How to Connect Bitquery to Excel (4 Methods Compared)

The Problem With Getting Workbook Data In and Out of Bitquery

You have an Excel workbook full of token contract addresses, wallet IDs, or liquidity pool pairs. You need Bitquery's indexed blockchain data — DEX trades, block stats, transfer history, aggregated metrics — pulled into your workbook for analysis, or you need your workbook-defined parameters fed into Bitquery queries so you're not retyping them every session.

Bitquery is good at what it does: deep, cross-chain indexed blockchain data served through a GraphQL API. But there's a gap between "I have a list of 20 token addresses in column A" and "I have the last 7 days of trade data for all of them in a flat table." The default path is writing GraphQL queries by hand, running them via curl or the Bitquery IDE, downloading the JSON, and importing it into Excel. Then doing it again next week.

Below are the four ways teams handle this. Only the last one doesn't require a developer.

Method 1: Manual Export and Import

The baseline for Excel users is slightly different — CSV exports more often than raw copy-paste. You run a Bitquery query in the IDE, download the result as JSON or CSV, open it in Excel, reformat the columns to match your workbook's schema, and paste into the right range.

With 20 token addresses, that's 20 query runs, 20 exports, 20 paste jobs. And every time the week closes and you need fresh numbers, you start from scratch.

The specific friction with Bitquery is that its output is nested — transaction arrays inside block arrays inside chain objects. Flattening that into Excel rows is its own task before analysis can begin. After the second or third weekly refresh, the data-fetching work starts taking longer than the analysis it's supposed to support.

Method 2: Power Automate

Power Automate has HTTP action support, which means you can POST to Bitquery's GraphQL endpoint on a schedule, receive the JSON response, and write values into Excel cells using the Excel Online connector.

Before going further: are you comfortable building HTTP actions in Power Automate? Have you parsed nested JSON using expressions like body('HTTP')?['data']?['EVM']?['DEXTrades']? Have you used an Apply to Each loop to write multiple rows? If those aren't familiar operations, this route will be frustrating — skip to Method 3 or 4.

Still here? The flow works. You set a recurrence trigger. You POST to the Bitquery endpoint with your GraphQL query hardcoded (or, if you're ambitious, loaded from a named cell). You iterate the results. You map fields to columns. You update the range.

But one row at a time through a loop is not a batch pull.

Twenty contract addresses means twenty iterations, twenty separate API calls worth of quota, and an error log where row 17's empty result looks identical to a successful zero-value response.

You probably just need the trading volume numbers. You probably haven't built a Power Automate flow before — and shouldn't have to in order to analyze blockchain data. So you ask whoever on your team maintains the Power Platform setup, and now you're in a meeting waiting for them to explain why the JSON expression isn't resolving.

Even when it's running, the flow only handles what it was built for. Adding a new filter — say, only trades above $100,000 — means editing the flow, not just changing a cell.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the most repeatable option for pulling blockchain API data into a workbook was a category of add-ins that let you save query configurations — endpoint URL, request body, output range — and re-execute them on demand.

That was a real improvement over manual exports. Configs were reusable. Output columns were stable. You didn't have to reformat every session.

But you were still writing the GraphQL query. You were still managing the field mapping. You were still deciding which response fields to include. The add-in moved the data through, but every analytical decision about what to ask and how to shape the output was still yours. And the moment the Bitquery schema updated or you wanted a new metric, the config needed to be updated by whoever built it.

This is the previous generation. It reduced the repetition without removing the complexity.

The Easy Way: Using SheetXAI in Excel

There is a different approach. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook — your token list, your wallet addresses, your pool pairs — and through its built-in Bitquery integration, it queries the API and writes the results back into your workbook for you. No query construction, no field mapping, no JSON parsing. You describe what you need.

Example 1: Pull DEX trade data for a token watchlist

For each token contract address in column A, query Bitquery for the last 7 days of Ethereum DEX trades and write total volume in USD, average price, and trade count into columns B, C, and D

Bitquery runs the query for each contract address, aggregates across the 7-day window, and writes three values per row. Addresses that returned no trades get a zero rather than a blank — so your formulas don't break.

Example 2: Pull large token transfers from the archive

Using the Bitquery archive database, pull all Ethereum token transfers over $1,000,000 in the last 24 hours and populate this sheet with sender, receiver, token symbol, amount, and transaction hash

Each qualifying transfer lands as its own row. No export step, no JSON to parse and flatten.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with a list of token addresses or wallet IDs, then ask it to pull the Bitquery data you need. The Bitquery integration is included in every SheetXAI plan.