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Bloom Trading Bot

# Bloom Trading Bot Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for Bloom Trading Bot measures the number of unique users who paid fees to the protocol over a rolling 30-day period. This metric identifies active traders by tracking fee-paying transactions sent to Bloom Trading Bot's designated fee collection addresses on the Solana blockchain. The calculation uses HyperLogLog sketches for efficient approximate counting of distinct users across time windows.

## 2. Data sources

- **Solana native token transfers**: SOL transfer events from Solana blockchain, specifically transfers directed to Bloom Trading Bot fee collection addresses
- **Solana transaction records**: Transaction metadata including signers (user addresses) and block timestamps
- **Protocol configuration**: Hardcoded fee collection account addresses specific to Bloom Trading Bot ('7HeD6sLLqAnKVRuSfc1Ko3BSPMNKWgGTiWLKXJF31vKM')

## 3. Step-by-step calculation

### Component A: Fee transaction identification
- **Purpose**: Identify users who paid fees to Bloom Trading Bot on each day
- **Logic**: Filter Solana native SOL transfers where the recipient address matches Bloom Trading Bot's fee collection accounts. Exclude transfers where the transaction signer is also a fee account to avoid counting internal protocol operations. Extract the `transaction_signer` as the user identifier and `block_timestamp` for temporal grouping.
- **Key fields**: `transaction_signer` (user address), `block_timestamp` (transaction time), `to_address` (fee recipient), `amount` (fee amount in SOL)

### Component B: Daily active user aggregation  
- **Purpose**: Calculate unique active users per day
- **Logic**: Group fee transactions by day using timestamp truncation to day granularity. Identify distinct transaction signers within each day to count unique users. Classify all users as 'Trader' user type since they're engaging with the trading bot through fee payments.
- **Key fields**: `timestamp` (day-level), `user_dau` (unique user address), `user_type` (set to 'Trader'), `chain` (Solana), `product_id` (protocol component)

### Component C: HyperLogLog sketch preparation
- **Purpose**: Convert daily user lists into efficient data structures for rolling window calculations
- **Logic**: Transform daily unique user lists into HyperLogLog sketches with 15-bit precision using `hll_count.init()`. This creates space-efficient approximate data structures that can be merged across time periods while maintaining reasonable accuracy for distinct counting.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (daily), `timestamp_grain` (granularity), `chain_id`, `user_type`, `product_id`

### Component D: Rolling window construction
- **Purpose**: Create 30-day rolling windows for MAU calculation
- **Logic**: Generate a datespine covering all days from the earliest data to present. For each day in the datespine, create a 30-day lookback window including the current day plus the previous 29 days. Use timestamp array generation to create all possible day combinations within each window.
- **Key fields**: `timestamp` (target day), `joinday` (each day within the 30-day window)

### Component E: Window population and HLL merging
- **Purpose**: Combine HLL sketches across each 30-day window
- **Logic**: Join the rolling window structure with daily HLL sketches, matching each window day with its corresponding HLL data. Merge all HLL sketches within each 30-day window using `hll_count.merge_partial()` to combine the approximate user counts. Extract the final distinct count using `hll_count.extract()` to get the approximate number of unique users over the 30-day period.
- **Key fields**: `hll` (merged sketch), `user_mau` (extracted distinct count)

### Final aggregation
- **Combining components**: Calculate MAU across multiple dimensional breakdowns - overall project level, by chain, by user type, and by product ID. Each breakdown aggregates HLL sketches at the appropriate grouping level and sets unused dimensions to NULL.
- **Currency conversion**: Not applicable for user counts
- **Time aggregation**: Rolling 30-day windows updated daily, with each day representing users active over the trailing 30-day period
- **Final calculation**: The MAU value represents the approximate number of distinct users who paid fees to Bloom Trading Bot over any 30-day rolling window, calculated using merged HyperLogLog sketches for computational efficiency

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

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

Bloom Trading Bot

Active users (monthly)

Active users (monthly)

Competitive landscape