We built a stock screener that ranks by outcome — and only trusts magnitude, not direction

Every stock screener ranks by the same things: RSI under 30, price above the 50-day, a volume spike, a MACD cross. They rank by what the chart looks like right now. None of them rank by the thing you actually care about — what setups that looked like this did next.

So we built one that does. And building it forced us to confront an uncomfortable, useful truth about chart patterns.

The idea: rank by the cohort's outcome

Chart Library retrieves, for any (symbol, date), a cohort of ~40–50 historically similar setups across 10 years and 19K+ symbols, then looks at what those analogs actually did over the next 1/3/5/10 days — the full forward-return distribution, every number a real historical fact, not a generation.

The Outcome Screener runs that across the whole market and ranks names by their cohort's expected move. Instead of "show me oversold stocks," you ask "show me names whose look-alikes historically made the biggest moves next." That's a fundamentally different — and more honest — sort key.

The uncomfortable truth: shape predicts size, not direction

Here's what fell out when we validated it on 300K+ historical cohort observations:

• Direction is ~unpredictable. The correlation between a cohort's historical mean return and the anchor's realized direction is ≈ −0.015 — a coin flip. A "bullish-looking" cohort wins 52% of the time vs a 54% baseline. There's no there there.
• Magnitude is predictable. The correlation between cohort dispersion and the realized absolute move is ≈ +0.20, and it's monotonic across quintiles — wider cohort, bigger realized move, reliably.

So we made a deliberate product decision: the screener ranks by expected magnitude, and refuses to rank by predicted direction. Most "AI alpha" tools quietly do the opposite — they sell you a direction they can't actually call. We'd rather ship the signal that survives validation.

And expected magnitude is useful even without a direction call — it's exactly what you need for option premium, position sizing, and stop placement. Knowing a name is primed for a 12% move (vs a 4% one) changes how you trade it regardless of which way it breaks.

A detail that mattered: robust estimators

The first version ranked a mega-cap to the top with a "320% expected move." It wasn't signal — it was an outlier-corrupted standard deviation. Naive std/mean over a cohort's forward returns gets wrecked by a single 10x penny-stock day.

The fix was to estimate dispersion robustly:

• For raw returns: MAD × 1.4826 (median absolute deviation — the consistent estimator of σ for a normal), falling back to std only when MAD degenerates.
• For stored percentiles: IQR / 1.349 (the normal-consistent σ from the interquartile range), with a sanity guard.

import numpy as np

def robust_sigma(returns: np.ndarray) -> float:
    """Dispersion that ignores the one insane bar instead of being dragged by it."""
    med = np.median(returns)
    mad = np.median(np.abs(returns - med))
    sigma = 1.4826 * mad
    return sigma if sigma > 0 else float(np.std(returns))

After the swap, the rankings became sane — and real. AMBA, for instance, surfaces with a ~12% expected move at ~1.9× its own baseline volatility, instead of a phantom mega-cap at 320%.

Use it three ways

1. UI: chartlibrary.io/screener, plus Strategy Lab to backtest cohort-conditioned rules against base rates.
2. API: GET /api/v1/screener returns ranked names with expected move + volatility ratio.
3. Agent / MCP: pip install chartlibrary-mcp gives Claude, Cursor, or a LangChain agent the same cohort tools. It's in the official MCP Registry.

The takeaway

A screener that ranks by outcome instead of appearance is more useful — but only if it's honest about what's actually predictable. Shape tells you how big, not which way. Build on the part that survives the backtest, and say so out loud.