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SolTradingBot

# SolTradingBot Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the count of unique users who have interacted with SolTradingBot within any 30-day rolling period. This metric identifies active traders by tracking fee payments to SolTradingBot's fee recipient addresses on the Solana blockchain. The calculation uses HyperLogLog (HLL) data structures to efficiently count unique users across overlapping time windows while providing dimensional breakdowns by chain, user type, and product.

## 2. Data sources

- **Solana native token transfers**: Raw SOL transfer events on the Solana blockchain, specifically transfers to SolTradingBot fee recipient addresses
- **Transaction records**: Solana transaction data containing transaction signers (user addresses) and timestamps
- **Fee recipient addresses**: Two hardcoded SolTradingBot fee collection addresses that identify bot usage
- **Chain availability metadata**: Data quality filters ensuring complete and reliable blockchain data coverage

## 3. Step-by-step calculation

### Component A: Fee transaction identification
- **Purpose**: Identifies users of SolTradingBot by tracking fee payments to the bot's collection addresses
- **Logic**: Filters Solana native token transfers to two specific fee recipient addresses (`F34kcgMgCF7mYWkwLN3WN7KrFprr2NbwxuLvXx4fbztj` and `96aFQc9qyqpjMfqdUeurZVYRrrwPJG2uPV6pceu4B1yb`). Excludes transactions where the signer is one of these fee addresses to avoid counting internal transfers. Each qualifying transfer represents a user interaction with SolTradingBot.
- **Key fields**: `block_timestamp` for timing, `transaction_signer` for user identification, `to_address` for fee recipient filtering, `amount` for transfer value, and `token_address` set to wrapped SOL address

### Component B: Daily active user aggregation  
- **Purpose**: Converts fee transactions into daily unique user counts
- **Logic**: Groups fee transactions by day using timestamp truncation. Extracts distinct transaction signers per day as Daily Active Users (DAU). Classifies all users as 'Trader' type since they're identified through trading fee payments. Maintains dimensional attributes for chain and product identification.
- **Key fields**: `timestamp` truncated to day granularity, `user_dau` containing unique signer addresses, `user_type` set to 'Trader', `chain` and `product_id` for dimensional tracking

### Component C: HyperLogLog preparation
- **Purpose**: Converts daily user lists into probabilistic data structures for efficient set operations
- **Logic**: Transforms daily user lists into HyperLogLog structures with precision parameter 15. Groups data by timestamp, chain, user type, and product dimensions. HLL structures enable efficient merging and unique counting across large datasets without storing individual user addresses.
- **Key fields**: `hll` containing the HyperLogLog structure, dimensional grouping fields preserved from daily aggregation

### Component D: Rolling window generation
- **Purpose**: Creates 30-day rolling windows for each date to calculate monthly active users
- **Logic**: Generates a datespine covering the full date range from minimum data date to current date minus one day. For each date in the spine, creates a 30-day lookback window using timestamp arrays. Joins each target date with its corresponding 30-day window of dates, creating the foundation for rolling MAU calculations.
- **Key fields**: `timestamp` as the target date, `joinday` representing each day within the 30-day window

### Component E: Window population with user data
- **Purpose**: Associates each rolling window with the corresponding daily user HLL structures
- **Logic**: Joins the rolling window structure with daily HLL data, matching window dates with actual user activity dates. This creates a comprehensive dataset where each target date has access to all user activity within its 30-day lookback period. Filters ensure only relevant time periods are processed during incremental updates.
- **Key fields**: `timestamp` for target date, dimensional fields from daily data, `hll` structures for each day in the window

### Component F: MAU calculation by dimensions
- **Purpose**: Computes unique user counts across 30-day windows for different dimensional breakdowns
- **Logic**: Groups window data by target timestamp and dimensional attributes. Merges HLL structures across the 30-day window using HLL merge operations, then extracts the final unique count. Creates separate calculations for project-level, chain-level, user type-level, and product-level breakdowns. Each breakdown nullifies non-relevant dimensions while preserving the target dimension.
- **Key fields**: `user_mau` containing the final unique user count, `dimensions` indicating the breakdown type, dimensional fields set appropriately for each breakdown

### Final aggregation
- **Combining components**: Unions all dimensional MAU calculations (project, chain, user type, product) into a single comprehensive dataset using UNION ALL operations
- **Time aggregation**: Uses daily timestamps as the basis for 30-day rolling calculations, with monthly partitioning for efficient incremental processing
- **Final calculation**: The `user_mau` field contains the exact count of unique users active within the 30-day period ending on each timestamp, calculated through HyperLogLog merge and extract operations across the rolling window

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

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

SolTradingBotSTBOT

Active users (monthly)

Active users (monthly)

Competitive landscape