Risk-aware signal overlay
Risk attribution, regime detection, and position-level signal overlay — wired into your existing execution stack with bounded latency.
Index / AssetModel
Structure on top
of noisy data.
A quant engine that turns signals into theses, and theses into risk-framed allocation. Built for ixprt's capital, available to firms that need the same discipline.
SYSOPERATIONAL
MKTNYSE · OPEN
FEEDS11/11
MODELS7 ACTIVE
INFER14ms
QUEUE0
LAT42ms
UTC14:32:08.117
RISK · ARMED
NAV
$184.2M
+$1.14M day
P&L · DAY
+0.62%
+62bps
MTD
+3.41%
α +1.6 vs SPX
YTD
+18.2%
α +12.1 vs SPX
SHARPE · 1Y
2.14
net of fees
SORTINO
3.06
downside-adj
MAX DD
−4.8%
since incept
Positions · 14:32:08 UTC ● 7 of 24
SIDESYMTHESISWGTP&L D5DCONV
LONGSPYearnings dispersion3.4%+0.41
LONGXLFcurve normalization2.1%+0.18
SHORTIWMbreadth deterioration1.6%+0.09
LONGXLEsupply tightness1.4%−0.06
LONGBTCflow + macro confluence0.9%+0.22
SHORTHYGspread risk0.7%+0.03
LONGGLDreal-rate inflection0.6%+0.14
Equity · 1Y vs SPX
strategy+18.2%
α
+12.1
β
0.42
VOL
6.1%
CALMAR
3.79
Live signal stream · ●
FLOWQQQ ask-side liquidity skew+0.412s
SENTcross-asset risk-on tilt+0.6211s
RISKvol-of-vol drift > floor−0.1828s
MACRO2s/10s steepening confirmed+0.2742s
EARNdispersion regime · week 4+0.341m
EXECrebalance proposed · sleeve EQ—1m
Inference graph · sleeve-eq · v2.41 ●
7 in6 · ReLU5 · ReLU4 out
Models · all sleeves
SLEEVE-EQv2.41 · acc 0.74 · drift 0.4σ
SLEEVE-RATESv1.18 · acc 0.71 · drift 0.6σ
SLEEVE-CREDITv0.92 · acc 0.69 · drift 1.2σ
SLEEVE-CRYPTOv3.04 · acc 0.66 · drift 0.3σ
REGIMEv2.07 · acc 0.81 · drift 0.2σ
RISK-OVLv4.12 · armed · last train 2h
Factor exposure
Equity
+0.64
Rates
−0.24
Credit
−0.16
FX
+0.12
Vol
−0.36
Crypto
+0.08
Sleeve correlation · 10×10
−1.00+1.0
Drawdown · 1Y
CURRENT
−0.2%
MAX
−4.8%
RECOV
14d
UPI
2.41
SPY524.18+0.41%
QQQ443.07+0.81%
IWM214.32−0.31%
XLF42.18+0.44%
XLE88.91−0.18%
GLD216.04+0.27%
TLT94.62−0.51%
HYG79.33−0.09%
BTC71,408+1.84%
DXY104.18−0.12%
VIX14.62+0.41%
2Y4.18%−2bp
10Y4.42%+1bp
SPY524.18+0.41%
QQQ443.07+0.81%
IWM214.32−0.31%
XLF42.18+0.44%
XLE88.91−0.18%
GLD216.04+0.27%
TLT94.62−0.51%
HYG79.33−0.09%
BTC71,408+1.84%
DXY104.18−0.12%
VIX14.62+0.41%
2Y4.18%−2bp
10Y4.42%+1bp
Who it's for
Discipline that adapts to how you trade.
AssetModel runs the same engine for ixprt's capital and for firms that license it. Sleeve coverage and integration footprint adapt to the audience.
Sleeve-level discipline
Structured signal-to-thesis-to-allocation discipline at the sleeve level. Full audit trail of why every position is on, and what would take it off.
Risk-framed allocation
Cross-sleeve allocation across equity, rates, credit, FX, and crypto with conviction scoring built into every decision and downside controlled by policy.
Signal-to-execution
Signal-to-execution pipeline — latency-bounded, drift-monitored, with an armed risk overlay that can pause execution autonomously on regime shifts.
How it works
Signals in. Allocation out. Risk in between.
01 · Signals
Diagest or your own
Cross-asset, cross-source structured signals. Bring Diagest, your existing feeds, or both.
02 · Engine
Inference + risk overlay
Per-sleeve models with confidence, regime detection, factor exposure framing, drift monitoring.
03 · Allocation
Decisions you can audit
Risk-framed positions with thesis labels, conviction bars, and full lineage from signal to trade.
FAQ
Frequently asked.
What is AssetModel?
AssetModel is a quant engine that turns structured signals into portfolio decisions: position theses, exposure framing, risk attribution, and allocation logic. It runs ixprt's own capital, and is available to firms that need the same discipline.
How does AssetModel relate to Diagest?
Diagest produces clean, AI-ready data. AssetModel consumes that data — and any other structured signal source — and produces the position-and-risk layer on top.
What asset classes does AssetModel cover?
Equities, rates, credit, FX, vol, and crypto sleeves, with cross-sleeve correlation and regime detection. New sleeves are added on request.
How is risk handled?
Risk is wired into every layer: per-position confidence, factor exposure budgets, sleeve correlation, drawdown limits, and a separate risk-overlay model that can pause execution. Net and gross are continuously monitored against policy.
Can it run on our data and signals, or only ixprt's?
Both. AssetModel can ingest your existing signal feeds, your existing risk framework, or run end-to-end on ixprt's stack — including Diagest as the data layer.
What is the integration footprint?
REST + WebSocket API for signals and decisions, plus a read-only dashboard. Optional FIX adapter for execution venues. Latency is bounded; integration time is typically two weeks for a focused sleeve.
From the blog
Notes on what we're building.
Buying Guide
What is a Quant Engine? A Buyer's Guide for Funds and Family Offices
A definition of the quant-engine category, the four functions it covers, and what funds and family offices should evaluate.
ComparisonRisk Attribution Models Compared: Barra vs Axioma vs Custom Approaches
How the major risk-attribution approaches compare — and which fits which kind of fund.
Industry ReportThe State of AI in Finance: 2026 Benchmark
What shipped, what stuck, and where the puck is going across data infrastructure, quant integration, and AI research.