website

twitter

Derivative exchanges

Photon

# Photon Average trading volume (per user) Methodology

## 1. Overview

Trading volume average per user measures the average USD trading volume per daily active user on the Photon DEX platform. This metric provides insights into user engagement depth by showing how much volume each active trader generates on average per day. The calculation divides total daily trading volume by the number of daily active users, where users are identified through fee-paying transactions.

## 2. Data sources

- **Solana DEX trade events**: Parsed Solana DEX swap transactions containing trade amounts, token addresses, and transaction signers
- **Solana native transfer events**: SOL transfer transactions used to identify fee payments to Photon
- **End-of-day token price feeds**: Daily average USD prices for Solana tokens used in trades
- **Photon fee account configuration**: Hardcoded fee account address (`AVUCZyuT35YSuj4RH7fwiyPu82Djn2Hfg7y2ND2XcnZH`) used to identify platform transactions

## 3. Step-by-step calculation

### Component A: Fee transaction identification
- **Purpose**: Identifies transactions that paid fees to Photon, establishing which DEX trades belong to the platform
- **Logic**: Extracts all Solana native SOL transfers where the recipient address matches Photon's fee account. Records the transaction ID, timestamp, signer address, and fee amount for each qualifying transfer
- **Key fields**: `transaction_id` for linking to trades, `signer` for user identification, `block_timestamp` for daily aggregation, `amount` for fee values

### Component B: Trading activity extraction  
- **Purpose**: Identifies actual DEX trades that correspond to fee-paying Photon transactions
- **Logic**: Joins Solana DEX trade events with the fee transactions using transaction ID matching. Extracts trade details including token pairs, trade amounts, and transaction signers. Only includes trades that have corresponding fee payments
- **Key fields**: `base_mint` and `quote_mint` for token addresses, `base_amount` and `quote_amount` for trade sizes, `signer` for trader identification

### Component C: USD value conversion with wash trading protection
- **Purpose**: Converts token amounts to USD values while filtering potential wash trading
- **Logic**: Joins trade data with end-of-day token price feeds for both tokens in each pair. When prices exist for both tokens, calculates USD values for both sides and uses the smaller amount to reduce wash trading impact. When only one price is available, uses that token's USD value. Aggregates these USD amounts daily
- **Key fields**: `trading_volume` as final USD amount per trade, `timestamp` truncated to day boundaries

### Component D: Daily active user counting
- **Purpose**: Counts unique users who paid fees to Photon each day
- **Logic**: Extracts distinct transaction signers from fee-paying transactions, grouping by day. Uses HyperLogLog aggregation to efficiently count unique users while enabling different dimensional breakdowns. Creates multiple aggregation levels including overall project level
- **Key fields**: `user_dau` as count of distinct signers, `dimensions` to specify aggregation level

### Final aggregation
- **Combining components**: Joins daily trading volume totals with daily active user counts using timestamp matching, specifically using the project-level DAU aggregation
- **Currency conversion**: USD conversion occurs during trade processing using end-of-day price feeds matched to trade dates
- **Time aggregation**: All data aggregated to daily boundaries using timestamp truncation to day level  
- **Final calculation**: Divides total daily trading volume by daily active user count using safe division to handle potential zero DAU days, producing average trading volume per user in USD

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

Average trading volume (per user)

Trading volume divided by daily active users.

Photon

Average trading volume (per user)

Average trading volume (per user)

Competitive landscape