Stock Rover Alternative: Screening on Filed Data
By Chad Hartman
Published · Last updated
Stock Rover is the best pure screening product retail investors can buy, and the numbers behind that claim are specific. Coverage across North American stocks, ETFs, and funds. Somewhere between 275 and 800-plus screenable metrics depending on tier. Up to twenty years of historical fundamentals. Ranked screening that weights criteria so results sort by strategy fit. Equation screening for custom formulas. More than 140 prebuilt screens. Portfolio analytics with broker sync, correlation analysis, and Monte Carlo simulation.
Nothing on this page argues the screener is weak. It is the strongest in its category.
The argument is that metric count is the wrong axis to evaluate it on, and that the number investors should care about is much smaller than 700.
Table of Contents
- Seven Hundred Metrics, Far Fewer Facts
- A Screen Inherits Every Definition It Uses
- Tier Gating Makes Data Depth a Pricing Decision
- Scores and Fair Value Are Another Opinion Layer
- What Screening on Filed Data Changes
- Screen First, Then Audit
- Frequently Asked Questions
Seven Hundred Metrics, Far Fewer Facts
Metric libraries expand faster than data does, and understanding why clarifies what depth actually means.
A financial statement contains a bounded set of reported line items. Everything on a screener beyond those is arithmetic performed on them. Return on equity is net income over equity. Return on invested capital is an operating result over a capital base. Free cash flow yield is a cash flow figure over a market value. Each ratio can then be produced in additional variants — trailing twelve months, five-year average, growth rate, percentile against sector, change versus prior period — and every variant counts as another metric.
Seven hundred metrics is therefore not seven hundred independent observations about a company. It is a much smaller set of filed facts, recombined.
That matters because the recombination is where definitions live. Two platforms reporting a different return on capital for the same company are usually not disagreeing about the filing. They are using different denominators, different tax treatments, or different decisions about what belongs in the capital base. The disagreement lives in the derivation, and the derivation is what a metric library does not show you.
Depth in a screener means how many derivations are offered. Depth in a dataset means how much of the filing survived.
A Screen Inherits Every Definition It Uses
Run a five-criterion screen and you have accepted five definitions you did not write.
Most of the time that is fine, because most definitions are conventional and the differences are small. The exceptions cluster in exactly the places serious screening cares about. Anything involving debt depends on whether lease obligations are inside or outside the debt figure. Anything involving capital depends on what the platform included in invested capital. Anything involving cash flow depends on how capital expenditure was classified, and anything involving earnings depends on whether the figure is GAAP or adjusted.
Change one of those and the population that passes the screen changes with it. Not marginally — a leverage threshold with leases in or out produces two different lists.
The practical test is simple and rarely run: take a company your screen returned, open the filing, and reconstruct the metric yourself from the reported line items. If the number matches, the definition was what you assumed. If it does not, the screen was answering a slightly different question than the one you asked.
That test needs as-filed line items. It cannot be run inside a library of derived metrics.
Tier Gating Makes Data Depth a Pricing Decision
There is a structural feature of the model worth naming, and it is common across the category rather than unique to any one product.
Screenable metric count scales with subscription tier, and so does historical depth. Entry tiers cap both. Equation screening, ranked screening, and screening against long historical fundamentals sit at higher tiers, which is a reasonable commercial design and produces an unusual consequence: the analytical question you are permitted to ask is set by what you paid.
An investor on a lower tier is not screening a smaller universe. They are screening the same universe through fewer fields and a shorter history, which quietly changes which patterns are findable. A metric that only becomes interesting across fifteen years is invisible to someone with five.
Filed data has no tiers. Every registrant's full filing history is public on EDGAR the day it lands, and the constraint is extraction rather than entitlement. GeminIQ's own Free plan is built on that same raw access: 3 years of history and 24 calculated metrics at no cost, with the full 17-plus-year archive and metric library reserved for the paid Annual or Monthly plan.
Scores and Fair Value Are Another Opinion Layer
Higher tiers add proprietary stock scores, fair value estimates, margin of safety figures, and warning flags. These are useful for triage, and they belong in the same category as every other composite discussed across this series.
A score selects inputs, weights them, and compresses the result into a rank. A fair value estimate assumes a relationship between historical figures and intrinsic worth. Both are defensible constructions and neither is a measurement, which means they cannot be checked — only accepted or ignored.
