Entity Stats

Entity stats tables can be used for the following:

1. Understanding balances for selected entities

2. Token transfers to and from entities by date and time data of selected entities

3. Tracking and benchmarking activity of entities.

Data for entities are grouped together under the aggregates section for each chain with entity_ as prefix. Please note that if the counterparty is not an entity, then the field is null at the moment.

Entity Stats

This is the balance sheet for all the entities and contains the following columns:

Field Name	Data Type	Description
entity	STRING	Name of the entity
primary_sector	STRING	The primary sector of this entity (the most frequent tag from attribution's entity list)
all_sectors	STRING	All sectors for this entity (all tags from attribution's entity list)
wei_balance	NUMERIC	Wei balance of the entity (transform to ETH by dividing with POW(10, CAST(18 AS NUMERIC) )
wei_balance_usd	FLOAT	Wei balance in USD of the entity, using current Coingecko prices
token_balance	RECORD	Array of structs with balance per token that the entity has ever held (in smallest unit for token). Balance can be zero and can also be negative since the data type is FLOAT64 which is not 100% accurate. Balance can also be negative for tokens that have custom events that are not included in the calculation or for rebase tokens. Balance in USD uses current Coingecko prices and only tokens with price data are included.
nft_balance_per_project	RECORD	Array of structs with number of NFTs per project_id currently held by the entity. There are cases where this number is not accurate, f.ex negative balances, due to not being able to capture the incoming transfer event or overflow issues. The median price is based on data from the past 24 hours (at run time of underlying table) or past 7 days. For some special collections, the median price for the past 90 days is used. The liquidity classification (Low, Medium, High) is based on number of sales the past 7 days. Also includes name of collection, flag for whether it is a blue chip collection and category, which can be either Art, Game, Metaverse or Social.

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Nansen Query

Entity USD Flow By Date

This table is an aggregation of all token transfers to and from entities by date & token. If the counter-party is not an entity, then the field is null at the moment.

Field Name	Data Type	Description
block_date	DATE	Block date of the token flow
token_address	STRING	Address of token sent/received
token_symbol	STRING	Symbol of token
token_name	STRING	Name of token
to_entity	STRING	Name of entity receiving the token
from_entity	STRING	Name of entity sending the token
value_usd	FLOAT	Value in USD of the token sent between named entities (or aggregated for non-entities)

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Entity Stats by Date

This table contains the activity level of crypto entities by date. This can be useful to track the rise of popular entities or to benchmark the relative level of activity between entities.

Field Name	Data Type	Description
block_date	DATE	Block date of the entity stats
entity	STRING	Name of the entity
primary_sector	STRING	The primary sector of this entity (the most frequent tag from attribution's entity list)
all_sectors	STRING	All sectors for this entity (all tags from attribution's entity list)
nof_peers_addresses	INTEGER	Number of other addresses the entity has transacted with (excluding same-entity addresses)
nof_peers_entities	INTEGER	Number of other entities the entity has transacted with
incoming_nof_transactions	INTEGER	Number of incoming transactions for this entity (excluding intra-entity transactions)
outgoing_nof_transactions	INTEGER	Numbere of outgoing transactions for this entity (excluding intra-entity transactions)
total_gas_spent_wei	NUMERIC	Total gas spent by this entity on outgoing transactions (intra-entity included)

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Examples:

Usecase #1: Understanding Entity Flow

Let's say you want to analyse how much volume has left Coinbase and to which entities has this volume gone to, you can use the nansen-query.aggregates_ethereum.entity_usd_flow_by_date table to do so. Here's a sample query that tracks total volume flow from Coinbase to other entities in the last 30 days:

SELECT
  SUM(value_usd) AS Volume_USD,
  to_entity,
  block_date
FROM
 nansen-query.aggregates_ethereum.entity_usd_flow_by_date
WHERE from_entity = 'Coinbase'
  AND block_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
Group By to_entity, block_date
Order By block_date

Usecase #2: Tracking entity performance across sectors

Another cool analysis that you can perform using entity stats is to compare sector level data. Here's an example that uses the nansen-query.aggregates_ethereum.entity_stats_by_date table to compare activity in exchange sector over the last one month:

SELECT
  entity,
  incoming_nof_transactions as incoming_transactions,
  outgoing_nof_transactions as Outgoing_Transactions,
  nof_peers_addresses as Active_Addresses,
  block_date
from
  nansen-query.aggregates_ethereum.entity_stats_by_date
WHERE
  primary_sector = "Exchange"
  AND block_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
Group By
  entity,
  incoming_nof_transactions,
  outgoing_nof_transactions,
  nof_peers_addresses,
  block_date

You can also use the same query to compare two sectors (Eg. comparing CEXs vs. DEXs based on total number of transactions, gas consumed or active_addresses)