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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Infrastructure

Zerion

# Zerion Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interacted with Zerion's infrastructure services within a rolling 7-day period. This metric answers the business question of user engagement patterns and growth across Zerion's multi-chain platform. The calculation uses probabilistic counting (HyperLogLog) for efficient user deduplication across large datasets and provides breakdowns by chain, user type, and product dimensions.

## 2. Data sources

- **Blockchain events**: Parsed `Executed` events from Zerion Router contracts deployed across six blockchains (BSC, Optimism, Polygon, Arbitrum, Avalanche, and Fantom)
- **Event metadata**: Transaction hashes, block timestamps, log indices, and sender addresses from blockchain event logs
- **Chain data**: Multi-chain transaction data with standardized schema across all supported networks

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user interactions with Zerion's infrastructure across supported chains
- **Logic**: Extracts `Executed` events from Zerion Router contracts on each blockchain. The `sender` field from each event identifies the active user address. Each interaction is classified as "Trader" user type within the "infrastructure" market sector. Data is standardized across all six chains with consistent field mapping including `block_timestamp`, `transaction_hash`, `log_index`, `chain`, and `user_address`
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `chain` for multi-chain tracking, `user_type` set to "Trader"

### Component B: Daily active user aggregation
- **Purpose**: Creates daily distinct user counts as the foundation for weekly calculations
- **Logic**: Groups user interactions by day using timestamp truncation to daily granularity. Creates distinct user counts per day while preserving dimensional attributes including chain, user type, market sector, business segment, and product identifiers. Applies chain availability filtering to ensure data quality and completeness
- **Key fields**: `timestamp` truncated to day level, distinct `user_address` counts, dimensional breakdowns by `chain`, `user_type`, `product_id`

### Component C: HyperLogLog probabilistic counting setup
- **Purpose**: Enables efficient user deduplication across large datasets using probabilistic counting
- **Logic**: Initializes HyperLogLog sketches for each day's user data with precision parameter 15. This allows approximate but highly accurate distinct user counting while using minimal memory and enabling fast aggregation across time windows. Each daily user set becomes an HLL sketch that can be efficiently merged with other days
- **Key fields**: `hll` sketches containing compressed user sets, maintaining all dimensional attributes for downstream aggregation

### Component D: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for each calendar date to calculate weekly active users
- **Logic**: Generates a complete datespine covering the full data range to avoid missing timestamps. For each date, creates associations with that date plus the 6 preceding days using timestamp arithmetic. This produces a comprehensive mapping where each target date is linked to its full 7-day lookback window, ensuring consistent weekly calculations even when some days have no activity
- **Key fields**: `timestamp` for target dates, `joinday` for dates within each 7-day window

### Component E: Weekly user aggregation with HLL merging  
- **Purpose**: Calculates unique users across each 7-day window using probabilistic counting
- **Logic**: Joins the rolling window structure with daily HLL user data. For each 7-day period, merges the HLL sketches from all days within the window using `hll_count.merge_partial()`. This produces a single HLL sketch representing all unique users active during the 7-day period, automatically handling deduplication for users active on multiple days. Extracts final user counts using `hll_count.extract()`
- **Key fields**: Merged `hll` sketches across 7-day windows, extracted `user_wau` counts

### Final aggregation
- **Combining components**: Creates four separate dimensional breakdowns of WAU data: project-level (combining all data), chain-level (grouped by blockchain), user-type level (grouped by user classification), and product-level (grouped by product identifier). Each breakdown aggregates HLL sketches appropriately while setting non-relevant dimensions to null
- **Time aggregation**: Uses daily boundary logic with 7-day rolling windows, ensuring each date represents users active in that date plus the 6 preceding days
- **Final calculation**: Unions all dimensional breakdowns and applies standardized metric finalization including schema enforcement and zero-filling for missing days. The final WAU value represents the approximate count of unique users who interacted with Zerion infrastructure during each rolling 7-day period

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

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

Zerion

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown