount0Out` and `amount1In` - `amount1Out`). Join with Ethereum transaction data to get the actual user address (`from_address`) rather than just the contract caller. Classify all users as 'Trader' type and assign to 'Exchange AMM' business line and 'dex' business segment.
- **Key fields**: `transaction_hash`, `log_index`, `user_address`, `token0_amount`, `token1_amount`, `block_timestamp`

### Component B: Trading pair definitions  
- **Purpose**: Define trading pairs with token metadata and fee information
- **Logic**: Extract `PairCreated` events from VerseDEX factory contracts to identify all valid trading pairs. For each pair, capture the two token addresses and assign a hardcoded fee tier of 0.3% (3000/1000000). Join with token metadata to get decimal precision for each token and create readable pair names by concatenating token symbols with a hyphen.
- **Key fields**: `account_address` (pair address), `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `account_name`, `fee_tier`

### Component C: Volume calculation with price protection
- **Purpose**: Calculate USD trading volumes with price manipulation protection
- **Logic**: Join trade events with trading pair data to get token decimal information. Normalize token amounts by dividing absolute values by 10 raised to the token's decimal precision. Convert both tokens to USD using end-of-day price feeds matched on timestamp, token address, and chain. Apply price manipulation protection by taking the lesser USD value between the two tokens in each trade (`least(token0_usd, token1_usd)`). If either price lookup fails, default to zero volume. Aggregate volumes daily by summing across all trades.
- **Key fields**: `trading_volume` (USD), `timestamp` (daily), `token0_address`, `token1_address`

### Component D: Daily active user counting
- **Purpose**: Count unique daily active users using probabilistic counting
- **Logic**: Extract distinct user addresses from the same trade events used for volume calculation, aggregating by day. Use HyperLogLog (HLL) probabilistic data structure to enable efficient approximate distinct counting across multiple dimensions. Generate breakdowns by project level ('project_dbt_tag'), chain, user type, and product, with the main metric using the project-level aggregation that counts all unique users across the entire protocol.
- **Key fields**: `user_dau` (distinct count), `dimensions` (aggregation level), `timestamp` (daily)

### Final aggregation
- **Combining components**: Join daily trading volumes with daily active user counts on matching timestamps, using only the project-level user DAU dimension ('project_dbt_tag') to get total protocol users
- **Currency conversion**: All volumes are already converted to USD in the volume calculation step using end-of-day price feeds
- **Time aggregation**: Both volume and user data use daily boundaries created by truncating timestamps to day precision
- **Final calculation**: Divide total daily trading volume by daily active user count using safe division to handle edge cases where DAU might be zero, resulting in the average trading volume per user in USD

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

Average trading volume (per user)

Trading volume divided by daily active users.

VerseVERSE

Average trading volume (per user)

Average trading volume (per user)

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