Miners process transactions and receive fees from users (supply-side fees).

Dogecoin is a distributed ledger for transfer and store of value (a blockchain). Dogecoin was founded by Billy Markus & Jackson Palmer and launched in 2013.

DOGE holders own and govern the protocol. They currently do not take a cut of the total transaction fees paid by users (revenue).

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Dogecoin

# Dogecoin Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique Dogecoin addresses that initiated at least one transaction during a rolling 7-day period. This metric provides insight into user engagement and network activity by tracking distinct transaction senders over time. The calculation uses probabilistic counting (HyperLogLog) to efficiently handle large volumes of address data while maintaining accuracy for distinct counts.

## 2. Data sources

The calculation relies entirely on native Dogecoin blockchain data:
- **Dogecoin transaction records**: Complete transaction history including block timestamps, transaction hashes, inputs, and outputs
- **Transaction input data**: JSON-structured input arrays containing references to previous transaction outputs via `txid` (transaction hash) and `vout` (output index)  
- **Transaction output data**: JSON-structured output arrays containing recipient address information in `scriptPubKey.addresses` fields
- **Block timestamps**: Transaction timing data for daily aggregation boundaries
- **Transaction references**: Hash-based links between transaction inputs and the outputs they consume

## 3. Step-by-step calculation

### Component A: Address resolution lookup table
- **Purpose**: Create a mapping between transaction outputs and their recipient addresses to later resolve transaction senders
- **Logic**: Parse each transaction's `outputs` array and extract the first address from each output's `scriptPubKey.addresses` JSON array. Store the mapping of transaction hash, output index, and recipient address. Use the explicit `n` field from outputs when available, otherwise fall back to the array offset position as the output index.
- **Key fields**: `transaction_hash`, `output_index`, `user_address` from parsed JSON structures

### Component B: Historical user activity (pre-2019)
- **Purpose**: Identify daily active addresses for the historical period before 2019
- **Logic**: For transactions before 2019-01-01, extract all inputs and resolve their sending addresses by joining each input's `txid` and `vout` reference with the address resolution lookup table. Truncate timestamps to daily boundaries and deduplicate addresses within each day.
- **Key fields**: Daily `timestamp`, resolved `user_address` from transaction senders

### Component C: Recent user activity (2019+) 
- **Purpose**: Identify daily active addresses for the recent period from 2019 onward using incremental processing
- **Logic**: For transactions from 2019-01-01 forward, extract all inputs and resolve their sending addresses using the same lookup process as historical data. Process incrementally with configurable lookback periods. Truncate timestamps to daily boundaries and deduplicate addresses within each day.
- **Key fields**: Daily `timestamp`, resolved `user_address` from transaction senders

### Component D: Daily active user aggregation
- **Purpose**: Combine historical and recent user data into a unified daily active user dataset
- **Logic**: Union the historical and recent user activity datasets to create a complete view of daily active addresses. Classify all users as "Transaction sender" type since Dogecoin active addresses are defined as addresses that initiated transactions.
- **Key fields**: `timestamp`, `user_address`, `user_type` (always "Transaction sender"), `chain` (always "dogecoin")

### Component E: Weekly active user calculation  
- **Purpose**: Calculate weekly active user counts using rolling 7-day windows with probabilistic counting
- **Logic**: Create HyperLogLog sketches for each day's active addresses to enable efficient distinct counting. Generate a complete date spine and create rolling 7-day windows by joining each day with the 6 preceding days. Merge HyperLogLog sketches across each 7-day window and extract distinct counts. Calculate breakdowns by different dimensions including chain-level and project-level aggregations.
- **Key fields**: `timestamp`, `user_wau` (weekly active users), `dimensions` (breakdown type), `chain_id`

### Final aggregation  
- **Combining components**: The weekly active user counts are directly mapped to active addresses since each active user represents a unique address that sent transactions
- **Time aggregation**: Uses rolling 7-day windows where each day's value represents the count of distinct addresses active in that day plus the 6 preceding days
- **Final calculation**: The `user_wau` field is renamed to `active_addresses_weekly` and filtered to include only chain-level and project-level dimension breakdowns. Missing days are filled with zeros using the finalize metric macro to ensure complete time series data.

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

Unique active addresses over a rolling 7-day window.

DogecoinDOGE

Active addresses (weekly)

Active addresses (weekly)

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