The metrics
Fourteen statistics. What each one measures, why it matters to a strategy, and where its band came from.
Where every band came from
Ten consecutive 252-day windows of 40 US large caps, 2015 to 2025, measured with this module's own estimators. Nine windows set each band. The tenth, the window that straddles the COVID crash, is reported separately as that band's crisis window.
So the panel is a claim about a typical year. Crisis behaviour is measured under pinned scenarios instead.
The fourteen
It sets the scale of every gain and every loss. Too high and every Sharpe ratio you measure is depressed and every stop is hit too often. Too low and risk looks free.
How often a day lands far from typical. Zero is a normal distribution, and real markets are strongly positive, because crashes and melt-ups happen far more often than a bell curve allows. This one decides whether tail risk means anything in your results.
Near zero in a real market. A positive value is free money for a momentum rule, so this is the row that decides whether a trend result here is a finding or an artifact.
Volatility clustering at one day. Present in every real market, and the reason a calm week is a poor forecast of the next one.
Clustering at lag five. Whether a volatile spell persists long enough for a weekly risk model to see it.
Clustering at lag twenty. This is where the decay-shape gap lives: the model holds the level here but reaches it with the wrong curve.
The average pairwise correlation. It decides whether a diversified book is actually diversified, and it is the row that concentration moves first.
Volume and absolute return move together in every real market. An execution algorithm that assumes constant depth is wrong in exactly the moments that matter.
Negative returns raise future volatility more than positive ones do. A symmetric model gets the shape of a drawdown wrong.
Negative in real markets: a heavy day is followed by a lighter one. Participation caps read against ADV depend on it.
Correlation rises in a selloff, which is when diversification is most wanted and least available.
Whether that coupling survives into the next session rather than being a one-day artifact.
How much more a name moves with its own industry than with the market. Undefined on a single-sector roster, because those are the same thing.
Correlation does not snap back the day after a crash. The single row pt-v14 misses on held-out seeds.
What one run actually shows you
Every banded number is a median across seeds. The spread around it is wide enough to change the answer, and tradefloor.envelope.intervals() reports it per statistic.
Read typical_straddles, not extremes_straddle
extremes_straddle fires when one seed of thirty crossed an edge. That is close to expected, so it is information rather than a finding.
typical_straddles says the middle eighty percent crosses. Then a reader running one seed is likely, not merely able, to measure out of band on a statistic whose median sits well inside.
Measured on pt-v10 over thirty seeds, nine of the fourteen straddle by that test. That is the previous era's dispersion. pt-v14 moved the medians and the spread around them has not been re-measured.
Three bands are not raw measurements
A band is what this measurement returns on real data, with three documented exceptions. Each names itself on its own provenance row.
The ceiling moves from the windows' 34 to 36, so the band admits a slightly more violent year than the sample held.
The floor is held at +0.02. Zero volatility clustering appears in no retrieved source and no observed window.
The ceiling is held at 0.00. A top above zero would certify a reversed leverage effect as real-market behaviour.