Someone at work asked me if I knew how to spot companies that eventually hit $300-500M valuations. The honest answer? Nobody really knows. But that got my brain spinning — what if we could automate the process of finding these early-stage gems?
I spent the last couple days building what I'm calling a "discovery intelligence platform." Sounds fancy, but it's really just a systematic way to screen for companies that might blow up later.
Here's what clicked for me: I've been jumping straight into code on every project, which always turns messy. This time I forced myself to blueprint everything first. Like actual architects do. You don't start laying foundation and figure out the bathroom later, right?
So I mapped out the entire pipeline before writing a single line. Universe screening, Edgar filings, Finra data, trend analysis, liquidity checks — the whole thing. Even built in validation tests and backtesting notebooks.
Today was all execution. Created about 15 files in roughly 4 hours. The pipeline pulls data from multiple sources, runs companies through disqualifier filters, scores them, and spits out ranked results. I even added a supply/demand module because market dynamics matter more than most people think.
The interesting part was setting up my AI rules upfront. Token efficiency became rule #1 because I'm not paying for more tokens. I told Claude to think like a quantitative trading engineer and system architect, and honestly? It takes those roles seriously. The code structure is cleaner than anything I've built before.
I'm testing it against historical data now to see if the scoring actually predicts anything useful. The backtest notebook should tell me if I'm onto something or completely wrong.
We'll see if this thing can actually spot the next breakout companies. At minimum, I've got a reusable framework for future projects. The blueprint approach is definitely sticking around.