Social

Story Protocol

# Story Protocol Active addresses (monthly) Methodology

## 1. Overview

Story monthly active addresses measures the number of unique addresses that initiated at least one transaction on the Story blockchain within any 30-day rolling window. This metric provides insight into user engagement and adoption trends for the Story L2 blockchain. The calculation uses HyperLogLog data structures for efficient computation of distinct address counts across rolling monthly periods.

## 2. Data sources

- **Story blockchain transactions**: Transaction records from the Story blockchain containing sender addresses, timestamps, and transaction metadata
- **Transaction metadata**: Block timestamps, transaction hashes, and sender addresses (`from_address` field)

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Extract and classify transaction initiators as blockchain users
- **Logic**: Parse Story blockchain transaction records to identify transaction senders. Each transaction's `from_address` field represents a user who initiated blockchain activity. All transaction senders are classified as "Transaction sender" user type. The component also assigns protocol metadata including chain identifier ('story'), market sector ('blockchains_l2'), and business segment ('blockchain').
- **Key fields**: `block_timestamp`, `transaction_hash`, `from_address` (renamed to `user_address`)

### Component B: Daily active user calculation
- **Purpose**: Aggregate users to daily distinct active users (DAU)
- **Logic**: Group transaction data by day using timestamp truncation to daily granularity. Count distinct user addresses per day to identify daily active users. Each unique address that sent at least one transaction on a given day contributes to that day's active user count. This creates a time series of daily unique address counts.
- **Key fields**: `timestamp` (daily), `user_address` (renamed to `user_dau`), `chain`, `user_type`

### Component C: Monthly active user aggregation with HyperLogLog
- **Purpose**: Calculate 30-day rolling monthly active users using efficient distinct counting
- **Logic**: Create HyperLogLog sketches for each day's active users to enable efficient merging across time windows. Build a datespine covering all days from the earliest data to yesterday. For each day, create a 30-day rolling window (29 days prior plus current day) using cross-joins. Merge HyperLogLog sketches across each 30-day window to calculate unique users. Extract final counts from merged HLL structures. Generate dimensional breakdowns for chain-level, project-level, user-type-level, and product-level aggregations.
- **Key fields**: `timestamp` (daily), `user_mau` (monthly active users), `dimensions` (breakdown type), `chain_id`

### Final aggregation
- **Combining components**: Select monthly active user counts specifically for chain-level and project-level dimensional breakdowns, filtering out user-type and product-level breakdowns
- **Currency conversion**: Not applicable - this is a count metric
- **Time aggregation**: Uses 30-day rolling windows where each day represents users active in the trailing 30-day period (including current day)
- **Final calculation**: Rename the `user_mau` field to `active_addresses_monthly` to reflect that this represents monthly active addresses. Apply zero-filling for any missing time periods to ensure complete time series coverage. The final value represents the count of unique addresses that initiated at least one transaction within any 30-day rolling period.

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

Unique addresses active over a rolling 30-day window.

Story ProtocolIP

Active addresses (monthly)

Active addresses (monthly)

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