The Scenario
Your quant model needs P/E, EV/EBITDA, revenue, net income, and total assets for 50 S&P 500 stocks. You built the scoring model last week. The sheet is waiting. The only thing missing is the data in columns B through F.
The bad version:
- Navigate to the Nasdaq Data Link SHARADAR/SF1 documentation, confirm which dimension value gives you the most recent annual filing (it's
ARY), note that down. - Write a Python script to loop through 50 tickers, make 50 API calls with the right datatable filter, parse the JSON, and write each row to the sheet via the Sheets API.
- Debug the EV/EBITDA field name, which turns out to be
evebitdanotev_ebitda, re-run, realize two tickers return multiple rows because they changed fiscal year end, and add deduplication logic.
The model is already built. The scoring weights are already calibrated. Spending three hours on ingestion plumbing is not part of the original time estimate.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Google Sheet. It reads your ticker list, queries SHARADAR/SF1 on Nasdaq Data Link for the most recent annual filing per ticker, and writes the fundamental metrics directly into the specified columns.
For each ticker in column A, query the SHARADAR/SF1 datatable on Nasdaq Data Link for the most recent annual filing and write revenue, net income, total assets, P/E ratio, and EV/EBITDA into columns B through F
What You Get
- Columns B–F populated with a header row: Revenue, Net Income, Total Assets, P/E Ratio, EV/EBITDA
- One row per ticker using the most recent ARY (annual restated) dimension from SHARADAR/SF1
- Revenue and total assets in USD millions as reported in SHARADAR; P/E and EV/EBITDA as decimals
- Any ticker with no SHARADAR coverage (recent IPO, foreign issuer not in the dataset) flagged explicitly rather than left blank
What If the Data Is Not Quite Ready
Some tickers have restated their financials and you want the latest restated figures, not the original filing
SHARADAR/SF1 has multiple dimension values. You want ARY (annual restated), not ART (trailing twelve months) or ARQ (quarterly).
For each ticker in column A, query SHARADAR/SF1 on Nasdaq Data Link using the ARY dimension and pull the most recent row — write revenue, net income, total assets, P/E ratio, and EV/EBITDA into columns B through F
You also need operating income and total debt to compute leverage ratios
Your model has expanded. Two more columns.
For each ticker in column A, query SHARADAR/SF1 on Nasdaq Data Link for the most recent annual filing and write revenue, net income, operating income, total assets, total debt, P/E ratio, and EV/EBITDA into columns B through H
You want to filter the pull to only tickers where market cap is above $5 billion
The small-cap names aren't relevant to this version of the model.
For each ticker in column A, query SHARADAR/SF1 for the most recent annual filing — only write data for tickers where market cap is above $5 billion; for the rest, leave the row blank and note "Below market cap threshold" in column B
You want fundamentals plus a quick-score calculated in one shot
For each ticker in column A, pull revenue, net income, total assets, P/E ratio, and EV/EBITDA from SHARADAR/SF1 into columns B through F — then add column G with a simple value score: "Cheap" if P/E is below 15 and EV/EBITDA is below 10, "Fair" if either is within 20% of those thresholds, and "Expensive" otherwise
Score the universe and pull the data in one prompt. The label lands in column G, ready for the sort.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with a column of S&P 500 tickers, then ask it to pull SHARADAR/SF1 fundamentals from Nasdaq Data Link for your entire scoring universe in one pass. You can also explore the spoke on enriching tickers with SHARADAR metadata or the Nasdaq overview.