About Trapeza Labs

That realization led to new challenges: how to recognize a truly good company, and perhaps even harder, how to tell when its shares are selling at a fair or undervalued price. Being an engineer at heart, I approached these questions systematically. I began studying what guided Buffett’s investment decisions and quickly found that there isn’t a precise, universally agreed‑upon formula. Books written by people close to Buffett, including his former daughter‑in‑law, shed some light, as did the works of Philip Fisher and others on financial fundamentals. Over time, I developed my own sense of which metrics matter most — the ones that give me peace of mind when investing. I came to believe that holding a portfolio of seven to fifteen companies that meet these quality thresholds offers a balanced chance of earning good returns without risking capital, as long as I stay patient and invest for the long term.

Still, even with a clear framework, the question of valuation remained. How do you know when a great company’s stock is priced low enough to buy? I explored various methods — price ratios, discounted cash flow, owner earnings — and found that even Buffett doesn’t rely on a single deterministic approach. What most value investors share, though, is a strong intuition about the Price‑to‑Earnings (P/E) ratio. It’s simple, accessible, and meaningful — provided the company has a solid history of earnings and free cash flow. I decided early on not to invest in new companies without that track record, no matter how compelling their story. A low P/E ratio became one of my key indicators of a margin of safety, complemented by other measures like P/FCF and P/S, though with less emphasis.

Once I had my principles in place, I assumed there must be tools to help apply them — but I soon discovered there weren’t. Most stock screeners are binary: a company either passes or fails a threshold. There’s no room for nuance, no way to weigh trade‑offs between metrics. My mathematical intuition told me there had to be a better way — one that could reflect the quality of a company more delicately. I realized that safety, and therefore peace of mind, comes from investing in companies that score well across multiple fundamentals, but that the scoring should be multiplicative, not additive. This way, a company with just a few mediocre metrics can still do well overall, but truly poor ones can’t be masked by strong results elsewhere. It ensures that companies with consistently good fundamentals stand out. I also wanted the score to be independent of market value — focused purely on business quality. That’s how the idea of the Trapeza Labs QScore was born.

To bring this idea to life, I turned to what I know best — software engineering. I experimented with AI and large language models but found their results too inconsistent and sometimes flawed (yes, the infamous hallucinations). So I built my own framework — one that collects reliable financial data directly from the SEC’s EDGAR database, calculates key metrics and statistics, and then uses an expert system I programmed to compute a final QScore for each company. I set the scope to include 900 publicly traded U.S. companies — those listed in the S&P 500 and Midcap 400 — and added valuation data to help assess whether each company’s stock was fairly priced.

Over time, this project grew more sophisticated and polished. Eventually, I realized it was too valuable to keep to myself. So I decided to share it with a wider community of thoughtful investors — people who, like me, want to invest intelligently and sleep well at night.

I hope you enjoy exploring the Trapeza Labs Dashboard and find it genuinely useful. Just remember — it’s an informational tool, not a buy‑or‑sell recommendation. Use it as your starting point, not your finish line. And if you have thoughts, ideas, or feedback, I’d love to hear from you. You can reach me at ronny.weisman@trapezalabs.io or connect with me on LinkedIn.