website

twitter

Infrastructure

Bloom Trading Bot

# Bloom Trading Bot Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Bloom Trading Bot measures the number of distinct users who conducted trading activity within any 7-day period. The metric is calculated using a rolling 7-day window applied to daily active users, where trading activity is identified through fee payments to the Bloom Trading Bot smart contract on Solana. The calculation uses HyperLogLog probabilistic data structures to efficiently count distinct users across multiple dimensional breakdowns.

## 2. Data sources

- **Solana native token transfers**: Native SOL transfer events on the Solana blockchain, specifically transfers sent to Bloom Trading Bot fee collection addresses
- **Transaction signer data**: Solana transaction records that include the transaction signer (wallet address that initiated and signed the transaction)
- **Bloom Trading Bot fee accounts**: Hardcoded smart contract address (`7HeD6sLLqAnKVRuSfc1Ko3BSPMNKWgGTiWLKXJF31vKM`) that receives trading fees

## 3. Step-by-step calculation

### Component A: Fee transaction identification
- **Purpose**: Identifies trading activity by detecting fee payments to Bloom Trading Bot
- **Logic**: Filters Solana native token transfer events where the recipient address matches the Bloom Trading Bot fee account (`7HeD6sLLqAnKVRuSfc1Ko3BSPMNKWgGTiWLKXJF31vKM`) and the sender is not the fee account itself (to exclude internal transfers). Each qualifying transfer represents a trading transaction that generated fees.
- **Key fields**: `block_timestamp` for timing, `transaction_signer` for user identification, `amount` for fee value, and recipient `to_address` for filtering

### Component B: Daily active user calculation
- **Purpose**: Aggregates fee transactions into daily unique user counts
- **Logic**: Groups fee transactions by day using `block_timestamp` truncated to daily granularity. Extracts unique `transaction_signer` addresses as daily active users, where each signer represents a distinct trader. All users are classified as 'Trader' type since trading activity is the only behavior captured.
- **Key fields**: `timestamp` (day-level), `signer` as `user_dau` (unique user identifier), `user_type` (always 'Trader'), `chain` (always 'solana'), and `product_id` (null for this project)

### Component C: HyperLogLog preparation
- **Purpose**: Converts daily user counts into probabilistic data structures for efficient weekly calculations
- **Logic**: Takes the daily active user data and creates HyperLogLog structures using `hll_count.init()` with 15-bit precision. This enables efficient distinct counting across rolling time windows without storing individual user identifiers.
- **Key fields**: `hll` (HyperLogLog structure), `timestamp`, `chain_id`, `user_type`, and `product_id` as grouping dimensions

### Component D: Rolling 7-day window construction
- **Purpose**: Creates overlapping 7-day periods for weekly active user calculation
- **Logic**: Generates a complete datespine covering the full date range to handle missing days, then creates a cross-join that associates each date with the previous 6 days of data. For each target date, this produces a 7-day window including the target date plus the 6 preceding days. Joins this window structure with the daily HLL data to capture all user activity within each 7-day period.
- **Key fields**: `timestamp` (target date), `joinday` (individual days within the 7-day window), and all dimensional fields from the HLL data

### Component E: Weekly aggregation by dimensions
- **Purpose**: Calculates distinct weekly active users across different analytical dimensions
- **Logic**: Performs separate aggregations for four dimensional breakdowns: project-level (overall WAU), chain-level (by blockchain), user-type level (by trader classification), and product-level (by product identifier). For each dimension, merges HyperLogLog structures using `hll_count.merge_partial()` to combine user sets across the 7-day window, then extracts the distinct count using `hll_count.extract()`.
- **Key fields**: `user_wau` (final distinct user count), `dimensions` (dimension type identifier), and the relevant dimensional fields (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a single result set with multiple rows per date (one for each dimensional breakdown)
- **Currency conversion**: Not applicable - this metric counts users, not monetary values
- **Time aggregation**: Daily timestamps represent the end date of each 7-day window. Each date shows the count of distinct users active within the 7-day period ending on that date
- **Final calculation**: The `finalize_metric_user_wau` macro applies standard schema formatting, handles missing dates by filling with zeros, and ensures consistent output structure across all user WAU metrics in the platform

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

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

Bloom Trading Bot

Active users (weekly)

Active users (weekly)

Competitive landscape