DEX aggregators

Oku Trade

# Oku Trade Active users (monthly) Methodology

## 1. Overview

Monthly Active Users (MAU) measures the number of unique users who interact with the OkuTrade protocol within any 30-day rolling window. This metric captures user engagement by tracking both trading activities and flash loan usage on the OkuTrade decentralized exchange. The calculation uses a rolling 30-day window approach with HyperLogLog data structures for efficient unique user counting across multiple dimensional breakdowns.

## 2. Data sources

The calculation relies on the following blockchain data sources:

- **OkuTrade v3 swap events**: Parsed `Pool_Swap_event` logs from OkuTrade v3 smart contracts on the XDC blockchain, capturing all trading transactions
- **OkuTrade v3 flash loan events**: Parsed `Pool_Flash_event` logs from OkuTrade v3 smart contracts on the XDC blockchain, capturing all flash loan activities
- **XDC transaction records**: Core XDC blockchain transaction data used to identify the actual user addresses that initiated protocol interactions

## 3. Step-by-step calculation

### Component A: Daily active user identification

- **Purpose**: Identifies unique users who interact with OkuTrade on each day through trading or flash loan activities
- **Logic**: The system parses `Pool_Swap_event` logs to extract users making trades via the `sender` field (mapped as `contract_user_address`). Separately, it processes `Pool_Flash_event` logs to identify flash loan users, joining these events with XDC transaction records to extract the actual initiating user address from the `from_address` field. All identified users are classified with user type 'Trader' and their activities are aggregated at daily granularity using timestamp truncation to day level
- **Key fields**: `block_timestamp` for temporal grouping, `contract_user_address` from swap events, `from_address` from transaction records for flash loans, `chain` identifier, and `product_id` for versioning

### Component B: HyperLogLog sketch creation

- **Purpose**: Converts daily user sets into memory-efficient HyperLogLog data structures for scalable unique counting
- **Logic**: Daily active user data is transformed into HyperLogLog sketches using BigQuery's `hll_count.init` function with precision parameter 15. This creates compact probabilistic data structures that can be efficiently merged across time periods while maintaining statistical accuracy for unique counting operations
- **Key fields**: `timestamp` for daily boundaries, `user_dau` containing unique user identifiers, `hll` containing the HyperLogLog sketch representation

### Component C: Rolling window construction

- **Purpose**: Creates a 30-day sliding window framework to calculate monthly active users for each day
- **Logic**: A datespine generates all calendar days from the earliest data point to the current date. For each target day, the system creates associations with the previous 29 days using timestamp array generation, effectively creating a 30-day rolling window. This ensures complete temporal coverage and handles missing data days appropriately
- **Key fields**: `timestamp` for the target date, `joinday` representing each day within the 30-day lookback period

### Component D: Window aggregation

- **Purpose**: Merges HyperLogLog sketches across the 30-day window to calculate unique monthly active users
- **Logic**: For each target day, the system joins the rolling window framework with daily HyperLogLog sketches. It then merges all HyperLogLog sketches within each 30-day window using `hll_count.merge_partial` and extracts the final unique count using `hll_count.extract`. This provides an accurate count of unique users active within any 30-day period
- **Key fields**: `timestamp` for the calculation date, merged `hll` sketches from the window period, extracted `user_mau` counts

### Final aggregation

- **Combining components**: The system creates four separate dimensional breakdowns from the window aggregation results: overall project level (project_dbt_tag), chain level, user type level, and product version level. Each breakdown groups the data differently while maintaining the same underlying MAU calculation methodology

- **Time aggregation**: All calculations maintain daily temporal resolution with each day representing the endpoint of a 30-day rolling window. The rolling nature means that each day's MAU calculation includes users active on that day or any of the previous 29 days

- **Final calculation**: The metric combines all dimensional breakdowns using union operations, applies standard Token Terminal metric formatting through the `finalize_metric_user_mau` macro, and fills missing periods with zero values. The final MAU value for any given day represents the count of unique users who were active within the 30-day window ending on that date

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

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

Oku Trade

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown