Mantra is a Decentralized Exchange (DEX), initially built on Ethereum and upgraded to the Mantra Chain, a blockchain built on the Cosmos SDK.

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MANTRA

# MANTRA Average trading volume (per user) Methodology

## 1. Overview

This metric calculates the average trading volume per daily active user (DAU) on Mantra, expressed in USD. It measures the daily trading intensity per user by dividing the total trading volume on Mantra Zone by the number of unique users active on the Mantra ecosystem each day. This metric helps assess user engagement and trading behavior depth on the Mantra blockchain.

## 2. Data sources

- **Mantra blockchain events**: Raw message events from the Mantra blockchain containing both transaction data and swap event data with JSON-encoded event attributes
- **Token metadata**: Reference data containing token decimals, identifiers, and chain information for proper amount normalization  
- **End-of-day token price feeds**: Daily USD price data for tokens traded on Mantra for currency conversion

## 3. Step-by-step calculation

### Component A: Swap data extraction
- **Purpose**: Extracts and parses raw swap transaction data from Mantra blockchain events
- **Logic**: Filters Mantra message events for swap transactions by selecting events with `event_type` of 'wasm', contract address `mantra1466nf3zuxpya8q9emxukd7vftaf6h4psr0a07srl5zw74zh84yjqagspfm` (Mantra Zone), and action `execute_swap_operations`. Parses JSON event attributes using regular expressions to extract key swap information including pool reserves, swap amounts, token addresses, sender address, and fee information
- **Key fields**: `sender` (swap initiator), `pool_identifier`, `amount_in` and `amount_out` (raw token amounts), `token_in` and `token_out` (token addresses), various fee components (`burn_fee`, `protocol_fee`, `swap_fee`)

### Component B: Trading volume calculation
- **Purpose**: Normalizes token amounts and converts to USD trading volume
- **Logic**: Joins swap data with token metadata to retrieve decimal places for each token. Normalizes raw token amounts by dividing by 10 raised to the token's decimal places. Joins with end-of-day price feeds to get USD prices for both input and output tokens. Applies anti-manipulation logic by taking the lesser of the two USD values (input amount × input token price vs output amount × output token price) to prevent inflated volume from price manipulation. Aggregates daily by summing all swap USD values
- **Key fields**: Normalized `token_in_amount` and `token_out_amount`, `price_usd` from price feeds, final `trading_volume` in USD

### Component C: Daily active user calculation  
- **Purpose**: Identifies and counts unique users active on Mantra each day
- **Logic**: Combines users from two sources - general transaction senders from all Mantra message events and specific swap senders from DEX transactions. Deduplicates by user address per day to ensure each user is counted only once regardless of transaction frequency. Uses HyperLogLog (HLL) aggregation to efficiently count approximate distinct users across different dimensional breakdowns (project-level, chain-level, user-type-level, product-level)
- **Key fields**: `user_address` from transaction events, `sender` from swap events, aggregated `user_dau` count

### Final aggregation
- **Combining components**: Joins daily trading volume with daily active user counts on timestamp, using the project-level DAU aggregate (`dimensions = 'project_dbt_tag'`) to get total unique users across all Mantra activities
- **Currency conversion**: All trading volume is converted to USD using end-of-day price feeds with anti-manipulation logic applied at the individual swap level
- **Time aggregation**: Both metrics are aggregated to daily granularity using timestamp truncation to day boundaries  
- **Final calculation**: Divides total daily trading volume by total daily active users using safe division to handle cases where DAU might be zero, resulting in the average trading volume per user in USD

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

Average trading volume (per user)

Trading volume divided by daily active users.

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Average trading volume (per user)

Average trading volume (per user)

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