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

Level Finance is a non-custodial exchange for spot and perpetual contracts built on BNB Chain. Level Finance was founded by an anonymous team and was launched in December 2022.

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

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Level Finance

# Level Finance Average fees per user (AFPU) Methodology

## 1. Overview

Average Fees Per User (AFPU) measures the average daily fees collected per active trader on Level Finance, a decentralized perpetual trading protocol. This metric answers the business question "How much revenue does Level Finance generate per active user?" and is calculated by dividing total daily fees by the number of daily active users. The metric combines fee data from both perpetual trading (leveraged positions) and spot trading activities across Level Finance's deployments on BSC (v1) and Arbitrum (v2).

## 2. Data sources

The calculation uses the following raw data sources:
- **Blockchain events**: Parsed `Pool_Swap`, `Pool_IncreasePosition`, `Pool_DecreasePosition`, and `Pool_LiquidatePosition` events from Level Finance smart contracts on BSC and Arbitrum
- **Contract data**: Event logs from Level Finance v1 contracts on BSC and v2 contracts on Arbitrum
- **Token metadata**: Token information including symbols and decimal places for proper fee calculation
- **External data**: End-of-day token price feeds for USD conversion of fees collected in various tokens
- **Transaction data**: Blockchain transaction records containing event logs with fee collection and user activity information

## 3. Step-by-step calculation

### Component A: Perpetual trading fees
- **Purpose**: Calculate fees collected from leveraged perpetual trading activities
- **Logic**: Extract fee data from `Pool_IncreasePosition`, `Pool_DecreasePosition`, and `Pool_LiquidatePosition` events. The `feeValue` field contains the fee amount in the protocol's internal representation (multiplied by 10^30). Fees are normalized by dividing by 10^30 to get the actual token amount. Each event represents a fee collection from position management activities like opening, closing, or liquidating leveraged positions.
- **Key fields**: `feeValue` (fee amount), `indexToken` (token address), `account` (user address), `block_timestamp` (transaction time)

### Component B: Spot trading fees  
- **Purpose**: Calculate fees collected from spot trading activities (token swaps)
- **Logic**: Extract fee data from `Pool_Swap` events where users pay fees for swapping tokens. The `fee` field contains the fee amount in the input token. Create trading pair symbols by joining token metadata and ordering symbols alphabetically to standardize pair names. Apply decimal correction based on token-specific decimal places.
- **Key fields**: `fee` (fee amount), `tokenIn` (input token), `tokenOut` (output token), `sender` (user address), `block_timestamp` (transaction time)

### Component C: Daily active users calculation
- **Purpose**: Count unique users who performed trading activities each day
- **Logic**: Extract user addresses from the same Pool events used for fee calculation. For perpetual trading, use the `account` field from position management events. For spot trading, use the `sender` field from swap events. All users are classified as 'Trader' type. Apply HyperLogLog aggregation to efficiently count distinct users across multiple dimensions while avoiding double-counting users who trade on multiple chains or in multiple markets.
- **Key fields**: `account` and `sender` fields as user addresses, aggregated by day using HyperLogLog

### Component D: Fee processing and normalization
- **Purpose**: Standardize and convert all fees to USD values
- **Logic**: Join fee data with token metadata to get token symbols and decimal places. Apply the decimal correction macro to normalize token amounts based on their specific decimal places. Convert all fee amounts to USD using end-of-day token prices matched by date, token address, and chain. Aggregate fees daily across all tokens and trading types.
- **Key fields**: Corrected `amount` values, `price_usd` from price feeds, daily timestamp truncation

### Final aggregation
- **Combining components**: Sum all perpetual and spot fees into total daily fees in USD. Count distinct daily active users using HyperLogLog aggregation at the project level (combining all chains and trading types)
- **Currency conversion**: All fees are converted to USD using end-of-day token prices matched by timestamp, token address, and chain
- **Time aggregation**: Data is aggregated to daily boundaries using timestamp truncation, with consistent timezone handling
- **Final calculation**: Divide total daily fees (USD) by daily active users count to get average fees per user. Apply the finalize metric macro to standardize output format and fill missing days with zero values for complete time series data

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

Level FinanceLVL

Average fees per user (AFPU)

Average fees per user (AFPU)

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