Users are able to use the Orca protocol to transact with digital assets on a peer-to-peer or peer-to-protocol basis by trading against liquidity  pools, which are created and seeded by other users providing liquidity of a pair of tokens into such liquidity pools in exchange for a stake of the liquidity and trading fees generated by such pools.

Orca is a non-custodial automated market maker (AMM) protocol built on Solana. Orca was founded by Yutaro Mori and Grace Kwan and launched in 2021.

ORCA tokenholders govern the Orca protocol treasury, which receives a portion of the trading fees paid by users of the protocol.

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Orca

Orca | Data Breakdown

# Orca Average trading volume (per trade) Methodology

## 1. Overview

The Orca trading volume average per trade count metric measures the average USD value traded per individual transaction on the Orca decentralized exchange on Solana. This metric provides insight into the typical trade size on the platform by dividing the total daily trading volume by the total daily trade count. It helps assess user trading patterns, market depth, and the platform's appeal to different trader segments (retail vs. institutional).

The calculation follows a straightforward approach: aggregate total USD trading volume per day, count total trades per day, then compute the ratio using safe division to handle edge cases.

## 2. Data sources

The calculation relies on several key data sources:

- **Solana DEX trade events**: Parsed swap transaction data from Solana's blockchain, specifically capturing trades executed through Orca's smart contracts
- **Program identification data**: Smart contract addresses and program calls that identify Orca's two main trading products (Whirlpools and Orca Token Swap V2)
- **End-of-day token price feeds**: Daily average USD price data for Solana-based tokens involved in trades
- **Transaction metadata**: Blockchain transaction details including timestamps, signers, token addresses, trade amounts, and pool identifiers
- **Token information**: Token contract addresses, decimal specifications, and metadata for proper amount calculations

## 3. Step-by-step calculation

### Component A: Trade data extraction and classification
- **Purpose**: Identify and extract all Orca trades from Solana DEX activity
- **Logic**: Filter Solana DEX trade events to include only transactions that involve Orca's two main programs: Whirlpools (program address `whirLbMiicVdio4qvUfM5KAg6Ct8VwpYzGff3uctyCc`) and Orca Token Swap V2 (program address `9W959DqEETiGZocYWCQPaJ6sBmUzgfxXfqGeTEdp3aQP`). The system checks both inner and outer program calls to capture all relevant transactions. Extract key trade details including base token address, quote token address, trade amounts for both tokens, pool address, transaction timestamp, and signer address.
- **Key fields**: `base_mint` and `quote_mint` (token addresses), `base_amount` and `quote_amount` (trade quantities), `pool_address` (trading pool identifier), `block_timestamp` (transaction time), `signer` (trader address)

### Component B: Token pricing integration
- **Purpose**: Obtain USD valuations for traded tokens to enable volume calculations
- **Logic**: For each trading day, calculate daily average USD prices for all Solana tokens by aggregating end-of-day price feeds. Join trade data with price data on two dimensions: the trade date (truncated to daily granularity) and the token contract address. This creates price lookups for both the base token and quote token in each trade.
- **Key fields**: `price_usd` (daily average token price), `token_address` (token contract identifier), `timestamp` (price date)

### Component C: Trading volume calculation
- **Purpose**: Convert trade amounts into USD trading volume while avoiding double-counting
- **Logic**: For each individual trade, calculate the USD value of both sides of the trade by multiplying token amounts by their respective daily USD prices. To avoid double-counting the same economic value, take the lesser of the two calculated values using the minimum function. This conservative approach ensures each trade contributes exactly once to the volume calculation. Aggregate these individual trade volumes by day, product type, and pool to get total daily trading volume.
- **Key fields**: `trading_volume` (USD value per trade), `amount_0` and `amount_1` (absolute trade amounts), calculated volume using minimum of both token valuations

### Component D: Trade count calculation  
- **Purpose**: Count the total number of individual trades executed daily
- **Logic**: Using the same filtered trade data, truncate transaction timestamps to daily granularity and count all individual trade records. Group by date, chain, and product type to get daily trade counts. Each row in the filtered trade dataset represents one trade, so the count function provides the total number of trading transactions per day.
- **Key fields**: `trade_count` (number of individual trades), `timestamp` (daily truncated date), `product_id` (Orca product classification)

### Final aggregation
- **Combining components**: Join the daily trading volume totals with daily trade count totals on the timestamp dimension. Both components use the same daily time boundaries, ensuring alignment between volume and count data.
- **Currency conversion**: All volume calculations are performed in USD using end-of-day token prices. No additional currency conversion is needed in the final step.
- **Time aggregation**: Both volume and trade count use daily time boundaries created by truncating transaction timestamps to the day level.
- **Final calculation**: Divide total daily trading volume by total daily trade count using safe division. The safe division function handles edge cases where trade count might be zero, preventing division errors and returning null for days with no trading activity. This produces the average USD volume per trade for each day.

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

OrcaORCA

Average trading volume (per trade)

Average trading volume (per trade)

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