Network participants process transactions and receive fees from users (supply-side fees).

Cosmos Hub is an interoperability & security platform for blockchains and applications. Cosmos was founded by Ethan Buchman & Jae Kwon and launched in 2019.

ATOM holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). Cosmos’s investors include Paradigm, IOSG Ventures, 1confirmation, etc.

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Cosmos Hub

# Cosmos Hub Active addresses (monthly) Methodology

## 1. Overview

Cosmos active addresses monthly measures the number of unique wallet addresses that have sent at least one transaction on the Cosmos Hub blockchain within a rolling 30-day period. This metric provides insight into user engagement and network activity by tracking distinct transaction initiators over monthly windows. The calculation uses a rolling window approach rather than calendar months to provide more granular and continuous monitoring of user activity trends.

## 2. Data sources

The calculation relies on the following raw data sources:

- **Cosmos Hub message events**: Parsed blockchain events from the Cosmos Hub network containing transaction activity and metadata
- **Event attributes**: JSON-formatted event data containing transaction details including sender addresses  
- **Block timestamps**: Temporal data from Cosmos blockchain blocks to establish transaction timing
- **Transaction metadata**: Additional context about transaction types and network activity

## 3. Step-by-step calculation

### Component A: Transaction sender extraction
- **Purpose**: Identifies unique wallet addresses initiating transactions on Cosmos Hub
- **Logic**: Extracts message events from Cosmos Hub blockchain data, filtering specifically for events where the `event_type` equals 'message'. From these message events, uses JSON parsing to extract the `sender` field from the `event_attributes` column. Each sender address represents a wallet that initiated a transaction. Associates each sender with the corresponding `block_timestamp` and `transaction_hash` for temporal tracking.
- **Key fields**: `sender` address from parsed JSON, `block_timestamp` for timing, `transaction_hash` for uniqueness, and `event_type` for filtering

### Component B: Daily active user aggregation  
- **Purpose**: Creates daily snapshots of unique active addresses to serve as building blocks for monthly calculations
- **Logic**: Takes the extracted transaction senders and truncates their `block_timestamp` to day granularity using timestamp truncation. Removes duplicate addresses within each day to ensure each unique address is counted only once per day. Preserves additional metadata like `chain`, `user_type`, `market_sector_id`, `business_segment_id`, and `product_id` for dimensional analysis. Creates HyperLogLog sketches for each daily distinct user count to enable memory-efficient aggregation.
- **Key fields**: Truncated `timestamp`, `user_address` (renamed from sender), `chain` identifier, and various dimensional metadata fields

### Component C: Rolling 30-day window creation
- **Purpose**: Establishes the mathematical framework for calculating monthly active addresses using overlapping 30-day periods
- **Logic**: Generates a complete date spine covering all days from the earliest transaction date to the present day. For each date in this spine, creates associations with the previous 29 days to form rolling 30-day windows. Uses cross-join logic where each target date is joined with a generated array of the 29 preceding dates plus itself. This approach ensures that every possible 30-day period is captured without gaps, even when some days have no transaction activity.
- **Key fields**: `timestamp` for the target date, `joinday` representing each day within the 30-day window

### Component D: Monthly active user calculation with HyperLogLog
- **Purpose**: Efficiently calculates unique user counts across 30-day rolling windows while handling large datasets
- **Logic**: Joins the rolling window structure with daily HyperLogLog sketches from the user aggregation step. For each 30-day window, merges the HLL sketches from all days within that window using `hll_count.merge_partial()` to combine the approximate distinct count data. Extracts the final unique count using `hll_count.extract()` which provides the estimated number of distinct addresses that were active during the 30-day period. This approach avoids memory issues that would occur from exact distinct counting across large time windows.
- **Key fields**: Merged HLL sketches, extracted `user_mau` count, window `timestamp`

### Component E: Dimensional breakdowns
- **Purpose**: Provides multiple analytical perspectives on monthly active addresses for different business use cases
- **Logic**: Creates separate aggregations of the monthly active user data across four key dimensions. The 'project_dbt_tag' dimension aggregates all Cosmos activity into a single project-level metric. The 'chain' dimension maintains chain-specific breakdowns for multi-chain analysis. The 'user_type' dimension allows analysis by different categories of users. The 'product_id' dimension enables product-specific analysis. Each breakdown uses union operations to combine the different dimensional views into a single dataset.
- **Key fields**: `dimensions` field indicating breakdown type, `chain_id`, `user_type`, `product_id`, and calculated `user_mau` values

### Final aggregation
- **Combining components**: The final model takes the dimensional MAU breakdowns and filters specifically for 'chain' and 'project_dbt_tag' dimensions, which are most relevant for the active addresses monthly metric. Renames the `user_mau` field to `active_addresses_monthly` to align with blockchain-specific terminology where users are typically referred to as addresses.
- **Currency conversion**: No currency conversion is required as this metric counts addresses rather than financial values
- **Time aggregation**: Uses rolling 30-day periods rather than calendar months, providing daily updates of the monthly active address count. Each day represents the count of unique addresses active in the 30-day period ending on that date.
- **Final calculation**: The final metric value is the HyperLogLog-estimated count of unique sender addresses that initiated at least one message transaction on Cosmos Hub within the rolling 30-day window, broken down by chain and project dimensions, with zero-filling applied for dates with no activity to ensure complete time series data.

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

Unique addresses active over a rolling 30-day window.

Cosmos HubATOM

Active addresses (monthly)

Active addresses (monthly)

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