The distinction that matters is between a screen and a verdict. A screen is a filter you specified: it returns companies meeting conditions you chose, and you can inspect the conditions. A score is a conclusion someone else reached, and its inputs are not yours to examine.
Using both is fine. Confusing them is where a screening workflow turns into an outsourced one.
What Screening on Filed Data Changes
GeminIQ approaches the same problem from the other end. It extracts 10-K and 10-Q data directly from SEC EDGAR, preserves each company's own reported line item structure, and keeps the XBRL tag attached to every value, so the inputs behind any metric are visible rather than assumed.
Stock Screeners run across that layer, and named strategy screens apply published criteria to filed data — the Piotroski F-Score Screener, the Magic Formula Screener, the High ROIC, Low Debt Screener, and the Net-Net Stocks Screener among them. When a company passes, Financial Statements show the reported captions that produced the result, and Custom Tables assemble the specific line items behind it. Calculated Metrics including Return on Invested Capital, Invested Capital, and Free Cash Flow publish what they are computed from, which is what makes the reconstruction test possible.
The metric library is smaller. The set of facts underneath it is the same set the company filed, and every one of them can be pointed at.
Screen First, Then Audit
The workflow that gets the most out of both products is a sequence, and most investors only run the first half.
Screening is a discovery operation. Its job is to reduce ten thousand companies to twenty, and for that job a deep metric library with ranked and equation-based filtering is exactly right. Stock Rover is excellent at it and no filings archive replaces the ergonomics of a well-built screen.
The half that gets skipped is the audit. Twenty names that passed a filter are twenty hypotheses, each resting on definitions you inherited. Checking two or three of them against the filings tells you whether the screen was measuring what you meant — and if it was not, every result from it was answering a different question.
For the shallower end of the same category, the Finviz alternative post covers what a one-row ratio snapshot cannot carry.
A screen is only as good as the fields behind it, and the only way to know how good those fields are is to open the filing that produced them.
Frequently Asked Questions
What are the best Stock Rover alternatives?
It depends on the job. For deep screening with ranked and equation-based filters across North American stocks, funds, and ETFs, the substitutes are other screening platforms with large derived-metric libraries. For auditing what a screen actually measured, the alternative is a platform that extracts from SEC EDGAR and preserves as-filed line items with XBRL tag traceability.
Does a screener with more metrics have better data?
Not necessarily. Most metrics on any screener are arithmetic performed on a much smaller set of reported line items, and additional variants — trailing twelve months, averages, growth rates, sector percentiles — each count separately. Metric count measures how many derivations are offered, not how much of the filing is preserved underneath them.
Why do two platforms report different ROIC for the same company?
Usually because they define the capital base differently, treat taxes differently, or make different decisions about which balance sheet items to include. Both can be reading the same filing. The disagreement lives in the derivation, which is why a metric whose inputs are published can be checked and one whose inputs are not cannot.
How do I verify a stock screener result?
Take a company the screen returned, open its most recent 10-K or 10-Q, and rebuild the metric from the reported line items. If your figure matches the screener's, the definition was what you assumed. If it does not, the difference is a methodology choice worth understanding before you act on the list.
Wall Street's data. Main Street's price.
Institutional terminals charge thousands a year for as-filed accuracy. GeminIQ gives you the same thing for a fraction of the cost: financials built directly from raw SEC EDGAR filings, not third-party APIs, with full XBRL traceability back to the original 10-K or 10-Q. No normalized guesswork, just calculated metrics, charts, screeners, and watchlists built on numbers exactly as the company reported them. Start researching now at GeminIQ.com.
Data Used / Sources
- Fundamental data sourced from XBRL-tagged SEC filings via GeminIQ.
- Stock Rover coverage, metric counts by tier, historical depth, ranked and equation screening capability, prebuilt screen count, portfolio analytics, and proprietary score and fair value features reviewed August 2, 2026 from Stock Rover materials and third-party platform reviews. Plan structure was restructured in 2026; verify current tiers and pricing before republication.
Disclaimer: The content in this blog is for educational and entertainment purposes only and does not constitute financial, legal, or tax advice. Investing involves risk, including the loss of principal. The views expressed are my own and not intended as financial advice or a guarantee of future performance.