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Data-driven stock research

Find stocks that beat
the S&P 500

A quantitative stock screener built on 10 metrics covering valuation, growth, profitability, momentum, and risk. Screen first. Research second. Invest with conviction.

14
Stocks tracked
10
Metrics scored
Live
Real-time prices
Free
No sign-up needed
Finding a promising stock is easy. Finding a promising stock systematically is much harder. My screener is built around one idea: use data to narrow the universe of stocks before spending time on deeper research.
Screen first. Research second. Invest with conviction only after understanding what you own.
01 · Quantitative
Start with the numbers
Revenue growth, earnings growth, margins, ROE, P/E, debt, free cash flow, market cap, and price momentum. Each tells a different part of the story.
02 · Quality filter
Growth + quality together
High growth alone isn't enough. The screener checks whether that growth is profitable, cash-generating, and priced reasonably by the market.
03 · Benchmarked
Compare against the S&P 500
Every stock is evaluated against the S&P 500 as a baseline. The goal is to find companies with characteristics that genuinely differentiate them from the index.
04 · Scored
One composite score
Fundamentals + Growth + Valuation + Market Data → a single 0–100 score. Companies that score well across multiple dimensions rise to the top.
05 · Scalable
Built with automation
Using Python and live market data, the system processes information across many companies simultaneously. It is impossible to do manually at scale.
06 · A starting point
Screening ≠ investing
The screener answers "which companies deserve a closer look?" Fundamental research including competitive advantages, management, industry dynamics, answers everything else.

Not financial advice · Real-time data via Finnhub · Built by Ishan Singh

Filters

Min composite score 60
Max P/E ratio 80
Min market cap ($T) 0.0
Beta max 3.0
Sectors
Preset screens
Beats S&P 500
Value picks (P/E < 25)
Low volatility (β < 1)
High growth (YTD > 20%)
results
Company Price Today YTD vs S&P P/E Score
⬡ AI analysis
Select a stock to generate analysis.

Market Heatmap

Today's price movement. Deeper green = bigger gain.

Market news

Curated feed with sentiment scoring

Your watchlist

Star any stock from the screener

Portfolio simulator

See how a hypothetical investment would have grown

Configure your portfolio

Starting $
Stock
Allocation%
$10,000
Estimated value today (based on YTD return)
$10,000
If you'd invested in SPY instead

Performance chart

Learn the metrics

Plain-English explanations of every number in the screener

Methodology

I built a stock screener going into sophomore year and had no idea what I was doing

Ishan Singh · Rising Sophomore, SCU · 6 min read

So I go to Santa Clara University, I'm studying finance, and I'm literally surrounded by this stuff every day. Silicon Valley is right there. I commute in and I'm passing by VC firms and semiconductor companies and tech campuses on the way to class. Everyone around me is talking about markets, startups, valuations. And the whole time I keep thinking the same thing: how do you actually find a stock worth buying?

Not theoretically. Like actually, in practice, how do you do it?

I couldn't find a clean answer anywhere so I just started building something. This is that thing.

Why I even needed a screener

There are like 8,000 publicly traded companies in the US alone. Even if you cut it down to just the S&P 500 you still have 500 companies to look at. I have classes, I have an internship, I'm commuting every day. I don't have time to go through 500 annual reports and cross-reference every earnings call by hand.

A screener basically does the first pass for you. You set your criteria, it filters everything down to a short list of companies that actually look interesting, and then you go do the real research on those. It's not doing the investing for you. It's just saving you from starting at square one every time.

My finance classes at SCU are solid but they keep everything pretty neat. The models work out cleanly, the cases have answers, the professor walks you through what to look at. Real markets are not like that at all. There are a hundred signals pointing in different directions and nobody tells you which one to trust. I wanted to build something that forced me to figure that out myself instead of just following a rubric.

The ten metrics and why I picked them

I didn't just randomly pick ten numbers and call it a screener. Each one is trying to answer something specific.

Revenue growth tells you if the business is actually getting bigger. EPS growth tells you if it's getting more profitable, not just bigger, which matters a lot more than people think. The P/E ratio is the classic valuation check, how much are you paying per dollar of earnings. The PEG ratio is the P/E adjusted for growth rate, Peter Lynch was big on this one. Free cash flow is honestly my favorite metric on here because it's the hardest one to fake. Return on equity shows how well management is actually using the money shareholders gave them. Debt to equity is just a gut check on how much risk is sitting on the balance sheet. Beta tells you how volatile the stock is compared to the broader market. Relative strength looks at recent price momentum. And dividend yield is what you actually get paid just for holding it.

All of these get combined into one score out of 100. Is it perfect? No. But it's a real starting point and it's way better than just going off whatever's trending on X or Reddit.

I had literally zero coding experience

When I started at SCU I had done some Excel. That was it. No Python, no JavaScript, nothing. Coding was not something I thought I'd ever really need for a finance career honestly.

This summer I've been interning at Achronix Semiconductor in Santa Clara. It's a chip company working on FPGA technology and edge AI. The work is pretty technical. I've been writing Python scripts, building data visualizations, working in Linux, doing market research on where the company sits competitively in the semiconductor space. Coming in with basically no technical background it was a steep learning curve.

But that's also where this screener came from. I started actually being able to build things instead of just analyzing things in spreadsheets. I connected a live market data API, built out the filtering and scoring logic, got it on my own domain. Real prices load automatically when you open the site, nobody visiting has to sign up for anything or enter any keys. A few months ago I wouldn't have had any idea how to do any of that.

Please don't use this to make actual trades

Seriously. A high score on this screener does not mean the stock is going up. A low score does not mean it's going down. This is not financial advice. I'm 19.

What this actually does is narrow the field. Instead of staring at thousands of tickers you get a short list of companies that look interesting across multiple metrics at once. From there you still have to go read the actual filings, understand what the business does, figure out if the growth makes sense, check what analysts are saying. The score gets you to the right neighborhood. You still have to look around yourself.

I think a lot of people see a tool like this and think it replaces the thinking. It really doesn't. It just makes the thinking start from a better place.

What building this actually taught me

Finance classes are great but they're clean. The numbers work out. The professor is there if you get stuck. There's a right answer at the end.

Building this forced me to make real calls with no rubric. Does a high P/E make this stock overvalued or is it justified by the growth rate? How much should beta matter for a long-term hold versus a short-term trade? What even counts as strong free cash flow for a tech company versus an industrial one? Those are the questions that actually matter and you don't really learn how to answer them until you're forced to commit to something.

I want to go into commercial banking after school. And honestly the thing I've realized is that the judgment calls, the ones that don't have textbook answers, are basically the whole job. Building something like this is as close as I can get to practicing that right now.

Anyway, go try the screener. Click over to the Screener tab, filter by whatever you care about, and see what comes up. If something seems off or wrong I genuinely want to hear it.

Screen first. Research second. Only invest in what you actually understand.