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Analytics Library

54 views across 12 categories
DEMO DATASimulated / demo · not client data
Portfolio

Current vs target allocation

Asset-class weight against strategic target; digital assets against its 5.0% maximum.

US Equity34.2%
International Equity14.8%
Fixed Income22.4%
Alternatives10.2%
Cash12.6%
Digital Assets5.8%
CurrentTarget / maximum
Current readingUS equity leads the overweight; cash 12.6% sits above its 10.0% floor.
Why it mattersShows which sleeves have drifted, and in which direction, before any trade is considered.
Canonical demo data
Allocation & Drift
Portfolio

Contribution to risk vs weight

Each asset class's share of portfolio risk (bar) against its share of capital (grey).

US Equity45.5%
International Equity16.8%
Fixed Income10.3%
Alternatives13.6%
Cash0.3%
Digital Assets13.5%
Risk shareCapital share
Current readingUS equity and digital assets carry more risk than capital.
Why it mattersWhere risk share exceeds capital share, that sleeve is driving the portfolio more than its size suggests.
Canonical demo data
Portfolio Overview
Portfolio

Allocation path: current → proposed → target

Where the combined proposed package (RB-2026-0911-A + PX-2026-0911-D) would take the three policy-relevant sleeves.

Technology18.7%
Digital assets5.8%
Cash12.6%
CurrentTarget / maximum / minimumProposed (approx.)
Current readingTechnology approx. 18.7% → 17.0%; digital approx. 5.8% → 5.0%; cash approx. 12.6% → 13.0%.
Why it mattersShows the package closes the breaches without overshooting the strategic structure.
Canonical demo data
Scenario Lab
Risk

Risk contribution waterfall

How each asset class builds up to 100% of portfolio risk.

US Equity+45.5%
International Equity+16.8%
Alternatives+13.6%
Digital Assets+13.5%
Fixed Income+10.3%
Cash+0.3%
Total100.0%
Current readingEquity sleeves account for most of total risk.
Why it mattersShows the order of magnitude of each sleeve's risk — the first two steps dominate.
Canonical demo data
Portfolio Overview
Risk

Correlation heatmap

Pairwise 6-month correlation between the main exposures.

US EqIntl EqTechCreditGovtDigital
US Eq1.000.820.910.48-0.220.46
Intl Eq0.821.000.740.44-0.180.39
Tech0.910.741.000.41-0.260.52
Credit0.480.440.411.000.180.21
Govt-0.22-0.18-0.260.181.00-0.08
Digital0.460.390.520.21-0.081.00
Current readingTechnology ↔ US equity 0.91; technology ↔ digital 0.52.
Why it mattersHigh equity–technology–digital correlation means the breaches are one risk, not three.
Simulated model analytics
Concentration
Factor

Factor exposure vs neutral

Standardized exposure to eight style and macro factors (tick = neutral).

Market1.06
Size-0.18
Value-0.31
Momentum0.42
Quality0.22
Volatility0.27
Duration-0.12
Crypto beta0.08
Neutral
Current readingMomentum +0.42 and value −0.31 are the largest style tilts.
Why it mattersMomentum and volatility tilts explain much of why the portfolio is regime-sensitive.
Simulated model analytics
Research
Market

Sector rotation quadrant

Relative strength against momentum rank (1 = strongest) for each sector.

ImprovingLeadingLaggingWeakeningMomentum (higher = stronger)Relative strengthXLUXLPXLVXLEXLFXLREXLBXLIXLCXLYXLK
Current readingUtilities and staples in the leading quadrant.
Why it mattersDefensive sectors lead while technology weakens — the rotation behind the proposal.
Canonical demo data
Sector Rotation
Regime

Regime transition matrix

Historical one-month probability of moving from each regime (row) to another (column).

Risk-onLate cycleVol transitionDefensive
Risk-on71%21%7%1%
Late cycle12%63%20%5%
Vol transition6%14%54%26%
Defensive9%7%19%65%
Current readingCurrent-state outlook: 54% remain, 28% move defensive.
Why it mattersVolatility transitions resolve defensive roughly one time in four — the base rate behind the posture shift.
Simulated model analytics
Market Regime
Liquidity

Normal vs stressed liquidity

Weight available in each liquidity tier, normally (bar) and after stress haircuts (grey).

Immediate · Same day12.6%
T+1 · 1 business day40.0%
T+2 · 2 business days26.9%
T+5 · Up to 5 days10.3%
Illiquid · Lock-up / gated10.2%
NormalStressed
Current readingCash 12.6% vs 10.0% minimum — within policy.
Why it mattersStress shrinks what can actually be sold — the gap is the liquidity at risk.
Canonical demo data
Liquidity
Event

EventEdge probability vs market odds

EventEdge model probability against the market-implied probability for each upcoming event.

Market-implied probability %EventEdge %August CPIFOMC decisionSeptember employmentLarge-cap technology
Current readingMost events sit near the diagonal; divergence is concentrated in a few.
Why it mattersPoints off the diagonal are where EventEdge disagrees with consensus — evidence, not a trade instruction.
Simulated model analytics
Event & Macro Context
Policy

Policy headroom matrix

Headroom to each percentage-based policy limit today (bar) and after the proposed package (grey).

US Equity range+0.8pp
International Equity range+1.8pp
Fixed Income range+1.4pp
Alternatives range+0.2pp
Cash range+3.4pp
Aggregate Technology sector exposure-1.7pp
Single issuer exposure+1.8pp
Correlation cluster exposure+2.9pp
Cash reserve+2.6pp
Illiquid allocation+4.8pp
Digital asset exposure-0.8pp
Family Trust — municipal minimum+0.1pp
Operating account balance+1.2pp
Modelled drawdown sensitivity+1.2pp
TodayAfter package
Current readingTechnology −1.7pp and digital −0.8pp today; both positive after the package.
Why it mattersNegative bars are breaches; the package moves both back inside policy.
Canonical demo data
Policy & Constraints
Scenario

Base vs minimum change vs proposal

Risk, drift and breaches under the current portfolio, the minimum-change option and the AI proposal.

Risk · base / min / prop
72 / 63 / 58
Drift
6.4 / 3.3 / 2.1
Breaches
2 / 0 / 0
Turnover
0% / 2.5% / 4.2%
Current readingRisk 72 → 63 (minimum) → 58 (proposal).
Why it mattersShows what the extra activity in the full proposal buys over the minimum fix.
Canonical demo data
Scenario Lab
Evidence

Evidence confidence distribution

How many Evidence Spine inputs fall in each confidence band.

<60%0
60–69%2
70–79%9
80–89%16
90%+4
Current reading31 evidence objects in the spine.
Why it mattersA conclusion is only as strong as its weakest heavily-weighted input.
Simulated model analytics
Research

Every view is labelled. Canonical demo data uses the same figures as every other screen. Simulated model analytics are deterministic demo fixtures, not production estimates and not client data.