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 Trade count Methodology

## 1. Overview

The Equalizer trade count metric measures the daily number of unique trading transactions on the Equalizer decentralized exchange. This metric counts distinct transaction hashes that contain swap events, providing insight into trading activity and user engagement on the platform. The calculation processes raw Fantom blockchain swap events from Equalizer V1 pairs, aggregates them by day, and applies business classification logic to produce standardized daily trade counts.

## 2. Data sources

- **Fantom blockchain event logs**: Parsed `Swap` events from Equalizer V1 pair contracts, containing trade execution details including token amounts and user addresses
- **Fantom blockchain event logs**: Parsed `PairCreated` events from Equalizer V1 pair factory contracts, used to map trading pair addresses to their constituent tokens
- **Fantom transaction records**: Base transaction data linking swap events to transaction initiators and providing transaction-level metadata
- **Token metadata**: Token symbol and decimal information for proper amount normalization and display formatting
- **End-of-day token price feeds**: Daily USD prices for tokens involved in swaps, used for volume-based filtering in intermediate processing

## 3. Step-by-step calculation

### Component A: Raw trade extraction
- **Purpose**: Extracts and processes individual swap events from Equalizer V1 pairs on Fantom blockchain
- **Logic**: Queries `Swap` events from Equalizer pair contracts, calculating net token flow by subtracting `amount0Out` and `amount1Out` from `amount0In` and `amount1In` respectively. Joins swap events with transaction records to identify the actual transaction initiator (user address) versus the contract caller. Filters for transactions occurring after November 14, 2022, when Equalizer V1 launched. Each swap event represents a single trade execution within a transaction
- **Key fields**: `transaction_hash` uniquely identifies each transaction, `log_index` distinguishes multiple swaps within the same transaction, `account_address` identifies the trading pair contract, `token0_amount` and `token1_amount` represent net token flows, `user_address` identifies the transaction initiator

### Component B: Trading pair dimensions
- **Purpose**: Creates a mapping between pair contract addresses and their constituent token details
- **Logic**: Processes `PairCreated` events from the Equalizer pair factory to identify all valid trading pairs. Joins with token metadata to retrieve decimal precision and symbol information for both tokens in each pair. Constructs human-readable pair names by concatenating token symbols with a hyphen separator. This dimensional data enables proper decimal correction and trade categorization
- **Key fields**: `account_address` links to trading pair contracts, `token0_address` and `token1_address` identify the constituent tokens, `token0_decimals` and `token1_decimals` enable proper amount normalization, `account_name` provides readable pair identification

### Component C: Trading volume aggregation
- **Purpose**: Aggregates raw trades by day, corrects token decimals, and applies USD conversion with volume-based filtering
- **Logic**: Groups trades by daily timestamp boundaries while preserving transaction-level granularity for downstream counting. Applies decimal correction by dividing raw token amounts by 10 raised to the token's decimal places, then taking absolute values to handle directional flows. Converts corrected token amounts to USD using end-of-day price feeds for both tokens in each pair. Applies volume filtering by taking the lesser of the two USD amounts to mitigate price manipulation, defaulting to zero if pricing data is unavailable
- **Key fields**: `timestamp` represents daily boundaries, `transaction_hash` and `log_index` maintain trade-level identity, `token0_amount` and `token1_amount` are decimal-corrected volumes, `trading_volume` represents USD-equivalent trade size for filtering

### Final aggregation
- **Combining components**: Joins the volume-aggregated trade data with all necessary dimensional information including chain, business segment classifications, and product categorizations
- **Currency conversion**: While USD conversion occurs in the intermediate processing for volume filtering, the trade count metric itself is not currency-dependent
- **Time aggregation**: Groups trades by daily timestamp boundaries using `timestamp_trunc` to day level, ensuring consistent daily reporting periods
- **Final calculation**: Counts distinct `transaction_hash` values per day, grouped by `chain_id`, `market_sector_id`, `business_segment_id`, and `product_id`. Applies the `finalize_metric_trade_count` macro with a "fill_zeros" strategy to ensure complete daily coverage and standardized output formatting. Filters final results based on chain availability settings to match Token Terminal's supported blockchain coverage

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

Number of trades executed on a decentralized exchange.

Equalizer ExchangeEQUAL

Trade count

Trade count

Competitive landscape

Chain breakdown