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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Derivative exchanges

Vertex

# Vertex Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who executed at least one trade on Vertex Protocol within a 30-day rolling window. This metric provides insight into user engagement and platform adoption across Vertex's multi-chain derivatives trading protocol. The calculation uses HyperLogLog probabilistic data structures to efficiently estimate distinct user counts over sliding monthly windows, with dimensional breakdowns by chain, user type, and product.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Smart contract events**: Parsed `FillOrder` events from Vertex Protocol and Blitz Protocol smart contracts across multiple blockchains
- **Arbitrum events**: `OffchainBook_FillOrder_event` and `OffchainExchange_FillOrder_event` logs containing trade execution data
- **Multi-chain events**: `OffchainExchange_FillOrder_event` logs from Base, Blast, Sonic, and Avalanche networks
- **Transaction data**: Blockchain transaction timestamps for temporal aggregation
- **Chain metadata**: Chain availability data for filtering and data quality assurance

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Extract unique daily active users from trading events across all supported chains
- **Logic**: The system identifies users from `FillOrder` events by extracting the `subaccount` parameter, which represents the trader's account identifier. Events are collected from `OffchainBook_FillOrder_event` (Arbitrum only) and `OffchainExchange_FillOrder_event` (all chains) tables. All identified users are classified as 'Trader' type within the 'derivatives' market sector and 'perpetual' business segment. Transaction timestamps are truncated to daily granularity to create distinct daily user counts.
- **Key fields**: `block_timestamp` for temporal grouping, `subaccount` as user identifier, `chain` for multi-chain analysis, and event-specific trading parameters

### Component B: HyperLogLog initialization and DAU preparation  
- **Purpose**: Convert daily user counts into probabilistic data structures for efficient MAU calculation
- **Logic**: Daily active user data is processed using HyperLogLog sketches with 15-bit precision to enable efficient distinct counting across large datasets. Each day's user set is initialized as an HLL sketch, grouped by timestamp, chain, user type, and product dimensions. This probabilistic approach allows for memory-efficient storage and fast aggregation of distinct user counts over time windows.
- **Key fields**: `timestamp` for temporal alignment, `user_dau` for distinct user identification, and dimensional attributes for breakdown analysis

### Component C: Complete time coverage with datespine
- **Purpose**: Ensure continuous time series without gaps for accurate rolling window calculations
- **Logic**: A complete datespine is generated from the earliest available data date to the current date using daily intervals. This addresses missing days where no trading activity occurred, which is essential for accurate 30-day rolling window calculations. The datespine provides the temporal framework for consistent MAU reporting regardless of daily activity fluctuations.
- **Key fields**: `timestamp` representing each day in the complete time range

### Component D: 30-day rolling window construction
- **Purpose**: Create sliding monthly windows for MAU calculation
- **Logic**: Each date in the datespine is joined with its preceding 29 days to create 30-day windows. This join operation generates combinations of current dates with their historical lookback periods, enabling the aggregation of user activity across monthly intervals. The window construction ensures that each day's MAU represents unique users active within the trailing 30-day period.
- **Key fields**: `timestamp` for the current date and `joinday` for dates within the 30-day lookback window

### Component E: Dimensional MAU breakdowns
- **Purpose**: Calculate MAU across different analytical dimensions using HLL merge operations
- **Logic**: The system creates four distinct dimensional views of MAU data. Project-level MAU aggregates all users regardless of chain or product. Chain-level MAU breaks down users by blockchain network. User type-level MAU segments by trader classification. Product-level MAU groups by specific product offerings. Each breakdown uses HLL merge operations to combine daily sketches within 30-day windows, then extracts the final distinct count estimates.
- **Key fields**: `dimensions` identifier, dimensional attributes (`chain_id`, `user_type`, `product_id`), and `user_mau` for the final count

### Final aggregation
- **Combining components**: All dimensional breakdown tables are combined using union operations to create a comprehensive MAU dataset with multiple analytical perspectives
- **Currency conversion**: Not applicable - this metric counts users rather than monetary values
- **Time aggregation**: Daily timestamps with 30-day rolling window logic, using monthly partitioning for efficient incremental processing
- **Final calculation**: HyperLogLog sketches are merged across 30-day windows using `hll_count.merge_partial()` operations, then distinct user estimates are extracted using `hll_count.extract()` to produce the final MAU values for each dimensional breakdown

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

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

VertexVRTX

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown