Zerion shows an aggregated view of a wallet's total crypto holdings. Through Zerion’s interface users are able to swap, send and borrow crypto assets.

Zerion is a non-custodial DeFi aggregator built on Ethereum and other adjacent blockchains. Zerion was founded by Evgeny Yurtaev, Alexey Bashlykov and Vadim Koleoshkin and launched in 2016.

Zerion owns and governs the platform. They currently charge a swap fee on the transactions that they route (revenue). Zerion’s investors include Gnosis, Wintermute, Blockchain Ventures etc.

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Zerion

# Zerion Active users (monthly) Methodology

## 1. Overview

The Zerion Monthly Active Users (MAU) metric measures the number of unique users who have executed trades through Zerion's router contracts within a rolling 30-day window. This metric provides insight into Zerion's user engagement and platform adoption across multiple blockchain networks. The calculation uses HyperLogLog sketches for memory-efficient approximate counting and creates multiple dimensional breakdowns to analyze user activity by chain, user type, and product.

## 2. Data sources

The MAU calculation is built from the following raw blockchain data sources:

- **Zerion router events**: Parsed `Router_Executed` events from Zerion's smart contracts deployed across six blockchain networks (BSC, Optimism, Polygon, Arbitrum, Avalanche, and Fantom)
- **Transaction metadata**: Block timestamps from blockchain transaction records to establish daily time boundaries
- **Chain availability data**: Blockchain network operational status information used for filtering

The core data comes from Zerion's router contract events, which capture when users execute trades or swaps through the Zerion platform. Each event includes the sender address (user), transaction hash, block timestamp, and chain identifier.

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify all user addresses that have executed trades through Zerion router contracts
- **Logic**: Extract `sender` addresses from `Router_Executed` events across all supported chains (BSC, Optimism, Polygon, Arbitrum, Avalanche, Fantom). Each user interaction is classified as "Trader" user type since all captured events represent trading activity. The extraction includes transaction metadata like block timestamp, transaction hash, and log index for tracking purposes.
- **Key fields**: `sender` address becomes `user_address`, block timestamps for temporal analysis, chain identifiers for multi-chain breakdowns, and static classification as "Trader" user type

### Component B: Daily user aggregation  
- **Purpose**: Calculate Daily Active Users (DAU) by aggregating unique users per day
- **Logic**: Group user addresses by day using timestamp truncation to daily granularity. Create distinct counts of user addresses for each combination of date, chain, user type, and product. This component establishes the base daily metrics that will be rolled up into monthly windows.
- **Key fields**: Daily timestamp boundaries, unique `user_address` counts per day, chain and user type dimensions for downstream breakdowns

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user counts into memory-efficient HyperLogLog sketches for scalable unique counting
- **Logic**: Initialize HyperLogLog sketches with 15-bit precision for each daily user count. This probabilistic data structure enables efficient approximate counting of unique users across large datasets and time windows. The sketches can be merged across time periods without double-counting users who appear on multiple days.
- **Key fields**: HLL sketches initialized from daily user address lists, maintaining dimensional breakdowns by chain, user type, and product

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for MAU calculation using datespine methodology
- **Logic**: Generate a complete datespine covering all days from the earliest data through yesterday. For each day in the spine, create joins to the 30 days prior (current day plus previous 29 days) to establish the rolling window boundaries. This ensures that MAU calculations don't skip days with zero activity and maintains consistent 30-day windows even when daily data is sparse.
- **Key fields**: Daily timestamps as window anchors, join dates covering 30-day periods, complete temporal coverage without gaps

### Component E: MAU window aggregation
- **Purpose**: Calculate MAU by merging HyperLogLog sketches across 30-day windows
- **Logic**: For each day and dimensional combination, merge HLL sketches from all days within the 30-day rolling window using `hll_count.merge_partial`. This combines the probabilistic sketches while avoiding double-counting users who appear on multiple days within the window. The merged sketches represent unique users across the entire 30-day period.
- **Key fields**: Merged HLL sketches representing unique users over 30-day windows, dimensional breakdowns preserved through the aggregation

### Component F: Dimensional breakdowns
- **Purpose**: Create multiple analytical perspectives of MAU data
- **Logic**: Generate four dimensional views of the MAU data: (1) Project-level aggregation combining all chains and dimensions, (2) Chain-level breakdown showing MAU per blockchain network, (3) User type breakdown (though currently all users are "Trader" type), (4) Product-level breakdown for future product differentiation. Each breakdown nullifies non-relevant dimension columns and aggregates across the remaining dimensions.
- **Key fields**: Dimension identifiers indicating the breakdown type, relevant dimensional values (chain, user type, product), nullified irrelevant dimensions

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing MAU for a specific day and dimensional combination
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Daily MAU values calculated using 30-day rolling windows, with each day showing unique users from that day plus the previous 29 days
- **Final calculation**: Extract final MAU counts from merged HyperLogLog sketches using `hll_count.extract`, applying finalization macros to standardize schema formatting and fill missing days with zero values where no user activity occurred

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

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

Zerion

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown