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 user) Methodology

## 1. Overview

The average trading volume per user metric measures the mean USD trading volume generated per daily active user on the Aperture protocol. This metric provides insight into user engagement intensity by dividing the total daily trading volume by the number of unique daily active users. Daily active users include both traders (users who executed swaps) and flash loan takers (users who took flash loans), providing a comprehensive view of protocol engagement.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Blockchain event logs**: Parsed `Pool_Swap_event` logs from Aperture V1 contracts on Manta blockchain containing trade execution data
- **Flash loan events**: Parsed `Pool_Flash_event` logs from Aperture V1 contracts capturing flash loan activity
- **Transaction data**: Manta blockchain transaction records providing user addresses and transaction metadata
- **Pool creation events**: Parsed `Factory_PoolCreated_event` logs containing trading pair configuration including token addresses, fee tiers, and pool addresses
- **External data**: End-of-day token price feeds for USD conversion and token metadata including decimals and symbols
- **Token metadata**: Token information including decimal places and symbols for proper amount normalization

## 3. Step-by-step calculation

### Component A: Trade data extraction
- **Purpose**: Extract and process raw trading activity from blockchain events
- **Logic**: Parse `Pool_Swap_event` logs from Aperture V1 contracts on Manta blockchain. Extract key fields including `sender` (contract user), token amounts (`amount0`, `amount1`), pool address, transaction hash, and timestamp. Join with transaction records to get the actual user address (`from_address`) as the transaction initiator. Filter for successful transactions and group by transaction details.
- **Key fields**: `transaction_hash`, `log_index`, `user_address`, `account_address` (pool), `token0_amount`, `token1_amount`, `block_timestamp`

### Component B: Flash loan data extraction  
- **Purpose**: Identify users who engaged with flash loan functionality
- **Logic**: Parse `Pool_Flash_event` logs from Aperture V1 contracts capturing flash loan usage. Extract flash loan amounts for both tokens and identify flash loan takers. Join with transaction records to get user addresses. This data contributes to the daily active user count but not to trading volume.
- **Key fields**: `user_address`, `token0_amount`, `token1_amount`, `account_address`, `block_timestamp`

### Component C: Trading pair metadata
- **Purpose**: Enrich trade data with token configuration and pool information
- **Logic**: Use `Factory_PoolCreated_event` logs to build a dimension table of trading pairs. Extract pool addresses, token0 and token1 addresses, fee tiers, and creation timestamps. Join with token metadata to get decimal places and symbols for each token. Create readable pool names by concatenating token symbols.
- **Key fields**: `account_address` (pool), `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `fee_tier`, `account_name`

### Component D: Trading volume calculation
- **Purpose**: Convert raw token amounts to USD trading volume with anti-manipulation measures
- **Logic**: Join trade events with trading pair metadata to get token addresses and decimal configurations. 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 on the trade date. Calculate USD values for both token amounts. Apply anti-manipulation filter by taking the minimum USD value between token0 and token1 amounts to prevent inflated volumes from price manipulation. Aggregate to daily level by summing all trade volumes per day.
- **Key fields**: `trading_volume` (minimum of token0_usd and token1_usd), `timestamp` (day), `chain`, `product_id`

### Component E: Daily active user counting
- **Purpose**: Count unique users who interacted with the protocol each day
- **Logic**: Combine user addresses from both trade events and flash loan events to capture all protocol interactions. Truncate timestamps to day boundaries. Count distinct user addresses per day using HyperLogLog algorithm for memory efficiency. Create separate breakdowns by chain, user type (trader vs flash loan taker), and product. Aggregate to project level to get total daily active users across all dimensions.
- **Key fields**: `user_dau` (distinct user count), `timestamp`, `dimensions`, `user_type`

### Final aggregation
- **Combining components**: Join daily trading volume data with daily active user data by matching timestamp (day level). Use inner join to ensure both metrics exist for the same day
- **Currency conversion**: All trading volume already converted to USD using end-of-day price feeds during the trading volume calculation step
- **Time aggregation**: Both components aggregated to daily boundaries using timestamp truncation to day
- **Final calculation**: Calculate average trading volume per user by dividing total daily trading volume by daily active user count using safe division to handle days with zero users. The formula is: `trading_volume_avg_per_user = safe_divide(trading_volume, user_dau)` where safe division returns null if user_dau is zero

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

Average trading volume (per user)

Trading volume divided by daily active users.

Aperture

Average trading volume (per user)

Average trading volume (per user)

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