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

website

twitter

discord

github

Blockchains (L1)

MANTRA

# MANTRA Average trading volume (per trade) Methodology

## 1. Overview

The average trading volume per trade metric measures the typical USD value of individual trades on the Mantra DEX (Mantra Zone). This metric is calculated by dividing the total daily trading volume in USD by the total number of distinct trades executed on the same day. It provides insight into the average trade size and market activity patterns on the Mantra protocol.

## 2. Data sources

- **Mantra blockchain events**: Raw message events from the Mantra blockchain, specifically filtering for `wasm` event types from the Mantra Zone contract address
- **Swap event data**: JSON-formatted event attributes containing swap execution details including pool identifiers, token amounts, reserves, fees, and transaction metadata
- **Token metadata**: Token registry providing decimal places and identifiers for proper amount conversion from raw blockchain units to human-readable values
- **End-of-day price feeds**: Daily token price data in USD for converting token amounts to standardized currency values

## 3. Step-by-step calculation

### Component A: Raw swap data extraction
- **Purpose**: Extract and parse raw swap transactions from Mantra blockchain events
- **Logic**: Filter Mantra blockchain message events for `wasm` events from the Mantra Zone contract address `mantra1466nf3zuxpya8q9emxukd7vftaf6h4psr0a07srl5zw74zh84yjqagspfm` with action `execute_swap_operations`. Parse JSON event attributes to extract structured swap data including sender address, pool identifier, swap details, and pool reserves. Use regex extraction to parse token amounts and identifiers from formatted strings within the swap and pool_reserves fields.
- **Key fields**: `block_timestamp`, `transaction_hash`, `sender`, `pool_identifier`, `amount_in`, `amount_out`, `token_in`, `token_out`, `reserve0`, `reserve1`, `token0`, `token1`, plus various fee amounts (`burn_fee`, `protocol_fee`, `swap_fee`)

### Component B: Token amount normalization  
- **Purpose**: Convert raw blockchain token amounts to human-readable decimal values
- **Logic**: Join swap data with token metadata to obtain decimal places for each token. Convert raw token amounts by dividing by 10 raised to the power of the token's decimal places. This transforms blockchain-native integer amounts into proper decimal representations (e.g., converting 1000000 wei to 1.0 ETH equivalent).
- **Key fields**: `token_in_amount` (normalized input amount), `token_out_amount` (normalized output amount), derived from raw amounts and token decimal metadata

### Component C: USD conversion with manipulation protection
- **Purpose**: Convert normalized token amounts to USD values while protecting against price manipulation
- **Logic**: Join normalized swap data with end-of-day price feeds for both input and output tokens. Calculate USD values for both the input amount (token_in_amount × input_token_price) and output amount (token_out_amount × output_token_price). Apply manipulation protection by taking the lesser of these two USD values using the `least()` function. This prevents inflated volume calculations from potentially manipulated token prices. Sum all protected USD values per day to get total trading volume.
- **Key fields**: `trading_volume` (final daily USD volume using the lesser of input/output USD values)

### Component D: Trade count calculation
- **Purpose**: Count the number of distinct trades executed each day
- **Logic**: Using the same base swap data, count distinct transaction hashes per day. Each unique transaction hash represents one trade execution, regardless of how many swap events or pool interactions occurred within that transaction. Group by daily timestamp and count distinct transaction hashes to get the total number of trades per day.
- **Key fields**: `trade_count` (count of distinct transaction hashes per day)

### Final aggregation
- **Combining components**: Join daily trading volume and daily trade count datasets on the timestamp field to align the metrics by day
- **Currency conversion**: Trading volume is already converted to USD in the earlier steps using end-of-day price feeds with manipulation protection
- **Time aggregation**: Both components use daily time boundaries created by truncating block timestamps to the day level
- **Final calculation**: Divide the total daily trading volume in USD by the total daily trade count using safe division. The safe division function handles edge cases where trade count might be zero, preventing division by zero errors and returning null for days with no trading activity

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

MANTRAMANTRA

Archived

Average trading volume (per trade)

Average trading volume (per trade)

Competitive landscape