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BONKbot

# BONKbot Active users (monthly) Methodology

## 1. Overview

The Bonkbot Monthly Active Users (MAU) metric measures the distinct count of users who interacted with the Bonkbot trading service within rolling 30-day windows. The metric identifies active users by tracking fee payments made to Bonkbot's designated fee collection address on the Solana blockchain. Users are classified as active when they pay trading fees in either SOL (native token) or SPL tokens, indicating they executed trades through the bot service. The calculation uses HyperLogLog probabilistic data structures for memory-efficient distinct counting across multiple dimensional breakdowns.

## 2. Data sources

The calculation relies on the following raw blockchain data sources:

- **Solana native token transfers**: Records of SOL transfers on the Solana blockchain, specifically filtering for transfers directed to Bonkbot's fee collection address
- **Solana SPL token transfers**: Records of SPL token transfers on Solana, filtered for transfers to Bonkbot's fee collection address where the recipient owner matches the fee address
- **Transaction metadata**: Blockchain transaction details including transaction signers (users), block timestamps, transaction IDs, and transfer amounts
- **Blockchain addresses**: The specific Bonkbot fee collection address (`ZG98FUCjb8mJ824Gbs6RsgVmr1FhXb2oNiJHa2dwmPd`) used to identify fee payments

## 3. Step-by-step calculation

### Component A: SOL fee collection tracking
- **Purpose**: Identifies users who paid trading fees in SOL (Solana's native token)
- **Logic**: Extracts all native SOL transfers directed to Bonkbot's fee collection address. Filters out transfers where the transaction signer is the fee address itself to avoid counting internal movements. Records the transaction signer as the user identifier, along with timestamps and amounts. Sets the token address as SOL's standard identifier and assigns null values for product-specific breakdowns since fees aren't currently segmented by product
- **Key fields**: `transaction_signer` as the user identifier, `block_timestamp` for temporal analysis, `amount` for fee values, and `transaction_id` for uniqueness

### Component B: SPL token fee collection tracking  
- **Purpose**: Identifies users who paid trading fees in SPL tokens (Solana Program Library tokens)
- **Logic**: Extracts SPL token transfers where the recipient owner address matches Bonkbot's fee collection address. Excludes transfers where the transaction signer is the fee address to avoid internal movements. Captures the specific token address being transferred, transaction signer as user identifier, timestamps and amounts. Like SOL fees, product segmentation is not currently implemented
- **Key fields**: `transaction_signer` as user identifier, `token_address` for the specific SPL token, `block_timestamp` for timing, and `amount` for fee quantities

### Component C: Daily active user aggregation
- **Purpose**: Combines both fee types to identify unique daily active users
- **Logic**: Unions the SOL and SPL fee data, then applies distinct counting on transaction signers per day. Truncates timestamps to daily granularity and classifies all users as 'Trader' type since they're engaging in trading activities. Creates a comprehensive list of unique users per day who interacted with Bonkbot services through fee payments
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, `user_type` set to 'Trader', and dimensional breakdowns for chain and product

### Component D: HyperLogLog initialization and windowing
- **Purpose**: Prepares daily user data for efficient monthly aggregation using probabilistic counting
- **Logic**: Converts daily distinct user lists into HyperLogLog sketches with precision parameter 15 for memory-efficient distinct counting. Creates a complete datespine covering all days from the earliest data through yesterday. Generates 30-day rolling windows by cross-joining each day with the previous 29 days, creating comprehensive time windows that don't skip missing dates
- **Key fields**: `hll` sketches containing user data, `timestamp` for the window end date, and `joinday` for the days included in each window

### Component E: Monthly active user calculation with dimensional breakdowns
- **Purpose**: Calculates MAU across different analytical dimensions
- **Logic**: Merges HLL sketches within each 30-day window to get distinct user counts. Creates four separate dimensional views: project-level (overall MAU without dimensional filters), chain-level (currently just Solana), user-type-level (currently just 'Trader'), and product-level (currently null since product segmentation isn't implemented). Each breakdown nullifies irrelevant dimensions while preserving the relevant grouping dimension
- **Key fields**: `user_mau` containing the distinct user count, `dimensions` indicating the breakdown type, and the relevant dimensional fields (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Unions all dimensional breakdowns into a single result set using union all operations, creating a comprehensive view with multiple analytical perspectives
- **Currency conversion**: Not applicable as this metric counts users rather than monetary values
- **Time aggregation**: Uses 30-day rolling windows where each day's MAU represents distinct users over the trailing 30 days including the current day
- **Final calculation**: The `finalize_metric_user_mau` macro applies standardized formatting, fills missing days with zero values, and adds metadata fields like data source identifiers. The final MAU value represents the HyperLogLog-estimated distinct count of users who paid fees to Bonkbot within each 30-day rolling window

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

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

BONKbot

Active users (monthly)

Active users (monthly)

Competitive landscape