
If you live in Google Sheets already, it’s the perfect canvas for stock price tracking. Instead of hopping between broker dashboards and finance sites, you can centralize tickers, prices, volumes, and performance in one familiar grid. The GOOGLEFINANCE function streams current and historical data directly into your workbook, so you can backtest strategies, monitor watchlists, and build lightweight dashboards without touching an API. Add charts, conditional formatting, and filters, and you’ve turned a simple spreadsheet into a living market console your whole team understands.
But once you care about dozens of tickers, multiple portfolios, and daily reporting, manual upkeep breaks. This is where an AI agent shines: it can open Google Sheets, adjust GOOGLEFINANCE ranges, copy results into summary tabs, sanity‑check data, timestamp updates, and even notify you if prices cross thresholds. Delegating this routine stock price maintenance to an AI computer agent keeps the sheet fresh while you focus on decisions, not data hygiene.
You probably didn’t start your agency, fund, or side hustle dreaming of copy‑pasting tickers. Yet many of us still babysit stock prices in Google Sheets, nudging formulas, fixing ranges, and refreshing tabs.
Let’s walk through the main ways to manage stock prices in Sheets—from hands‑on to fully delegated to an AI computer agent—so you can choose the right level of automation.
This is where most people start: one sheet, a few tickers, and the GOOGLEFINANCE function.
NASDAQ:AAPL)=GOOGLEFINANCE(A2, "price")=GOOGLEFINANCE(A2, "high52")=GOOGLEFINANCE(A2, "low52")=GOOGLEFINANCE(A2, "close", TODAY()-30, TODAY(), "DAILY")=SPARKLINE(F2:F31) to see a mini price trend per ticker.When you’re tracking hundreds of tickers or need richer fundamentals, add‑ons like SheetsFinance can take you further.
=SF("AAPL")) to pull real‑time quotes, deep historical series, or ratios.At some point, the real bottleneck isn’t the formula—it’s you. You’re the one opening Sheets, copying templates for each client, updating tickers, checking for #REF! errors, and exporting PDFs.
This is exactly the kind of repetitive, click‑heavy work Simular’s AI computer agents are built to take over.
An agent running on Simular Pro can:
GOOGLEFINANCE or SheetsFinance formulas.GOOGLEFINANCE formulas.The moment you realize you’re spending more time maintaining the spreadsheet than interpreting the numbers, you’re ready to let an AI agent drive the workflow while you steer the strategy.
In Google Sheets, list your tickers in column A (e.g. NASDAQ:AAPL). In B2, use `=GOOGLEFINANCE(A2, "price")` and drag down for all rows. Add more attributes like `"volume"` or `"high52"` in other columns. Remember that data can be delayed up to 20 minutes and is for informational use, so always cross‑check before trading.
Use GOOGLEFINANCE with a start and end date. For example: `=GOOGLEFINANCE("NASDAQ:AAPL", "close", DATE(2024,1,1), DATE(2024,3,31), "DAILY")`. Place the formula in an empty cell with space below, because it returns an array of dates and prices. You can then chart that range or reference it from summary formulas like AVERAGE or MAX.
Create a table with ticker, quantity, and average buy price. Use `GOOGLEFINANCE` in a separate column to fetch the latest price, then calculate market value with `=quantity*price` and unrealized P&L with `=(price‑buy_price)*quantity`. Add a totals row with SUM, then insert charts and conditional formatting to highlight top winners and losers.
Standardize your sheet: fixed columns for ticker, price, and attributes. Use array formulas like `=ARRAYFORMULA(GOOGLEFINANCE(A2:A, "price"))` where possible. For larger workflows—multiple sheets or client files—delegate the update routine to a Simular AI agent so it opens each sheet, refreshes data, logs dates, and exports reports on a schedule.
Spot‑check a sample of tickers against a reliable source such as your broker or a major finance site. Confirm exchange prefixes (e.g. NASDAQ: vs NYSE:) are correct. For historical series, ensure date ranges and intervals match. You can even have a Simular AI agent pull reference quotes in a separate tab and flag discrepancies beyond a tolerance you define.