Validators process transactions and receive validator rewards. Super representatives and super representative partners receive voting rewards. TRX stakers receive energy and bandwidth incentives.

Tron is a smart contract platform for decentralized applications (a blockchain). Tron was founded by Justin Sun and launched in 2018.

TRX holders own and govern the protocol. They currently burn all transaction fees paid by users (revenue). It is important to note that the incentives related to energy and bandwidth are effectively returned as revenue, and this is reflected in the earnings.

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Tron

# Tron Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that initiated at least one transaction on the Tron blockchain within a rolling 30-day window. This metric provides insight into user engagement and network activity by tracking distinct addresses that actively participate in the network over time. The calculation uses a rolling window approach to provide daily updates of monthly activity levels.

## 2. Data sources

- **Tron blockchain transactions**: Raw transaction records from the Tron network containing transaction metadata including sender addresses, timestamps, and transaction hashes
- **Transaction logs**: Tron transaction traces and execution data that identify transaction initiators

## 3. Step-by-step calculation

### Component A: User identification
- **Purpose**: Identify and extract unique user addresses from daily transaction activity
- **Logic**: Transaction sender addresses are extracted from raw Tron transaction records as the primary identifier of user activity. Each transaction's `from_address` field represents a user who initiated blockchain activity. Block timestamps are truncated to daily boundaries to create daily snapshots of user activity. The model classifies all transaction senders as 'Transaction sender' user types and assigns them to the 'blockchains_l1' market sector and 'blockchain' business segment.
- **Key fields**: `from_address` (user address), `block_timestamp` (transaction timing), `transaction_hash` (unique transaction identifier)

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily unique user counts using efficient distinct counting methodology
- **Logic**: Daily active users are calculated by identifying distinct user addresses per day from the transaction sender data. HyperLogLog sketches are applied to each day's user set for memory-efficient storage and processing of large cardinality data. The daily data is partitioned by timestamp, chain, user type, and product dimensions to enable flexible aggregation patterns.
- **Key fields**: `user_dau` (daily active user address), `timestamp` (daily boundary), `hll` (HyperLogLog sketch for distinct counting)

### Component C: Rolling window construction
- **Purpose**: Create 30-day rolling windows for monthly active user calculation
- **Logic**: A complete datespine is generated to ensure no missing days in the calculation window. For each day, a 30-day lookback window is constructed by joining the target day with the previous 29 days of data. This creates overlapping windows where each day represents the endpoint of a 30-day period. The datespine methodology ensures consistent window sizes even when certain days have no transaction activity.
- **Key fields**: `timestamp` (target day), `joinday` (days within the 30-day window)

### Component D: Monthly active user calculation
- **Purpose**: Aggregate unique users across the 30-day rolling window periods  
- **Logic**: HyperLogLog sketches from all days within each 30-day window are merged using HLL union operations to calculate the distinct count of users across the entire period. This approach efficiently handles the overlap of users across multiple days while maintaining accuracy for large-scale distinct counting. The calculation produces multiple dimensional breakdowns including chain-level, project-level, user type, and product-level aggregations.
- **Key fields**: `user_mau` (monthly active users), `dimensions` (aggregation level), `chain_id` (blockchain identifier)

### Final aggregation
- **Combining components**: The monthly active user counts are filtered to include only chain-level and project-level dimensional breakdowns. The `user_mau` field is renamed to `active_addresses_monthly` to reflect the blockchain context where addresses represent active participants in the network.
- **Time aggregation**: Daily endpoints represent 30-day rolling windows, providing a smooth time series of monthly activity levels updated daily
- **Final calculation**: The metric represents the count of unique addresses that initiated at least one transaction within any 30-day period ending on the specified date

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

Unique addresses active over a rolling 30-day window.

TronTRX

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape