Stock Screener and Backtesting Platform

August 12, 2026 · 8 min read

A stock screener and backtesting platform is what people want when they are done jumping between Finviz and a separate backtest tab. You want one place that finds the names and shows whether the way you found them was any good — then a path to trade the list. Quant-Builder.ai is built as that combined loop: ranked morning picks from a trained approach, historical review of how it behaved, then size and exits.

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One Platform — Not Two Toys

A screener with no historical check invents confidence
A backtest that never becomes tomorrow’s ranked list stays a hobby
After the list appears you still need size and exits

Buy a stock screener and backtesting platform that ends in trades.

Quant-Builder.ai

Bring the numbers you already trust. Train. Review how the process looked on past markets. Get the overnight ranking. Trade with stops, targets, and exit dates. Free demo at /learn. Paid plans on /pricing.

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Two Jobs That Belong in One Place

Screening and backtesting are usually sold separately, and the separation is the problem rather than an inconvenience.

A screener answers what qualifies today. A backtester answers whether an approach worked historically. Kept apart, you get an unpleasant situation: the thing producing your daily candidates has never been tested, and the thing you tested is not what produces your candidates. You end up trusting a screen because a broadly similar idea backtested acceptably somewhere else, which is not evidence about the screen.

In one product they are the same object. The model that ranks tomorrow is the model whose history you examined. What you validated is what you are trading.

What Backtesting a Screen Actually Requires

Harder than it sounds, and the difficulty is the reason most tools do not offer it honestly.

Point-in-time fundamentals. Companies restate figures. Testing a screen from 2019 using today's revised numbers uses information nobody had.
Delisted companies. If your history contains only firms that still exist, every failure has been quietly removed and results are inflated in a way you cannot see.
Correct adjustment. Splits and dividends must be handled or every technical condition is corrupted at every split date.
Realistic execution. A screen run on the close cannot be filled on that close. Slippage and spread matter, especially in smaller names.
No reuse of the test set. If you adjust the screen after seeing results, then re-test, the second result is contaminated.

That last point is where most retail backtesting quietly fails, and it fails through diligence rather than laziness — trying variations until one looks good feels like work.

Why Combining Them Changes the Daily Routine

When selection and validation are one thing, several new questions become answerable. How has this model behaved over the last quarter compared with its validation. Is the ranking degrading. Which inputs mattered then and which matter now.

Separate tools cannot answer any of those, because there is no continuity between the tested idea and today's list. Continuity is the real product of putting them together.

The Whole Chain in One Place

Universe definition, 600+ features across 3,000+ US stocks with point-in-time history, model training, rolling out-of-sample validation, nightly scoring into a ranked list, and execution with target, stop, trail and hard exit date attached per lot. One object throughout, so the thing validated is the thing traded.

Free demo at /learn. Paid plans start at $25/month.

Frequently Asked Questions

Why can most screeners not be backtested?

Because they were built to evaluate today. Reconstructing the past faithfully requires point-in-time data and delisted companies, which is a different and more expensive data problem.

What makes a backtest trustworthy?

Point-in-time data, delisted names included, correct split and dividend adjustment, realistic execution assumptions, and results from periods the model never saw.

Can I test my existing screen?

Your filters can become model inputs, and the resulting model can be validated across past periods. That is a more honest test than most screeners can offer.

Is backtesting enough to trust a strategy?

No. It tells you an idea was not obviously broken historically. Paper trading and small live size are how you find out the rest.

Where do I see both together?

Free demo at /learn.