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 Average trading volume (per trade) Methodology

## 1. Overview

The trading volume average per trade count metric measures the average USD value of trades on the Equalizer decentralized exchange. This metric is calculated by dividing the total daily trading volume in USD by the number of unique trades executed on that day. It provides insight into the typical trade size and helps understand user trading behavior on the platform.

## 2. Data sources

- **Equalizer protocol events**: Parsed `Swap` events from Equalizer v1 pairs on Fantom blockchain containing token swap amounts and transaction details
- **Trading pair definitions**: Parsed `PairCreated` events from Equalizer's PairFactory contract defining token pairs and their addresses
- **Blockchain transaction data**: Fantom transaction records providing user addresses and transaction metadata
- **Token metadata**: Token contract information including decimal places and symbols for amount normalization
- **External price data**: End-of-day token price feeds providing USD conversion rates for all traded tokens

## 3. Step-by-step calculation

### Component A: Trade data extraction
- **Purpose**: Extract and process individual swap transactions from Equalizer v1 pairs
- **Logic**: Query `Swap` events from Equalizer v1 pair contracts on Fantom blockchain, filtering transactions from November 14, 2022 onwards. Calculate net token changes by subtracting `amount0Out` from `amount0In` for token0 and `amount1Out` from `amount1In` for token1. Join with transaction data to obtain user addresses and enrich with business classification (Exchange AMM, exchanges market sector, dex business segment).
- **Key fields**: `block_timestamp`, `transaction_hash`, `log_index`, `account_address` (pair contract), `contract_user_address` (swap initiator), `user_address` (transaction sender), `token0_amount`, `token1_amount`

### Component B: Trading pair metadata
- **Purpose**: Define trading pairs and their token composition with normalization parameters
- **Logic**: Extract trading pairs from `PairCreated` events in Equalizer's PairFactory contract. Join with token metadata to obtain decimal places for both tokens in each pair. Create human-readable pair names by combining token symbols with hyphen separator.
- **Key fields**: `account_address` (pair contract), `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `account_name` (token0-token1 format)

### Component C: Trading volume calculation
- **Purpose**: Convert raw token amounts to USD trading volumes with price manipulation protection
- **Logic**: Join swap data with trading pair metadata to access token addresses and decimal information. Normalize token amounts by dividing absolute values by 10 raised to the power of respective token decimals. Convert both token amounts to USD using end-of-day price feeds matched by timestamp, token address, and chain. Apply price manipulation filtering by taking the lesser USD value between token0 and token1 amounts for each swap, defaulting to zero if either price is unavailable.
- **Key fields**: `trading_volume` (lesser of token0 and token1 USD values), `timestamp` (daily truncation), `transaction_hash`, `log_index`

### Component D: Daily trade count
- **Purpose**: Count unique trades per day to establish denominator for average calculation
- **Logic**: Use the same trading volume data but count distinct `transaction_hash` values grouped by day, chain, market sector, business segment, and product. This ensures each unique trade transaction is counted exactly once regardless of multiple swap events within the transaction.
- **Key fields**: `trade_count` (count of distinct transaction hashes), `timestamp`, `chain_id`, `market_sector_id`, `business_segment_id`

### Final aggregation
- **Combining components**: Sum daily trading volumes and trade counts separately across all relevant dimensions before joining on timestamp and timestamp grain
- **Currency conversion**: USD conversion occurs at the individual swap level using end-of-day price feeds, with price manipulation protection applied
- **Time aggregation**: All data is aggregated to daily boundaries using timestamp truncation to day level
- **Final calculation**: Divide total daily trading volume by total daily trade count using safe division to handle zero trade count scenarios, applying zero-fill strategy for missing data periods

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

Equalizer ExchangeEQUAL

Average trading volume (per trade)

Average trading volume (per trade)

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