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 users (monthly) Methodology

## 1. Overview

Bitcoin Monthly Active Users (MAU) measures the number of unique Bitcoin addresses that have sent transactions within any 30-day rolling window. This metric answers the business question: "How many distinct users are actively transacting on the Bitcoin network each month?" The calculation uses HyperLogLog probabilistic counting to efficiently estimate distinct users across multiple dimensions, aggregating daily active users over 30-day periods.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Bitcoin blockchain transaction data**: Complete Bitcoin transaction records including transaction IDs (`txid`), block timestamps, and transaction structure
- **Transaction inputs and outputs**: Detailed input and output arrays for each transaction, containing references to previous transactions and destination addresses  
- **Transaction output scripts**: `scriptPubKey.address` fields that contain the actual Bitcoin addresses receiving funds
- **Blockchain timestamps**: Block-level timestamps used to determine when transactions occurred

## 3. Step-by-step calculation

### Component A: Address resolution from transaction outputs
- **Purpose**: Creates a lookup table mapping transaction outputs to their receiving Bitcoin addresses
- **Logic**: Extracts Bitcoin addresses from transaction output arrays by unnesting the `outputs` field and extracting the `scriptPubKey.address` for each output. Each output is identified by its `transaction_hash` and `output_index` position within the transaction
- **Key fields**: `transaction_hash`, `output_index`, `user_address`, `block_timestamp`

### Component B: User identification from transaction inputs  
- **Purpose**: Resolves the actual sending addresses for Bitcoin transactions by tracing input references
- **Logic**: Bitcoin transaction inputs reference previous transaction outputs using `txid` (previous transaction hash) and `vout` (output index). To find who sent a transaction, the system unnests transaction `inputs` arrays and joins each input reference with the address resolution lookup table using `input_transaction_hash` = `transaction_hash` and `input_output_index` = `output_index`. This resolves each input to its originating Bitcoin address
- **Key fields**: `input_transaction_hash`, `input_output_index`, resolved `user_address`

### Component C: Daily user activity aggregation
- **Purpose**: Aggregates unique Bitcoin addresses that sent transactions each day, split between historical and recent periods
- **Logic**: Groups resolved sending addresses by day using `timestamp_trunc` to day level. Creates two datasets: historical users (before 2019-01-01) and recent users (2019-01-01 onwards). Each unique address per day represents one daily active user. Applies distinct deduplication to ensure each address is counted once per day regardless of transaction volume
- **Key fields**: `timestamp` (day-level), `user_address`

### Component D: Daily active users (DAU) standardization
- **Purpose**: Combines historical and recent user data into standardized DAU records
- **Logic**: Unions historical and recent user datasets, adding standard metadata fields including `timestamp_grain` set to 'day', `chain` identifier as 'bitcoin', `user_type` as 'Transaction sender', and null `product_id`. Each record represents one unique address active on a specific day
- **Key fields**: `timestamp`, `timestamp_grain`, `chain`, `user_type`, `product_id`, `user_dau` (the Bitcoin address)

### Component E: HyperLogLog sketch preparation  
- **Purpose**: Converts daily user addresses to probabilistic counting sketches for efficient distinct counting
- **Logic**: Groups DAU records by timestamp and metadata dimensions, then initializes HyperLogLog sketches from user addresses using `hll_count.init()` with precision parameter 15. This creates approximate distinct count structures that can be efficiently merged across time periods
- **Key fields**: `timestamp`, dimensional grouping fields, `hll` sketch

### Component F: Rolling window creation
- **Purpose**: Creates 30-day rolling windows to calculate monthly active users
- **Logic**: Generates a datespine covering all days from the earliest DAU timestamp to yesterday. For each day in the datespine, creates a cartesian product with the previous 29 days (plus the current day = 30 total days) using `generate_timestamp_array()`. Joins this window structure with the HLL sketches to associate each day with its corresponding 30-day lookback period
- **Key fields**: `timestamp` (window end date), `joinday` (individual days within window), `hll` sketches

### Component G: Multi-dimensional MAU calculation
- **Purpose**: Calculates monthly active users across different analytical dimensions
- **Logic**: Creates four dimensional breakdowns by grouping the windowed data: (1) project_dbt_tag level (overall), (2) chain level (by blockchain), (3) user_type level (by user category), (4) product_id level (by product). For each dimension and time window, merges all HLL sketches using `hll_count.merge_partial()` and extracts the final distinct count using `hll_count.extract()`. This produces the estimated number of unique addresses active within each 30-day period
- **Key fields**: `timestamp`, `dimensions`, dimensional identifiers, `user_mau` (final count)

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns using `union all` to create a single result set containing MAU calculations across all analytical dimensions
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows where each day represents the MAU for the trailing 30-day period ending on that date
- **Final calculation**: The final `user_mau` value represents the estimated distinct count of unique Bitcoin addresses that sent at least one transaction within the 30-day window, calculated using HyperLogLog probabilistic counting for computational efficiency

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

Unique revenue-generating users over a rolling 30-day window.

BitcoinBTC

Active users (monthly)

Active users (monthly)

Competitive landscape