Common Analytical Queries

To illustrate the kinds of questions DeepBlock can answer, here are some common analytical queries that our platform simplifies. These would typically require complex scripting or multiple data sources, but with DeepBlock, they can be done with a single GraphQL query or a quick AI prompt.

Capital Flow Tracing

“Trace the flow of funds from a particular address or contract.”

Query:

What are the top destinations of funds that exited the ABC DeFi protocol last month?

DeepBlock can follow the on-chain trail: from the protocol’s contract to user addresses, then see where those users moved the funds next (exchanges, other protocols, off-ramp, etc.). This helps identify where value goes after leaving a platform.

Entity Clustering (Address Relationships)

“Identify clusters of addresses that likely belong to the same entity.”

Query:

Which addresses frequently interact with each other or share common ownership signals?

DeepBlock’s graph can reveal groups of addresses that always act together (indicative of an entity using multiple addresses) or that have all interacted with a common set of addresses (indicative of a structured operation). This is useful for detecting Sybil attacks, understanding whale activity (one entity controlling many wallets), or just de-anonymizing patterns for research.

Cross-Chain Bridge Usage

“Analyze usage of cross-chain bridges.”

Query:

How much ETH was bridged from Ethereum to Polygon via Bridge X in the past week, and what were the largest transfers?

With DeepBlock, you can easily filter transactions on the Ethereum side that went into Bridge X’s contract and link them to the corresponding events on Polygon (mint/burn events representing the bridged assets). The data might show spikes on certain days or identify a handful of very large movers (perhaps institutions or arbitrage bots).

Protocol Performance Queries

“Compare protocol metrics over time.”

Query:

What was the Total Value Locked (TVL) of Protocol A versus Protocol B over the last quarter, and how did user counts differ?

DeepBlock can provide time-series data because we index historical states. A single query could fetch quarterly snapshots of TVL for both protocols and even the number of unique active addresses interacting with them. This kind of query is great for market research or internal KPI tracking, combining multiple metrics across protocols seamlessly.

Whale Monitoring

“Find large holders or movers of assets.”

Query:

Who are the top 10 holders of Token X and have any of them made significant movements in the last week?

DeepBlock can identify the largest addresses holding a given token (via analyzing on-chain balances) and then check recent transfer activity from those addresses. If, say, one of the top holders suddenly moved 20% of their stack, that’s something you might want to know (and possibly act on, if you’re managing risk or looking for market signals).

Each of these analytical questions can be answered without setting up multiple databases or writing a custom ETL pipeline; they are essentially one or two queries to DeepBlock. This dramatically accelerates the insight-gathering process for analysts and enables more exploratory questions (because asking a new question is cheap when you don’t have to re-engineer your data pipeline for it).