The Problem: Too Much Manual Work, Not Enough Automation
When you are running a small investment-focused startup, your analysts should be spending time on insights — not copy-pasting ETF prices into a spreadsheet every morning. That was exactly the situation I found myself in. Our team was using Google Finance as the primary source for ETF data — prices, volumes, performance metrics — and every day someone had to manually refresh and update our internal tracking file.
It was slow, error-prone, and completely unsustainable at scale. I knew the answer was an automated Excel workbook that could pull data directly from Google Finance without anyone touching it. The question was how to actually build it.
What I Tried First
I am comfortable in Excel. Formulas, pivot tables, conditional formatting — none of that intimidates me. So I started by exploring what was natively possible. I knew Google Sheets had a built-in GOOGLEFINANCE function, but our team's workflow was built entirely around Excel. Replatforming everything to Google Sheets was not an option.
I looked into Power Query as a way to pull in web data, and I got partway there. I could fetch some page content from Google Finance URLs, but the data was inconsistent, the structure kept shifting, and anything resembling real-time or scheduled refresh was far more complex than I had anticipated. I also started experimenting with VBA macros to automate the refresh cycle, but building a robust connection that handled multiple ETFs, custom filters, and clean data output was a different problem entirely.
After a few days of progress that kept unraveling, I had to be honest with myself: this was not just an Excel task. It was a data engineering problem wrapped inside a spreadsheet.
Handing It Off to People Who Had Done This Before
That is when I came across Helion360. I explained what I needed — an Excel workbook that would automatically fetch ETF data from Google Finance, support customizable ETF filters, display clean metrics, and refresh on a set schedule without requiring manual intervention. I also mentioned that VBA or any scripting layer was on the table if it helped.
Their team understood the brief immediately. They asked the right clarifying questions: how many ETFs did we need to track, what metrics mattered most, how often did we want the data to refresh, and did we need any visual summary layer on top of the raw data. Within a short turnaround, they came back with a working solution.
What the Final Workbook Looked Like
The delivered workbook was cleaner and more functional than anything I had managed to sketch out on my own. The Google Finance connection was handled through a combination of Power Query and a lightweight VBA layer that managed scheduled refresh triggers. The data — ETF prices, daily volumes, percentage changes, and a few other key metrics — populated automatically into a structured table every time the workbook opened or when the refresh was manually triggered.
Customizable filters let any team member select specific ETFs or categories from a dropdown, and the visible data would update accordingly. Formulas were documented inline so anyone maintaining the file later could understand the logic without needing to reverse-engineer anything. The whole thing felt production-ready rather than experimental.
What I Took Away From This
The experience reinforced something I already knew but sometimes ignore under deadline pressure: knowing the goal is not the same as knowing the path. I understood perfectly what the workbook needed to do. But bridging Google Finance data into a reliable, automated Excel environment required a specific combination of Power Query knowledge, VBA scripting, and data structure experience that I did not have at the depth the project demanded.
For a startup where analyst time is expensive and data reliability is non-negotiable, getting this right the first time was worth more than iterating through failed attempts on my own.
If you are trying to build something similar — an automated Excel workbook that pulls live financial data, handles ETF tracking, or streamlines any kind of repeating data workflow — Helion360 is worth reaching out to. They took a problem I had been circling for days and turned it into a working solution our team actually uses.