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BONKbot

# BONKbot Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Bonkbot measures the count of unique users who paid fees to Bonkbot services within any 7-day rolling window. This metric identifies active traders using Bonkbot by tracking fee payments in both SOL (native Solana tokens) and SPL tokens to Bonkbot's designated fee collection address. The calculation uses HyperLogLog data structures for efficient unique user counting across large datasets and provides breakdowns by different dimensions including chain, user type, and product.

## 2. Data sources

The calculation relies on the following raw data sources:
- **Solana native transfers**: Records of SOL token transfers on the Solana blockchain, capturing transactions where users send native SOL tokens to specific addresses
- **Solana SPL token transfers**: Records of SPL (Solana Program Library) token transfers, capturing transactions where users send various SPL tokens to specific addresses
- **Transaction metadata**: Solana transaction records containing signer information, timestamps, amounts, and token addresses
- **Blockchain event logs**: Parsed Solana transaction data providing structured access to transfer events and transaction signers

## 3. Step-by-step calculation

### Component A: SOL fee collection tracking
- **Purpose**: Identifies users who paid fees to Bonkbot using native SOL tokens
- **Logic**: Extracts all native SOL transfers directed to Bonkbot's fee collection address `ZG98FUCjb8mJ824Gbs6RsgVmr1FhXb2oNiJHa2dwmPd`. Filters out transactions where the fee collection address itself is the signer to avoid self-transactions. Captures the transaction signer as the user identifier, representing the trader who initiated the fee payment.
- **Key fields**: `block_timestamp` for timing, `transaction_signer` as user identifier, `amount` for fee value, and hardcoded chain as 'solana'

### Component B: SPL token fee collection tracking
- **Purpose**: Identifies users who paid fees to Bonkbot using SPL tokens
- **Logic**: Extracts all SPL token transfers where the recipient owner address matches Bonkbot's fee collection address `ZG98FUCjb8mJ824Gbs6RsgVmr1FhXb2oNiJHa2dwmPd`. Excludes transactions where the fee collection address is the transaction signer. Records both the token address and amount to capture different SPL token types used for fee payments.
- **Key fields**: `block_timestamp`, `transaction_signer`, `token_address` for SPL token identification, and `amount` converted to bignumeric format

### Component C: Daily active user aggregation
- **Purpose**: Creates daily snapshots of unique users who paid any type of fee to Bonkbot
- **Logic**: Combines users from both SOL and SPL fee payments, grouping by day using timestamp truncation. Assigns all users the classification 'Trader' as the user type since fee payment indicates active trading behavior. Creates distinct combinations of timestamp, chain, user type, product ID, and user identifier to eliminate duplicate user counts within the same day.
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing unique user addresses, and standardized dimensions

### Component D: HyperLogLog preparation
- **Purpose**: Converts daily user lists into HyperLogLog data structures for efficient unique counting
- **Logic**: Initializes HyperLogLog sketches from the daily active user data with precision parameter 15, enabling approximate but highly accurate unique user counting. Groups by timestamp, timestamp grain, chain, user type, and product ID to maintain dimensional breakdowns while creating mergeable data structures.
- **Key fields**: `hll` containing the HyperLogLog sketch data structure for each day and dimension combination

### Component E: Rolling window creation
- **Purpose**: Generates 7-day lookback windows for each day to calculate weekly active users
- **Logic**: Creates a datespine covering all days from the earliest available data to yesterday. For each day in the datespine, generates an array of 7 timestamps representing the current day plus the 6 preceding days. Joins this window specification with the daily HyperLogLog data to create a dataset where each row represents one day within a 7-day window for WAU calculation.
- **Key fields**: `timestamp` for the WAU calculation date and `joinday` for each day within the 7-day lookback window

### Component F: Dimensional WAU calculation
- **Purpose**: Calculates weekly active users across different dimensional breakdowns
- **Logic**: For each dimension (project tag, chain, user type, product ID), merges HyperLogLog sketches across the 7-day windows and extracts the final unique user count. Uses HyperLogLog merge operations to combine daily user sets while avoiding double-counting users who were active on multiple days within the window. Creates separate aggregations for each dimensional view, setting non-relevant dimensions to null.
- **Key fields**: `user_wau` containing the final weekly active user count, `dimensions` identifying the breakdown type, and relevant dimensional identifiers

### Final aggregation
- **Combining components**: Unions all dimensional breakdown views (project tag, chain, user type, product ID) into a single result set, creating a comprehensive view of weekly active users across different analytical perspectives
- **Time aggregation**: Uses daily boundaries with 7-day rolling windows, where each day's WAU represents unique users active within that day plus the 6 preceding days
- **Final calculation**: Applies the `finalize_metric_user_wau` macro with 'bonkbot' as the data identifier and 'fill_zeros' strategy to standardize schema, handle missing data points, and ensure consistent time series output

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

Unique revenue-generating users over a rolling 7-day window.

BONKbot

Active users (weekly)

Active users (weekly)

Competitive landscape