Liquidity providers deposit funds into Clipper and receive returns generated by Clipper’s costless diversified rebalancing liquidity pools (supply-side fees). LP yields are generated from the rebalancing process itself rather than from fees charged to traders.

Clipper is a non-custodial exchange protocol built on Ethereum. Clipper was built by Shipyard Software and launched in 2021.

AdmiralDAO and SAIL token holders own and govern the protocol. They currently receive all trading fees paid by traders (revenue). Clipper's investors include Polychain, Robot Ventures, 0x Labs, etc.

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# Clipper Average trading volume (per user) Methodology

## 1. Overview

The trading volume average per user metric measures the average USD trading volume per daily active user on the Clipper DEX protocol. This metric provides insight into user engagement intensity by dividing total daily trading volume by the number of unique daily active traders. The calculation aggregates swap activity across all Clipper exchange contracts on Ethereum, Polygon, Optimism, and Arbitrum chains.

## 2. Data sources

The calculation relies on several raw data sources:

- **Blockchain events**: Parsed `Swapped` events from multiple Clipper exchange contracts across Ethereum, Polygon, Optimism, and Arbitrum, including ApproximateCaravelExchange, VerifiedCaravelExchange, CaravelExchange, ExchangeInterface, VerifiedExchange, DirectExchange, PackedVerifiedExchange, and PackedExchange variants
- **Transaction data**: Ethereum, Polygon, Optimism, and Arbitrum transaction records containing user addresses and transaction metadata
- **External data**: Token metadata including decimal precision and symbols for proper amount conversion, and end-of-day token price feeds for USD conversion

## 3. Step-by-step calculation

### Component A: Raw trade data extraction
- **Purpose**: Extract all swap transactions from Clipper exchange contracts across supported chains
- **Logic**: Query `Swapped` events from multiple Clipper contract variants on Ethereum, Polygon, Optimism, and Arbitrum. Each event contains `inAsset` and `outAsset` token addresses, `inAmount` and `outAmount` quantities, transaction hash, and log index. Join with transaction records to identify the `from_address` as the actual trader/user. Filter by incremental time boundaries for efficient processing.
- **Key fields**: `inAsset` and `outAsset` (token addresses), `inAmount` and `outAmount` (raw token quantities), `from_address` (user address), `block_timestamp`, `transaction_hash`, `log_index`

### Component B: Trading volume calculation  
- **Purpose**: Convert raw token amounts to USD trading volumes with anti-manipulation safeguards
- **Logic**: Join trade data with token metadata to get decimal precision, then divide raw amounts by 10^decimals to get actual token quantities. Join with end-of-day price feeds to get USD prices for both tokens in each swap. Calculate USD value for each token side of the swap, then use the minimum (LEAST function) of the two USD values to prevent price manipulation. Aggregate by day, transaction hash, and log index to avoid double-counting.
- **Key fields**: `token0_amount` and `token1_amount` (adjusted quantities), `price_usd` (end-of-day prices), `trading_volume` (minimum USD value between token pair)

### Component C: Daily active users calculation
- **Purpose**: Count unique users who executed trades each day
- **Logic**: Extract distinct user addresses from trade data, truncate timestamps to day level, and classify all users as 'Trader' type. Use HyperLogLog (HLL) approximate distinct counting for scalable computation across large datasets. Create multiple dimensional breakdowns including project-level aggregation across all chains.
- **Key fields**: `user_address` (trader identifier), `timestamp` (day-level), `user_dau` (distinct user count)

### Final aggregation
- **Combining components**: Join daily trading volume totals with daily active user counts on matching timestamps. The join uses the project-level DAU aggregation to get total users across all chains matched with total trading volume across all chains.
- **Currency conversion**: All trading volumes are converted to USD using end-of-day price feeds, with anti-manipulation logic selecting the minimum USD value between the two tokens in each swap
- **Time aggregation**: Data is aggregated at daily boundaries using timestamp truncation to day level
- **Final calculation**: Divide total daily trading volume by daily active user count using safe division to handle edge cases where DAU might be zero. Apply standardization and gap-filling through finalization macro to ensure consistent time series data.

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

Average trading volume (per user)

Trading volume divided by daily active users.

ClipperSAIL

Average trading volume (per user)

Average trading volume (per user)

Competitive landscape