Liquidity providers deposit funds into Equalizer and stake their LP tokens to receive EQUAL tokens (token incentives).

Equalizer is a non-custodial exchange protocol built on Fantom. Equalizer was founded by Blake Hooper and launched in 2022.

EQUAL tokenholders own and govern the protocol. They vote for pools where they wish to receive trading fees and external bribes from, while pools receive EQUAL emissions in respect to their share of votes.

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Exchanges (DEX)

Equalizer Exchange

# Equalizer Exchange Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who performed at least one trade on Equalizer within any 7-day rolling window. This metric provides insight into Equalizer's user engagement and trading activity over weekly periods. The calculation uses HyperLogLog data structures to efficiently count unique users across overlapping time windows while providing dimensional breakdowns by chain, user type, and product.

## 2. Data sources

- **Blockchain events**: Parsed `Pair_Swap_event` logs from Equalizer v1 contracts on Fantom blockchain
- **Transaction data**: Fantom blockchain transaction records containing user address information
- **Chain availability data**: Configuration data that validates which blockchain networks should be included in metrics

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Extract and process all trading activity from Equalizer DEX contracts
- **Logic**: Parse `Pair_Swap_event` logs from Equalizer v1 pair contracts on Fantom. Calculate net token flows by subtracting `amount0Out` from `amount0In` for token0, and `amount1Out` from `amount1In` for token1. Join swap events with Fantom transaction records to identify the actual user performing each trade using the transaction's `from_address`. Filter trades to include only those occurring after November 14, 2022. Classify all trading activity under 'Exchange AMM' business line with 'trader' user type.
- **Key fields**: `transaction_hash`, `user_address`, `block_timestamp`, `amount0In`, `amount0Out`, `amount1In`, `amount1Out`

### Component B: Daily active user aggregation
- **Purpose**: Aggregate trading activity into daily unique user counts
- **Logic**: Truncate `block_timestamp` to day-level granularity using `timestamp_trunc`. Extract distinct `user_address` values for each day, creating one record per unique user-day combination. Apply chain availability filtering to ensure only valid blockchain data is included. All users are classified as 'trader' type since Equalizer is exclusively a DEX platform.
- **Key fields**: `timestamp` (daily), `user_address`, `chain`, `user_type`, `product_id`

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user data into HyperLogLog sketches for efficient unique counting
- **Logic**: Transform daily active user records into HyperLogLog data structures using `hll_count.init` with precision 15. Group by timestamp, chain, user type, and product to create HLL sketches that can be merged across time periods while maintaining cardinality accuracy.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id`, `hll` sketch

### Component D: Rolling window generation
- **Purpose**: Create rolling 7-day windows for WAU calculation
- **Logic**: Generate a complete date spine covering all days from the first recorded trade to yesterday. For each day in the spine, create a 7-day lookback window by generating an array of the current day plus the previous 6 days. This creates overlapping windows where each day appears in multiple weekly calculations, enabling true rolling WAU metrics.
- **Key fields**: `timestamp` (target day), `joinday` (days within the 7-day window)

### Component E: Weekly aggregation with HyperLogLog
- **Purpose**: Calculate unique users across each 7-day window
- **Logic**: Join the rolling windows with HLL sketches from daily data. For each 7-day window, merge all HLL sketches using `hll_count.merge_partial` to combine unique user counts while avoiding double-counting users who traded on multiple days. Extract final unique count using `hll_count.extract`.
- **Key fields**: `timestamp`, `user_wau` (unique weekly active users)

### Component F: Dimensional breakdowns
- **Purpose**: Provide multiple views of WAU data for different analytical needs
- **Logic**: Create four separate dimensional views of the WAU data: project-level aggregation (combining all chains and products), chain-level breakdown (unique users per blockchain), user-type breakdown (though all are 'trader' for Equalizer), and product-level breakdown. Each breakdown maintains the same underlying unique user counts but provides different grouping perspectives.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_wau`

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with null values filling unused dimension columns for each breakdown type
- **Time aggregation**: Daily timestamps represent the end date of each 7-day rolling window
- **Schema standardization**: Apply `finalize_metric_user_wau` macro to ensure consistent output format, including standardized column names, data types, and project metadata
- **Missing data handling**: Fill missing days with zero values to maintain continuous time series for visualization and analysis purposes
- **Final calculation**: Each row represents the count of unique users who traded within the 7-day period ending on the specified timestamp, broken down by the specified dimensional category

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

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

Equalizer ExchangeEQUAL

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown