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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Fraxtal

# Fraxtal Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that sent at least one transaction on the Fraxtal blockchain during a 7-day rolling window period. This metric answers the question "How many distinct users are actively transacting on Fraxtal each week?" and serves as a key indicator of network adoption and user engagement. The calculation uses probabilistic counting techniques to efficiently track distinct addresses across rolling weekly windows.

## 2. Data sources

- **Fraxtal transaction records**: Complete transaction history from the Fraxtal blockchain, including transaction metadata such as sender addresses, timestamps, and transaction hashes
- **Chain availability configuration**: Data governance rules that define valid time periods and chain operational status to ensure metric accuracy

## 3. Step-by-step calculation

### Component A: Daily user extraction
- **Purpose**: Identify unique addresses that sent transactions each day
- **Logic**: Extract all Fraxtal blockchain transactions and identify the sender address (`from_address`) for each transaction. Group transactions by day using truncated block timestamps, then collect all unique sender addresses for each day. Each address is classified as a 'Transaction sender' user type and tagged with Fraxtal chain metadata including market sector as 'blockchains_l2' and business segment as 'blockchain'.
- **Key fields**: `block_timestamp` for daily grouping, `from_address` as the user identifier, `transaction_hash` for transaction tracking

### Component B: HyperLogLog daily aggregation  
- **Purpose**: Convert daily user lists into memory-efficient probabilistic sketches
- **Logic**: Take the daily unique addresses and initialize HyperLogLog sketches with precision 15 for each day. This probabilistic data structure allows efficient approximate counting of distinct values while using minimal memory. Group by timestamp, chain, user type, and product dimensions to create separate HLL sketches for different breakdowns.
- **Key fields**: `timestamp` for daily boundaries, `hll` sketches containing encoded user addresses, dimensional fields for grouping

### Component C: Rolling window creation
- **Purpose**: Establish 7-day rolling windows for weekly calculations
- **Logic**: Create a datespine covering all days from the earliest available data to yesterday. For each day in the datespine, generate a 7-day window that includes the current day plus the 6 preceding days. This creates overlapping windows where each day serves as the endpoint of its own weekly period. Join the datespine windows with the daily HLL data to align time periods.
- **Key fields**: `timestamp` as the window endpoint, `joinday` for connecting to daily HLL data

### Component D: Weekly active user calculation
- **Purpose**: Count distinct users across each 7-day window
- **Logic**: For each 7-day window, merge all HyperLogLog sketches within that window using HLL merge operations. Extract the final distinct count from the merged sketch to get the number of unique addresses that were active during any day in the 7-day period. This produces a rolling weekly active user count where each day represents the endpoint of a weekly window.
- **Key fields**: `user_wau` containing the weekly distinct address count, dimensional breakdown fields

### Component E: Dimensional breakdowns
- **Purpose**: Calculate weekly active users across different dimensional views
- **Logic**: Create separate aggregations for different analytical perspectives. Generate breakdowns by 'chain' dimension (Fraxtal-level metrics), 'project_dbt_tag' dimension (protocol-level aggregation), 'user_type' dimension (transaction sender analysis), and 'product_id' dimension (product-level breakdowns). Each breakdown uses the same HLL merging logic but groups by different dimensional fields.
- **Key fields**: `dimensions` field indicating breakdown type, appropriate dimensional identifiers, `user_wau` values

### Final aggregation
- **Combining components**: Filter the weekly active user data to include only 'chain' and 'project_dbt_tag' dimensional breakdowns, as these are the relevant views for the active addresses metric. Rename the `user_wau` field to `active_addresses_weekly` since users are identified by their blockchain addresses.
- **Currency conversion**: No currency conversion required as this is a count-based metric
- **Time aggregation**: Data is already aggregated into rolling 7-day windows with daily endpoints. Each timestamp represents the final day of a weekly measurement period.
- **Final calculation**: The final metric value is the distinct count of addresses that sent transactions during any day within the 7-day rolling window period, calculated using HyperLogLog probabilistic counting for efficiency and accuracy.

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

Unique active addresses over a rolling 7-day window.

FraxtalFXTL

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown