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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Blockchains (L1)

MANTRA

# MANTRA Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that have interacted with the Mantra ecosystem (either the blockchain or DEX) within a rolling 30-day window. This metric combines both Layer 1 blockchain users and DEX participants to provide a comprehensive view of user engagement. The calculation uses HyperLogLog data structures for efficient distinct counting across large datasets and sliding time windows.

## 2. Data sources

The calculation draws from the following raw blockchain data:
- **Mantra blockchain message events**: Transaction logs containing sender addresses from all blockchain transactions
- **Mantra DEX swap events**: Parsed smart contract events from the Mantra DEX contract (`mantra1466nf3zuxpya8q9emxukd7vftaf6h4psr0a07srl5zw74zh84yjqagspfm`) containing swap operation details
- **Event metadata**: JSON-formatted event attributes containing user addresses, contract interactions, and transaction details
- **Incremental processing timestamps**: Table modification timestamps used to optimize incremental data processing

## 3. Step-by-step calculation

### Component A: Blockchain user extraction
- **Purpose**: Identifies unique daily users from Mantra blockchain transactions
- **Logic**: Extracts user addresses from message events where `event_type` equals `message`. The `sender` field is parsed from the JSON `event_attributes` to identify transaction originators. Each unique address per day represents a blockchain user, classified as "Transaction sender" user type under the "blockchains_l1" market sector
- **Key fields**: `block_timestamp` for daily aggregation, `sender` address from JSON attributes, `transaction_hash` for deduplication

### Component B: DEX user extraction  
- **Purpose**: Identifies unique daily users from Mantra DEX swap activities
- **Logic**: Extracts user addresses from wasm contract events where `event_type` equals `wasm`, the contract address matches the Mantra DEX contract, and the `action` field equals `execute_swap_operations`. The `sender` field is parsed from JSON event attributes to identify swap participants. Users are classified as "DEX user" type under the "exchanges" market sector and "Mantra Zone" product
- **Key fields**: `block_timestamp` for daily aggregation, `sender` address from JSON attributes, swap details including `amount_in`, `amount_out`, `pool_identifier`

### Component C: Daily user aggregation
- **Purpose**: Combines blockchain and DEX users into daily unique address counts
- **Logic**: Union distinct users from both blockchain transactions and DEX swaps, truncating timestamps to daily granularity. Each unique address per day is counted once regardless of transaction volume. Users maintain their source classification (blockchain vs DEX) and associated metadata like `product_id` and `user_type`
- **Key fields**: `timestamp` truncated to day, `user_dau` containing the unique address, classification metadata

### Component D: HyperLogLog preprocessing
- **Purpose**: Converts daily user lists into HyperLogLog sketches for efficient distinct counting
- **Logic**: Groups daily users by timestamp, chain, user type, and product, then initializes HyperLogLog sketches with precision 15 bits. This enables efficient approximate distinct counting when merging across time windows without storing raw address lists
- **Key fields**: `hll` sketch containing compressed user data, grouping dimensions for later aggregation

### Component E: Rolling window construction
- **Purpose**: Creates 30-day rolling windows for MAU calculation
- **Logic**: Generates a datespine covering the full time range, then creates joinable records for each day linking to the previous 29 days (30 days total). This sliding window approach ensures each day's MAU calculation includes users active in the current day plus the prior 29 days
- **Key fields**: `timestamp` for the calculation date, `joinday` for the days included in the 30-day window

### Component F: Multi-dimensional MAU calculation
- **Purpose**: Calculates MAU across different dimensional breakdowns
- **Logic**: Merges HyperLogLog sketches across each 30-day window and extracts the distinct count. Creates four dimensional views: project-level (overall), chain-level, user-type level, and product-level. Each dimension uses null values for non-applicable fields to maintain consistent schema structure
- **Key fields**: `user_mau` containing the distinct user count, `dimensions` indicating the aggregation level, relevant dimensional identifiers

### Final aggregation
- **Combining components**: The final model selects only chain-level and project-level MAU calculations, filtering out user-type and product-level breakdowns
- **Currency conversion**: Not applicable - this is a count-based metric
- **Time aggregation**: Uses rolling 30-day windows calculated daily, with results partitioned monthly for storage efficiency
- **Final calculation**: Renames `user_mau` to `active_addresses_monthly` and applies the standard metric finalization process including schema standardization and zero-filling for missing periods

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

Unique addresses active over a rolling 30-day window.

MANTRAMANTRA

Archived

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown