Integral provides low slippage on large trades by executing orders using a time-weighted average price (TWAP).

Integral is a non-custodial TWAP-based exchange on Ethereum and Arbitrum that specializes in handling large trades with low slippage. Integral was founded by a group of pseudonymous developers, mathematicians and financial engineers, and their flagship product Integral SIZE was launched in 2022.

ITGR tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue).

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Integral

# Integral Average trading volume (per trade) Methodology

## 1. Overview

This metric calculates the average trading volume per trade for the Integral protocol by dividing total daily trading volume by the number of unique trades executed that day. It provides insight into the typical trade size on the Integral decentralized exchange, helping to understand user trading patterns and market liquidity characteristics. The calculation uses anti-manipulation techniques to ensure accurate volume representation.

## 2. Data sources

- **Blockchain events**: Parsed Ethereum and Arbitrum `TwapPair_Swap_event` logs from Integral V1 contracts containing trade execution data
- **Trading pair data**: Parsed `TwapFactory_PairCreated_event` logs from Integral V1 contracts on Ethereum and Arbitrum to identify valid trading pairs and token addresses
- **Fee tier data**: Parsed `TwapPair_SetSwapFee_event` logs from Integral V1 contracts to track fee rates for each trading pair over time
- **External data**: End-of-day token price feeds for USD conversion and token metadata including decimal precision and symbol information
- **Transaction data**: Ethereum and Arbitrum transaction hashes to identify unique trades

## 3. Step-by-step calculation

### Component A: Trading pair identification
- **Purpose**: Establishes the universe of valid Integral trading pairs and their token composition
- **Logic**: Extract all `TwapFactory_PairCreated_event` logs from Integral V1 contracts on Ethereum and Arbitrum. Each event contains the trading pair contract address, token0 address, and token1 address. Join with token metadata to retrieve decimal precision and symbol information for both tokens in each pair. Generate human-readable pair names by concatenating token symbols.
- **Key fields**: `pair` address, `token0` address, `token1` address, token decimals, and constructed pair names

### Component B: Fee tier tracking
- **Purpose**: Captures fee rates for each trading pair over time to enable fee-based analysis
- **Logic**: Process `TwapPair_SetSwapFee_event` logs from Integral V1 contracts. Calculate daily average fee rates by dividing raw fee values by 10^18 and averaging across any fee changes within a day. Apply forward-filling to ensure each trading pair has a fee tier value for every day, using the most recent available fee rate.
- **Key fields**: Trading pair `address`, daily `fee_tier` rates, and temporal coverage through forward-filling

### Component C: Raw trade extraction
- **Purpose**: Identifies individual swap transactions and calculates token amount changes
- **Logic**: Extract `TwapPair_Swap_event` logs from Integral V1 contracts on Ethereum and Arbitrum. Each swap event contains `amount0In`, `amount0Out`, `amount1In`, and `amount1Out` fields. Calculate net token movements by computing `amount0In` minus `amount0Out` for token0, and `amount1In` minus `amount1Out` for token1. Take absolute values to represent trade volume regardless of direction. Each event includes transaction hash and log index for unique identification.
- **Key fields**: `transaction_hash`, `log_index`, `account_address` (trading pair), calculated `token0_amount` and `token1_amount` in raw units

### Component D: Volume calculation with anti-manipulation
- **Purpose**: Converts raw token amounts to USD trading volume while preventing price manipulation attacks
- **Logic**: Join raw trades with trading pair data to identify token addresses and decimal precision. Normalize token amounts by dividing by 10^decimals for each token. Join with end-of-day price feeds to get USD prices for both tokens involved in each swap. Calculate USD value for both token amounts. Apply anti-manipulation logic by taking the LEAST USD value between token0 and token1 amounts - this prevents artificially inflated volumes from price manipulation where one token in a pair has unreliable pricing. Aggregate volumes by day, transaction, and log index to maintain trade-level granularity.
- **Key fields**: Normalized `token0_amount` and `token1_amount`, USD prices, final `trading_volume` using LEAST logic

### Component E: Trade count calculation
- **Purpose**: Counts the number of unique trades executed each day
- **Logic**: Use the same processed trade data from the volume calculation. Group by day and count distinct transaction hashes to determine the number of unique trades. This approach ensures that each transaction is counted once as a single trade, regardless of how many swap events occurred within that transaction or across multiple trading pairs.
- **Key fields**: Daily aggregated `trade_count` based on distinct `transaction_hash` values

### Final aggregation
- **Combining components**: Join daily trading volume totals with daily trade counts on the timestamp dimension. Both components are aggregated to the daily level before joining.
- **Currency conversion**: USD conversion occurs at the individual trade level using end-of-day price feeds, with anti-manipulation logic applied before daily aggregation.
- **Time aggregation**: All data is aggregated to daily boundaries using day-truncated timestamps. Both volume and trade count use the same daily aggregation approach.
- **Final calculation**: Calculate the average volume per trade by dividing the total daily trading volume by the daily trade count using safe division (which returns null for zero trade count scenarios rather than causing division errors). The result represents the mean USD volume per unique transaction on each day.

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

IntegralITGR

Average trading volume (per trade)

Average trading volume (per trade)

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