Social

Story Protocol

# Story Protocol Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that initiated at least one transaction on the Story blockchain during a rolling 7-day period. This metric answers the business question: "How many distinct users are actively engaging with the Story blockchain each week?" The calculation uses a rolling window approach where each day's metric represents the count of unique addresses that were active on that day or any of the previous 6 days. This provides a smoothed view of user engagement that reduces daily volatility while capturing weekly usage patterns.

## 2. Data sources

The metric relies on the following raw data sources:
- **Story blockchain transaction records**: Complete transaction history from the Story blockchain containing sender addresses, transaction hashes, and block timestamps
- **Transaction metadata**: Block timestamps used to determine daily and weekly time boundaries for aggregation
- **Address identification**: Transaction sender addresses (`from_address`) used as the primary identifier for active users

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies active users from transaction data and calculates daily active users
- **Logic**: The system extracts all transactions from the Story blockchain and identifies the sender address (`from_address`) of each transaction as an active user. Each sender is classified with the user type "Transaction sender" and associated with the Story chain and blockchain L2 market sector. Transactions are then grouped by day using timestamp truncation to day boundaries, and distinct sender addresses are counted for each day to produce daily active user counts (`user_dau`)
- **Key fields**: `block_timestamp` for temporal grouping, `from_address` as the user identifier, `transaction_hash` for transaction identification, and `user_dau` as the daily distinct address count

### Component B: HyperLogLog initialization for efficient counting
- **Purpose**: Prepares daily user data for efficient distinct counting across rolling windows
- **Logic**: For each day's active users, the system initializes HyperLogLog sketches with precision parameter 15. This probabilistic data structure allows for efficient approximate distinct counting when combining multiple days of data. The daily user addresses are processed into HyperLogLog sketches grouped by timestamp, timestamp grain, chain, user type, and product
- **Key fields**: `hll` sketches containing compressed representations of daily active address sets, enabling memory-efficient distinct counting across time windows

### Component C: Rolling window construction
- **Purpose**: Creates 7-day rolling windows for weekly active user calculation
- **Logic**: A datespine is generated covering all dates from the earliest transaction date to yesterday. For each date in the datespine, a 7-day window is constructed by joining with the current day plus the previous 6 days. This creates overlapping 7-day periods where each day's weekly metric represents activity from that day back through the previous 6 days. The system joins the window dates with the daily HyperLogLog data to gather all relevant user sketches for each 7-day period
- **Key fields**: `timestamp` for the reporting date, `joinday` for the individual days within each 7-day window, and `hll` sketches for each day in the window

### Component D: Weekly aggregation with dimensional breakdowns
- **Purpose**: Aggregates daily HyperLogLog sketches into weekly active user counts across different dimensions
- **Logic**: For each 7-day window, the system merges the daily HyperLogLog sketches using the `hll_count.merge_partial()` function and extracts the final distinct count using `hll_count.extract()`. This process is performed across multiple dimensional breakdowns: chain-level (grouped by chain), project-level (grouped by project tag), user-type-level (grouped by user type), and product-level (grouped by product). Each dimensional breakdown produces weekly active user counts (`user_wau`) with appropriate grouping fields
- **Key fields**: `user_wau` as the weekly active user count, `dimensions` indicating the breakdown level, and relevant grouping fields like `chain_id`, `user_type`, or `product_id`

### Final aggregation
- **Combining components**: The different dimensional breakdowns are combined using union operations to create a comprehensive dataset with weekly active users across all dimension levels
- **Currency conversion**: No currency conversion is required as this metric counts addresses rather than monetary values
- **Time aggregation**: The metric uses rolling 7-day windows where each day's value represents unique addresses active during that day plus the previous 6 days, providing smoothed weekly engagement trends
- **Final calculation**: The final weekly active addresses metric is generated by selecting the chain-level and project-level weekly active user counts from the dimensional breakdowns and renaming `user_wau` to `active_addresses_weekly`. The `finalize_metric_active_addresses_weekly` macro applies standardized schema formatting and fills missing dates with zero values to ensure complete time series coverage

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

Unique active addresses over a rolling 7-day window.

Story ProtocolIP

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape