I’m a junior analyst on Wall Street and I’m sick of manual data entry. So I’m building a tool to automate it.

I’ve been in Equity Research for about two years now and the amount of time spent manually updating models is honestly wild.  If you’ve ever wondered what it’s like to work in this field, it’s 80% being a glorified data entry clerk and 20% talking to people. Every time a 10-K drops or the Fed breathes, I’m stuck copy-pasting numbers into Excel models all day. How is it 2026 and we’re still doing this? I got fed up and started building a side project to automate the grunt work for me. I also figured this would be a great tool for this subreddit so people won’t have to waste as much time as I did, rather than making something for the guys with $20k Bloomberg terminals. So to sum it up, my **goal is to create a tool that handles the heavy lifting:** * **Automated Data Scrape:**  An automated function that scrapes the Income Statement, Balance Sheet, and Cash Flow directly from filings. * **Living DCF:** A fair value estimator that updates daily based on news and sentiment (instead of just sitting in a static spreadsheet gathering dust). * **The Macro Overlay:**  A dynamic comparison engine that benchmarks tickers against peers and factors in macro data (rates/inflation) so you can see if a stock is actually "cheap" or just moving with the sector. To clarify, I’m not selling anything or pitching a "system”, I just want to build something that makes the research process less soul-crushing but also want to make sure its something people would actually use. Would appreciate any perspectives on:  * What’s the most tedious part of visualizing metrics or keeping your fair value estimates updated?  * Is there a specific calculation or data point that takes you forever to adjust whenever news drops? Please let me know what would actually make your research process suck less by filling out this Google form [here](https://forms.gle/HQVV146HFqL13twB8)