Liquidity providers deposit funds into Ramses and stake their LP tokens to receive RAM tokens in proportion to their share of the votes (token incentives).

Ramses is a non-custodial exchange protocol built on Arbitrum. Ramses was founded by pseudonyms Dog and North in 2023.

RAM tokenholders own and govern the protocol. They vote which pools they wish to earn trading fees from (revenue).

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Ramses Exchange

# Ramses Exchange Average trading volume (per trade) Methodology

## 1. Overview

The average trading volume per trade metric for Ramses exchange measures the typical size of individual trades by calculating the ratio of total daily trading volume to total daily trade count. This metric provides insight into trading behavior patterns, indicating whether the exchange primarily serves retail traders (lower average) or institutional/large traders (higher average). The calculation combines data from both Ramses V1 legacy pairs and V2 concentrated liquidity pools on Arbitrum, using anti-manipulation techniques to ensure accurate volume measurement.

## 2. Data sources

The calculation utilizes several types of blockchain and external data:

- **Blockchain events**: Parsed `Swap` events from Ramses V1 `LegacyPair` contracts and `Swap` events from Ramses V2 `Pool` contracts on Arbitrum
- **Transaction data**: Arbitrum transaction records to identify actual users behind contract interactions
- **Trading pair metadata**: `PairCreated` events from Ramses V1 Factory contracts and `PoolCreated` events from Ramses V2 Factory contracts to map token pairs and their properties
- **Token metadata**: Token symbol and decimal information for accurate amount calculations
- **External data**: End-of-day price feeds for standard tokens and specialized CoinGecko price feeds for RAM token (`0xaaa6c1e32c55a7bfa8066a6fae9b42650f262418`)

## 3. Step-by-step calculation

### Component A: Raw trade extraction
- **Purpose**: Extract and standardize swap events from both Ramses protocol versions
- **Logic**: Collect `Swap` events from V1 `LegacyPair` contracts, calculating token amount changes as `amount0In - amount0Out` and `amount1In - amount1Out`. Collect `Swap` events from V2 `Pool` contracts using direct `amount0` and `amount1` values. Join with transaction data to identify actual user addresses rather than contract addresses. Filter events to start from launch date of March 14, 2023.
- **Key fields**: `block_timestamp`, `transaction_hash`, `log_index`, contract `address`, `sender` address, token amounts for both tokens in the pair

### Component B: Trading pair mapping
- **Purpose**: Map contract addresses to their underlying token pairs and metadata
- **Logic**: Extract trading pairs from Factory creation events, combining `PairCreated` events from V1 (legacy pairs) and `PoolCreated` events from V2 (concentrated liquidity pools). Join with token metadata to obtain decimal places and symbols for both tokens in each pair. Create human-readable names by concatenating token symbols.
- **Key fields**: `account_address` (pair contract), `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals`, `account_name`

### Component C: Volume calculation with decimal correction
- **Purpose**: Convert raw token amounts to standardized decimal-corrected values
- **Logic**: Join swap events with trading pair metadata to access token decimal information. Apply decimal correction by dividing absolute token amounts by 10 raised to the respective token's decimal places. Truncate timestamps to daily boundaries for aggregation. Take absolute values of token amounts to handle different swap directions consistently.
- **Key fields**: Daily `timestamp`, corrected `token0_amount`, corrected `token1_amount`, `transaction_hash`, `log_index`

### Component D: Price integration and USD conversion
- **Purpose**: Convert token amounts to USD values using comprehensive price feeds
- **Logic**: Create combined price feed by joining standard end-of-day price feeds with specialized CoinGecko pricing for RAM token, excluding RAM from standard feeds to avoid double counting. Join decimal-corrected volumes with price data for both tokens in each swap. Calculate USD values by multiplying token amounts with corresponding prices. Apply anti-manipulation filter by taking the lesser of the two USD values (token0_amount × price0 vs token1_amount × price1) to prevent trades with manipulated prices from inflating volumes.
- **Key fields**: `price_usd` for both tokens, calculated USD values, filtered `trading_volume`

### Component E: Trade count calculation  
- **Purpose**: Count unique trades per day across all Ramses protocols
- **Logic**: Use the same base swap events from the volume calculation but focus on counting distinct transactions. Count unique `transaction_hash` values per day, grouped by chain, product type (legacy pair vs concentrated liquidity pool), and other categorical dimensions. This ensures each transaction is counted exactly once regardless of how many swap events it generates.
- **Key fields**: `trade_count` as count of distinct `transaction_hash` per day

### Final aggregation
- **Combining components**: Sum daily trading volumes across all product types and versions to get total daily volume. Sum daily trade counts across all product types and versions to get total daily trades. Join the two aggregated datasets on timestamp to align volume and count data.
- **Currency conversion**: All volumes are converted to USD using end-of-day price feeds, with RAM token specifically priced via CoinGecko integration
- **Time aggregation**: Both metrics use daily timestamp truncation with consistent boundary logic across all components
- **Final calculation**: Divide total daily trading volume by total daily trade count using safe division that handles zero-trade days by returning null rather than causing division errors. Apply fill_zeros strategy to populate missing days with zero values for consistent time series data.

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

Ramses ExchangeRAM

Average trading volume (per trade)

Average trading volume (per trade)

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