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Derivative exchanges

Photon

# Photon Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Photon measures the number of unique traders who paid fees to the protocol during any 7-day rolling window. This metric identifies genuine protocol usage by tracking users who actually executed trades and incurred fees, rather than just viewing or interacting with non-transactional features. The calculation uses HyperLogLog probabilistic counting to efficiently handle large-scale user deduplication across time windows.

## 2. Data sources

- **Solana native token transfers**: SOL transfer events from Solana blockchain transaction logs, specifically transfers to known Photon fee collection addresses
- **Transaction signer data**: User wallet addresses that initiated and signed the fee-paying transactions on Solana
- **Protocol fee accounts**: Hardcoded list of Photon fee collection addresses used to identify protocol-related transactions
- **Blockchain timestamps**: Transaction timestamps from Solana blocks for temporal aggregation

## 3. Step-by-step calculation

### Component A: Fee payment extraction
- **Purpose**: Identify actual protocol usage through fee payments to establish the base user activity dataset
- **Logic**: Extract all native SOL transfers from Solana transaction logs where the recipient address matches the Photon fee collection address `AVUCZyuT35YSuj4RH7fwiyPu82Djn2Hfg7y2ND2XcnZH`. Capture the `block_timestamp`, `transaction_id`, `signer` (user wallet address), and `amount` for each fee payment. Set `chain` as 'solana', `product_id` as 'photon', and `token_address` as the native SOL token address
- **Key fields**: `signer` represents the user who paid the fee, `block_timestamp` provides timing for aggregation, `amount` contains the fee value in native SOL

### Component B: Daily active user calculation  
- **Purpose**: Aggregate fee-paying users into daily unique counts to create the foundation for weekly calculations
- **Logic**: Group all fee payment records by day using timestamp truncation to daily boundaries. Extract unique `signer` addresses for each day to identify distinct users. Classify all users as 'Trader' type since they represent users who executed fee-generating transactions. Maintain `chain`, `product_id`, and daily `timestamp` groupings
- **Key fields**: `user_dau` contains the unique signer address, `timestamp` truncated to day boundaries, `user_type` set to 'Trader'

### Component C: HyperLogLog sketch creation
- **Purpose**: Convert daily user lists into probabilistic data structures that enable efficient unique counting across time windows
- **Logic**: Transform each day's unique user list into a HyperLogLog sketch using precision 15. Group by `timestamp`, `timestamp_grain`, `chain_id`, `user_type`, and `product_id`. Initialize HLL sketches from the daily user lists to prepare for temporal window merging
- **Key fields**: `hll` contains the HyperLogLog sketch representing daily unique users with 15-bit precision for accurate deduplication

### Component D: Temporal window construction
- **Purpose**: Create a complete 7-day rolling window framework that doesn't skip missing dates and ensures continuous WAU calculations
- **Logic**: Generate a continuous datespine from the earliest DAU date to current date minus one day using daily intervals. For each date in the datespine, create a 7-day lookback window by generating timestamps for the current day plus the previous 6 days. This creates joinable date pairs where each target date connects to its corresponding 7-day historical period
- **Key fields**: `timestamp` for the target date, `joinday` for each day within the 7-day window

### Component E: Weekly window HLL merging
- **Purpose**: Combine daily HyperLogLog sketches across 7-day windows to count unique weekly users while avoiding double-counting
- **Logic**: Join the temporal windows with daily HLL sketches where each window day matches a DAU record date. This creates multiple HLL sketches for each target date representing the 7 days of user activity. Maintain all dimensional groupings (`chain_id`, `user_type`, `product_id`) during the join to preserve breakdown capabilities
- **Key fields**: HLL sketches are carried forward with their dimensional attributes for subsequent merging operations

### Final aggregation
- **Combining components**: Merge HLL sketches across each 7-day window using HyperLogLog merge operations to count unique users without duplication. Create separate aggregation levels by grouping on different dimensional combinations: project-level (all nulls), chain-level (by `chain_id`), user-type-level (by `user_type`), and product-level (by `product_id`). Extract final unique counts from merged HLL sketches using HLL extract functions
- **Currency conversion**: Not applicable as this metric measures user counts rather than monetary values
- **Time aggregation**: Use daily timestamp boundaries with 7-day rolling windows. Each date represents the unique user count for that day plus the previous 6 days. Maintain monthly partitioning for efficient incremental processing
- **Final calculation**: The final WAU value represents the estimated number of unique wallet addresses that paid fees to Photon during any 7-day period ending on the specified date. Union all dimensional breakdowns (project, chain, user type, product) into a single result set with appropriate dimension labeling. Apply schema standardization and fill missing dates with zero values to ensure complete time series coverage

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

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

Photon

Active users (weekly)

Active users (weekly)

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