Liquidity providers deposit funds into Velodrome and stake their LP tokens to receive VELO tokens in proportion to their share of the votes (token incentives).

Velodrome is a non-custodial exchange protocol built on Optimism. Velodrome was founded by veDAO members and launched in 2022.

VELO tokenholders own and govern the protocol. They vote which pools they wish to earn trading fees from (revenue), while the pools' unstaked liquidity providers earn the remainder of the trading fees (supply-side fees).

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Exchanges (DEX)

Velodrome

# Velodrome Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who executed at least one trade on the Velodrome decentralized exchange within a rolling 30-day window. This metric provides insights into user engagement and platform adoption across Velodrome's different protocol versions (V1, V2, and Slipstream/V3). The calculation uses HyperLogLog probabilistic counting to efficiently handle large-scale user activity data while maintaining accuracy for unique user counts.

## 2. Data sources

- **Velodrome swap events**: Parsed `Swap` events from Velodrome V1 `Pair` contracts, V2 `Pool` contracts, and V3 `CLPool` contracts on Optimism blockchain
- **Transaction data**: Optimism transaction records containing user addresses and transaction metadata
- **Denylist data**: Hardcoded list of trading pair contract addresses excluded due to limited price feed support for low-liquidity tokens
- **Chain metadata**: Protocol version information and business classification data

## 3. Step-by-step calculation

### Component A: Trade event collection
- **Purpose**: Gather all Velodrome trading activity across protocol versions
- **Logic**: Extract `Swap` events from three Velodrome protocol versions on Optimism. For V1 and V2, calculate token amount changes using `amount0In` minus `amount0Out` and `amount1In` minus `amount1Out`. For V3 Slipstream, use direct `amount0` and `amount1` values. Join swap events with transaction records to obtain user addresses (`from_address`) and exclude any trades involving denylisted trading pair addresses.
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address`, `account_address` (trading pair), `product_id` (protocol version)

### Component B: Daily active user calculation
- **Purpose**: Count unique users who traded each day
- **Logic**: Group all trade events by day using `timestamp_trunc` to daily boundaries. For each day, count distinct `user_address` values to identify unique traders. Classify all users as 'Trader' type and assign appropriate business metadata including market sector ('exchanges'), business segment ('dex'), and product version ('V1', 'V2', or 'slipstream').
- **Key fields**: `timestamp` (daily), `user_address`, `user_type`, `product_id`, `chain`

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user counts to probabilistic data structures for efficient aggregation
- **Logic**: Transform daily active user lists into HyperLogLog sketches using `hll_count.init` with precision parameter 15. This enables approximate but highly accurate unique counting when merging data across multiple days. Group sketches by timestamp, chain, user type, and product to maintain dimensional breakdowns.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp`, `chain_id`, `user_type`, `product_id`

### Component D: Rolling window construction  
- **Purpose**: Create 30-day lookback windows for each date
- **Logic**: Generate a datespine covering all days from the first Velodrome trade to yesterday. For each date in the spine, create associations with the current day plus the previous 29 days (total 30 days) using `generate_timestamp_array`. This creates a comprehensive mapping where each date knows all days in its 30-day lookback period.
- **Key fields**: `timestamp` (current date), `joinday` (each day in the 30-day window)

### Component E: Window aggregation
- **Purpose**: Merge HLL sketches across 30-day windows
- **Logic**: For each date and its 30-day lookback period, collect all relevant HyperLogLog sketches from the daily user data. Join the window mapping with daily HLL sketches on matching dates. Group by the target timestamp and merge HLL sketches using `hll_count.merge_partial` to combine unique user counts across the full 30-day period.
- **Key fields**: `timestamp` (target date), merged `hll` sketches by dimension

### Final aggregation
- **Combining components**: Extract final unique user counts from merged HLL sketches using `hll_count.extract` and create separate breakdowns for overall project level, individual chains, user types, and product versions through separate groupings
- **Time aggregation**: Use daily boundaries with 30-day rolling windows, updating each day with a fresh 30-day lookback period
- **Final calculation**: Monthly Active Users equals the approximate unique count of users who executed at least one trade within the 30-day rolling window ending on each date, calculated via HyperLogLog sketch merging across all relevant daily user activity

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

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

VelodromeVELO

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown