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 (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who have executed at least one trade on zkSwap within a 30-day rolling window. This metric uses HyperLogLog (HLL) probabilistic counting to efficiently aggregate unique users across time periods while maintaining accuracy. The calculation identifies active traders from zkSwap v1 AMM trading activity on zkSync Era and provides breakdowns across multiple dimensions including chain, user type, and product.

## 2. Data sources

- **zkSync Era blockchain events**: Parsed `Swap` events from zkSwap v1 `Pair` contracts containing trade execution details, token amounts, and participant addresses
- **zkSync Era transaction data**: Transaction records providing user address mappings and transaction metadata for trade events
- **HyperLogLog functions**: BigQuery's probabilistic counting functions for efficient unique user aggregation across rolling time windows
- **Generated datespines**: Algorithmically generated continuous timestamp arrays to ensure complete time series coverage without gaps

## 3. Step-by-step calculation

### Component A: Trade event processing
- **Purpose**: Extracts and structures individual trading events from raw blockchain data
- **Logic**: Processes `Swap` events from zkSwap v1 `Pair` contracts on zkSync Era, joining with transaction records to identify user addresses. Calculates token amount changes for both tokens in each swap by subtracting `amount_out` from `amount_in` values. Enriches events with business classification metadata including market sector ('exchanges'), business segment ('dex'), and business line ('Exchange AMM')
- **Key fields**: `block_timestamp` for timing, `user_address` from transaction records, `contract_user_address` from swap events, `token0_amount` and `token1_amount` for trade sizes

### Component B: Daily active user identification  
- **Purpose**: Aggregates unique daily users from trading activity
- **Logic**: Groups trade events by day using timestamp truncation, extracting unique `user_address` values for each day. Classifies all trading participants as 'Trader' user type. Applies incremental processing with partition-based updates for efficiency
- **Key fields**: `timestamp` (daily truncated), `user_address` for unique user counting, `user_type` set to 'Trader', `chain` for zkSync Era identification

### Component C: HyperLogLog sketch creation
- **Purpose**: Converts daily user sets into efficient probabilistic counting structures
- **Logic**: Initializes HyperLogLog sketches for each day's unique users using BigQuery's `hll_count.init` function with 15-bit precision. Groups by timestamp, chain, user type, and product dimensions to maintain granular breakdowns while enabling efficient aggregation
- **Key fields**: `hll` sketches containing compressed user sets, dimensional grouping fields for breakdown categories

### Component D: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Generates a continuous datespine covering the full time range, then creates join relationships between each target date and its preceding 29 days using `generate_timestamp_array`. This ensures complete coverage even for days with no trading activity
- **Key fields**: `timestamp` for target dates, `joinday` for historical dates in each window

### Component E: Window-based aggregation
- **Purpose**: Combines HLL sketches across 30-day rolling windows
- **Logic**: Joins the rolling window structure with daily HLL sketches, grouping by target timestamp and dimensional attributes. Merges HLL sketches from all days within each 30-day window using `hll_count.merge_partial`, then extracts final unique user counts with `hll_count.extract`
- **Key fields**: Merged `hll` sketches representing 30-day user sets, dimensional breakdown fields

### Final aggregation
- **Combining components**: Creates separate dimensional breakdowns for project-level (all chains combined), chain-level, user-type-level, and product-level MAU calculations. Each breakdown nullifies non-relevant dimension fields and aggregates HLL sketches accordingly
- **Currency conversion**: No currency conversion required as this metric counts users rather than monetary values
- **Time aggregation**: Uses 30-day rolling windows with daily granularity, ensuring each day represents the count of unique users active in that day plus the previous 29 days
- **Final calculation**: Extracts final MAU values from merged HLL sketches and applies the `finalize_metric_user_mau` macro to standardize schema, handle missing periods with zero-filling, and format output according to Token Terminal standards

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

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

zkSwap FinanceZF

Active users (monthly)

Active users (monthly)

Competitive landscape