I'll take a deep breath and approach this carefully. To find the 5 companies with a market cap above $200 billion that have the highest earnings per share (EPS), 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.price_data` and `my_project.my_dataset.earnings` tables to list all of the fields including the date field(s) for `price_data` (if applicable), the date field(s) for `earnings` (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_market_cap AS (SELECT symbol, MAX(date) AS max_date, MAX(marketCap) AS marketCap FROM `my_project.my_dataset.price_data` WHERE marketCap > 200000000000 GROUP BY symbol), latest_eps AS (SELECT symbol, MAX(date) AS max_date, MAX(earningsPerShareBasic) AS earningsPerShareBasic FROM `my_project.my_dataset.earnings` GROUP BY symbol) SELECT lm.symbol, si.name, lm.marketCap, le.earningsPerShareBasic, lm.max_date AS marketcap_date, le.max_date AS eps_date FROM latest_market_cap lm JOIN latest_eps le ON lm.symbol = le.symbol JOIN `my_project.my_dataset.stockindustries` si ON lm.symbol = si.symbol ORDER BY le.earningsPerShareBasic DESC LIMIT 5"
}