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pump.fun

# pump.fun Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Pump.fun measures the number of unique users who have executed at least one trade on the Pump.fun decentralized exchange platform within a rolling 30-day period. This metric provides insights into Pump.fun's user engagement and platform adoption by tracking distinct traders over monthly windows. The calculation uses HyperLogLog algorithms for memory-efficient distinct user counting across large datasets and provides multiple dimensional breakdowns for comprehensive analysis.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Solana DEX trades**: Trade execution data from Solana's decentralized exchange infrastructure, specifically filtering for trades executed through Pump.fun's program (`6EF8rrecthR5Dkzon8Nwu78hRvfCKubJ14M5uBEwF6P`)
- **Token metadata**: Solana token registry containing decimal precision information for accurate amount calculations
- **Chain availability filters**: Configuration data that ensures metrics are only calculated for periods when Solana chain data is reliably available

## 3. Step-by-step calculation

### Component A: Trade data extraction and processing
- **Purpose**: Extracts and processes raw Pump.fun trading activity from Solana
- **Logic**: The system identifies all trades executed through Pump.fun's specific program address on Solana. Each trade record includes the transaction signer (trader address), base and quote token addresses, trade amounts, and block timestamps. The system joins this trade data with token metadata to apply proper decimal adjustments, converting raw integer amounts to human-readable token quantities by dividing by 10 raised to the power of each token's decimal precision. Trade amounts are converted to absolute values to handle both buy and sell directions consistently.
- **Key fields**: `transaction_signer` (trader address), `base_mint_address` and `quote_mint_address` (traded tokens), `base_amount` and `quote_amount` (trade sizes), `block_timestamp` (trade timing), `product_id` ('pump.fun')

### Component B: Daily active users (DAU) aggregation
- **Purpose**: Identifies unique daily traders and prepares data for MAU calculation
- **Logic**: The system groups all trades by calendar day using timestamp truncation, then identifies distinct users per day based on their transaction signer addresses. All users are classified as 'Trader' type since the metric focuses on trading activity. The daily user data is organized by timestamp, chain ('solana'), user type, and product identifier, creating the foundation for rolling monthly calculations.
- **Key fields**: `timestamp` (daily truncated), `user_dau` (distinct trader address), `user_type` ('Trader'), `chain` ('solana')

### Component C: HyperLogLog initialization for efficient distinct counting
- **Purpose**: Prepares daily user data for memory-efficient distinct counting across monthly windows
- **Logic**: The system initializes HyperLogLog sketches for each day's unique users, using precision parameter 15 for optimal accuracy-memory balance. This probabilistic data structure allows efficient merging of distinct user counts across multiple days without storing actual user addresses. Each daily HLL sketch represents the unique users active on that specific day, organized by timestamp, chain, user type, and product dimensions.
- **Key fields**: `hll` (HyperLogLog sketch of daily users), grouping dimensions for later aggregation

### Component D: Date spine and rolling window creation
- **Purpose**: Creates a complete time series and defines 30-day rolling windows for MAU calculation
- **Logic**: The system generates a complete date spine covering all days from the earliest available data to the most recent complete day, ensuring no gaps in the time series. For each day in this spine, it creates a 30-day rolling window by generating an array of the previous 29 days plus the current day. This approach uses nested unnest operations with generate_timestamp_array to create a comprehensive mapping where each target date is associated with all days that should contribute to its 30-day MAU calculation.
- **Key fields**: `timestamp` (target MAU date), `joinday` (contributing days within the 30-day window)

### Component E: Rolling window HLL aggregation  
- **Purpose**: Merges daily user data across 30-day windows to calculate monthly active users
- **Logic**: The system joins the date spine windows with the daily HLL data, matching each contributing day within a window to its corresponding HLL sketch. For each target date, it collects all HLL sketches from the 30-day period and merges them using HyperLogLog merge operations. This process efficiently combines distinct user counts across the monthly period without double-counting users who were active on multiple days. The merging occurs within each dimensional grouping (chain, user type, product) to maintain proper segmentation.
- **Key fields**: Combined HLL sketches representing unique users across 30-day windows, dimensional grouping fields

### Component F: Dimensional breakdown generation
- **Purpose**: Creates multiple analytical perspectives of MAU data across different business dimensions
- **Logic**: The system generates four separate dimensional breakdowns from the windowed HLL data. The project-level breakdown aggregates all users across all dimensions to show total Pump.fun MAU. The chain-level breakdown (though only Solana applies here) maintains chain segmentation. The user-type breakdown separates different user categories (only 'Trader' in this case). The product-level breakdown separates different products (only 'pump.fun' here). Each breakdown nullifies non-relevant dimensions and merges HLL sketches appropriately, then extracts the final distinct count using hll_count.extract operations.
- **Key fields**: `dimensions` (breakdown type), `user_mau` (distinct monthly active users), relevant dimension identifiers

### Final aggregation
- **Combining components**: The four dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with multiple analytical perspectives. Each row represents MAU for a specific date-dimension combination.
- **Schema standardization**: The finalize_metric_user_mau macro applies consistent schema formatting, adds metadata fields like data_id ('pump.fun'), and implements a fill_zeros strategy that populates missing time periods with zero values rather than leaving gaps.
- **Time aggregation**: Daily timestamps serve as the final temporal granularity, with each day's MAU representing the unique users active during that day plus the previous 29 days. Monthly partitioning optimizes query performance for time-based analysis.
- **Final calculation**: The final user_mau value represents the distinct count of Solana addresses that executed at least one trade on Pump.fun during the 30-day period ending on each specific date, extracted from merged HyperLogLog sketches and organized across multiple dimensional breakdowns for comprehensive business analysis.

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

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

pump.funPUMP

Active users (monthly)

Active users (monthly)

Competitive landscape