How to Connect EODHD APIs to Google Sheets (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of EODHD APIs

You have a spreadsheet full of tickers, ISINs, country codes, or fund names. You need historical prices, live quotes, macro indicators, or fundamentals loaded into it — or data already in the sheet pushed somewhere else — without rebuilding the pipeline from scratch every Tuesday.

EODHD is good at supplying institutional-grade financial data across global equities, fixed income, forex, and macro indicators. But bridging that data into your spreadsheet the way you actually want it — organized by ticker, split across tabs, with the right column headers — is where the friction starts. The default flow is to export from the EODHD dashboard or hit the API manually, then paste what you got into the right cells, then realize the column order is wrong, then fix that, then do it again next week.

Below are the four common ways teams handle this. Only the last one scales.

Method 1: Manual Copy-Paste

The default. Open the EODHD developer portal or data download interface, pull the export for each ticker or indicator, bring it into the sheet by hand.

For one or two tickers, this is fine. For a watchlist of 30 or 40, you are running the same export 40 times, renaming columns, aligning dates across tabs, and hoping nothing got offset when you pasted.

The part that grinds people down is the structure. EODHD returns data ticker-by-ticker. Your sheet wants one tab per ticker, properly date-aligned, with the same column order every time. Getting from one to the other is not analysis — it's assembly work. And it compounds the moment a new ticker gets added or a date range needs extending.

Method 2: Zapier or Make

Both platforms have EODHD connector options. You can wire up a schedule trigger, call the EODHD historical endpoint, and write the returned data into a specific sheet range.

Quick reality check before you go further — do you know what a REST connector is? API pagination? Dynamic field mapping across a JSON response array? If those terms require a second read, this path is not the right one for you. Skip ahead to Method 3 or 4.

If you're still here: yes, you can build a working automation. You set up the trigger cadence, configure the EODHD endpoint call with your API key, map the response fields to sheet columns, and handle the write.

The structural ceiling appears fast.

EODHD returns time-series arrays — potentially thousands of rows per ticker. Zapier and Make are built around one-record-at-a-time execution. Running 40 tickers through a Zap means 40 separate API calls, 40 task executions, and a run log that becomes impossible to audit when ticker 17 fails silently because the exchange code changed.

You probably just need the OHLCV data in the right tabs. You probably have no idea how to paginate a JSON array response into a spreadsheet write without writing custom JavaScript in a Code step. So you hand it to whoever on your team builds these automations — and now you're waiting on a Slack reply from someone who has four other things queued.

And once you need to join across tickers, calculate cross-tab averages, or do anything that requires the full dataset in memory at once, you've left what these tools can do for you.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best repeatable path was a category of add-ons that let you configure EODHD API calls as saved templates. You picked your endpoint, set the parameters, mapped the output columns, and re-ran it on demand.

That was a genuine improvement over manual exports. Configs were reusable. Output was consistent. You weren't reformatting every time.

But you still owned the endpoint logic. Which fields to request. How to split tickers across tabs. What to do when a field came back null. The column mapping schema. The add-on got the data through — but every decision about structure, organization, and handling was still yours to make. And if you renamed a column in your sheet, the config broke until someone went back and fixed it.

That was the previous generation. It handled the mechanical transfer. It didn't handle the thinking.

The Easy Way: Using SheetXAI in Google Sheets

There is a different way entirely. SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands what you are looking at, and through its built-in EODHD APIs integration it can pull prices, fundamentals, macro data, and identifiers for you. No endpoint configuration, no field mapping, no per-ticker iteration. You just ask.

Example 1: Pull historical prices for every ticker in the sheet

Pull 5 years of daily OHLCV data from EODHD for every ticker in column A of my 'Watchlist' sheet and create a separate tab for each ticker with date, open, high, low, close, and volume columns.

SheetXAI iterates across the list, calls EODHD once per ticker, creates the tabs, and writes the aligned time series. The column headers are consistent across every tab without any template setup.

Example 2: Fetch macro indicators and fill a comparison matrix

For each country code in column A of my 'Macro Data' sheet, pull GDP, inflation, and unemployment rate for the past 10 years from EODHD and write the annual values into columns B onward.

The pattern: instead of building an API call and then then reshaping the response, you describe the output you want and SheetXAI handles the rest — the request, the parsing, and the layout.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet containing tickers, ISINs, or country codes, then ask it to pull the data from EODHD. The EODHD APIs integration is included in every SheetXAI plan.