Traders exchange funds through request for quotation system built on 0x protocol and aggregated AMMs. Tokenlon collects a trading fee (revenue) from trades.

Tokenlon is a non-custodial exchange protocol built on Ethereum. Tokenlon was founded by imToken team and launched in 2018.

LON tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). Tokelon's investors include IDG Capital, Qiming Venture Partners, Breyer Capital, etc.

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Tokenlon

# Tokenlon Average trading volume (per user) Methodology

## 1. Overview

The trading volume average per user metric measures the mean USD trading volume per daily active user on the Tokenlon DEX aggregator. This metric provides insight into user engagement intensity by dividing total daily trading volume by the number of unique traders. The calculation combines trade event data from multiple Tokenlon protocol versions across Ethereum and Arbitrum chains, converts token amounts to USD values, and computes the daily average per active user.

## 2. Data sources

- **Tokenlon contract events**: Parsed events from multiple Tokenlon protocol versions including `AMMWrapper_Swapped`, `AMMWrapperWithPath_Swapped`, `PMM_FillOrder`, `RFQ_FillOrder` events on Ethereum, `LimitOrder_LimitOrderFilledByProtocol` events on Arbitrum, and `ZeroExchange_Fill` events
- **Transaction data**: Ethereum and Arbitrum transaction records containing user addresses (`from_address`) and transaction metadata
- **Token metadata**: Token symbols, decimal places, and contract addresses for proper amount normalization
- **End-of-day price feeds**: USD price data for tokens involved in trades, used for currency conversion
- **Chain data**: Blockchain transaction logs and event data from Ethereum and Arbitrum networks

## 3. Step-by-step calculation

### Component A: Trade event extraction
- **Purpose**: Collect all Tokenlon trade events across supported protocol versions and chains
- **Logic**: Extract trade events from multiple Tokenlon contract types including AMMWrapper, PMM (Price Making Market), RFQ (Request for Quote), and LimitOrder systems. Filter events to include only those where Tokenlon's fee recipient address (`0xb9e29984fe50602e7a619662ebed4f90d93824c7`) appears in either `feeRecipientAddress` or `takerAddress` fields. Handle different event schemas across protocol versions by mapping fields like `takerAssetAddr`/`makerAssetAddr` for token addresses and `takerAssetAmount`/`makerAssetAmount` for quantities
- **Key fields**: `transaction_hash`, `log_index`, `block_timestamp`, token addresses (`takerAssetAddr`, `makerAssetAddr`), token amounts (`takerAssetAmount`, `makerAssetAmount`), contract address

### Component B: User identification and token processing
- **Purpose**: Link trades to actual users and normalize token data
- **Logic**: Join trade events with transaction records to identify the initiating user via `from_address`. Extract token addresses from trade events, converting native token identifiers (`0x0000000000000000000000000000000000000000`) to their wrapped equivalents (WETH on Ethereum: `0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2`, WETH on Arbitrum: `0x82af49447d8a07e3bd95bd0d56f35241523fbab1`). Parse token amounts as large integers to handle high-precision calculations
- **Key fields**: `user_address`, `token0_address`, `token1_address`, `token0_amount`, `token1_amount`, `chain`

### Component C: Decimal correction and USD conversion
- **Purpose**: Normalize token amounts and convert to USD values
- **Logic**: Join with token metadata to apply decimal corrections by dividing raw token amounts by 10^decimals. Match each token with end-of-day price feeds based on timestamp, token address, and chain. Convert both tokens in each trade pair to USD values by multiplying normalized amounts by respective USD prices. Use the lesser of the two USD values to mitigate price manipulation attempts and ensure conservative volume estimates
- **Key fields**: `token0_amount` (normalized), `token1_amount` (normalized), `trading_volume` (USD)

### Component D: Daily trading volume aggregation  
- **Purpose**: Sum total USD trading volume per day
- **Logic**: Group all trades by day using `timestamp_trunc` to daily boundaries. Sum USD trading volumes across all trades within each day. Maintain chain, business line, and version dimensions for proper categorization. Apply the finalize metric macro to ensure consistent schema and fill missing days with zeros
- **Key fields**: `timestamp`, `trading_volume` (daily total)

### Component E: Daily active user counting
- **Purpose**: Count unique users per day across all trading activity
- **Logic**: Extract distinct user addresses from daily trades grouped by day. Use HyperLogLog probabilistic counting (`hll_count.init` and `hll_count.merge_partial`) for memory-efficient distinct user counting across large datasets. Aggregate users at the project level (`project_dbt_tag` dimension) to get total platform DAU
- **Key fields**: `user_dau`, `timestamp`, `dimensions`

### Final aggregation
- **Combining components**: Join daily trading volume totals with daily active user counts on matching timestamps. The trading volume table contains total USD volume per day, while the DAU table contains unique user counts per day
- **Currency conversion**: All amounts already converted to USD in earlier steps using end-of-day price feeds matched by timestamp and token address
- **Time aggregation**: Both components aggregated to daily boundaries using `timestamp_trunc` to day level
- **Final calculation**: Divide total daily trading volume by daily active users using `safe_divide` function to handle edge cases where DAU might be zero. Apply the finalize metric macro to ensure proper schema structure and gap filling with zeros for missing days

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

Average trading volume (per user)

Trading volume divided by daily active users.

TokenlonLON

Average trading volume (per user)

Average trading volume (per user)

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