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 Tradeable pairs Methodology

## 1. Overview

The Orca tradeable pairs metric measures the total number of unique trading pairs available on the Orca decentralized exchange (DEX) on Solana. This metric tracks the cumulative count of trading pairs that have been created over time, providing insight into the liquidity pool diversity and market depth of the Orca platform. The calculation identifies when each trading pair was first initialized and maintains a running total of all available pairs.

## 2. Data sources

- **Solana instruction logs**: Parsed Solana blockchain instruction data from Orca smart contracts, specifically three types of pool initialization instructions
- **Whirlpool initialization events**: `InitializePool` and `InitializePoolV2` instruction logs from Orca's Whirlpool AMM contracts that create concentrated liquidity pools
- **TokenSwap initialization events**: `Initialize` instruction logs from Orca's TokenSwapV2 contracts that create traditional constant product AMM pools
- **Instruction metadata**: Block timestamps, account addresses, and token mint addresses from the initialization instructions

## 3. Step-by-step calculation

### Component A: Trading pair identification
- **Purpose**: Extract and catalog all trading pairs created on Orca across different AMM types
- **Logic**: Parse three types of Orca pool initialization instructions from Solana blockchain data. From `Whirlpool_InitializePool` and `Whirlpool_InitializePoolV2` instructions, extract the `whirlpool` account address along with `tokenMintA` and `tokenMintB` addresses. From `TokenSwapV2_Initialize` instructions, extract the `tokenSwapAccount` address along with `tokenA` and `tokenB` addresses. Map each instruction type to its corresponding product category: Whirlpool instructions map to "whirlpools" product while TokenSwap instructions map to "Orca Token Swap V2" product.
- **Key fields**: `account_address` (unique pair identifier), `token0_address` and `token1_address` (constituent tokens), `block_timestamp` (creation time), `product_id` (AMM type classification)

### Component B: First appearance tracking  
- **Purpose**: Determine when each unique trading pair was first created to avoid double-counting
- **Logic**: For each unique `account_address` representing a trading pair, find the earliest daily timestamp when it was initialized. Group by `product_id` and `chain` to maintain separation between different AMM types and networks. Truncate block timestamps to daily boundaries to establish consistent time buckets for the metric.
- **Key fields**: `timestamp` (first appearance date), `account_address` (unique pair identifier), `product_id` and `chain` (grouping dimensions)

### Component C: Daily pair creation counts
- **Purpose**: Count how many new trading pairs were created each day
- **Logic**: Group the first appearances by daily timestamp, product, and chain, then count distinct account addresses to determine how many new pairs became available each day. This creates a time series of daily pair additions across the platform.
- **Key fields**: `timestamp` (daily grain), `pair_count` (new pairs created that day)

### Component D: Time series completion
- **Purpose**: Ensure continuous daily time series with no missing dates
- **Logic**: Apply zero-filling logic to insert missing dates between the earliest and latest pair creation dates. For any days where no new pairs were created, insert records with `pair_count` of zero. This maintains data integrity and enables accurate cumulative calculations.
- **Key fields**: `pair_count` (zero-filled for missing dates)

### Final aggregation
- **Combining components**: Sum the daily `pair_count` values using a cumulative window function partitioned by `chain` and `product_id`, ordered by `timestamp`
- **Currency conversion**: Not applicable - this metric measures count of trading pairs, not monetary values
- **Time aggregation**: Daily timestamps with cumulative summing to track total available pairs over time
- **Final calculation**: Running total where each day shows the cumulative number of unique trading pairs that have been created on Orca since inception, calculated as `sum(pair_count) over (partition by chain, product_id order by timestamp)` to maintain separate totals for whirlpools vs traditional AMM pools

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

Number of unique token pairs available for trading on the exchange.

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Tradeable pairs

Tradeable pairs

Competitive landscape