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 user) Methodology

## 1. Overview

Average trading volume (per user) is measured as the average USD value of trading activity per daily active user on the Orca protocol. The business question of how much trading volume is generated on average per active user per day is addressed by this metric. Daily total trading volume is divided by active users (daily) to analyze user engagement intensity and trading behavior patterns across the Whirlpools and Orca Token Swap V2 products.

## 2. Data sources

- **Blockchain instructions**: Solana DEX trade records, `InitializePool`, `InitializePoolV2`, and `InitializePoolWithAdaptiveFee` from the `Whirlpool` program, `TokenSwapV2_Initialize` from the `TokenSwapV2` program, and `Traded` from the `Whirlpool` program, covering Solana
- **External data**: End-of-day price feeds for traded assets and asset metadata

## 3. Step-by-step calculation

### Component A: Orca trade identification

- **Purpose**: Trading activity on the Orca protocol is identified and extracted from raw Solana DEX trade records.
- **Logic**: Solana DEX trades are filtered by verifying if the executing program matches specific Orca program accounts. Program accounts are mapped to canonical product names: `whirLbMiicVdio4qvUfM5KAg6Ct8VwpYzGff3uctyCc` is mapped to "Whirlpools" and `9W959DqEETiGZocYWCQPaJ6sBmUzgfxXfqGeTEdp3aQP` is mapped to "Orca Token Swap V2". Trade details, including the transaction signer account, token accounts, normalized token amounts, pool accounts, and block timestamps, are extracted.
- **Key fields**: `signer` (user account), `token_0_address` and `token_1_address` (asset accounts), `amount_0` and `amount_1` (normalized asset amounts), `pool_address`, `block_timestamp`

### Component B: USD volume calculation

- **Purpose**: Normalized asset amounts are converted to USD trading volume to enable cross-asset comparison and aggregation.
- **Logic**: Trade records are joined with end-of-day asset prices. For each trade, the USD value of both asset amounts is calculated by multiplying the normalized amount by the daily USD price. If one asset is resolved as a tokenized stock and the other is not, the trading volume is valued based on the tokenized stock leg. Otherwise, the minimum USD value between the two legs is selected to prevent double-counting.
- **Key fields**: `amount_0` and `amount_1` (normalized asset amounts), `price_usd` (daily asset prices), `trading_volume` (calculated USD value)

### Component C: Daily trading volume aggregation

- **Purpose**: Orca trading activity is aggregated into daily total volumes.
- **Logic**: USD-converted trades are grouped by day using timestamp truncation to the day boundary. All `trading_volume` values are summed within each day across all products, pools, and trading pairs. Breakdowns by chain and product are maintained for detailed analysis.
- **Key fields**: `timestamp` (day-truncated), `trading_volume` (daily sum), `chain_id`, `product_id`

### Component D: Daily active user counting

- **Purpose**: Distinct users who traded on Orca each day are counted to determine active users (daily).
- **Logic**: Unique users are identified by extracting distinct transaction signer accounts from trades each day. Distinct users are aggregated across multiple dimensions, including project-wide totals, chain-level breakdowns, user type classifications, and product-specific counts. The project-wide aggregation is utilized to provide the total active users (daily) across all Orca products and pools.
- **Key fields**: `user_dau` (active users (daily)), `dimensions` (aggregation level), `timestamp`, `user_type` ("Trader")

### Component E: Average trading volume calculation

- **Purpose**: The average trading volume generated per active user is calculated.
- **Logic**: Daily trading volume is joined with active users (daily) on the same timestamp. An inner join is utilized to ensure both volume and user data exist for each day, specifically using the project-wide user count where the dimension is set to `project_dbt_tag`. Daily total trading volume is divided by active users (daily) using safe division to handle cases where the user count is zero.
- **Key fields**: `trading_volume` (daily total volume), `user_dau` (active users (daily)), `trading_volume_avg_per_user` (average volume per user)

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

Average trading volume (per user)

Trading volume divided by daily active users.

OrcaORCA

Average trading volume (per user)

Average trading volume (per user)

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