Liquidity providers deposit funds into SolidLizard and stake their LP tokens to receive SLIZ tokens (token incentives).

SolidLizard is a non-custodial exchange protocol built on Arbitrum. SolidLizard was founded by an anonymous team and launched in 2023.

SLIZ tokenholders own and govern the protocol. They vote for pools where they wish to receive trading fees and external bribes from, while pools receive SLIZ emissions in respect to their share of votes.

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SolidLizard

# SolidLizard Average trading volume (per user) Methodology

## 1. Overview

The trading volume average per user metric measures the average USD trading volume generated per daily active user on the SolidLizard decentralized exchange. This metric provides insight into user engagement intensity by calculating how much trading volume each active user generates on average per day. The calculation divides total daily trading volume by the number of distinct daily active users (traders) to produce an average volume per user metric.

## 2. Data sources

- **SolidLizard swap events**: Parsed `Swap` events from SolidLizard v1 trading pairs on Arbitrum, containing token amounts swapped in each direction
- **Trading pair creation events**: Parsed `PairCreated` events from the SolidLizard PairFactory contract on Arbitrum, identifying valid trading pairs and their token compositions
- **Arbitrum transaction records**: Transaction data providing the actual user addresses (from_address) that initiated swap transactions
- **Token metadata**: Token registry containing symbol names and decimal precision for proper amount normalization
- **End-of-day price feeds**: Daily USD price data for tokens traded on SolidLizard, used for volume conversion to USD

## 3. Step-by-step calculation

### Component A: Trading volume extraction and processing
- **Purpose**: Extract and process individual swap transactions to calculate USD trading volume
- **Logic**: Parse `Swap` events from SolidLizard v1 trading pairs on Arbitrum, extracting the net token amount changes for each swap transaction. For each swap, calculate the token amount differences by subtracting amounts out from amounts in for both token0 and token1. Join swap events with Arbitrum transaction records to identify the actual user who initiated each swap transaction. Combine swap data with trading pair metadata to obtain token addresses, decimal precision, and pair information necessary for proper amount calculation.
- **Key fields**: `amountIn` and `amountOut` values for both tokens in each swap, `from_address` from transaction records, trading pair `account_address`, `token0_address` and `token1_address`, token decimal precision

### Component B: USD volume conversion and aggregation
- **Purpose**: Convert token amounts to USD values and aggregate to daily trading volume
- **Logic**: Normalize token amounts by dividing by the appropriate decimal precision (10^decimals) to get the actual token quantities. Convert both token amounts to USD values using end-of-day price feeds for the corresponding tokens and trading date. To prevent price manipulation from affecting volume calculations, take the lesser USD value between token0 and token1 amounts for each swap. Sum all USD swap values by day to calculate total daily trading volume. Truncate timestamps to day boundaries for daily aggregation.
- **Key fields**: Normalized `token0_amount` and `token1_amount`, `price_usd` from price feeds, calculated `trading_volume` in USD

### Component C: Daily active user identification  
- **Purpose**: Identify and count distinct daily active users who performed swaps
- **Logic**: Extract unique user addresses from swap transactions by using the transaction sender address for each swap event on each day. Classify all swap participants as trader-type users. Use timestamp truncation to day boundaries to group users by trading day. Apply HyperLogLog aggregation to efficiently count distinct users across multiple dimension breakdowns including project-level, chain-level, user-type-level, and product-level groupings.
- **Key fields**: `user_address` from transaction records, `user_type` classification, daily `timestamp` boundaries, HLL aggregated `user_dau` counts

### Final aggregation
- **Combining components**: Join daily trading volume totals with daily active user counts using the same timestamp (day) as the join key, specifically using the project-level DAU aggregation that covers all SolidLizard users
- **Currency conversion**: All volume amounts are converted to USD using end-of-day token price feeds, with manipulation protection by taking the minimum USD value between the two tokens in each swap
- **Time aggregation**: Both trading volume and user counts are aggregated to daily boundaries using timestamp truncation to day level  
- **Final calculation**: Divide the total daily trading volume by the daily active user count using safe division to handle cases where there are no active users on a given day, producing the average trading volume per user metric

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

Average trading volume (per user)

Trading volume divided by daily active users.

SolidLizardSLIZ

Average trading volume (per user)

Average trading volume (per user)

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