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 trade) Methodology

## 1. Overview

The trading volume average per trade count metric measures the average USD value per trade executed on the Tokenlon protocol. This metric provides insight into the typical trade size and helps assess user trading behavior and market liquidity. It is calculated by dividing the total daily trading volume in USD by the total number of trades executed on the same day.

## 2. Data sources

- **Blockchain events**: Parsed trading events from Tokenlon's various contract versions across Ethereum and Arbitrum, including `ZeroExchange_Fill_event`, `AMMWrapper_Swapped_event`, `AMMWrapperWithPath_Swapped_event`, `PMM_FillOrder_event`, `RFQ_FillOrder_event`, and `LimitOrder_LimitOrderFilledByProtocol_event`
- **Transaction data**: Ethereum and Arbitrum transaction records to identify user addresses and validate event context
- **Token metadata**: Token contract information including decimal places and symbols for proper amount conversion
- **External data**: End-of-day token price feeds in USD for volume calculations

## 3. Step-by-step calculation

### Component A: Raw trade data extraction
- **Purpose**: Collect all trading events from Tokenlon's various protocol versions and contract addresses
- **Logic**: Extract trading events from multiple Tokenlon contract versions across Ethereum and Arbitrum chains. Events include various swap types, order fills, and limit order executions. Filter events by specific Tokenlon contract addresses including `0xb9e29984fe50602e7a619662ebed4f90d93824c7` for fee recipient filtering. Convert native token addresses (`0x0000000000000000000000000000000000000000`) to wrapped equivalents (WETH on Ethereum: `0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2`, WETH on Arbitrum: `0x82af49447d8a07e3bd95bd0d56f35241523fbab1`) for consistency
- **Key fields**: Transaction hash, log index, token addresses (`takerAssetAddr`, `makerAssetAddr`), token amounts (`takerAssetAmount`, `makerAssetAmount`), timestamps, user addresses from transaction data

### Component B: Volume calculation with decimal correction
- **Purpose**: Convert raw token amounts to human-readable values and calculate USD trading volumes
- **Logic**: Join trading events with token metadata to get decimal places and symbols. Convert raw token amounts from blockchain units to human-readable amounts by dividing by 10 raised to the token's decimal places. Aggregate trades by day using timestamp truncation. Join with end-of-day price feeds to convert token amounts to USD values for both tokens in each trading pair
- **Key fields**: Corrected `token0_amount` and `token1_amount`, token symbols, USD prices, daily timestamps

### Component C: Price manipulation protection
- **Purpose**: Filter out potentially manipulated trades by comparing token valuations
- **Logic**: For each trade, calculate the USD value of both tokens involved in the swap. Take the minimum USD value between the two tokens to determine the final trading volume. This approach helps eliminate trades where one token may have an artificially inflated or manipulated price, ensuring more accurate volume measurements
- **Key fields**: `trading_volume` as the lesser of the two token USD values, with zero fallback for invalid calculations

### Component D: Trade count calculation
- **Purpose**: Count the number of distinct trades executed daily
- **Logic**: Use the same processed trade data but focus on counting unique transactions rather than calculating volumes. Apply approximate distinct count (`approx_count_distinct`) on transaction hashes to determine the number of unique trades per day. Group by timestamp, chain, market sector, business segment, and product dimensions to maintain proper aggregation levels
- **Key fields**: `trade_count` as distinct transaction hash count, daily timestamp grain

### Final aggregation
- **Combining components**: Join the daily trading volume data with daily trade count data using timestamp as the common key. Both datasets are pre-aggregated at the daily level with the same time boundaries
- **Currency conversion**: All volumes are converted to USD using end-of-day price feeds, applied after decimal correction and before the final volume calculation
- **Time aggregation**: Data is aggregated at daily boundaries using timestamp truncation to day level, ensuring consistent time alignment between volume and count calculations
- **Final calculation**: Calculate the average volume per trade by dividing the total daily trading volume by the total daily trade count using safe division to handle cases where trade count might be zero, resulting in null values for those days

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

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Average trading volume (per trade)

Average trading volume (per trade)

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