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

Katana is a non-custodial exchange protocol built on Ronin. Katana was founded by Sky Mavis and launched in 2021.

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

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Exchanges (DEX)

Katana (Ronin)

# Katana Average fees per user (AFPU) Methodology

## 1. Overview

The Average Fees Per User (AFPU) metric measures the average daily fees generated per active trader on the Katana DEX. This metric is calculated by dividing the total daily fees collected from trading activity by the number of daily active users (traders). AFPU helps assess the revenue efficiency per user and trading behavior patterns on the platform.

## 2. Data sources

- **Blockchain swap events**: Parsed Ronin blockchain `Pair_Swap_event` logs from Katana V1 DEX smart contracts containing token swap details
- **Trading pair creation events**: Ronin blockchain `Factory_PairCreated_event` logs that define trading pairs and their fee tier configurations
- **Transaction data**: Ronin blockchain transaction records used to identify user addresses executing trades
- **Price feeds**: End-of-day token price feeds for USD conversion of trading volumes
- **Token metadata**: Token information including decimal places and symbols for proper amount calculations
- **Manual token configurations**: Protocol-specific token metadata for tokens not in standard datasets (e.g., SLP - Smooth Love Potion)

## 3. Step-by-step calculation

### Component A: Raw trade data extraction
- **Purpose**: Extract individual swap transactions from blockchain events
- **Logic**: Parse `Pair_Swap_event` logs from Katana V1 contracts on Ronin blockchain. For each swap, calculate the net change in token amounts by subtracting outflow from inflow (`amount0In` - `amount0Out` for token0, `amount1In` - `amount1Out` for token1). Join with transaction data to identify the user address (`from_address`) executing each trade. Each record represents a single swap transaction with token amount changes and user identification.
- **Key fields**: `transaction_hash`, `user_address`, `token0_amount`, `token1_amount`, `account_address` (trading pair contract), `block_timestamp`

### Component B: Trading pair configuration
- **Purpose**: Define trading pairs, fee tiers, and token metadata
- **Logic**: Extract trading pair information from `Factory_PairCreated_event` logs which contain the pair contract address, token0 address, token1 address, and fee tier. Fee tiers are stored as integers (e.g., 3000) and converted to decimal percentages (0.003 = 0.3% fee). Enrich each pair with token metadata including decimal places and symbols from both standard token datasets and manual configurations. Manual tokens like SLP (Smooth Love Potion) are handled with hardcoded metadata.
- **Key fields**: `account_address`, `token0_address`, `token1_address`, `fee_tier`, `token0_decimals`, `token1_decimals`, `account_name`

### Component C: Trading volume calculation
- **Purpose**: Convert raw token amounts to USD trading volumes
- **Logic**: Join swap data with trading pair configurations to get token metadata. Take absolute values of token amount changes and divide by 10^decimals to correct for token decimal places. Convert both corrected token amounts to USD using end-of-day price feeds. To prevent price manipulation, use the lesser of the two USD values (minimum of token0 USD value and token1 USD value) as the final trading volume for each swap. Aggregate volumes daily by trading pair.
- **Key fields**: `trading_volume` (USD), `timestamp` (daily), `token0_amount`, `token1_amount`, `fee_tier`

### Component D: Fee calculation
- **Purpose**: Calculate fees collected from trading activity
- **Logic**: Multiply the daily trading volume for each trading pair by its fee tier percentage. For example, a pair with 0.3% fee tier and $1000 trading volume generates $3 in fees. Sum all fees across all trading pairs to get total daily fees for the protocol.
- **Key fields**: `fees` (total daily fees in USD), `timestamp`, `chain_id`

### Component E: Daily active user counting
- **Purpose**: Count unique users who traded each day
- **Logic**: Identify unique `user_address` values from all trading transactions for each day. Use HyperLogLog (HLL) probabilistic counting for scalable distinct user aggregation across different dimensional breakdowns (project-level, chain-level, user-type-level). Merge HLL sketches to get final daily active user counts while maintaining approximate accuracy for large datasets.
- **Key fields**: `user_dau` (daily active users count), `timestamp`, `dimensions`, `user_type` ("Trader")

### Final aggregation
- **Combining components**: Join daily fees data with daily active user counts by timestamp to get both metrics for the same date
- **Currency conversion**: All fees are already converted to USD using end-of-day price feeds during the volume calculation step
- **Time aggregation**: Data is aggregated daily with timestamp truncated to day boundaries
- **Final calculation**: Divide total daily fees by daily active users (AFPU = Daily Fees ÷ DAU) using the `calculate_arpu` macro. Apply zero-filling strategy for dates with missing data to maintain continuous time series. The result represents the average fees generated per active trader per day.

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

Katana (Ronin)

Average fees per user (AFPU)

Average fees per user (AFPU)

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