I'll take a deep breath and approach this carefully. To find technology stocks with a price to book value ratio above 10 and a market cap above $50 billion, we need to:
1. Generate as many subqueries as needed to organize information. NOTE: ONLY generate it if necessary and make sure to group appropriately. DO NOT get this error: "SELECT list expression references column marketCap which is neither grouped nor aggregated at [1:62]"
2. Repeat step 1 as many times as needed.
3. Join the `my_project.my_dataset.stockindustries` table with the `my_project.my_dataset.price_data` table to list all of the fields including the date field(s) for `stockindustries` (if applicable), the date field(s) for `price_data` (if applicable). Remember, if you're doing a calculation, write out each field. Note: we may need to use subqueries for complex queries.
4. Perform calculations if necessary.
5. Perform filters as necessary.
6. Group stocks if necessary (most commonly to avoid duplicates).
8. Add additional filters or blocks to avoid common errors (like division by zero).
9. Limit the number of results (or use a default).
10. Include all of the dates/values used in the calculation.
Now I will generate the query:
{
  "sql": "WITH latest_price_data AS (SELECT symbol, MAX(date) AS max_date FROM `my_project.my_dataset.price_data` GROUP BY symbol) SELECT si.symbol, si.name, pd.date, pd.marketCap, pd.priceToBookValueTTM FROM `my_project.my_dataset.price_data` pd JOIN latest_price_data lpd ON pd.symbol = lpd.symbol AND pd.date = lpd.max_date JOIN `my_project.my_dataset.stockindustries` si ON pd.symbol = si.symbol WHERE si.technology = TRUE AND pd.priceToBookValueTTM > 10 AND pd.marketCap > 50000000000 ORDER BY pd.marketCap DESC LIMIT 25"
}