Stock Screener

August 15, 2026 · 8 min read

You typed stock screener because you want stocks to trade. That is the right search. A screener’s job is simple: take a huge market and spit out a list you can actually work. Quant-Builder.ai does that same job. It just does not do it with a row of filters you guessed last Tuesday.

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What a Stock Screener Actually Is

A stock screener is a filter. You say RSI under 30, PE under 20, volume over a million, price above the 50-day. Everything that passes shows up. Everything that fails disappears. The names that pass are treated like they are equal. The screener does not know if that combo ever made money. It only knows the stock cleared your cuts today.

That is why people live in screeners and still feel lost. You can change the filters forever. You still have a flat list and no idea which name is the actual trade.

Same Job — Different Engine

Quant-Builder is the same job: start with thousands of stocks, end with a shortlist for today. You still decide what to take. You still size it. You still skip names you do not want.

The difference is the engine. You can still use your indicators — RSI, moving averages, volume, valuation, momentum, the stuff you already think in. You just do not turn them into hard yes/no gates. You put them into a model. The model looks at years of those same numbers and learns which mixes actually lined up with the move you care about. Then every morning it ranks the market. High confidence at the top. That is your list.

Before you trust that list with real money, you can run it on past market days and see how the idea behaved — wins, losses, rough patches — instead of hoping today’s filter combo is lucky. That is the quiet advantage over a normal screener: you are not only finding names, you are checking whether the method held up.

A screener asks: “Did it pass my rule?” Quant-Builder asks: “How much does this look like the setups that worked?” That is why it is better. It is still a screener in the only sense that matters — it finds stocks to trade.

The Threshold Problem Nobody Talks About

Every filter you set has an edge, and the edge is arbitrary. RSI under 30 keeps a stock at 29.8 and throws away the one at 30.2. Those two stocks are the same stock. Your screener treats one as a candidate and the other as if it does not exist.

Now stack five filters like that. Each one has its own arbitrary cliff, and a name only survives if it clears all five. You are not describing a setup anymore. You are describing a coincidence — and the tighter you make the filters, the fewer names come back, which feels like precision but is really just a smaller accident.

A model does not have cliffs. RSI at 29.8 and 30.2 contribute almost identically, because the model learned how much that number mattered instead of being told where to cut it.

Two Traders, Same Screener, Different Results

Hand the same screener and the same filters to two people and they will make different money. The list came back with 40 names. One trader took the first four alphabetically. The other took the four with the nicest-looking charts. Nothing in the screener said which four to take.

That is the real gap. The screener did the easy part — cutting 3,000 names to 40 — and then quit right before the part that decides your return. Ranking is not a nicer way to display the list. It is the missing half of the job.

Where Each Kind of Screener Stops

Broker screeners — free with the account, a handful of filters, no history, no way to check whether the filter combination ever worked
Charting screeners — TradingView, Finviz and similar. Far more filters, good visuals, still an unordered list of whatever passed today
Premium scanners — faster, real-time, more exotic conditions. Same architecture: your guessed rules, applied to today, output unranked
A trained model — learns from years of outcomes which combinations preceded the move, then ranks tomorrow's candidates by how strongly each one resembles those setups

The first three differ in polish and speed. Only the last one changes what the output means.

You Keep Your Indicators

This is the part people assume they have to give up. You do not. RSI, moving averages, MACD, volume, momentum, valuation ratios, earnings growth, sector behaviour — all of it stays. Those are exactly the inputs the model reads.

What changes is their job. In a screener each indicator is a gate that admits or rejects. In a model each one is evidence, weighted by how much it actually mattered historically. And afterwards you can look at which inputs carried the most weight, so you find out whether the indicator you have trusted for years is really doing anything.

Most people discover at least one favourite filter that contributes nothing. That alone is worth the exercise.

Checking the Method, Not Just Today's Names

A screener can only ever tell you what passes right now. It has no memory, so it cannot tell you whether the approach has been working or quietly falling apart for six months.

Because a model is built on history, you can run it across past market periods before you put money behind it and see the shape of it — hit rate, average gain, the ugly stretches, how long the drawdowns lasted. You are not just collecting names. You are checking whether the method holds up, which is a question a filter list cannot answer.

What Your Morning Looks Like

The market closes and the day's data lands
The model scores every stock in your universe overnight
Your ranked list is waiting before the open, strongest candidates first
You take what you want from the top, at the size you chose in advance
Targets, stops and a maximum holding period are attached when the position opens

No filter tweaking at 9:15. No staring at 40 equal-looking names trying to pick four.

What You Do With the List

On Quant-Builder.ai the list is not a research toy. You build the model, you see whether it held up on past data, you get the ranked picks after the close, and you can trade them from the same place — stops, targets, how long you will hold. Free demo at /learn. Paid plans on /pricing.

Frequently Asked Questions

Is this a stock screener?

It does the same job — thousands of stocks in, a short list to trade out. It does it by ranking with a trained model instead of filtering on thresholds you picked, so the list comes back in order of confidence rather than alphabetically.

Do I need to know how to code?

No. You configure the universe, the target and the inputs in the interface, and the agent can walk you through setting one up.

How many stocks does it cover?

3,000+ US stocks, with 600+ inputs per stock, updated nightly after the close.

Can I use my own indicators?

Yes. Technical indicators, fundamentals and sector or macro context are all available as inputs, and you can see afterwards which ones actually influenced the ranking.

Can I check it before risking money?

Yes. Test the model against past periods first, then run it against a paper account before going live.

How often does the list update?

Every night. Scoring runs after the close, so the ranking is ready before the next open without you rerunning anything.

What does it cost?

The demo at /learn is free. Paid plans start at $25/month.

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