Synapse allows users to send tokens from one chain to another almost immediately by using AMMs deployed across 16+ EVM and non-EVM blockchains. 

Synapse is a cross-chain liquidity network that enables fully non-custodial transfers between EVM and non-EVM compatible chains and L2 systems. Synapse was launched in 2021.

SYN tokenholders own and govern the protocol. They currently take a cut of the transaction fees paid by the users (revenue). Synapse’s investors include CMS Holdings, Alameda Research, Primitive Ventures, etc.

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Synapse

# Synapse Active users (weekly) Methodology

## 1. Overview

Synapse Weekly Active Users (WAU) measures the number of unique users who performed cross-chain bridge transactions through the Synapse protocol within any 7-day rolling window. This metric provides insights into user engagement and adoption across Synapse's multi-chain bridge infrastructure. The calculation aggregates daily active users over 7-day periods and provides breakdowns by blockchain, user type, and product dimensions.

## 2. Data sources

- **Synapse bridge events**: Parsed smart contract events from Synapse bridge contracts across 8 blockchains (Ethereum, Arbitrum, Avalanche, Base, BSC, Fantom, Optimism, Polygon)
- **Bridge operation events**: `TokenMint`, `TokenWithdraw`, `TokenDeposit`, `TokenRedeem`, and `TokenRedeemV2` events capturing standard bridge operations
- **Combined bridge-swap events**: `TokenMintAndSwap`, `TokenWithdrawAndRemove`, `TokenDepositAndSwap`, `TokenRedeemAndSwap`, and `TokenRedeemAndRemove` events for operations that combine bridging with swapping
- **CCTP events**: Cross-Chain Transfer Protocol `CircleRequestSent` and `CircleRequestFulfilled` events from Circle's USDC bridge (available on Ethereum, Arbitrum, Avalanche, and Optimism)
- **Blockchain transaction data**: Transaction timestamps and hashes from all supported blockchain networks
- **Event parameters**: User address fields including `to`, `sender`, and `recipient` parameters from bridge events

## 3. Step-by-step calculation

### Component A: User identification from bridge events
- **Purpose**: Extract all unique user addresses that interacted with Synapse bridge contracts
- **Logic**: Parse events from Synapse bridge smart contracts across 8 different blockchains. For each event type, extract the relevant user address field - `to` field for most bridge events, `sender` for CCTP send events, and `recipient` for CCTP fulfillment events. Classify all users as "Trader" type and categorize activities under "bridges" market sector and "amm" business segment. Include transaction timestamp and blockchain identifier for each user interaction.
- **Key fields**: `block_timestamp` for timing, `user_address` for user identification, `chain` for blockchain context, `user_type` set to "Trader"

### Component B: Daily active user aggregation
- **Purpose**: Calculate daily active users by identifying unique addresses per day
- **Logic**: Group user addresses by day using timestamp truncation to day boundaries. Create distinct user counts for each combination of date, chain, user type, and product. Convert daily user counts into HyperLogLog sketches to enable efficient approximate distinct counting when combining across time periods. This approach allows for accurate union operations when creating weekly windows while maintaining computational efficiency.
- **Key fields**: `timestamp` truncated to day, `user_dau` containing unique user addresses, `chain` and `user_type` for dimensional grouping

### Component C: Rolling 7-day window construction
- **Purpose**: Create 7-day rolling windows for weekly active user calculation
- **Logic**: Generate a complete datespine covering all dates from the earliest data point to the current date minus one day. For each date in the datespine, create a 7-day window by joining with the current date plus the 6 preceding days. This ensures every date has a complete 7-day lookback window and prevents gaps in the time series that could occur from missing daily data.
- **Key fields**: `timestamp` for the window end date, `joinday` for each day within the 7-day window

### Component D: Weekly active user calculation
- **Purpose**: Aggregate daily HyperLogLog sketches across 7-day windows to calculate weekly active users
- **Logic**: Join the 7-day windows with daily HyperLogLog data, matching each window day with the corresponding daily user sketch. Merge all HyperLogLog sketches within each 7-day window using HLL merge operations, then extract the final approximate distinct count. This provides the number of unique users who were active on any day within the 7-day period.
- **Key fields**: `hll` sketches for efficient distinct counting, merged across the 7-day window

### Component E: Dimensional breakdowns
- **Purpose**: Create multiple views of WAU data across different dimensions
- **Logic**: Calculate WAU at four different dimensional levels: (1) Project level combining all chains and user types, (2) Individual chain level showing WAU per blockchain, (3) User type level (currently only "Trader" type), and (4) Product level for future product-specific breakdowns. Each breakdown uses the same HyperLogLog merging logic but groups by different dimension combinations. Null values are used for non-applicable dimensions in each breakdown.
- **Key fields**: `dimensions` field indicating the breakdown type, `chain_id`, `user_type`, and `product_id` for dimensional grouping

### Final aggregation
- **Combining components**: Union all dimensional breakdowns into a single result set, with each row representing WAU for a specific dimension combination on a specific date
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to standardize column names, data types, and add metadata fields including data source identifier
- **Gap filling**: Fill any missing dates with zero values to ensure a complete time series without gaps
- **Final calculation**: The final `user_wau` value represents the approximate count of distinct users who performed at least one bridge transaction within any 7-day rolling window, calculated using HyperLogLog algorithms for computational efficiency

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

Unique revenue-generating users over a rolling 7-day window.

SynapseSYN

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown