Fraxtal batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

Fraxtal is an Ethereum Layer 2 (L2) blockchain built on the OP Stack by Frax Finance. It uses frxETH as the gas token and features a blockspace incentive system called Flox. Fraxtal launched in February 2024.

Frax Finance team owns and governs the protocol. Frax Finance's investors include Dragonfly Capital, Mechanism Capital, Galaxy Digital, etc.

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Blockchains (L2)

Fraxtal

# Fraxtal Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique addresses that initiated at least one transaction on the Fraxtal blockchain within a rolling 7-day window. This metric provides insight into user engagement and platform adoption by counting distinct transaction senders over weekly periods. The calculation uses HyperLogLog sketches for efficient approximate counting and maintains multiple dimensional breakdowns for comprehensive analysis.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Fraxtal blockchain transactions**: Complete transaction records from the Fraxtal Layer 2 blockchain including transaction hashes, timestamps, and sender addresses
- **Transaction sender addresses**: The `from_address` field identifying who initiated each transaction
- **Block timestamps**: Precise timing data for when each transaction was included in a block

## 3. Step-by-step calculation

### Component A: User identification from transactions
- **Purpose**: Identify unique users from daily transaction activity on Fraxtal
- **Logic**: Extract all transaction sender addresses from Fraxtal blockchain transaction records. Each transaction includes a `from_address` field representing the wallet address that initiated the transaction. Transaction timestamps are truncated to day boundaries to group activity by calendar day. Users are classified as "Transaction sender" type since they represent addresses actively initiating transactions.
- **Key fields**: `from_address` becomes `user_address`, `block_timestamp` truncated to daily granularity, chain identifier set to "fraxtal", market sector classified as "blockchains_l2"

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily unique active users as the foundation for weekly calculations
- **Logic**: Group transaction sender addresses by truncated daily timestamps to get distinct users per day. Remove duplicate addresses within each day to count each user only once per day, regardless of how many transactions they sent. Include business metadata like chain, user type, and sector classifications for dimensional analysis.
- **Key fields**: `timestamp` (daily), `user_dau` (distinct user addresses), `chain`, `user_type`, `product_id`

### Component C: HyperLogLog sketch initialization
- **Purpose**: Convert daily user sets into efficient sketches for approximate distinct counting
- **Logic**: Initialize HyperLogLog sketches for each day's user set using precision parameter 15. HLL sketches enable efficient storage and merging of large distinct count sets while maintaining acceptable accuracy. Each daily user set becomes an HLL sketch that can be combined with other days.
- **Key fields**: `hll` sketches created from `user_dau` sets, grouped by timestamp, chain, user type, and product dimensions

### Component D: Rolling window datespine creation
- **Purpose**: Generate complete date ranges and 7-day rolling windows without gaps
- **Logic**: Create a datespine covering the full date range from minimum available data to current date minus one day. For each date in the spine, generate a 7-day window consisting of the current day plus the 6 preceding days using timestamp array functions. This ensures every day has a complete 7-day lookback window even if some intermediate days have no transaction activity.
- **Key fields**: `timestamp` (current day), `joinday` (each day in the 7-day window)

### Component E: Weekly window HLL merging
- **Purpose**: Combine daily HLL sketches across 7-day rolling windows
- **Logic**: Join the rolling window dates with daily HLL sketches to gather all relevant sketches for each 7-day period. For each window, collect the HLL sketches from all days in the window (current day plus 6 previous days). This creates the foundation for merging sketches to count unique users across the weekly period.
- **Key fields**: `timestamp` (window end date), `hll` sketches for each day in the window, dimensional grouping fields

### Component F: Multi-dimensional WAU calculation
- **Purpose**: Calculate WAU across different business dimensions
- **Logic**: Group the weekly HLL sketches by different dimensional combinations and merge sketches within each group. Four breakdowns are created: project-level (all dimensions null), chain-level (grouped by chain), user-type-level (grouped by user type), and product-level (grouped by product ID). For each breakdown, use HLL merge functions to combine sketches across the 7-day window and extract the final approximate distinct count.
- **Key fields**: `dimensions` (breakdown type), `user_wau` (distinct count), dimension-specific fields like `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set containing WAU calculations at project, chain, user-type, and product levels
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows calculated daily, with each day representing the end of a weekly period
- **Final calculation**: Extract approximate distinct user counts from merged HyperLogLog sketches using `hll_count.extract(hll_count.merge_partial(hll))` to get the final WAU values for each dimensional breakdown

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

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

FraxtalFXTL

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown