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

Bitcoin is a distributed ledger for transfer and store of value (a blockchain). Bitcoin was founded by a pseudonym Satoshi Nakamoto and launched in 2009.

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

github

block explorer

Blockchains (L1)

Bitcoin

# Bitcoin Active addresses (weekly) Methodology

## 1. Overview

Bitcoin weekly active addresses measures the count of unique Bitcoin addresses that sent at least one transaction within a 7-day rolling window. This metric indicates network activity and user engagement by tracking addresses that actively initiated Bitcoin transactions. The calculation uses a rolling 7-day window approach with HyperLogLog probabilistic counting for efficient computation across Bitcoin's large transaction volume.

## 2. Data sources

The calculation relies on several key Bitcoin blockchain data sources:

- **Bitcoin transaction records**: Raw Bitcoin blockchain transactions containing transaction IDs, timestamps, and input/output arrays
- **Transaction inputs and outputs**: Detailed input and output data from Bitcoin transactions, including references to previous transaction outputs
- **ScriptPubKey address data**: Bitcoin address information extracted from transaction output scripts
- **Block timestamp data**: Timing information for when transactions were included in Bitcoin blocks

## 3. Step-by-step calculation

### Component A: Address resolution lookup table
- **Purpose**: Creates a mapping between transaction outputs and their recipient Bitcoin addresses
- **Logic**: Processes Bitcoin transaction records and extracts recipient addresses from each transaction output's `scriptPubKey` field. Each output is associated with its transaction hash and output index position, creating a lookup table that can later be used to resolve which addresses received Bitcoin in specific transactions.
- **Key fields**: `transaction_hash`, `output_index`, `user_address`, `block_timestamp`

### Component B: Historical user activity tracking
- **Purpose**: Identifies Bitcoin addresses that sent transactions before January 1, 2019
- **Logic**: Takes Bitcoin transactions from before 2019 and explodes the `inputs` array to get individual input references. Each input contains a reference to a previous transaction output (via `txid` and `vout` fields). Joins these input references with the address resolution lookup table to determine which address originally received the Bitcoin being spent, thus identifying the sending address for each transaction input.
- **Key fields**: `timestamp` (daily truncated), `user_address`

### Component C: Recent user activity tracking  
- **Purpose**: Identifies Bitcoin addresses that sent transactions from January 1, 2019 onward
- **Logic**: Uses the same input-to-address resolution process as historical tracking but processes more recent transactions. Implements incremental updates to efficiently handle new data. Explodes transaction inputs and resolves sending addresses by matching input transaction references with the address lookup table.
- **Key fields**: `timestamp` (daily truncated), `user_address`

### Component D: Daily active users aggregation
- **Purpose**: Combines historical and recent user data into a unified daily active user dataset
- **Logic**: Unions the historical and recent user tracking results, creating a comprehensive view of all addresses that sent Bitcoin transactions on each day. Each record represents a unique address being active on a specific date, with additional metadata including user type classification as "Transaction sender".
- **Key fields**: `timestamp`, `user_address`, `chain`, `user_type`, `timestamp_grain`

### Component E: Weekly active users calculation
- **Purpose**: Converts daily active users into weekly active users using a 7-day rolling window
- **Logic**: First creates HyperLogLog sketches for each day's unique addresses to enable efficient probabilistic counting. Generates a complete date spine and creates 7-day rolling windows by associating each date with the 7 preceding days (including itself). For each date, merges HyperLogLog sketches from all 7 days in the window and extracts the distinct count estimate. Creates multiple dimensional breakdowns including chain-level, project-level, user-type-level, and product-level aggregations.
- **Key fields**: `timestamp`, `user_wau`, `dimensions`, `chain_id`, `timestamp_grain`

### Final aggregation
- **Combining components**: Selects weekly active user counts from the chain-level and project-level dimensional breakdowns, filtering to only include these specific dimension types
- **Currency conversion**: Not applicable - this metric counts addresses rather than monetary values
- **Time aggregation**: Uses 7-day rolling windows where each date represents the count of unique addresses active in the current day plus the 6 preceding days
- **Final calculation**: Renames the `user_wau` field to `active_addresses_weekly` to reflect that for Bitcoin, active users are equivalent to active addresses since Bitcoin uses address-based transactions rather than account-based systems

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

Unique active addresses over a rolling 7-day window.

BitcoinBTC

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape