AI Screener Validator

AI Screener Validator in ScreenerHub is the AI review layer for draft and saved screeners. Use it to check whether a screener is too narrow, too loose, internally inconsistent, or missing an important constraint before you run it repeatedly, share it, or attach it to a watchlist workflow.

What AI Screener Validator is for

The validator is useful after you already have a first workable screener definition. It is not there to invent a strategy from nothing. Its job is to review the logic you wrote and help you spot issues that are easy to miss when you have been staring at the same criteria for too long.

Three questions the validator should help you answer:

Does this screener express one coherent idea, or did I mix several styles together?
Are my criteria likely to produce too few or too many results to be useful?
Which blind spots should I fix before I save, monitor, or share this screen?

In practice, that means catching things like a value screener that forgot a quality guardrail, a dividend screener that ignores payout sustainability, or a momentum screen that layers so many thresholds together that almost nothing can pass.

How to use it

Start with a clear first draft

The validator works best when the screener already contains the core of your idea. Build the first version in Studio: choose the universe, add the main factors, and save the screener if you plan to revisit it.

This first draft does not need to be perfect. It does need to be specific enough that the AI can evaluate the trade-offs. A draft with P/E Ratio TTM < 15, ROE > 15%, and Debt / Equity < 1 is reviewable. A draft with one vague filter and no real structure is not.

Run AI review from the screener workflow

Open the screener you want to inspect and launch the AI review from that workflow. The validator reads the current criteria as they are configured now, so review the same definition you actually intend to run.

That matters if you are iterating quickly. A validator result is only useful when it matches the exact screener state you are about to save or share.

Read conflicts, warnings, and improvement suggestions separately

Not every review item means the same thing.

Conflicts point to logic that is hard to satisfy together or directly contradictory.
Warnings usually mean the screen may be too broad, too narrow, or too dependent on one class of metric.
Improvement suggestions are prompts to make the screen easier to interpret, not hard errors.

For example, a screener that combines deep-value multiples with very high growth thresholds may be logically possible but uncommon enough to deserve a second look. A dividend screen with Dividend Yield > 5% and no check on payout ratio or free-cash-flow coverage may deserve a warning even if it still returns names.

Revise the criteria and rerun the review

The validator is most valuable as an iteration loop, not as a final score. Adjust the weakest part of the logic first, then run the review again.

Typical revisions inside ScreenerHub look like this:

replace stacked valuation filters with one core valuation rule and one quality rule
add a balance-sheet check such as Current Ratio or Debt / Equity when yield or value rules are aggressive
remove duplicate filters that all express nearly the same idea
split one overloaded screener into two separate strategies instead of forcing one definition to do everything

If you are unsure whether a change made the screener better, compare the revised result set in Studio, then use Monitoring Runs or your own review notes to see whether the logic now behaves more consistently.

Common issues the validator catches

Contradictory logic

Some criteria can coexist mathematically but still work against each other strategically. A screen that asks for both deep cyclicals at distressed multiples and highly predictable compounders may describe two different businesses rather than one investable universe.

Missing guardrails

A screener often starts with the exciting part of the thesis and forgets the protective part. The validator is useful for spotting missing balance-sheet, profitability, or sustainability filters that would make the output easier to trust.

Overly narrow rule stacks

If you keep adding one more threshold to improve the list, you can end up with a definition that only finds a few names under one market regime. The validator can flag when the logic looks so tight that it risks overfitting.

Criteria that are hard to interpret together

Even good metrics can become noisy when combined without a clear hierarchy. If the screener uses valuation, momentum, dividend, and turnaround criteria all at once, the validator can help you decide whether the strategy needs one primary axis.

When to ignore a suggestion

AI review is a second pass, not an investment committee. You should ignore a suggestion when the trade-off is intentional and you can explain it clearly.

Examples:

you want an unusually concentrated screener because it is meant to surface only exceptional setups
you are deliberately screening for stressed balance sheets in a turnaround strategy
a metric is missing because you plan to judge that factor manually on the company page

The standard is simple: if you can defend the choice in plain language, keep it. If you cannot explain why the rule belongs there, revise it.

What it is not

AI Screener Validator is not a stock-rating engine. It reviews the quality of the screener logic, not whether a specific stock is a buy.

It is not a replacement for Screener Quick Check. Quick Check evaluates one stock against a screener. The validator evaluates the screener itself.

It is not a historical backtest. If you want to pressure-test a saved definition over time, combine it with Monitoring Lab and the process described in How to Backtest a Stock Screen.

Feature overview: Stock Screener
Build and edit the underlying logic: Studio
Check operators and thresholds: Operators
Validate one stock against a screen: Screener Quick Check
Track how a saved definition behaves over time: Monitoring Lab