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 (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the count of distinct users who traded on Level Finance protocol within any 30-day rolling window. This metric tracks user engagement across Level Finance's perpetual futures trading platform on BSC and Arbitrum networks. The calculation uses a rolling 30-day window approach, where each day's MAU represents the number of unique users who performed any trading activity in the current day plus the previous 29 days. This provides a smoothed view of user activity that accounts for intermittent usage patterns while maintaining daily granularity.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **BSC blockchain event logs**: Parsed `IncreasePosition`, `DecreasePosition`, and `Swap` events from Level Finance v1 Pool contracts
- **Arbitrum blockchain event logs**: Parsed `IncreasePosition`, `DecreasePosition`, and `Swap` events from Level Finance v2 Pool contracts
- **Transaction metadata**: Block timestamps and transaction hashes for each trading event
- **Chain availability data**: Network-specific filters to ensure data completeness

## 3. Step-by-step calculation

### User identification from trading events

- **Purpose**: Identify all users who interact with Level Finance trading protocols
- **Logic**: Extract user addresses from three types of Pool contract events across BSC and Arbitrum networks. `IncreasePosition` events capture users opening or adding to perpetual futures positions using the `account` parameter. `DecreasePosition` events capture users closing or reducing positions using the `account` parameter. `Swap` events capture users exchanging tokens using the `sender` parameter. All identified users are classified as 'Trader' type within the 'derivatives' market sector and 'perpetual' business segment.
- **Key fields**: `block_timestamp` for timing, `transaction_hash` for deduplication, `account` or `sender` fields for user addresses, chain identifiers for network attribution

### Daily active users (DAU) aggregation

- **Purpose**: Create daily snapshots of distinct active users as the foundation for MAU calculation
- **Logic**: Aggregate the identified trading events to daily granularity by truncating `block_timestamp` to day level. Count distinct `user_address` values for each day, maintaining dimensional attributes like chain, user type, and product identifiers. Apply chain availability filtering to ensure only complete data days are included.
- **Key fields**: `timestamp` truncated to day, `user_address` for distinct counting, dimensional attributes (`chain`, `user_type`, `product_id`)

### HyperLogLog sketch creation

- **Purpose**: Convert daily user counts into probabilistic data structures for efficient 30-day window calculations
- **Logic**: Transform each day's distinct user list into HyperLogLog sketches using precision parameter 15. This enables efficient merging of user sets across the 30-day window without storing individual user addresses. Group sketches by timestamp, chain, user type, and product dimensions.
- **Key fields**: `hll` sketches containing probabilistic user count data, dimensional grouping fields

### Rolling 30-day window construction

- **Purpose**: Create overlapping 30-day periods for each calendar day to calculate rolling MAU
- **Logic**: Generate a complete date spine covering the full data range to avoid gaps in the time series. For each target date, create a window spanning that date plus the previous 29 days (30 days total). Use timestamp arrays to generate all dates within each window, then join with the daily HLL sketches to collect user data for the full 30-day period.
- **Key fields**: `timestamp` for the target date, `joinday` for dates within the window, dimensional attributes preserved from daily data

### MAU calculation by dimension

- **Purpose**: Calculate monthly active users across different analytical dimensions
- **Logic**: Merge HyperLogLog sketches within each 30-day window to count distinct users, then extract the final count. Create separate breakdowns for project-level analysis (all dimensions aggregated), chain-level analysis (BSC vs Arbitrum), user-type analysis (Trader classification), and product-level analysis (though product_id is null for Level Finance). Each breakdown nullifies non-relevant dimensions while preserving the target dimension for analysis.
- **Key fields**: `user_mau` containing the distinct user count, `dimensions` indicating the breakdown type, relevant dimensional fields

### Final aggregation and formatting

- **Purpose**: Combine all dimensional breakdowns and apply standard metric formatting
- **Logic**: Union all dimensional breakdowns into a single result set. Apply the `finalize_metric_user_mau` macro to standardize schema, fill missing dates with zero values, and add metadata fields like data source identifier. Use incremental processing with safety margins to handle data updates efficiently while maintaining historical accuracy.
- **Key fields**: Standardized metric fields including `timestamp`, `user_mau`, `dimensions`, and metadata fields for tracking data lineage and quality

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

Unique revenue-generating users over a rolling 30-day window.

Level FinanceLVL

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown