StockScorer rates stocks by disclosed, rule-based criteria instead of gut feeling. This page covers who runs the project, where the data comes from, how a score is built, and where the backtests honestly reach their limits.
Sunday night, 11:12 pm. Fourteen open tabs, two spreadsheets, three opinions from three forums, and in the end it still comes down to a feeling. Sounds familiar. That's exactly why this software exists.
Metrics, prices, and index compositions come from leeway, a commercial market data provider. An automated pipeline updates them once a day after market close. There are no real-time or intraday quotes. For a rule set that looks at annual figures, that would only be noise anyway.
Every stock page shows how fresh the data is: with a timestamp, not with "live".
Every data run measures its own success rate. If it drops below a minimum, say during a provider outage, downstream steps like score recalculation are skipped automatically. A score from yesterday beats a wrong one from today.
A scoring profile is a list of rules: each rule checks one metric against a value you set with a slider, and awards points. You can build your own profile from over 90 metrics, take the StockScorer profile as-is, or clone a profile from the library and tune it to your taste. Evaluation is purely rule-based, no black-box AI, no hidden weighting.
Return on capital, margins, leverage, growth, valuation. You decide what counts and what you ignore.
ROC greater than 20, gross margin greater than 40, debt-to-equity less than 0.5. Every rule gets its own point value, negative points included. Rules can be grouped, negated, or set as a hard exclusion.
The score is the sum of the triggered rules. It is not normalized to a 0-to-10 scale, and a score can be negative. Four points mean four points.
You decide how many points make a stock a low, medium, or high match with your profile, split by large, mid, and small cap if you want. This classification is deliberately neutral and is not a buy or sell recommendation: it only describes how well a stock fits a rule set.
The "Moat Quality" profile looks for companies with durably high returns on capital, a defended gross margin, and strong cash flow. Eight rules, exactly as they appear in the editor:
Every line is yours to touch: drag the threshold slider, change the points, negate a rule, compare against another metric instead of a constant (this year's gross margin against last year's), or mark a rule as a hard exclusion. Missing data is its own case and is never silently scored as "passed".
That's your call, not mine. Two thresholds per profile, optionally split by cap class, because the same point total means something different for a small cap than for a large-cap giant:
If you don't like the split, turn it off and use one threshold for everything. On a tie, a metric of your choice decides, ROC in this example, higher is better. And if you have no idea where to start: the Strategy Optimizer backtests other thresholds and rules and proposes concrete changes. Purely informational, you adopt them or you don't.
This is the full math behind the card at the top of the page. Eight rules, six of them triggered, two not. Nothing gets smoothed over until it fits.
Because there's no normalization, a score is only comparable within the same profile. A 7 from one profile and a 7 from another are two different things, and that's the point: it's your scale.
A profile that gives every second stock top marks helps no one. This is how the scores of the "Moat Quality" profile spread across 16,452 stocks. On the left are the names with negative points, on the right the handful that clear every bar.
As of 2026-07-23 · profile "Moat Quality" · illustrative snapshot
Historical simulations almost always suffer from the same two problems. StockScorer addresses both explicitly, not because it's convenient, but because a backtest that looks too good is worse than none at all.
Looking only at companies that still exist today writes the failures out of history. StockScorer reconstructs index membership at every point in time: delisted companies stay in the history and exit at their last real price instead of simply disappearing.
A period's score and entry rely solely on data that was actually available at that time. Returns are only measured in the following period, never in the same window the selection came from.
Every backtest also shows its data quality and its gaps. Every strategy page additionally runs a real out-of-sample history since the profile's publication date. That one runs forward from day one, not backward.
Once a day after market close, via an automated nightly pipeline. There are no real-time quotes, and every stock page shows the exact timestamp of the data.
No. A score only expresses how strongly a stock matches a profile's rules. The presentation is purely informational and does not replace individual investment advice.
Because normalizing throws information away. The score is the plain sum of the points your rules award, and it can go negative too. In exchange, you see for every stock exactly which rule contributed which point. A score is comparable within one profile, not across different profiles.
No. You can use the StockScorer profile as-is, clone a profile from the library, or build your own from over 90 metrics. Most people start with a ready-made profile and tune it after a few weeks.
Yes, since the corresponding rollout: index composition is reconstructed at every point in time, delisted companies stay in the history and exit at their last real price. Every backtest states the exact start date of the survivorship correction.
Create a profile, run it against 16,452 stocks, check it against the history. Start for free, no sales call, there's no one who could make one.