First, clarify the definitions of 'active trader' and 'power trader' and the time window. Then, write a SQL query that filters trades to the last 30 days, aggregates by user to count trades, and joins with the users table to identify active traders. Finally, compute the counts and ratio, ensuring correct handling of users with zero trades.
Pro tip: Always confirm whether 'active traders' are defined as users who made at least one trade in the last 30 days or users who were active on the platform (e.g., logged in) regardless of trades. This distinction drastically changes the result and shows attention to detail.
Define 'active trader' (e.g., users with at least one trade in the last 30 days) and 'power trader' (e.g., users with at least 3 trades in the last 30 days). Confirm the time window and whether to include all users or only those with trades.
Use a WHERE clause to select trades within the last 30 days from the current date. Ensure the date column is correctly identified and that the time zone is consistent.
Group by user_id and count the number of trades. This gives the trade count per user in the window.
Join the aggregated trade counts with the users table to get all users (or only those with trades). Compute the count of active traders (those with >=1 trade) and power traders (those with >=3 trades).
Compute the ratio as power_traders / active_traders (as a decimal). Handle division by zero if there are no active traders. Present the three values clearly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 'include users who never traded' part is where people probably drop the ball and I almost did.
Start by clarifying the definition of a signup cohort (e.g., by signup date) and the retention metric (trade on exactly day 7 after signup). Then outline a SQL-based approach: generate a cohort table of all users with signup dates, left join to trade activity filtered to day 7, and aggregate to compute the percentage of users who traded on day 7, including those with no trades.
Pro tip: Emphasize that retention is calculated on a per-cohort basis and that users who never traded are included in the denominator, which is crucial for accurate retention rates. Also, mention the importance of handling time zones consistently to avoid off-by-one errors in day calculations.
Confirm what defines a signup cohort (e.g., daily, weekly) and the exact retention window (day 7 after signup). Ensure understanding that 'exactly the 7th calendar day' means the trade must occur on that specific date, not within a range.
Locate the users table (with signup dates) and trades table (with user IDs and trade timestamps). Determine how to join them and filter trades to the 7th day after signup.
Create a cohort of all users by signup date. For each user, check if they have at least one trade on the 7th day after signup. Use a LEFT JOIN to include users with no trades, and flag retention as 1 if they traded on day 7, else 0.
Group by cohort (signup date) and calculate the retention rate as the sum of retained users divided by the total number of users in the cohort. Ensure the denominator includes all users, even those who never traded.
Sanity-check the results (e.g., retention rates between 0 and 1). Consider edge cases like users who signed up less than 7 days ago (they should be excluded or handled appropriately). Present findings with clear labels and possibly a visualization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.