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 (monthly) Methodology

## 1. Overview

Monthly active users (MAU) for Equalizer measures the count of unique addresses that executed at least one trade on the Equalizer decentralized exchange within any rolling 30-day period. This metric answers the question: "How many distinct users actively traded on Equalizer in the past 30 days?" The calculation uses a rolling window approach with HyperLogLog algorithms for efficient distinct user counting across time periods.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Blockchain event logs**: Parsed `Pair_Swap_event` logs from Equalizer V1 smart contracts deployed on the Fantom blockchain
- **Transaction data**: Fantom blockchain transaction records that provide user address mapping and transaction context
- **Contract metadata**: Equalizer protocol contract addresses and version information
- **Token metadata**: Token contract addresses and decimal specifications for proper amount calculations

All data is filtered to include activity from November 14, 2022 onwards, when reliable Equalizer data became available.

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Identifies all swap transactions executed on Equalizer DEX
- **Logic**: Extracts `Pair_Swap_event` logs from Equalizer V1 contracts on Fantom blockchain. Each swap event contains `amount0In`, `amount0Out`, `amount1In`, and `amount1Out` fields representing token flows. The system joins swap events with corresponding Fantom transaction records to obtain the user address (`from_address`) who initiated each trade. All activity is classified as 'Exchange AMM' business line within the 'dex' business segment and 'exchanges' market sector
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `transaction_hash` and `log_index` for unique event identification, and token amount fields for trade validation

### Component B: Daily active user aggregation  
- **Purpose**: Converts individual trade events into daily distinct user counts
- **Logic**: Groups all trade events by day using `block_timestamp` truncated to daily granularity. For each day, counts distinct `user_address` values to identify unique traders. All users are classified with user type 'trader' since they executed trading activity. The aggregation maintains dimensions for chain, user type, product ID, and market sector for downstream analysis
- **Key fields**: `timestamp` (daily), `user_address` as `user_dau`, `chain`, `user_type`, and dimensional breakdowns

### Component C: HyperLogLog conversion
- **Purpose**: Converts daily user lists to efficient HyperLogLog sketches for scalable distinct counting
- **Logic**: Transforms each day's distinct user count into a HyperLogLog (HLL) sketch using `hll_count.init()` with precision 15. HLL sketches enable efficient merging of distinct counts across multiple time periods without storing the full list of user addresses. This is essential for calculating rolling window metrics at scale
- **Key fields**: `hll` containing the HyperLogLog sketch, maintaining all dimensional attributes from the daily aggregation

### Component D: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Builds a complete datespine covering all days from the earliest available data to the current date, ensuring no gaps in the time series. For each day in the datespine, creates a join table that connects that day to the previous 29 days, forming a 30-day window. Joins this window structure with the daily HLL data to associate each day's user activity with its corresponding 30-day lookback period
- **Key fields**: `timestamp` for the current day, `joinday` for each day within the 30-day window, and all HLL and dimensional data

### Component E: MAU aggregation with dimensional breakdowns
- **Purpose**: Calculates final MAU values across different dimensional views
- **Logic**: Merges HyperLogLog sketches across each 30-day window using `hll_count.merge_partial()` to combine distinct user counts, then extracts the final count using `hll_count.extract()`. Creates separate dimensional breakdowns: project-level (all chains and products combined), chain-level (by individual blockchain), user-type-level (by trader category), and product-level (by specific product). Each breakdown nullifies non-relevant dimensions while preserving the specific dimension being analyzed
- **Key fields**: `user_mau` containing the final distinct user count, `dimensions` indicating the breakdown type, and dimension-specific fields (`chain_id`, `product_id`, `user_type`)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL to create a comprehensive view with multiple granularities in a single table
- **Currency conversion**: Not applicable - this metric counts users, not monetary values
- **Time aggregation**: Uses daily timestamps with rolling 30-day windows, where each day represents the MAU for the trailing 30-day period ending on that date
- **Final calculation**: The final `user_mau` value represents the count of distinct addresses that executed at least one trade within the 30-day period ending on the specified timestamp, calculated using HyperLogLog merge operations for computational efficiency

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

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

Equalizer ExchangeEQUAL

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown