📅 Date | 💫 Daily Transactions | 👥 Unique Users | 📊 Tx per User | ↩️ Previous Day Tx | 📈 Day-over-Day Change % | |
|---|---|---|---|---|---|---|
1 | 2025-03-12 00:00:00.000 | 20842491 | 2136217 | 9.76 | 25675081 | -18.82 |
2 | 2025-03-11 00:00:00.000 | 25675081 | 1976849 | 12.99 | 11903796 | 115.69 |
3 | 2025-03-10 00:00:00.000 | 11903796 | 1182650 | 10.07 | 9595439 | 24.06 |
4 | 2025-03-09 00:00:00.000 | 9595439 | 1294814 | 7.41 | 10708955 | -10.4 |
5 | 2025-03-08 00:00:00.000 | 10708955 | 1511389 | 7.09 | 10752657 | -0.41 |
6 | 2025-03-07 00:00:00.000 | 10752657 | 1640739 | 6.55 | 10392027 | 3.47 |
7 | 2025-03-06 00:00:00.000 | 10392027 | 1501760 | 6.92 | 3086200 | 236.73 |
8 | 2025-03-05 00:00:00.000 | 3086200 | 539225 | 5.72 |
Monad Data Enginewarm-chocolate
Updated 2025-03-12
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WITH daily_transactions AS (
SELECT
DATE_TRUNC('day', block_timestamp) AS date,
COUNT(*) AS daily_tx_count,
COUNT(DISTINCT from_address) AS unique_users
FROM MONAD.testnet.fact_transactions
WHERE block_timestamp >= CURRENT_TIMESTAMP - INTERVAL '7 days'
GROUP BY 1
)
SELECT
date AS "📅 Date",
daily_tx_count AS "💫 Daily Transactions",
unique_users AS "👥 Unique Users",
ROUND(daily_tx_count::DECIMAL / unique_users, 2) AS "📊 Tx per User",
LAG(daily_tx_count, 1) OVER (ORDER BY date) AS "↩️ Previous Day Tx",
ROUND(((daily_tx_count::DECIMAL / LAG(daily_tx_count, 1) OVER (ORDER BY date)) - 1) * 100, 2) AS "📈 Day-over-Day Change %"
FROM daily_transactions
ORDER BY date DESC;
Last run: 17 days ago
8
515B
10s