Liquidity providers deposit funds into LFJ and receive trading fees from traders (supply-side fees).

LFJ (previously Trader Joe) is a non-custodial exchange protocol built on Avalanche (originally a fork of SushiSwap). LFJ was founded by an anonymous team and launched in 2021.

JOE tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue). LFJ's investors include Three Arrows Capital, DeFiance Capital, GBV Capital, Mechanism Capital, etc.

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Exchanges (DEX)

# LFJ Active users (weekly) Methodology

## 1. Overview

TraderJoe Weekly Active Users (WAU) measures the number of unique users who executed at least one trade on the TraderJoe decentralized exchange within a rolling 7-day window. This metric uses HyperLogLog (HLL) probabilistic counting for efficient calculation across multiple protocol versions and blockchain networks. The metric answers the business question: "How many distinct users are actively trading on TraderJoe each week?" The calculation aggregates daily active users from all TraderJoe protocol versions (v1, v2, v2.1, v2.2) across supported networks, then applies a 7-day rolling window to determine weekly activity.

## 2. Data sources

- **Swap events from TraderJoe contracts**: Parsed `Swap` events from TraderJoe v1 Pair contracts, v2 LBPair contracts, v2.1 LBPairV2 contracts, and v2.2 LBPairV22 contracts across multiple networks
- **Transaction records**: Transaction data from BSC, Avalanche, Ethereum, and Arbitrum networks to identify the actual users initiating trades
- **Protocol-specific event schemas**: Different event structures for each TraderJoe version with varying parameter encoding methods
- **Denylisted trading pairs**: Hardcoded list of trading pair addresses excluded due to limited price feed support for low-liquidity tokens

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identify unique daily active users across all TraderJoe protocol versions and networks
- **Logic**: Extract swap events from four TraderJoe protocol versions. For v1, parse `Pair_Swap_event` records from BSC and Avalanche contracts, excluding denylisted trading pairs. For v2, parse `LBPair_Swap_event` records from Arbitrum, BSC, and Avalanche. For v2.1, parse `LBPairV2_Swap_event` records from Ethereum, Arbitrum, BSC, and Avalanche, using JavaScript functions to decode packed amount parameters. For v2.2, parse `LBPairV22_Swap_event` records from Arbitrum and Avalanche with similar parameter decoding. Join each swap event with corresponding transaction records to obtain the `from_address` as the actual user address. Truncate all `block_timestamp` values to day level and union distinct across all protocol versions to ensure each user is counted only once per day regardless of how many trades they executed or which protocol versions they used.
- **Key fields**: `block_timestamp` truncated to day, `user_address` from transaction `from_address`, `chain` identifier, `user_type` classified as 'Trader'

### Component B: HyperLogLog sketch creation
- **Purpose**: Convert daily user sets into HyperLogLog sketches for efficient approximate counting
- **Logic**: Group daily active users by timestamp, chain, user type, and product dimensions. Apply `hll_count.init` function with precision parameter 15 to create HyperLogLog sketches from the daily user address sets. This probabilistic data structure allows efficient storage and merging of large user sets while maintaining acceptable accuracy for counting operations.
- **Key fields**: `timestamp` at day level, dimensional grouping fields, `hll` sketch containing compressed user set

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for calculating weekly active users
- **Logic**: Generate a continuous date spine covering the full time range of available data using `generate_timestamp_array` with daily intervals. For each date in the spine, create a 7-day window by generating timestamps from 6 days prior through the current date. Join this window specification with the daily HLL sketches to identify which daily sketches should be included in each weekly calculation. This approach ensures consistent 7-day windows even when some days have no trading activity.
- **Key fields**: `timestamp` representing the end date of each 7-day window, `joinday` representing individual days within the window

### Component D: Dimensional aggregations
- **Purpose**: Calculate weekly active users across different business dimensions
- **Logic**: Create four separate dimensional views by grouping the windowed data differently. For project-level breakdown, merge all HLL sketches across chains, products, and user types. For chain-level breakdown, group by chain while merging across other dimensions. For user-type-level breakdown, group by user type while merging across other dimensions. For product-level breakdown, group by product while merging across other dimensions. Use `hll_count.merge_partial` to combine HLL sketches within each group, then `hll_count.extract` to obtain the final approximate user count.
- **Key fields**: `dimensions` indicating the breakdown type, relevant dimension fields (chain_id, product_id, user_type), `user_wau` containing the final count

### Final aggregation
- **Combining components**: Union all four dimensional breakdowns into a single result set with consistent schema structure
- **Schema standardization**: Apply the `finalize_metric_user_wau` macro to standardize field names, data types, and add metadata fields like `data_id` set to 'traderjoe'
- **Missing data handling**: Fill missing days with zero values using the 'fill_zeros' strategy to ensure continuous time series
- **Final calculation**: The metric represents the approximate count of distinct users who executed at least one trade within each 7-day rolling window, calculated using HyperLogLog probabilistic counting with precision 15 for memory efficiency while maintaining high accuracy

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

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

LFJJOE

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown