Liquidity providers deposit funds into Vertex and earn the spread between maker and taker orders.

Vertex is a non-custodial exchange built on Arbitrum that offers spot, perpetuals, and money markets with universal cross-margin accounts. Vertex launched in 2023, and was founded by Darius Tabatabai and Alwin Peng.

The Vertex team own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). Vertex's investors include Wintermute, Hack VC, Jane Street, etc.

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Vertex

# Vertex Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique traders who executed at least one trade on Vertex Protocol during any 7-day rolling window. This metric captures user engagement and trading activity across Vertex Protocol's decentralized derivatives exchange, providing insights into the protocol's user base growth and retention. The calculation uses a 7-day sliding window approach with HyperLogLog data structures for efficient computation across multiple blockchain networks.

## 2. Data sources

- **Blockchain events**: Parsed `FillOrder` events from Vertex Protocol's smart contracts across five blockchain networks (Arbitrum, Base, Blast, Sonic, and Avalanche)
- **Contract types**: Both `OffchainExchange` and `OffchainBook` contract events on Arbitrum, and `OffchainExchange` events on other networks
- **Event data**: Trade execution logs containing `subaccount` addresses that identify unique traders, along with transaction timestamps and chain identifiers
- **Chain coverage**: Multi-chain data from Arbitrum (Vertex v1), Base (Vertex v1), Blast (Blitz v1), Sonic (Vertex v1), and Avalanche (Vertex v1) networks

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identifies unique traders who executed trades on each day across all supported chains
- **Logic**: Processes `FillOrder` events from OffchainExchange and OffchainBook contracts across five blockchain networks. Each event represents a completed trade and contains a `subaccount` field identifying the trader. The system truncates the `block_timestamp` to day granularity and extracts distinct `subaccount` addresses for each day. All traders are classified as 'Trader' user type in the 'derivatives' market sector and 'perpetual' business segment
- **Key fields**: `block_timestamp` for timing, `subaccount` for user identification, `chain` for network identification, and trade execution metadata

### Component B: HyperLogLog sketch creation  
- **Purpose**: Creates efficient probabilistic data structures to estimate cardinality of unique users for each day
- **Logic**: Converts the daily distinct user data into HyperLogLog sketches with 15-bit precision using BigQuery's `hll_count.init()` function. Each sketch represents the unique user set for a specific day, grouped by chain, user type, and product dimensions. This approach enables efficient storage and computation when creating rolling windows across large user sets
- **Key fields**: Daily HLL sketches grouped by `timestamp`, `chain_id`, `user_type`, and `product_id` dimensions

### Component C: Date spine and rolling window generation
- **Purpose**: Creates a complete time series framework and 7-day sliding windows for WAU calculation
- **Logic**: Generates a continuous date spine covering the full time range from the earliest available data to the current date minus one day, ensuring no gaps in the time series. For each date in the spine, creates a 7-day window by generating timestamps for that date plus the previous 6 days using `generate_timestamp_array`. This cross-join approach creates all necessary date combinations for the rolling window calculation
- **Key fields**: `timestamp` for the target date and `joinday` for each day within the 7-day window

### Component D: Weekly aggregation with HLL merging
- **Purpose**: Calculates unique user counts across each 7-day rolling window
- **Logic**: Joins the 7-day windows with the daily HLL sketches to collect all relevant user data for each rolling period. Uses BigQuery's `hll_count.merge_partial()` function to combine HLL sketches from all days within each 7-day window, then extracts the final unique user count using `hll_count.extract()`. This approach efficiently handles overlapping time periods and provides accurate unique user counts across the rolling windows
- **Key fields**: Merged HLL sketches producing `user_wau` counts for each 7-day period

### Component E: Dimensional breakdowns
- **Purpose**: Creates WAU metrics across different analytical dimensions for comprehensive reporting
- **Logic**: Generates separate aggregations for four key dimensions: project-level (combining all chains and user types), chain-level (breakdown by individual blockchain networks), user-type-level (currently only 'Trader' type), and product-level (breakdown by specific products within Vertex Protocol). Each breakdown nullifies irrelevant dimension fields while preserving the target dimension for grouping. All breakdowns are unioned together to create a comprehensive dimensional dataset
- **Key fields**: `dimensions` field indicating the breakdown type, along with relevant dimension values like `chain_id`, `user_type`, or `product_id`

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a single comprehensive dataset with multiple analytical perspectives
- **Time aggregation**: Daily boundaries are based on UTC timestamps truncated to day level, with 7-day rolling windows calculated for each date
- **Schema standardization**: The `finalize_metric_user_wau` macro applies consistent schema formatting, fills missing dates with zero values using a 'fill_zeros' strategy, and adds standard metadata fields
- **Final calculation**: Each record contains a `user_wau` value representing the count of unique traders who executed at least one trade during the 7-day period ending on that date, broken down by the specified dimension (project, chain, user type, or product)

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

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

VertexVRTX

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown