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Blockchains (L2)

Shape

# Shape Active addresses (daily) Methodology

## 1. Overview

Active addresses daily measures the number of unique blockchain addresses that initiated at least one transaction on Shape blockchain per day. This metric provides insight into daily user activity and adoption by counting distinct transaction senders, treating each unique address as an active user. The calculation uses HyperLogLog (HLL) data structures for efficient distinct counting and provides dimensional breakdowns by chain, project tag, user type, and product.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Shape blockchain transactions**: Complete transaction records from the Shape blockchain containing sender addresses, transaction hashes, block timestamps, and transaction metadata
- **Transaction metadata**: Additional context about transaction types and business classifications

## 3. Step-by-step calculation

### Component A: User fact extraction
- **Purpose**: Extract and standardize user activity data from raw blockchain transactions
- **Logic**: Process raw Shape blockchain transaction data to identify transaction senders. Extract the `from_address` field as the user address, along with the block timestamp. Apply data type casting to ensure proper timestamp and string formats. Classify each transaction sender with metadata including chain identifier ('shape'), market sector ('blockchains_l2'), business segment ('blockchain'), and user type ('Transaction sender').
- **Key fields**: `block_timestamp`, `transaction_hash`, `from_address` (renamed to `user_address`), `chain`, `user_type`

### Component B: Daily user aggregation
- **Purpose**: Group transaction activity by day and identify distinct active users
- **Logic**: Truncate block timestamps to daily boundaries using day-level granularity. For each day, identify all unique user addresses that initiated transactions. Apply incremental processing logic to handle new data efficiently, with lookback periods for data consistency. Create distinct records for each unique combination of day, chain, user type, and user address.
- **Key fields**: `timestamp` (daily boundary), `timestamp_grain` ('day'), `user_address`, `user_type`, `chain`

### Component C: HLL-based distinct counting
- **Purpose**: Efficiently count distinct active addresses across multiple dimensional breakdowns
- **Logic**: Initialize HyperLogLog (HLL) data structures for each user address to enable efficient distinct counting. Create multiple dimensional views of the same underlying data: project-level aggregation (all users regardless of breakdown), chain-level breakdown, user type breakdown, and product-level breakdown. For each dimension, merge HLL values and extract the final distinct count using `hll_count.merge_partial` and `hll_count.extract` functions.
- **Key fields**: `user_dau` (distinct user count), `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project tag, chain, user type, product) into a single comprehensive dataset with different granularity levels
- **Currency conversion**: Not applicable - this is a count metric without monetary values
- **Time aggregation**: Daily boundaries are established by truncating block timestamps to day level, ensuring each day captures all unique addresses that were active during that 24-hour period
- **Final calculation**: The final `active_addresses_daily` value is the distinct count of user addresses extracted from the HLL data structure, renamed from `user_dau` to align with metric naming conventions. Missing days are filled with zero values to ensure complete time series, and chain availability filters ensure data quality.

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

Unique active addresses over a fixed 24-hour window.

ShapeSHAPE

Active addresses (daily)

Active addresses (daily)

Competitive landscape