I'll take a deep breath and approach this carefully. To find the average free cash flow of the top 10 e-commerce stocks by market cap, we need to:
1. Generate a subquery to identify the top 10 e-commerce stocks by market cap. This involves joining the `stockindustries` and `price_data` tables to filter for e-commerce companies and get their most recent market cap.
2. Join the result of step 1 with the `earnings` table to get the most recent free cash flow data for these companies.
3. Calculate the average free cash flow for these top 10 e-commerce stocks, while also showing individual stock data.
4. Ensure we're using the most recent data available for both market cap and free cash flow.
5. Filter for e-commerce stocks using the `ecommerce` field in the `stockindustries` table.
6. Use appropriate aggregation functions (MAX for market cap and date, MAX for free cash flow to get the most recent value) to avoid errors related to non-aggregated columns.
7. Group by symbol in subqueries to ensure we're getting one entry per company and avoiding duplicates.
8. Include stocks with negative free cash flow in the results and calculation to provide a comprehensive view.
9. Limit the inner query to 10 results to get only the top 10 e-commerce stocks by market cap.
10. Include all relevant fields: symbol, name, market cap, free cash flow, and dates in the results for transparency.
Now I will generate the query:
This query does the following:
1. Identifies the top 10 e-commerce stocks by market cap using the most recent data.
2. Retrieves the most recent free cash flow data for these 10 stocks.
3. Calculates the average free cash flow across all 10 stocks.
4. Shows individual stock data including symbol, name, market cap, free cash flow, and relevant dates.
5. Includes stocks with negative free cash flow in both the results and the average calculation.
6. Uses appropriate grouping and aggregation to avoid errors with non-aggregated columns.
7. Joins tables efficiently to get all required data.
8. Orders the results by market cap descending to show the top e-commerce stocks first.
9. Doesn't exclude any stocks based on free cash flow value, allowing for negative values.
10. Uses window functions (AVG OVER ()) to calculate the average while still showing individual stock data.
This approach gives a comprehensive view of the top 10 e-commerce stocks by market cap, their individual free cash flow figures, and the average free cash flow across all of them, including those with negative values.
{
  "sql": "WITH top_ecommerce AS (SELECT si.symbol, si.name, MAX(pd.marketCap) AS marketCap, MAX(pd.date) AS marketcap_date FROM `my_project.my_dataset.stockindustries` si JOIN `my_project.my_dataset.price_data` pd ON si.symbol = pd.symbol WHERE si.ecommerce = TRUE GROUP BY si.symbol, si.name ORDER BY marketCap DESC LIMIT 10), recent_fcf AS (SELECT e.symbol, MAX(e.date) AS fcf_date, MAX(e.freeCashFlow) AS freeCashFlow FROM `my_project.my_dataset.earnings` e JOIN top_ecommerce te ON e.symbol = te.symbol GROUP BY e.symbol) SELECT te.symbol, te.name, te.marketCap, te.marketcap_date, rf.freeCashFlow, rf.fcf_date, AVG(rf.freeCashFlow) OVER () AS avg_free_cash_flow FROM top_ecommerce te JOIN recent_fcf rf ON te.symbol = rf.symbol ORDER BY te.marketCap DESC"
}