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SolTradingBot

# SolTradingBot Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for SolTradingBot measures the number of unique users who paid fees to the SolTradingBot protocol over rolling 7-day periods. This metric identifies active traders by tracking fee payments made to SolTradingBot's designated fee collection addresses on the Solana blockchain. The calculation uses HyperLogLog sketches for efficient approximate distinct counting across rolling time windows and provides breakdowns by project level, chain, user type, and product dimensions.

## 2. Data sources

- **Solana native transfers**: Parsed native SOL transfer events on the Solana blockchain, specifically transfers organized by recipient address
- **SolTradingBot fee addresses**: Two hardcoded fee collection addresses that receive payments from SolTradingBot users:
  - `F34kcgMgCF7mYWkwLN3WN7KrFprr2NbwxuLvXx4fbztj`
  - `96aFQc9qyqpjMfqdUeurZVYRrrwPJG2uPV6pceu4B1yb`
- **Transaction metadata**: Solana transaction records containing signer information, timestamps, and transfer amounts
- **Token reference data**: Wrapped SOL token address (`So11111111111111111111111111111111111111112`) for standardization

## 3. Step-by-step calculation

### Component A: Fee transaction identification
- **Purpose**: Extract all fee payments made to SolTradingBot to identify active users
- **Logic**: Filter Solana native transfer events where the recipient address matches one of the two SolTradingBot fee collection addresses. Exclude internal transfers where the transaction signer is also one of the fee accounts to avoid counting operational transfers. Each qualifying transfer represents a fee payment by an active user, with the transaction signer field identifying the user who paid the fee.
- **Key fields**: `block_timestamp` for timing, `transaction_signer` as the user identifier, `to_address` for filtering fee recipients, `amount` for transfer value

### Component B: Daily active user aggregation  
- **Purpose**: Group fee transactions by day and identify unique active users
- **Logic**: Truncate transaction timestamps to day level and group by date. Extract distinct transaction signers for each day to identify unique daily active users. Classify all users as 'Trader' type since they are actively paying fees to use the protocol. Map all activity to the Solana chain and wrapped SOL token for consistency.
- **Key fields**: `timestamp` (truncated to day), `signer` as unique user identifier, `chain` set to 'solana', `user_type` set to 'Trader'

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user lists into efficient data structures for approximate distinct counting
- **Logic**: Transform daily active user lists into HyperLogLog sketches using `hll_count.init` with 15-bit precision. This enables efficient merging and counting of distinct users across multiple time periods without storing raw user lists. Group sketches by timestamp, chain, user type, and product dimensions.
- **Key fields**: `hll` sketch containing compressed user information, dimensional grouping fields

### Component D: Rolling window construction
- **Purpose**: Create 7-day lookback windows for each date to calculate weekly metrics
- **Logic**: Generate a complete datespine covering all dates from the earliest fee transaction to yesterday. For each date in the spine, create joins with the current day plus the previous 6 days to form 7-day rolling windows. Use inner joins with the daily HLL data to populate the windows, ensuring only dates with actual data contribute to the calculations.
- **Key fields**: `timestamp` as the window end date, `joinday` representing each day within the 7-day window

### Component E: Weekly user aggregation
- **Purpose**: Calculate approximate distinct users across each 7-day window
- **Logic**: For each 7-day window, merge the HyperLogLog sketches from all days in the window using `hll_count.merge_partial`, then extract the final distinct count using `hll_count.extract`. This provides an approximate count of unique users who were active at any point during the 7-day period.
- **Key fields**: `user_wau` as the final weekly active user count, maintaining dimensional groupings

### Component F: Dimensional breakdowns
- **Purpose**: Provide WAU metrics across different analytical dimensions
- **Logic**: Calculate separate WAU aggregations for four different dimensional views: project level (all dimensions collapsed), chain level (grouped by chain), user type level (grouped by user type), and product level (grouped by product). For each breakdown, aggregate the appropriate dimensional combinations while setting unused dimensions to null. Combine all breakdowns using union operations.
- **Key fields**: `dimensions` indicating the breakdown type, dimensional fields (`chain_id`, `user_type`, `product_id`) with appropriate nulls

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, maintaining the same schema across all breakdown types
- **Currency conversion**: Not applicable for user count metrics  
- **Time aggregation**: Rolling 7-day windows updated daily, with each date representing the WAU for that date plus the previous 6 days
- **Final calculation**: The final `user_wau` value represents the approximate number of unique users who paid fees to SolTradingBot during any day within the 7-day rolling window ending on the specified date

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

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

SolTradingBotSTBOT

Active users (weekly)

Active users (weekly)

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