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 | Data Breakdown

# Orca Trade count Methodology

## 1. Overview

The Orca trade count metric measures the daily number of swap transactions executed on Orca's decentralized exchange protocols on Solana. This metric counts all trades across Orca's two main products: Whirlpools (concentrated liquidity) and Orca Token Swap V2 (constant product). The calculation aggregates individual swap transactions by day, providing insight into trading activity and user engagement on the Orca platform.

## 2. Data sources

- **Solana DEX trade events**: Parsed swap transaction data from Solana blockchain containing trade execution details, token pairs, amounts, and program execution information
- **Program execution data**: Information about which Solana programs were invoked during transactions, including both outer and inner program calls
- **Token metadata**: Mint addresses for base and quote tokens involved in trades
- **Transaction metadata**: Block timestamps for temporal aggregation and transaction signer addresses
- **Liquidity pool data**: Pool addresses where trades were executed

## 3. Step-by-step calculation

### Component A: Trade extraction and filtering
- **Purpose**: Identifies and extracts trades specifically executed on Orca protocols from broader Solana DEX activity
- **Logic**: Filters Solana DEX trade events to include only transactions where either the outer program or inner program matches Orca's specific program addresses. The filtering targets two program addresses: `whirLbMiicVdio4qvUfM5KAg6Ct8VwpYzGff3uctyCc` for Whirlpools and `9W959DqEETiGZocYWCQPaJ6sBmUzgfxXfqGeTEdp3aQP` for Orca Token Swap V2. This dual-check approach ensures trades are captured regardless of whether Orca programs are called directly or through other program interactions.
- **Key fields**: Extracts `block_timestamp` for temporal aggregation, `signer` as the transaction initiator, `base_mint` and `quote_mint` as the traded token pair, `pool_address` for the specific liquidity pool, and `base_amount` and `quote_amount` for trade sizes (converted to absolute values)

### Component B: Product classification
- **Purpose**: Categorizes trades by Orca product type to enable product-specific analysis
- **Logic**: Maps program addresses to human-readable product identifiers. Trades involving the Whirlpools program address are classified as `whirlpools` product, while trades using the Orca Token Swap V2 program are classified as `Orca Token Swap V2` product. Any trades that don't match these specific programs are marked as `unknown`, though this should not occur given the initial filtering.
- **Key fields**: Creates `product_id` field with standardized product names and sets `chain` field to `solana` for all trades

### Component C: Daily aggregation
- **Purpose**: Counts the total number of trades per day for each product
- **Logic**: Groups all extracted trades by truncated daily timestamp, chain identifier, and product type. Each individual trade transaction contributes a count of 1 to the daily total. The timestamp is truncated to day boundaries to ensure consistent daily reporting regardless of the specific time trades occurred.
- **Key fields**: Aggregates by `timestamp` (daily), `chain_id`, and `product_id`, producing `trade_count` as the total number of trades

### Final aggregation
- **Combining components**: The daily trade counts are processed through the `finalize_metric_trade_count` macro which standardizes the output format and applies a zero-filling strategy
- **Zero-filling strategy**: Ensures that days with no trading activity still appear in the dataset with a trade count of zero, maintaining consistent daily records for downstream analysis
- **Chain filtering**: Applies chain availability filtering that excludes Blast chain (though not applicable for Solana-only data)
- **Final calculation**: The final metric represents the total count of unique swap transactions executed on Orca protocols each day, segmented by product type (Whirlpools vs Orca Token Swap V2)

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

Number of trades executed on a decentralized exchange.

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Trade count

Trade count

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