Stakers deposit funds into Frax and receive a return on investment in the form of staking rewards (supply-side fees).


Frax Ether is a liquid staking derivative product created by the Frax Finance protocol. Frax Finance was founded by Sam Kazemian, Stephen Moore, Jason Huoan & Travis Moore and launched in 2019.


FRAX tokenholders own and govern the Frax Finance protocol, including the Frax Ether business line. They currently take a cut of the total staking rewards earned by the protocol (revenue). Frax’s investors include Crypto.com Capital & Ascensive Assets.


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Frax Ether

# Frax Ether Active users (weekly) Methodology

## 1. Overview

The Frax weekly active users (WAU) metric measures the number of unique users who interacted with Frax liquid staking products within rolling 7-day periods. This metric answers the business question: "How many distinct users are actively engaging with Frax's liquid staking ecosystem on a weekly basis?" The calculation uses HyperLogLog probabilistic counting to efficiently aggregate daily active users into weekly periods while maintaining accuracy for large user sets.

## 2. Data sources

- **Blockchain events**: Parsed Ethereum `ETHSubmitted` events from the FrxETHMinter contract that capture liquid staking deposits
- **Transaction data**: Ethereum transaction records containing timestamps, transaction hashes, and log indexes for event correlation
- **Chain availability data**: Configuration data that defines which blockchain networks are available for analysis and filtering

## 3. Step-by-step calculation

### Component A: User interaction extraction
- **Purpose**: Identifies all user addresses that have interacted with Frax liquid staking products
- **Logic**: Extracts data from Ethereum `ETHSubmitted` events emitted by the FrxETHMinter contract. Each event represents a user depositing ETH to receive frxETH liquid staking tokens. The `sender` parameter from these events identifies the depositing user address. All interactions are classified as 'Depositor' user type within the 'liquid_staking' market sector and business segment. The extraction includes transaction metadata like `block_timestamp`, `transaction_hash`, and `log_index` for downstream processing
- **Key fields**: `sender` (converted to `user_address`), `block_timestamp`, `transaction_hash`, `log_index`, with hardcoded classifications for `chain`, `market_sector_id`, `business_segment_id`, and `user_type`

### Component B: Daily active user calculation  
- **Purpose**: Aggregates user interactions into daily distinct active user counts
- **Logic**: Groups the extracted user interactions by day using timestamp truncation to day granularity. For each day, counts the distinct user addresses that performed any interaction. Maintains dimensional attributes including chain, user type, market sector, business segment, and product identifiers. Applies chain availability filtering to ensure only data from supported networks is included in the calculations
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` (set to 'day'), `chain`, `user_type`, `product_id`, and `user_dau` (the distinct user address)

### Component C: HyperLogLog sketch preparation
- **Purpose**: Converts daily active user data into probabilistic data structures for efficient weekly aggregation
- **Logic**: Transforms each day's user data into HyperLogLog sketches using 15-bit precision. HyperLogLog is a probabilistic algorithm that allows efficient counting of distinct elements in large sets while using minimal memory. Each daily record gets converted into an HLL sketch that can be merged with other sketches to count unique users across multiple days without double-counting
- **Key fields**: `hll` (HyperLogLog sketch initialized from `user_dau` data), maintaining all dimensional attributes from the daily aggregation

### Component D: Rolling window generation
- **Purpose**: Creates 7-day rolling windows to ensure complete coverage without gaps in time series
- **Logic**: Uses a datespine methodology to generate a continuous series of timestamps from the earliest available data to the current date minus one day. For each timestamp, creates a 7-day lookback window by generating an array of the 7 days ending on that timestamp (including the current day and the 6 preceding days). This ensures that every day has a complete 7-day window for WAU calculation, even if some days within the window had no user activity
- **Key fields**: `timestamp` (the end date of each 7-day window), `joinday` (each individual day within the 7-day window)

### Component E: Weekly active user aggregation
- **Purpose**: Combines daily HyperLogLog sketches across 7-day windows to calculate weekly active users
- **Logic**: Joins the rolling window structure with the daily HLL sketches, matching each day in a 7-day window to its corresponding daily user data. For each 7-day window, merges all available HyperLogLog sketches using partial merge operations, then extracts the final distinct count. This approach efficiently handles overlapping windows and ensures accurate deduplication of users who were active on multiple days within the same week
- **Key fields**: All dimensional attributes plus `user_wau` (extracted from merged HLL sketches)

### Final aggregation
- **Combining components**: Creates four separate dimensional breakdowns of the weekly active user data: by overall project (project_dbt_tag level), by blockchain chain, by user type, and by product identifier. Each breakdown aggregates the HyperLogLog sketches at the appropriate dimensional level and extracts the final distinct user count
- **Currency conversion**: Not applicable for this user-based metric as no monetary values are involved
- **Time aggregation**: Uses 7-day rolling windows with daily granularity, where each day represents the end of a 7-day period. The `timestamp_grain` remains 'day' to indicate daily calculation frequency
- **Final calculation**: The final `user_wau` value represents the count of distinct users who were active within any 7-day period ending on the given timestamp, calculated by merging HyperLogLog sketches and extracting the estimated distinct count. Missing days are filled with zeros, and incremental processing ensures efficient updates by only recalculating recent periods when new data arrives

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

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

Frax Ether

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown