Liquidity providers deposit funds into zkSwap and receive trading fees from traders (supply-side fees). Traders receive token rewards for their trades through the Swap2Earn incentive program (token incentives).

zkSwap Finance is a non-custodial Swap-to-Earn DeFi exchange protocol built on zkSync Era (originally a fork of Uniswap). zkSwap Finance was founded by pseudonymous ThunderNoah, etc., and launched in 2023. 

ZF token holders own and govern the protocol via Snapshot vote. They currently take a cut of the total trading fees paid by traders (revenue).

governance

github

twitter

discord

etherscan

website

Exchanges (DEX)

zkSwap Finance

# zkSwap Finance Active users (weekly) Methodology

## 1. Overview

The zkSwap weekly active users (WAU) metric measures the count of unique users who executed trades on the zkSwap V1 decentralized exchange on zkSync Era within any 7-day rolling window. This metric provides insights into user engagement and activity patterns on the platform. The calculation uses HyperLogLog data structures to efficiently handle user deduplication across the rolling time windows and provides breakdowns across multiple dimensions including project-level, chain-level, user type, and product ID.

## 2. Data sources

The metric relies on the following raw data sources:
- **zkSync Era blockchain events**: Parsed `Pair_Swap` events from zkSwap V1 DEX smart contracts, containing trade execution details including token amounts, participant addresses, and transaction metadata
- **zkSync Era transaction records**: Complete transaction data from the zkSync Era network, providing transaction initiator addresses and execution context
- **Processing metadata**: Internal cache data used for incremental data processing optimization

## 3. Step-by-step calculation

### Component A: Trade event processing
- **Purpose**: Identifies all trading activity and associated users on zkSwap V1
- **Logic**: Extracts all `Pair_Swap` events from zkSwap V1 smart contracts on zkSync Era network. Each swap event contains token amounts in and out for both tokens in the pair. The system joins these events with zkSync Era transaction records to identify the transaction initiator. Users are classified as 'Trader' user type, and trades are categorized under 'Exchange AMM' business line within the 'exchanges' market sector and 'dex' business segment.
- **Key fields**: `block_timestamp` for timing, `user_address` from transaction initiator, `transaction_hash` for deduplication, token amount calculations from `amount0In`, `amount0Out`, `amount1In`, `amount1Out` fields

### Component B: Daily active user aggregation  
- **Purpose**: Converts trade events into daily unique user counts
- **Logic**: Groups all trading activity by day using timestamp truncation to daily boundaries. For each day, extracts unique user addresses who executed at least one trade. Users are aggregated across chain, market sector, business segment, and product dimensions. The system applies chain availability filtering to ensure data quality.
- **Key fields**: Daily `timestamp` boundaries, unique `user_address` identifiers, dimensional attributes like `chain`, `user_type`, `product_id`

### Component C: HyperLogLog initialization
- **Purpose**: Prepares daily user data for efficient set operations across rolling windows
- **Logic**: Converts daily active user data into HyperLogLog data structures using 15-bit precision. HyperLogLog enables scalable approximate counting of unique users across multiple days without storing individual user identifiers. Each daily user set is initialized as an HLL sketch grouped by timestamp, chain, user type, and product dimensions.
- **Key fields**: `hll` data structures containing compressed user sets, grouping dimensions for aggregation boundaries

### Component D: Date spine and rolling window construction
- **Purpose**: Creates complete 7-day rolling windows for WAU calculation
- **Logic**: Generates a complete date spine covering the full range of available data to ensure no missing days in the analysis. For each day in the spine, creates a 7-day lookback window including the current day (days t-6 through t). Uses cross join with date arrays to expand each target date into its corresponding 7-day window. Joins this window structure with the HLL daily data to collect all relevant user sets for each window.
- **Key fields**: `timestamp` for target dates, `joinday` for window components, joined `hll` data for each day in the window

### Component E: Multi-dimensional WAU aggregation
- **Purpose**: Calculates unique weekly users across different analytical dimensions
- **Logic**: For each 7-day window, merges the HLL data structures across all days in the window using HLL merge operations. This produces a deduplicated count of unique users active within any day of the 7-day period. Calculations are performed separately for four dimensions: project-level aggregation (all dimensions combined), chain-level breakdown, user type breakdown, and product ID breakdown. Unused dimensions are set to null for each respective breakdown.
- **Key fields**: Merged `hll` structures, extracted `user_wau` counts, dimension identifiers like `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: The different dimensional breakdowns are combined using union operations to create a single result set containing WAU metrics across all requested dimensions
- **Currency conversion**: Not applicable - this metric counts users rather than monetary values
- **Time aggregation**: Uses 7-day rolling windows where each day represents the count of unique users active in that day plus the previous 6 days
- **Final calculation**: The final WAU value for each day and dimension combination represents the approximate count of unique users who executed at least one trade within the 7-day period ending on that date, calculated using HyperLogLog merge and extract operations

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

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

zkSwap FinanceZF

Active users (weekly)

Active users (weekly)

Competitive landscape