How to scrape finance market data from Zerodha using the Minexa.ai extension

Zerodha publishes sector-level stock listings that are genuinely useful for anyone tracking Indian equity markets by industry. The agriculture sector page, for example, lists every company in that category with its market capitalization and current trading price. The data is public, it updates regularly, and it covers dozens of companies per sector. The challenge is that it sits inside a web page with no export button and no API. Getting it into a spreadsheet the manual way means copying row by row, which stops being practical the moment you want more than one sector or want to repeat the exercise next week.

This guide walks through how to extract that data using Minexa.ai, a Chrome extension that detects the structure of any web page and exports the data to Excel, Google Sheets, or JSON without any code.

https://www.youtube.com/watch?v=_OxurlRDGMA

The video above covers the full workflow from start to export. The sections below walk through each stage in detail with screenshots.

What you end up with

Before going through the steps, it helps to know what the output looks like. Here is a sample of the structured data Minexa.ai extracts from the Zerodha agriculture sector page:

[
  {
    "company_name": "Bombay Burmah Trdg.",
    "numeric_value": "10,813",
    "price": "8.7",
    "stock_link": "/markets/stocks/NSE/BBTC/"
  },
  {
    "company_name": "Kaveri Seed",
    "numeric_value": "4,688",
    "price": "15.85",
    "stock_link": "/markets/stocks/NSE/KSCL/"
  },
  {
    "company_name": "Jubilant Agri & Con.",
    "numeric_value": "3,198",
    "price": "25.01",
    "stock_link": "/markets/stocks/NSE/JUBLCPL/"
  }
]

Each row is one company. The numeric_value field holds the market capitalization figure, which lets you sort or filter companies by size within the sector without any extra calculation. The price field holds the current trading price as a string. When no live quote is available for a stock, this field returns a double dash rather than a blank or a zero, so downstream logic can distinguish between a stock with no active price and one that simply has not loaded yet. The stock_link field returns a relative path that embeds both the exchange (NSE or BSE) and the ticker symbol, giving you everything needed to construct a direct link to each company's detail page on Zerodha.

You have the page open and Minexa.ai ready

Start by navigating to zerodha.com/markets/sector/agriculture/ in Chrome. Then open the Minexa.ai extension from your browser toolbar.

The extension popup appears with a button confirming you are on the right page. Clicking it tells Minexa.ai to begin analyzing the page structure. This is the only manual confirmation step before detection runs automatically.

Pagination is detected and you decide how far to go

Minexa.ai scans the page and identifies any pagination present. For the Zerodha sector page, it detects the navigation controls and lists them so you can confirm before continuing. You do not need to configure anything here.

After confirming pagination, you choose whether to extract only the list data or to also follow each stock link and pull information from the individual detail pages. For most sector-level analysis tasks, the list data alone covers what you need.

The scraping mode is selected and the container is confirmed

Next you choose between simple and advanced scraping mode. Simple mode handles the majority of cases and is the right choice here. Minexa.ai then highlights the repeating data container it identified on the page automatically.

The highlighted container shows exactly which section of the page Minexa.ai has locked onto. This is the repeating block that contains each company row. Confirming it takes one click.

All data points are extracted and ready to review

Once the container is confirmed, Minexa.ai extracts all data points it finds within each row. You can see a preview of the structured output before running the full job, including the company name, market cap figure, price, and stock link for each entry.

At this stage you also see the code samples Minexa.ai generates if you want to use the data programmatically later. Clicking 'Complete Configuration' finalizes the scraper setup.

The job is configured and you can schedule it

After configuration, you reach the job summary screen. This is where you can connect Google Sheets for automatic export or set a recurring schedule so the job runs without manual triggering.

Scheduling is particularly useful for sector data that changes over time. Setting a daily or weekly run means you build a historical record of how market caps and prices shift across the agriculture sector without doing anything manually after this point.

Get started with Minexa.ai

The job runs and the data is ready to export

From the jobs list, you trigger the run with one click. The extraction starts immediately and you can watch rows populate in real time.

Once the job finishes, the full dataset is available. You can export it to Excel, download it as JSON, or have it pushed directly to a Google Sheet.

What this makes possible

With the data in a spreadsheet, you can sort the agriculture sector by market cap to identify the largest players, filter out stocks with no active price quote, or build a simple tracker that flags when a company's price crosses a threshold. Running the same scraper across multiple Zerodha sector pages gives you a cross-sector dataset that would take hours to assemble manually.

The scraper configuration is saved after the first run. Every subsequent run on the same page type starts almost instantly without repeating any setup steps.

If you work across multiple sectors or want to track changes over time, the Minexa.ai Chrome extension handles the full workflow from detection through export in a single session.

For more on extracting financial and market data from structured web sources, see this related guide: Scraping tax and accounting data from IDX using the Minexa API.