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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Derivative exchanges

Level Finance

# Level Finance Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who interact with Level Finance's derivatives protocol within any 7-day rolling period. This metric captures user engagement across the protocol's perpetual trading functionality on BSC and Arbitrum networks. The calculation uses HyperLogLog data structures to efficiently count unique users across multiple dimensional breakdowns while handling the large-scale user data typical of DeFi protocols.

## 2. Data sources

- **BSC blockchain event logs**: Parsed Level Finance v1 Pool contract events including position management and token swap activities
- **Arbitrum blockchain event logs**: Parsed Level Finance v2 Pool contract events for derivatives trading activities
- **Trading events**: Specific event types captured include `IncreasePosition`, `DecreasePosition`, and `Swap` events from Level Finance Pool contracts
- **User identification data**: Wallet addresses extracted from event parameters that identify traders interacting with the protocol

## 3. Step-by-step calculation

### Component A: User identification from blockchain events
- **Purpose**: Identifies all users who actively trade on Level Finance by extracting wallet addresses from protocol events
- **Logic**: Processes blockchain event logs from Level Finance Pool contracts on BSC (v1) and Arbitrum (v2). For position events (`IncreasePosition` and `DecreasePosition`), extracts user addresses from the `account` parameter. For `Swap` events, extracts addresses from the `sender` parameter. All identified users are classified as 'Trader' type within the 'derivatives' market sector and 'perpetual' business segment. Events are timestamped using `block_timestamp` and deduplicated using `transaction_hash`
- **Key fields**: `user_address`, `block_timestamp`, `transaction_hash`, `chain`, `user_type`

### Component B: Daily active users aggregation  
- **Purpose**: Creates daily unique user counts as the foundation for weekly calculations
- **Logic**: Groups the user activity data by day using timestamp truncation to day granularity. Deduplicates user addresses within each day to count unique daily active users. Maintains dimensional groupings by chain, user type, and product ID. This creates the base dataset of daily user activity that feeds into the weekly aggregation
- **Key fields**: `timestamp` (day-truncated), `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau`

### Component C: HyperLogLog initialization for efficient counting
- **Purpose**: Prepares the daily user data for efficient unique counting across rolling windows using probabilistic data structures  
- **Logic**: Initializes HyperLogLog data structures for each day's user count using `hll_count.init()` with precision parameter 15. This enables efficient approximate counting of unique users when merging across multiple days without storing individual user addresses
- **Key fields**: `hll` (HyperLogLog structure), `timestamp`, dimensional grouping fields

### Component D: Seven-day rolling window creation
- **Purpose**: Creates the 7-day rolling windows needed to calculate weekly active users
- **Logic**: Generates a complete datespine covering all days from the first user activity to the present to ensure no days are skipped. For each day, creates joins to the previous 6 days using timestamp array generation, resulting in 7-day windows. Joins this window structure with the daily HyperLogLog data to create user activity data for each 7-day period
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within window), `hll` (daily user counts)

### Component E: Weekly unique user calculation
- **Purpose**: Calculates unique users across each 7-day window using HyperLogLog merging
- **Logic**: For each 7-day window, merges the HyperLogLog structures from all days in the window using `hll_count.merge_partial()`. Extracts the final unique count using `hll_count.extract()`. This provides an approximate count of unique users who were active at least once during the 7-day period
- **Key fields**: `user_wau` (weekly active users count), `timestamp`, dimensional fields

### Component F: Dimensional breakdowns
- **Purpose**: Creates multiple views of WAU data across different analytical dimensions
- **Logic**: Calculates separate WAU aggregations for four different dimensional breakdowns: project-level (overall protocol), chain-level (BSC vs Arbitrum), user type-level (Trader classification), and product-level (specific products within the protocol). Each breakdown aggregates the HyperLogLog data at the appropriate dimensional level and sets non-relevant dimension fields to null using `safe_cast(null as string)`
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `product_id`, `user_type`, `user_wau`

### Final aggregation
- **Combining components**: Unions all dimensional breakdown views (project, chain, user type, product) into a single comprehensive dataset using `union all` operations
- **Time aggregation**: Uses daily timestamps as the basis for 7-day rolling windows, with each day representing the end date of a 7-day period
- **Standardization**: Applies the `finalize_metric_user_wau` macro with 'fill_zeros' strategy to standardize the output schema, handle missing data, and ensure consistent formatting across all Level Finance WAU metrics
- **Final calculation**: The final WAU value represents the approximate count of unique wallet addresses that interacted with Level Finance contracts (through position management or swapping) within any consecutive 7-day period, calculated using HyperLogLog probabilistic counting for computational efficiency

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

Unique revenue-generating users over a rolling 7-day window.

Level FinanceLVL

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown