The original ten-member study remains frozen; 2 additional audited candidates are now exposed in the constructor, off by default. All 12 are priced under one composite shared execution basis, with real entry and exit impact and one causal cash balance. Frame 25 Aug 00:00 → 3 Sep 02:00 UTC; headline window forward, 27 Aug → 3 Sep; holdout 31 Aug 15:30 → 2 Sep 12:00.
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69,763 legs / 20,667 events / 23 members
exact grid 8,595 settlements
live layer ON · P2 pass
P2 worst error $5.8e-11 on 100 scenarios
Toggle a member by clicking its name, drag any scale, move the capital limit. The whole portfolio re-settles on every change — same engine, same physics as the tables below, verified against it to $5.8e-11. Each chart holds its y max so two settlements stay comparable while you drag; release it with fixed / auto, and a hatched top edge means the line runs above the ceiling. A sleeve can also be handed its own cap $ budget on top of the shared limit, and either time chart can be split into one line per sleeve. Whatever you change is written into the page URL, so the link reopens the same portfolio.
| member | scale | trades | joint c50 | standalone c50 | joint / standalone | $ / 1k cap-h |
|---|
Standalone is the same sleeve, same scale, same bankroll, alone. Below 50% the sleeve is capital-contended (E4) and is never recommended on headline PnL.
Deep coordinate search on the verified engine, then re-settled by the canonical Python engine. Reject share is held at or below 1.0%. Italic rows are protected-core diagnostics — they quantify what an incumbent is worth jointly and are never a recommendation.
E1 a proposal must beat the live six by ≥15% on forward c50 $/day, each at its own
optimum, reject share ≤1.0%. E2 the same comparison must be non-negative on the holdout.
E3 a removal is proposed only for t21 or t23.
E4 a sleeve whose joint contribution is under half its standalone is capital-contended.
Each row is standalone sum → joint uncapped → joint at $24k for one untuned vector. The first step is what the sleeves cost each other physically; the second is what the bankroll refuses. Cross impact is the part of the drag that only exists because the sleeves trade together.
Oversubscribing the bankroll means the cash queue refuses some
baskets, so the result depends on which of two near-simultaneous baskets reached the wallet first. The left
table perturbs exactly that arrival order — nothing else — and reports the dispersion over 24 draws.
The right table scales t20 on its own, which is a different question from its marginal value
inside a crowded book.
A pristine ordering is the optimistic end of its own distribution; that gap is the honest haircut on any oversubscribed number.
t20 alone, scaledDollars keep rising well past 2x while capital efficiency decays; the sleeve does not break, it just gets expensive. Its marginal value inside the original ten-model book is a separate number and falls much sooner.
The table above optimises on the same window it reports, which flatters every row equally but flatters them. Here each portfolio is fitted on 27–31 Aug only and then read out on 31 Aug 15:30 → 3 Sep 02:00, which the fit never saw.
The original ten members were replayed from frozen weights on
shock_v2_20260903_0500_256b. The added r11_g8_a6_t180 ung q80 sleeve was
materialized from its frozen 246b logical book against the compatible physical snapshot, then joined only
where exact event keys or unique physical quote signatures proved shared identity. Stage 1's original
study and every historical table below are unchanged.
Two candidate exits whose physical proceeds were zero but whose saved logical return was positive are conservatively pinned to a total loss. Their original values remain in the source receipt; the constructor uses the corrected physical basis.
The settlement is the same one lib.portfolio_execution performs: same-minute entry clusters
walk the pool sequentially in score order, exits walk it in value order, and one shared causal cash balance
admits or refuses whole entry baskets. standalone sum → joint uncapped → joint finite capital
separates own impact, cross-model impact and cash scarcity.
The page recomputes rather than looks up. It ships the execution basis and the settlement, not a cached surface, so any combination you build is engine-exact and not an interpolation. Its parity against the Python engine was measured on 100 random scenarios: worst c50 error $5.82e-11, identical admitted sets and trade counts.