Footprint Analytics and build some NEAR statistics
About this topic:
This topic is about how to use Flipside to build some NEAR blockchain statistics
Footprint Analytics link: https://www.footprint.network/
To learn more about NEAR blockchain, visit https://polygon.technology/.
NEAR price
How do I find a suitable table for this query?
by using search
We will search for tables and select the required table.
By clicking on the table we want, we can browse and display the fields in it.
Now back to NEAR price
The suggested SQL statement is the following:
SELECT date("footprint"."token_daily_stats"."on_date") AS "time",
sum("footprint"."token_daily_stats"."price")
AS "price"
FROM "footprint"."token_daily_stats"
WHERE "footprint"."token_daily_stats"."token_symbol" = 'NEAR'
GROUP BY date("footprint"."token_daily_stats"."on_date")
ORDER BY date("footprint"."token_daily_stats"."on_date") ASC
After adding the query, a graphic form can be added based on it.
NEAR TVL
The suggested SQL statement is the following:
SELECT
date("footprint"."defi_protocol_daily_stats"."on_date")
AS "time",
sum("footprint"."defi_protocol_daily_stats"."tvl")
AS "tv"
FROM
"footprint"."defi_protocol_daily_stats"
WHERE
"footprint"."defi_protocol_daily_stats"."chain"
= 'Near'
GROUP BY
date("footprint"."defi_protocol_daily_stats"."on_date")
ORDER
BY date("footprint"."defi_protocol_daily_stats"."on_date")
ASC
NEAR market cap
The suggested SQL statement is the following:
SELECT
date("footprint"."token_daily_stats"."on_date")
AS "time",
sum("footprint"."token_daily_stats"."market_cap")
AS "market_cap"
FROM
"footprint"."token_daily_stats"
WHERE
("footprint"."token_daily_stats"."token_symbol"
= 'NEAR'
AND "footprint"."token_daily_stats"."on_date"
>= CAST('2021-01-02 00:00:00Z' AS timestamp with time zone))
GROUP
BY date("footprint"."token_daily_stats"."on_date")
ORDER
BY date("footprint"."token_daily_stats"."on_date")
ASC
NEAR TVL period
The suggested SQL statement is the following:
SELECT
date("footprint"."defi_protocol_daily_stats"."on_date")
AS "time",
sum("footprint"."defi_protocol_daily_stats"."tvl")
AS "tv"
FROM
"footprint"."defi_protocol_daily_stats"
WHERE
"footprint"."defi_protocol_daily_stats"."chain"
= 'Near'
GROUP BY
date("footprint"."defi_protocol_daily_stats"."on_date")
ORDER
BY date("footprint"."defi_protocol_daily_stats"."on_date")
ASC
NEAR market cap period
The suggested SQL statement is the following:
SELECT
date("footprint"."token_daily_stats"."on_date")
AS "time",
sum("footprint"."token_daily_stats"."market_cap")
AS "market_cap"
FROM
"footprint"."token_daily_stats"
WHERE
("footprint"."token_daily_stats"."token_symbol"
= 'NEAR'
AND "footprint"."token_daily_stats"."on_date"
>= CAST('2021-01-02 00:00:00Z' AS timestamp with time zone) AND
"footprint"."token_daily_stats"."on_date"
>= date(date_add('day', -360, now())) AND
"footprint"."token_daily_stats"."on_date"
< date(now()))
GROUP BY
date("footprint"."token_daily_stats"."on_date")
ORDER
BY date("footprint"."token_daily_stats"."on_date")
ASC
Now build a dashboard.
We can build dashboards.
Just add the queries to the dashboard and coordinate and arrange.
