Liquidity providers deposit funds into IDEX and receive trading fees from traders (supply-side fees).

IDEX is hybrid-liquidity DEX, combining a high-performance order book with an Automated Market Making matching engine, built on Polygon. IDEX was founded by Alex Wearn and launched in 2019.

IDEX tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). IDEX's investors include Gnosis, Collider Ventures, Borderless Capital, G1 Ventures, etc.

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# IDEX Average trading volume (per trade) Methodology

## 1. Overview

The IDEX trading volume average per trade count metric measures the average USD value traded per individual trade transaction on the IDEX decentralized exchange. This metric provides insight into the typical trade size on the platform by dividing total daily trading volume by the number of unique trade transactions. The calculation focuses exclusively on taker-side trades and only includes trades denominated in USDC and ETH as quote assets.

## 2. Data sources

The metric relies on the following raw data sources:

- **IDEX v3 Exchange events**: Parsed Polygon blockchain event logs from the IDEX v3 Exchange contract, specifically `PoolTradeExecuted`, `OrderBookTradeExecuted`, and `HybridTradeExecuted` events
- **Transaction data**: Polygon transaction records containing block timestamps, transaction hashes, and log indices for trade identification
- **End-of-day price feeds**: Token price data for USD conversion of trading volumes
- **Token metadata**: Token decimal information and contract addresses for proper amount calculations
- **Quote asset definitions**: Predefined list of acceptable quote tokens (USDC and ETH) with their contract addresses

## 3. Step-by-step calculation

### Component A: Trade data extraction
- **Purpose**: Extract and standardize trade events from IDEX v3 Exchange contract
- **Logic**: Parse four types of trading events from Polygon blockchain logs. `PoolTradeExecuted` events capture AMM pool trades using `baseQuantityInPips` and `quoteQuantityInPips` fields. `OrderBookTradeExecuted` events capture traditional order book trades with the same quantity fields. `HybridTradeExecuted` events capture hybrid trades that interact with both pools and order books, generating separate entries for pool components (`poolbaseQuantityInPips`, `poolquoteQuantityInPips`) and order book components (`orderBookbaseQuantityInPips`, `orderBookquoteQuantityInPips`). Only taker-side trades are included by filtering for `takerSide = 1`. Trade amounts are converted from pips to decimal format by dividing by 10^8.
- **Key fields**: `block_timestamp` for daily aggregation, `transaction_hash` and `log_index` for unique trade identification, `baseAssetSymbol` and `quoteAssetSymbol` for token identification, quantity fields converted to `token0_amount` and `token1_amount`

### Component B: Quote asset filtering
- **Purpose**: Restrict analysis to trades with approved quote assets
- **Logic**: Filter trades to only include those where the quote token (token1) matches predefined quote assets. Currently includes USDC (contract address `0x2791bca1f2de4661ed88a30c99a7a9449aa84174`) and ETH (contract address `0x7ceb23fd6bc0add59e62ac25578270cff1b9f619`) on Polygon. This ensures volume calculations focus on the most liquid and commonly used quote currencies.
- **Key fields**: `token1_symbol` matched against approved quote asset symbols, `token_address` for price feed lookup

### Component C: Trading volume calculation
- **Purpose**: Calculate daily USD-denominated trading volume
- **Logic**: Aggregate the quote token amounts (`token1_amount`) from filtered trades, grouping by day using timestamp truncation. Convert aggregated amounts to USD using end-of-day price feeds, with automatic decimal correction based on token metadata. Apply chain availability filters to ensure data quality and completeness. Use fill-zeros strategy for days with no trading activity.
- **Key fields**: Daily aggregated `trading_volume` in USD, `timestamp` truncated to day boundaries, `chain_id` for filtering

### Component D: Trade count calculation  
- **Purpose**: Count unique trade transactions per day
- **Logic**: Count distinct transaction hashes from the same filtered trade data, grouping by daily timestamps. Each unique transaction hash represents one trade regardless of how many events it generated. Apply the same chain availability filters and fill-zeros strategy as the volume calculation.
- **Key fields**: `trade_count` as count of distinct `transaction_hash` values, daily `timestamp` boundaries

### Final aggregation
- **Combining components**: Join the daily trading volume and trade count data on matching timestamps to ensure corresponding periods are compared
- **Currency conversion**: All volume amounts are converted to USD using end-of-day price feeds with proper decimal correction applied during the volume calculation phase
- **Time aggregation**: Both components use daily timestamp truncation with day-level granularity for consistent time boundaries
- **Final calculation**: Divide total daily trading volume by daily trade count using safe division to handle zero trade count scenarios, resulting in the average USD volume per trade transaction

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

IDEXIDEX

Average trading volume (per trade)

Average trading volume (per trade)

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