Lenders deposit funds into  Fraxlend and receive interest payments from borrowers (supply-side fees).

Fraxlend is a non-custodial lending protocol built on Ethereum. Fraxlend is part of Frax Finance and was launched in 2022.

FRAX tokenholders own and govern the FRAX Finance protocol. Fraxlend is a part of Frax Finance. They currently do not take a cut of the total interest paid by borrowers (revenue).

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Lending

Fraxlend

# Fraxlend Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Fraxlend measures the number of unique users who performed any lending-related activity on the Fraxlend protocol within a 30-day rolling window. This metric answers the business question: "How many distinct users are actively engaging with Fraxlend's lending and borrowing features over a monthly period?" The calculation uses HyperLogLog cardinality estimation to efficiently handle large user sets and provides dimensional breakdowns by project, chain, user type, and product.

## 2. Data sources

- **Fraxlend contract events**: Parsed events from Fraxlend v1 smart contracts on Ethereum including `BorrowAsset`, `RepayAsset`, `AddCollateral`, `RemoveCollateral`, and `Withdraw` events
- **Ethereum transaction data**: Transaction records containing sender addresses and metadata for linking events to actual user addresses
- **Blockchain event logs**: Raw Ethereum event logs from Fraxlend protocol smart contracts
- **Protocol metadata**: Configuration data mapping event types to user classifications and business segments

## 3. Step-by-step calculation

### Component A: User event extraction and classification
- **Purpose**: Identifies all user interactions with Fraxlend protocol and classifies users by activity type
- **Logic**: Extracts five specific event types from Fraxlend v1 Ethereum contracts. Each event type represents a distinct user action: `BorrowAsset` events identify Borrowers, `RepayAsset` events identify Repayers, `AddCollateral` events identify Depositors, `RemoveCollateral` events identify Withdrawers, and `Withdraw` events also identify Withdrawers. Events are joined with Ethereum transaction data to extract the actual user address from the transaction `from_address` field.
- **Key fields**: `block_timestamp` for temporal analysis, `transaction_hash` for joining with transaction data, event-specific receiver/sender fields for user identification, and `from_address` from transaction data as the final user identifier

### Component B: Daily active user aggregation  
- **Purpose**: Calculates unique daily active users with proper deduplication and classification
- **Logic**: Groups user events by day using timestamp truncation to daily boundaries. Aggregates unique user addresses for each combination of date, chain, user type, market sector, business segment, and product. Applies distinct counting to ensure each user is counted only once per day regardless of how many transactions they performed.
- **Key fields**: `timestamp` truncated to day level, `chain` identifier, `user_type` classification, `user_address` for uniqueness counting, and business classification fields

### Component C: HyperLogLog encoding for efficient cardinality
- **Purpose**: Converts daily user sets into HyperLogLog structures for memory-efficient storage and fast merging operations
- **Logic**: Initializes HyperLogLog sketches from daily active user data using precision parameter 15. This probabilistic data structure allows efficient estimation of cardinality (unique user counts) while using minimal memory. Groups data by timestamp, chain, user type, and product to create separate HLL sketches for each dimensional combination.
- **Key fields**: `timestamp`, `chain_id`, `user_type`, `product_id` for grouping, and `hll` containing the encoded user set

### Component D: Rolling window generation
- **Purpose**: Creates 30-day rolling windows to calculate monthly active users
- **Logic**: Generates a complete datespine covering all days from the earliest DAU data to the current date. For each day in the datespine, creates a 30-day window by generating timestamps from 29 days prior through the current day. This ensures every day has a complete 30-day lookback window even if some days had no user activity. Joins the datespine with the daily HLL data to create the windowed dataset.
- **Key fields**: `timestamp` as the end date of each 30-day window, `joinday` representing each day within the window, and all dimensional fields carried forward from the HLL data

### Final aggregation
- **Combining components**: Merges HyperLogLog sketches across each 30-day window using HLL merge operations to combine user sets while maintaining uniqueness. Creates separate aggregations for different dimensional breakdowns: project-level (all dimensions null), chain-level (chain specified, others null), user type-level (user type specified, others null), and product-level (product specified, others null).
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows with daily granularity. Each day's MAU represents unique users active in that day plus the previous 29 days.
- **Final calculation**: Extracts the final user count from merged HLL sketches using HLL extract function. Combines all dimensional breakdowns using union operations to create a unified output with separate rows for each breakdown type. Applies zero-filling for days with no activity to ensure complete time series.

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

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

Fraxlend

Active users (monthly)

Active users (monthly)

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