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Drift Protocol

# Drift Protocol Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Drift Protocol measures the number of unique users who have interacted with the protocol within any 7-day rolling window. This metric provides insight into user engagement and platform adoption by counting distinct wallet addresses that execute transactions on the Drift Protocol smart contract over weekly periods. The calculation uses HyperLogLog probabilistic data structures to efficiently handle large-scale user counting while maintaining accuracy.

## 2. Data sources

The WAU calculation relies on the following raw blockchain data:

- **Solana instruction data**: On-chain instruction execution records from the Solana blockchain, specifically targeting the Drift Protocol program
- **Transaction metadata**: Solana transaction records containing signer addresses, timestamps, and transaction identifiers
- **Program filtering**: Data is filtered to only include instructions executed by the specific Drift Protocol program address `dRiftyHA39MWEi3m9aunc5MzRF1JYuBsbn6VPcn33UH`

## 3. Step-by-step calculation

### Component A: Interaction data extraction
- **Purpose**: Identify all user interactions with Drift Protocol on Solana
- **Logic**: Extract instruction execution records from Solana blockchain data, filtering specifically for transactions that interact with the Drift Protocol smart contract. Each interaction captures the transaction signer (user wallet address), block timestamp, and transaction identifier. This creates a comprehensive log of all protocol activity.
- **Key fields**: `timestamp` (block execution time), `transaction_id` (unique transaction identifier), `signer` (wallet address executing the transaction), `chain_id` (set to 'solana'), `product_id` (set to 'driftprotocol')

### Component B: Daily active user aggregation  
- **Purpose**: Calculate unique daily active users from raw interactions
- **Logic**: Aggregate the interaction data by day, counting distinct signer addresses for each calendar day. All users are classified as 'Trader' type since Drift Protocol is a trading platform. The aggregation ensures each unique wallet address is counted only once per day, regardless of how many transactions they performed.
- **Key fields**: `timestamp` (truncated to day), `user_type` (set to 'Trader'), `user_dau` (unique signer address), `timestamp_grain` (set to 'day')

### Component C: HyperLogLog initialization
- **Purpose**: Convert daily user sets into probabilistic data structures for efficient counting
- **Logic**: Transform each day's unique user set into a HyperLogLog data structure with 15-bit precision. This probabilistic approach allows for memory-efficient storage and fast set operations when calculating overlapping users across multiple days. Each day's HLL structure represents the set of unique users for that specific date.
- **Key fields**: `hll` (HyperLogLog data structure containing user set), dimensional breakdowns by chain, user type, and product

### Component D: Date spine generation
- **Purpose**: Create a continuous timeline without gaps for rolling window calculations
- **Logic**: Generate a complete sequence of daily timestamps from the earliest interaction date to the current date minus one day. This ensures that every calendar day is represented in the final output, even if no interactions occurred on specific dates. The date spine is essential for accurate rolling window calculations.
- **Key fields**: `timestamp` (continuous daily sequence)

### Component E: Seven-day rolling window construction
- **Purpose**: Create overlapping 7-day periods for each date
- **Logic**: For each date in the date spine, generate the corresponding 7-day window by creating timestamps for the 6 preceding days plus the current date. This creates a cross-join structure where each target date is associated with all dates in its 7-day lookback period. The rolling windows ensure that WAU is calculated consistently across all dates.
- **Key fields**: `timestamp` (target date), `joinday` (each day within the 7-day window)

### Component F: Window data aggregation
- **Purpose**: Collect HyperLogLog data for each 7-day window
- **Logic**: Join the rolling window structure with the daily HLL data, matching each window date with its corresponding daily user data. This creates a dataset where each target date has access to all HLL structures from its 7-day lookback period, enabling the calculation of unique users across the entire window.
- **Key fields**: Combined HLL structures for each 7-day period, with dimensional attributes preserved

### Component G: Dimensional breakdowns
- **Purpose**: Calculate WAU across different analytical dimensions
- **Logic**: Create separate aggregations for different analytical perspectives: project-level (overall protocol WAU), chain-level (Solana-specific), user-type-level (Trader category), and product-level (Drift Protocol specific). Each breakdown merges the relevant HLL structures and extracts the unique count, providing flexibility for different analytical use cases.
- **Key fields**: `dimensions` (breakdown category), `user_wau` (extracted unique count), dimension-specific identifiers

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single comprehensive dataset, ensuring each analytical perspective is represented with appropriate null values for non-applicable dimensions
- **Currency conversion**: Not applicable for user counting metrics
- **Time aggregation**: Daily boundaries with 7-day rolling windows, where each day represents the unique users active in the preceding 7-day period
- **Final calculation**: Extract unique user counts from merged HyperLogLog structures using `hll_count.extract(hll_count.merge_partial(hll))`, which provides the probabilistic count of distinct users across the 7-day window while accounting for users who were active on multiple days within the period

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

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

Drift ProtocolDRIFT

Active users (weekly)

Active users (weekly)

Competitive landscape