Liquidity providers deposit funds into Aperture and receive trading fees from traders (supply-side fees).

Aperture is a non-custodial DeFi exchange protocol built on Manta Pacific (originally a fork of Uniswap). Aperture was founded by Gao Han, Lian Zhu, and Peiqian Li in 2021.

Aperture team & investors own and govern the protocol. They do not currently take a cut of the total trading fees paid by traders (revenue). Aperture's investors include ParaFi Capital, Arrington XRP Capital, Costanoa Ventures and Divergence Ventures.

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Aperture

# Aperture Average trading volume (per trade) Methodology

## 1. Overview

This metric calculates the average trading volume per trade for the Aperture decentralized exchange on Manta. It measures the typical USD value of each trade by dividing the total daily trading volume by the total number of unique trades. This provides insight into the average trade size and trading patterns on the platform.

The calculation follows a two-step approach: first computing total trading volume and trade count separately, then dividing volume by count to get the average per trade.

## 2. Data sources

The calculation relies on several key data sources from the Manta blockchain and external systems:

- **Blockchain events**: Parsed Aperture v1 `Pool_Swap_event` logs containing token swap amounts and pool addresses
- **Pool creation events**: Aperture v1 `Factory_PoolCreated_event` logs defining trading pairs, tokens, and fee tiers
- **Transaction data**: Manta blockchain transaction records providing user addresses and transaction metadata
- **Token metadata**: Token information including symbols and decimal places for proper amount calculations
- **External data**: End-of-day token price feeds providing USD conversion rates for all traded tokens

## 3. Step-by-step calculation

### Component A: Trading pair identification
- **Purpose**: Establishes all available trading pools with their token pairs and fee structures
- **Logic**: Processes `Factory_PoolCreated_event` logs to extract pool addresses, token pairs (token0 and token1), and fee tiers. Joins with token metadata to get decimal places and symbols for each token. Creates trading pair names by concatenating token symbols. Each pool is classified as 'Exchange AMM' business line with 'apertureswapv1' product identifier.
- **Key fields**: `account_address` (pool address), `token0_address`, `token1_address`, `fee_tier`, `token0_decimals`, `token1_decimals`, `account_name` (token pair symbol)

### Component B: Trade extraction and processing
- **Purpose**: Captures all individual swap transactions with raw token amounts
- **Logic**: Extracts all `Pool_Swap_event` logs containing swap data including `amount0` and `amount1` representing token quantity changes. Joins with transaction data to link swaps to user addresses and transaction hashes. Each swap event represents one trade execution within a liquidity pool.
- **Key fields**: `transaction_hash`, `log_index`, `account_address` (pool), `token0_amount`, `token1_amount`, `user_address`, `block_timestamp`

### Component C: Volume calculation with USD conversion
- **Purpose**: Converts raw token amounts to standardized USD trading volumes
- **Logic**: Combines trade data with trading pair information to apply decimal corrections by dividing token amounts by 10 raised to the token's decimal places. Takes absolute values of amounts to handle directional swaps. Joins with end-of-day price feeds to convert both token amounts to USD values. Implements anti-manipulation protection by taking the lesser USD value between the two tokens in each swap, preventing inflated volumes from manipulated prices. Aggregates to daily level by truncating timestamps to day boundaries.
- **Key fields**: `trading_volume` (USD value), `timestamp` (daily), `chain`, `product_id`

### Component D: Trade count aggregation
- **Purpose**: Counts unique trading transactions per day
- **Logic**: Groups trade data by daily timestamps and counts distinct transaction hashes to determine the number of unique trades. This represents the total number of trading transactions rather than individual swap events, as one transaction may contain multiple swaps.
- **Key fields**: `trade_count` (distinct transactions), `timestamp` (daily)

### Final aggregation
- **Combining components**: Trading volume and trade count data are joined on matching timestamps to ensure alignment between volume and count calculations
- **Currency conversion**: All volumes are already converted to USD using end-of-day price feeds during the intermediate processing step
- **Time aggregation**: Both components use daily boundaries created by truncating block timestamps to day level
- **Final calculation**: The average trading volume per trade is computed by dividing the total daily trading volume by the total daily trade count using safe division to handle days with zero trades. This produces the average USD value per trading transaction for each day.

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

Aperture

Average trading volume (per trade)

Average trading volume (per trade)

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