I'll take a deep breath and approach this carefully. To find the 10 companies with a market cap of $100 billion or more that had the lowest price to free cash flow ratio on June 16th, 2021, we need to:
1. Generate a subquery to get the most recent price data on or before June 16th, 2021, for each company. This ensures we have data even if June 16th was a non-trading day.
2. Filter for companies with a market cap of $100 billion or more.
3. Join the `price_data` table with the `stockindustries` table to get company names.
4. Use the price to free cash flow ratio (TTM) from the price_data table, as it's already calculated for us.
5. Order the results by the price to free cash flow ratio in ascending order to get the lowest ratios.
6. Limit the results to 10 companies.
7. Include all relevant fields: symbol, name, date, market cap, and price to free cash flow ratio.
8. Add a filter to exclude any potential division by zero errors or null values in the price to free cash flow ratio.
9. Group by symbol to ensure we only get one entry per company.
10. Use appropriate timestamp operations to handle the date filtering.
Now I will generate the query:
This query:
1. Uses a subquery (latest_data) to get the most recent date on or before June 16th, 2021, for each symbol.
2. Joins this subquery with the price_data table to get the most recent data for each company.
3. Joins with the stockindustries table to include company names.
4. Filters for companies with a market cap of $100 billion or more.
5. Excludes companies with negative or zero price to free cash flow ratios to avoid potential errors.
6. Orders the results by the price to free cash flow ratio in ascending order.
7. Limits the results to 10 companies.
8. Includes all relevant fields: symbol, name, date, market cap, and price to free cash flow ratio.
9. Uses appropriate timestamp operations (TIMESTAMP('2021-06-16')) for date filtering.
10. Groups by symbol implicitly through the MAX(date) in the subquery, ensuring one entry per company.
This approach avoids common errors like division by zero, ensures we get data for the correct date even if it's a non-trading day, and provides a clear, efficient query to meet the user's requirements.
{
  "sql": "WITH latest_data AS (SELECT symbol, MAX(date) AS max_date FROM `my_project.my_dataset.price_data` WHERE date <= TIMESTAMP('2021-06-16') GROUP BY symbol) SELECT pd.symbol, si.name, pd.date, pd.marketCap, pd.priceToFreeCashFlowTTM FROM `my_project.my_dataset.price_data` pd JOIN latest_data ld ON pd.symbol = ld.symbol AND pd.date = ld.max_date JOIN `my_project.my_dataset.stockindustries` si ON pd.symbol = si.symbol WHERE pd.marketCap >= 100000000000 AND pd.priceToFreeCashFlowTTM > 0 ORDER BY pd.priceToFreeCashFlowTTM ASC LIMIT 10"
}