Liquidity providers deposit funds to the lending pool and receive a portion of the interest (supply-side fees).

TrueFi is a non-custodial lending protocol built on Ethereum offering undercollateralized loans for verified borrowers. TrueFi was founded by Rafael Cosman and launched in 2020.

TRU tokenholders own and govern the protocol. They currently take a cut of the interest and an origination fee (revenue). TrueFi’s investors include a16z, BlockTower Capital, Alameda Research etc.

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Lending

TrueFi

# TrueFi Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for TrueFi represents the number of unique users who interacted with TrueFi's lending protocol within any 30-day rolling window. This metric tracks user engagement across TrueFi's three protocol versions (v1, v2, v3) on Ethereum, counting users who performed lending activities such as depositing funds into pools or withdrawing from managed portfolios. The calculation uses HyperLogLog data structures for memory-efficient unique user counting across rolling 30-day windows.

## 2. Data sources

- **Ethereum event logs**: Parsed events from TrueFi v1 and v2 Pool contracts and v3 ManagedPortfolio contracts
- **User interaction events**: `Pool_Joined` and `Pool_Exited` events from TrueFi v1 and v2 Pool contracts
- **Lending activity events**: `ManagedPortfolio_Deposited` and `ManagedPortfolio_Withdrawn` events from TrueFi v3 contracts
- **Transaction metadata**: Block timestamps for temporal analysis
- **Contract addresses**: TrueFi protocol contract addresses across different versions

## 3. Step-by-step calculation

### Component A: User event extraction
- **Purpose**: Identify and classify all user interactions with TrueFi protocol contracts
- **Logic**: Extract user addresses from TrueFi contract events across three protocol versions. For v1 and v2, capture `Pool_Joined` events (users joining lending pools) and `Pool_Exited` events (users exiting pools), extracting the `staker` parameter as the user address. For v3, capture `ManagedPortfolio_Deposited` events (users depositing into managed portfolios) and `ManagedPortfolio_Withdrawn` events (users withdrawing), extracting the `lender` parameter as the user address. Classify users as 'Depositor' for joining/depositing events and 'Withdrawer' for exiting/withdrawing events. Tag all activity with 'lending' as both market sector and business segment, Ethereum as the chain, and include transaction hash and block timestamp
- **Key fields**: `user_address` (extracted from event parameters), `user_type` (Depositor/Withdrawer), `block_timestamp`, `chain`, `transaction_hash`

### Component B: Daily active user calculation  
- **Purpose**: Count distinct users per day who performed any TrueFi interaction
- **Logic**: Group user events by day using timestamp truncation to daily granularity. For each day, identify all unique user addresses who performed any TrueFi activity, regardless of user type or number of transactions. Maintain dimension attributes including chain, user type, market sector, business segment, and product ID for downstream aggregation
- **Key fields**: `timestamp` (truncated to day), `user_dau` (unique user addresses), `user_type`, `chain`, `product_id`

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user lists into memory-efficient data structures for unique counting
- **Logic**: Transform daily distinct user address lists into HyperLogLog data structures using 15-bit precision. Group by timestamp, timestamp grain, chain, user type, and product ID. Initialize HLL structures for each dimension combination to enable efficient merging across rolling windows without storing individual user addresses
- **Key fields**: `hll` (HyperLogLog data structure), dimension grouping fields

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for MAU calculation
- **Logic**: Generate a complete datespine from the first day of TrueFi activity through yesterday to ensure no missing timestamps. For each day in the datespine, create a 30-day lookback window including the current day plus the previous 29 days. Join this window structure with daily HLL data to capture all user activity within each 30-day period. This approach ensures consistent MAU calculation even for days with no direct user activity
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within 30-day window), `hll` (daily user data)

### Component E: Multi-dimensional MAU aggregation
- **Purpose**: Calculate MAU across different business dimensions
- **Logic**: Merge HLL structures within each 30-day window and extract unique user counts for four different dimension levels. For project-level MAU, aggregate across all chains, user types, and products. For chain-level MAU, group by individual chains while aggregating across user types and products. For user type MAU, group by Depositor vs Withdrawer categories while aggregating across chains and products. For product-level MAU, group by individual lending products while aggregating across chains and user types. Set non-relevant dimensions to null for each aggregation level
- **Key fields**: `user_mau` (final unique user count), `dimensions` (aggregation level), dimension-specific fields

### Final aggregation
- **Combining components**: Union all dimensional MAU calculations into a single result set, maintaining separate rows for each dimension combination and time period
- **Currency conversion**: Not applicable - MAU is a user count metric
- **Time aggregation**: Each row represents a 30-day rolling window ending on the specified timestamp, calculated daily to provide continuous MAU tracking
- **Final calculation**: Extract unique user count from merged HyperLogLog structures using `hll_count.extract(hll_count.merge_partial(hll))`, apply the `finalize_metric_user_mau` macro to standardize schema and fill missing periods with zeros, and filter results based on incremental update requirements

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

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

TrueFiTRU

Active users (monthly)

Active users (monthly)

Competitive landscape