Liquidity providers deposit funds into DFX and receive a share of trading fees (supply-side fees).

DFX is a decentralized foreign exchange protocol deployed on Polygon, Ethereum & Arbitrum. DFX was founded in 2021 by Kevin Zhang and Adrian Li.

DFX tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue).

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Exchanges (DEX)

DFX Finance

# DFX Finance Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) for DFX measures the number of unique addresses that have executed at least one trade on the DFX protocol within a trailing 30-day period. This metric uses a rolling window approach rather than calendar months, providing a continuous view of user engagement across the decentralized exchange platform. The calculation employs HyperLogLog sketches for efficient approximate distinct counting across multiple blockchain networks and protocol versions.

## 2. Data sources

- **Blockchain events**: Parsed `Trade` events from DFX protocol smart contracts across multiple versions and chains, including `Curve_Trade_event`, `CurveV2_Trade_event`, and `CurveV22_Trade_event`
- **Transaction data**: Ethereum, Polygon, and Arbitrum transaction records containing user addresses and timestamps
- **Protocol versions**: Data from DFX v1 (Ethereum and Polygon) and DFX v2 (Ethereum, Polygon, and Arbitrum)
- **Chain coverage**: Multi-chain data spanning Ethereum mainnet, Polygon, and Arbitrum networks

## 3. Step-by-step calculation

### Component A: Trade event collection
- **Purpose**: Identify all trading activity across DFX protocol versions and chains
- **Logic**: The system collects trade events from seven different protocol deployments covering DFX v1 on Ethereum and Polygon, plus DFX v2 across Ethereum, Polygon, and Arbitrum. Each trade event contains token swap details including origin token address (`origin`), target token address (`target`), and respective amounts (`originAmount`, `targetAmount`). Trade events are joined with blockchain transaction records to identify the user address through the transaction's `from_address` field, establishing who initiated each trade.
- **Key fields**: `transaction_hash` for linking events to users, `block_timestamp` for temporal grouping, token addresses and amounts for trade validation, `from_address` as the unique user identifier

### Component B: Daily active user calculation  
- **Purpose**: Count unique users per day who executed trades
- **Logic**: Trade data is grouped by day using timestamp truncation to day granularity. All identified users are classified as 'Trader' type since activity is based solely on trading behavior. The system counts distinct user addresses per day and stores these counts as HyperLogLog sketches with precision 15, enabling memory-efficient storage and later aggregation of large user counts. Daily counts are broken down by chain, user type, market sector, business segment, and product dimensions.
- **Key fields**: `timestamp` truncated to day, `user_address` for distinct counting, `chain` for multi-chain breakdown, classification fields for dimensional analysis

### Component C: Rolling window construction
- **Purpose**: Create 30-day trailing windows for each day to calculate MAU
- **Logic**: A complete datespine is generated covering all days from the earliest available data through the previous day. For each date in the datespine, the system creates a 30-day lookback period by generating timestamps for the current day plus the previous 29 days. This rolling window approach ensures that MAU represents users active in any trailing 30-day period rather than calendar month boundaries. The datespine prevents gaps in the time series that could occur from days with no trading activity.
- **Key fields**: `timestamp` as the reference date, `joinday` representing each day in the 30-day window

### Component D: HyperLogLog aggregation
- **Purpose**: Merge daily user counts across the 30-day windows to calculate MAU
- **Logic**: For each day's 30-day window, the system retrieves the corresponding daily HyperLogLog sketches and merges them using HLL merge operations. The merged sketch represents all unique users who were active during any day within the 30-day period. The final distinct count is extracted from the merged HyperLogLog sketch to produce the MAU value. This approach handles overlapping users across days efficiently while maintaining approximate accuracy.
- **Key fields**: `hll` sketches for merging, extracted `user_mau` counts from merged sketches

### Component E: Multi-dimensional breakdown
- **Purpose**: Calculate MAU across different analytical dimensions
- **Logic**: The system generates MAU calculations for four different dimensional breakdowns. Project-level MAU combines all chains and provides an overall protocol view. Chain-level MAU breaks down users by individual blockchain networks (Ethereum, Polygon, Arbitrum). User-type MAU segments by user classification, currently only 'Trader' type. Product-level MAU would break down by specific product offerings if applicable. Each breakdown aggregates the appropriate HyperLogLog sketches and extracts distinct counts, with unused dimensions set to null.
- **Key fields**: `dimensions` identifying the breakdown type, dimensional fields like `chain_id` and `user_type`, aggregated `user_mau` values

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive view with multiple analytical perspectives in a single result set
- **Currency conversion**: Not applicable as this metric counts users rather than monetary values
- **Time aggregation**: Each record represents a specific day's MAU calculated using a trailing 30-day window, with monthly partitioning for efficient data management
- **Final calculation**: The final MAU value for any given day represents the approximate count of unique user addresses that executed at least one trade within the 30-day period ending on that date, calculated through HyperLogLog sketch merging and extraction across the rolling window

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

Unique revenue-generating users over a rolling 30-day window.

DFX FinanceDFX

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown