website

twitter

Infrastructure

SolTradingBot

# SolTradingBot Active users (daily) Methodology

## 1. Overview

The Solana trading bot user DAU metric measures the daily count of unique active users interacting with Solana-based trading bot services. This metric identifies active users by tracking fee payments made to specific trading bot fee collection accounts on the Solana blockchain. The calculation uses HyperLogLog (HLL) algorithms to ensure accurate distinct user counting across multiple dimensional breakdowns while maintaining computational efficiency.

## 2. Data sources

- **Solana native transfers**: Native SOL token transfer records on the Solana blockchain, capturing all transfers of the native Solana token between addresses
- **Solana transaction records**: Transaction metadata including transaction signers and timestamps for identifying user activity
- **Token Terminal chain availability metadata**: Data quality filters to ensure consistent reporting across time periods

## 3. Step-by-step calculation

### Component A: Fee collection event identification
- **Purpose**: Identifies trading bot usage by detecting fee payments to known trading bot fee collection accounts
- **Logic**: Filters Solana native SOL transfers to identify payments made to two specific hardcoded fee collection addresses: `F34kcgMgCF7mYWkwLN3WN7KrFprr2NbwxuLvXx4fbztj` and `96aFQc9qyqpjMfqdUeurZVYRrrwPJG2uPV6pceu4B1yb`. Excludes transfers where the transaction signer is one of these fee accounts themselves to avoid counting internal operations. Sets all token addresses to the native SOL token identifier and assigns null values to `product_id`
- **Key fields**: `block_timestamp` for timing, `transaction_id` for unique identification, `transaction_signer` as the user identifier, `to_address` for fee account matching, `amount` for transfer value

### Component B: Daily user aggregation
- **Purpose**: Converts fee payment events into daily active user records
- **Logic**: Groups fee collection events by day using timestamp truncation to daily granularity. Extracts the `transaction_signer` as the unique user identifier representing the trading bot user. Classifies all users with the user type 'Trader' since they are actively using trading bot services. Maintains chain and product_id information for dimensional analysis
- **Key fields**: `timestamp` truncated to day, `timestamp_grain` set to 'day', `chain` as 'solana', `user_type` as 'Trader', `signer` as `user_dau` identifier

### Component C: HyperLogLog initialization
- **Purpose**: Prepares data for accurate distinct counting across multiple dimensions using probabilistic data structures
- **Logic**: Initializes HyperLogLog sketches for each combination of timestamp, timestamp grain, chain, user type, and product_id. Uses HLL precision parameter of 15 for balancing accuracy and memory usage. Groups data by all dimensional attributes to create base HLL sketches for subsequent aggregations
- **Key fields**: All dimensional fields plus `hll` sketch containing user identifiers

### Component D: Multi-dimensional aggregations
- **Purpose**: Creates different dimensional views of daily active users using HLL merge operations
- **Logic**: Generates four separate aggregation levels by merging HLL sketches and extracting final counts. Project-level aggregation merges all dimensions into a single project view. Chain-level breakdown maintains chain dimension while merging others. User type breakdown preserves user type classification. Product ID breakdown maintains product-level granularity. Each aggregation uses HLL merge operations to combine sketches and extract final distinct counts
- **Key fields**: `dimensions` to identify aggregation level, dimension-specific fields like `chain_id`, `product_id`, `user_type`, and `user_dau` as the final count

### Final aggregation
- **Combining components**: Unions all dimensional aggregations (project, chain, user_type, product_id) into a single result set with consistent schema structure
- **Currency conversion**: Not applicable as this is a count-based metric measuring users rather than monetary values
- **Time aggregation**: Data is aggregated at daily boundaries using timestamp truncation, with HyperLogLog ensuring accurate distinct counting across time periods
- **Final calculation**: Applies the finalize_metric_user_dau macro with zero-fill strategy to populate missing dates with zero values, ensuring continuous time series. Chain availability filters are applied to maintain data quality and exclude periods where blockchain data may be incomplete or unreliable

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

Unique revenue-generating users over a fixed 24-hour window.

SolTradingBotSTBOT

Active users (daily)

Active users (daily)

Competitive landscape