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

Merkle Trade is a non-custodial perpetual exchange protocol built on Aptos.

MKL tokenholders own and govern the protocol. They do currently take a cut of the total trading fees paid by traders (revenue). Merkle Trade's investors include Arrington Capital, Hashed, etc.

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Merkle Trade

# Merkle Trade Average fees per user (AFPU) Methodology

## 1. Overview

Average Fees Per User (AFPU) measures the daily average fee revenue generated per active user on the MerkleTree protocol. This metric answers the business question "How much fee revenue does each active user generate on average per day?" and is calculated by dividing total daily fees by the number of daily active users. MerkleTree operates as a decentralized derivatives exchange on the Aptos blockchain, where users trade perpetual futures using lzUSDC (LayerZero USDC) as collateral.

## 2. Data sources

- **Aptos blockchain events**: Parsed `Trading_PositionEvent_event` logs from the MerkleTree v1 protocol that capture trade executions and position changes
- **Fee distribution events**: Parsed `FeeDistributor_DepositFeeEvent_event` logs from Aptos that record protocol fee collections distributed to liquidity providers, developers, and staking rewards
- **Rebate events**: Parsed `Referral_RebateEvent_event` logs from Aptos that capture referral rebate payments to users
- **External data**: End-of-day token price feeds for USD conversion of lzUSDC denominated fees
- **Token metadata**: Decimal and symbol information for the lzUSDC token used as collateral on MerkleTree

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Identify and count unique users who execute trades on MerkleTree each day
- **Logic**: Extract user addresses from Aptos `Trading_PositionEvent_event` logs where position events represent order executions. Filter for events where the order ID is not zero to ensure only actual trade executions are counted. Truncate timestamps to daily boundaries and extract the `user` parameter from each event as the trader address. Use HyperLogLog sketching to create distinct user counts that can be efficiently aggregated across multiple dimensions including project level, chain level, user type, and product ID.
- **Key fields**: `block_timestamp` for daily aggregation, `user` address for trader identification, `user_type` as "Trader" classification

### Component B: Fee collection tracking
- **Purpose**: Calculate total protocol fees generated from trading activities and referral programs
- **Logic**: Combine two sources of fee data. First, extract fee distributions from `FeeDistributor_DepositFeeEvent_event` logs and sum the `lp_amount`, `dev_amount`, and `stake_amount` fields to get total fees distributed to different protocol participants. Second, extract rebate amounts from `Referral_RebateEvent_event` logs representing fees paid back to users through referral programs. All fee amounts are denominated in lzUSDC token. Truncate timestamps to daily boundaries and aggregate total fees per day.
- **Key fields**: `lp_amount` for liquidity provider fees, `dev_amount` for development fees, `stake_amount` for staking rewards, `rebate` amount for user incentives, `token_address` identifying lzUSDC collateral

### Component C: Currency conversion and decimal correction
- **Purpose**: Convert lzUSDC denominated fees to USD values with proper decimal handling
- **Logic**: Apply decimal correction to raw fee amounts based on lzUSDC token metadata to convert from blockchain integer representation to human-readable amounts. Convert corrected lzUSDC amounts to USD using end-of-day price feeds that provide exchange rates between lzUSDC and USD. This ensures all fee calculations are standardized in USD for consistent metric reporting.
- **Key fields**: Raw fee `amount` values, `token_address` for price lookup, converted `fees_usd` amounts

### Final aggregation
- **Combining components**: Join the daily active user counts with the daily fee totals using the daily timestamp as the common key. Both user counts and fee amounts are aggregated at the same daily granularity.
- **Currency conversion**: All fees are converted to USD through the lzUSDC to USD exchange rate using end-of-day price feeds, with decimal corrections applied based on token metadata.
- **Time aggregation**: Data is aggregated using daily boundaries created by truncating blockchain timestamps to the day level, ensuring consistent time-based grouping.
- **Final calculation**: Divide the total daily fees in USD by the daily active user count to produce the average fees per user. Apply a zero-filling strategy to ensure continuous daily time series data, where missing days are filled with zero values rather than null values.

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

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