Methodology

Data Workflow

We retrieve data directly from ledger canisters through our self-hosted Rosetta Node. Once collected, the data is stored in high-performance databases optimized to handle large transaction volumes. These databases retain complete transaction histories, account details, and all other relevant ledger information.

Our proprietary Python-based algorithms then process and index this data to extract meaningful insights. They normalize raw records, aggregate transactional activity, and compute advanced metrics used across our platform. These algorithms are continuously refined to improve data quality and analytical depth.

Our database infrastructure is designed for fast querying, scalable growth, and robust transaction storage as the Internet Computer ecosystem expands. Built-in redundancy and frequent backups help safeguard against data loss and ensure uninterrupted operations, even in the event of infrastructure disruptions.

Limitation

While the system is designed for reliability, occasional discrepancies may occur due to factors such as algorithmic issues, server infrastructure challenges, or external anomalies in the Rosetta Node or underlying blockchain data.

Although we take extensive measures to ensure the integrity of our data and analytics, some factors remain beyond our direct control. Users are therefore encouraged to independently verify critical information and report any inconsistencies for further investigation.

Metrics related to exchange reserves are derived from our extensive database of labeled addresses. These labels are compiled using a combination of officially published exchange information (when available) and our proprietary clustering algorithms. While we strive to keep these balances as accurate as possible, they may not always fully reflect an exchange’s total reserves—particularly in cases where exchanges do not publicly disclose all of their addresses.