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explainer · 8 min read · 24 Apr 2026

How the Framler score is calculated — a plain-English walkthrough

Thirteen academic factor families, one 0-100 composite, honest uncertainty intervals. What each component contributes and how to read the number on any ticker page.

If you've opened any ticker page on Framler you've seen a number like 67 / 100 with a small range underneath, e.g. [52 – 74], and maybe a pattern label like QUALITY COMPOUNDER. That single number is the composite score. This post walks through what's inside it, what the range means, and why you can read the pattern label as a shortcut to the underlying story.

The 13 factor families

the engine runs thirteen separate 0-100 scorers over every ticker every day. Each scorer is a direct implementation of a published academic factor — no black-box features, no "alternative data" we can't describe.

Each returns higher = more bullish. The thirteen raw scores are combined with weights derived from each factor's published information coefficient (IC), shrunk Bayesian toward a uniform prior when out-of-sample data is thin.

Why combining works better than cherry-picking one factor

Any single factor has an IC of maybe 0.03 — meaning 3% of next-period return variance is explained by the factor score today. That sounds small because it is. The magic is that thirteen mostly-independent factors combined with appropriate weights give a composite IC closer to 0.06-0.08 — because the noise components cancel while the signals stack. That's the entire case for multi-factor investing, going back to Grinold-Kahn's 1999 fundamental law of active management.

Framler measures this explicitly and publishes it on the coherence audit page as the "effective breadth" — how many genuinely-independent factors you actually have out of thirteen raw ones. Live value is currently around 6-7, which is what you want.

What the [lower – upper] range means

The number in brackets under the score is a 90% prediction interval — not a forecast range, not a confidence band, specifically a conformal interval. It says: if the method is calibrated properly, 90% of similar tickers will land inside this range when we check their realised forward return 30 days from now.

ExampleA ticker with score 65 and range [52 – 74] is being flagged as mildly bullish, but the interval is wide enough that the method is honest about its own uncertainty. A narrower range around the same score would mean the method is confident about placement; a wider range says "similar setups have produced mixed outcomes, size accordingly".

Where most quant dashboards show a point estimate and stop, we publish the interval alongside it because lying about precision is the oldest mistake in applied statistics. Interval width itself shifts by regime — tighter in settled bull and bear phases, wider in transition — and that shift is driven by the same Bayesian regime posterior that drives the factor weights. One piece of math, multiple uses.

The pattern label as a shortcut

On top of the multi-factor composite, a pattern engine checks whether any of a library of academic setups fire on today's factor values. Examples: VALUE TRAP (cheap + low quality + bad accruals), SHORT SQUEEZE (crowded short + rising price + bullish options), QUALITY COMPOUNDER (high profitability + reasonable valuation + clean accounting).

When a pattern fires, the composite gets overridden by the pattern's published effect size — that's why the number on a ticker with a pattern label may not match the simple weighted average of its factors. The pattern is telling you the interaction is the story, not the list of twelve numbers. The full catalogue of patterns with citations lives on the Pattern Library.

Reading the number in practice

A sensible first pass:

  1. Score above 60 — bullish lean. If the pattern label is one of the bullish variants (QUALITY COMPOUNDER, PEAD DRIFT, SHORT SQUEEZE SETUP), the story is consistent. Check the interval width before sizing.
  2. Score between 45 and 60 — neutral to mildly bullish. Pattern is likely NO EDGE or CONFLICTING SIGNALS. The ticker is unremarkable today; wait for the picture to clarify.
  3. Score below 40 — bearish lean. If the pattern is one of the bearish variants (VALUE TRAP, QUALITY CRACK, PRICED FOR PERFECTION) the story is coherent — expensive or declining without fundamental support.

None of this is investment advice. It's a starting point for asking the next question — does the current catalyst calendar, position sizing, and regime environment support the thesis? The composite is a research tool, not a trading signal on its own.

Where we're honest about limitations

The stack deployed to production in April 2026. Forward-return tracking — actually measuring IC, Sharpe, conformal coverage against realised prices — opened on 16 May 2026 once the first short-horizon windows accumulated. The 1d + 7d horizons are now live; 30d and 90d roll in as their windows mature (~26 Jun and ~15 Sep respectively). For the still-maturing horizons we show live pipeline diagnostics and literature effect sizes, not out-of-sample results we don't yet have. The Track Record page lists exactly what publishes when.

Universe is curated for liquidity and academic-factor coverage — roughly a thousand names spanning the S&P 500, selected semis, biotechs, fintech, plus EU and Asian listings. International NLP coverage (20-F filings, foreign filings) is the active expansion front. We'd rather publish a curated list well than chase the long tail poorly.

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Methodology →Pattern Library →Track Record →
How the Framler score is calculated — a plain-English walkthrough | Framler