Equity-sleeve coverage83.6%83.5% covered of 99.9% retained equity weight.
Total-fund coverage83.5%Company fundamentals as a share of 100.0% reported fund weight; no reallocation.
Non-company exposure0.1%Cash, derivatives, and other non-equity rows are excluded · tradingview_etf_holdings · Jul 20, 2026 · current.
Partial mapped company sleeve28/28 mapped companies have stored fundamentals, covering 83.6% of the retained company sleeve and 83.5% of fund weight. The remaining 0.1% is non-company exposure, not missing companies. Separately, 79 equity holdings (16.4% of fund weight) remain unmapped.
Return factors and risk factors built from stored company fundamentals, price tape, themes, event evidence, liquidity, and flow datasets.
Companies28Ranked inside this universe
Factors5034 ready · 16 partial
Coverage100%Average ready-factor coverage
As ofJul 21, 9:30 PM GMT+8Latest stored factor input
Full factor lab detail queuedThe initial factor map is loaded from the compact snapshot; full histories, heatmaps, validation matrices, and lenses hydrate on demand.
Factor workflow
Navigate the factor engine without scrolling the whole report
Start with attribution quality, move into the action book, then inspect individual factor evidence only when needed.
Daily stock-level cross-sectional regressions estimate factor returns from company returns and reusable economic factor exposures. Sector, industry, theme, and country are reporting lenses, not dummy factors used to force-fit the universe return.
For each stored trading day, company returns are regressed against reusable company-level factor exposures. The common market column captures the broad universe tape; style factors explain tilts around that tape.
Lens policyNo fake fit from buckets
Sector, industry, country, and theme are reporting lenses only; they are not dummy variables used to force-fit returns.
Attribution splitWhat is investable versus only explanatory
19 usable factors · 30 diagnostic factors
Common tape-24.69%Broad universe move before active factor interpretation.Rewarded / usable factors+0.56%11 rewarded · 8 risk-controlDiagnostic factors+2.61%30 diagnostic · 0 blockedResidual-0.02%Idiosyncratic return that should flow into company-level research.
Attribution policy. Separate common tape, investable rewarded factors, diagnostic factors, and residual. Diagnostic factors explain movement but are not portfolio-sizing inputs.
Factor action bookWhat to overweight, hedge, block, or research next
Overweight · +1.36% actionable return
Active tilts11Risk controls8Blocked0Research-only30Regime-adjusted11
Small / validation pendingConviction 60 · risk budget n/a%
Contribution
+0.96%
Factor return
+3.17%
Exposure
+0.30
Energy-security leverage is a rewarded factor with positive contribution and usable breadth.
Regime-aware sizingSmall / validation pending
No stored regime evidence · best n/a · worst n/a · n/a% survival
No stored regime validation yet; keep sizing small until regime behavior is refreshed.
Coverage passValidation watchPayoff watchCrowding failRisk budget watchRegime fit watch
Use the action book as a review order. Active tilts require validation, breadth, uniqueness, controlled crowding, and regime survival; diagnostic rows remain research-only.
Model fit disciplineWhen factor attribution is reliable enough to use
Fragile factor fit
Current stock-level fit69%Weighted R-squared across latest company returns.Average daily fit46%How often the model explains day-to-day cross-sectional moves.Fit stability49%Share of stored sessions with usable stock-level factor fit.Residual cost+7.82%Average stock-level error; gross residual +3.74%.
Not an accounting identity. R-squared is a stock-level fit diagnostic, not a promise that factors explain every portfolio return dollar. The residual board remains the source of company-specific work.
Use factors as the first pass, then verify residual groups and company-specific outliers before sizing.
Factor trust dashboardCan this attribution guide positioning?
Usable with checks
Trust score59
Use smaller factor tilts; validate residual clusters and crowded factors before sizing.
Positioning guidanceUsable with checks
Use smaller factor tilts; validate residual clusters and crowded factors before sizing.
Weakest fit dayJul 2, 2026
4% fit · RMSE +0.32%
Residual-dominated days18
Latest fit 76% · average 46%
Required checks before sizing1 passed
Stock-level fitwatch46 · Stock-level fit is usable but should be checked against residual outliers.
Residual loadpass12 · Residual sleeve is controlled enough for a factor-first review.
Validation depthwatchn/a · Run forward validation before treating factor payoffs as durable.
Crowding controlfail100 · Top factors or redundant factors dominate; cap position size and avoid double counting.
Diagnostic factor loadwatch26 · Diagnostic-only factors are meaningful; use them for triage rather than direct sizing.
Failure modes6 checks
Weak stock-level fitmediumThe cross-sectional model is not consistently explaining stock returns.
Weakest fit dayhigh2026-07-02 had only 4.0% stock-level explanatory fit.
Validation depthmediumForward validation is not strong enough to treat every factor payoff as durable.
Crowding controlhighA small number of sleeves or overlapping factors can dominate the attribution.
Diagnostic factor loadmediumToo much movement is explained by diagnostic-only factors that should not be direct sizing inputs.
Residual-dominated daysmedium18 stored sessions had more residual than modeled factor fit.
Factor reliability boardWhich factors are investable, confirming, or noisy
54 average reliability · 0 decision-grade factors
Decision-grade tilts0Validated enough for active factor interpretation
No factor clears all reliability gates yet.
Usable confirmation3Useful when independent evidence agrees
ValueUse as a confirming factor alongside other independent evidence.
Reliability
68
Validation
n/a
Crowding
76
Insider alignmentUse as a confirming factor alongside other independent evidence.
Reliability
61
Validation
n/a
Crowding
62
Growth accelerationUse as a confirming factor alongside other independent evidence.
Reliability
60
Validation
n/a
Crowding
25
Noisy or incomplete3Needs better validation, coverage, or stability
Drawdown repairUse as a descriptive exposure until forward tests are populated.
Reliability
56
Validation
n/a
Crowding
62
Earnings revision pressureUse as a descriptive exposure until forward tests are populated.
Reliability
50
Validation
n/a
Crowding
76
Sentiment reversal setupUse as a descriptive exposure until forward tests are populated.
Theme alignmentUse only as a warning or confirmation; avoid double-counting this factor.
Reliability
42
Validation
n/a
Crowding
100
Institutional flowUse only as a warning or confirmation; avoid double-counting this factor.
Reliability
59
Validation
n/a
Crowding
100
AI infrastructure intensityUse only as a warning or confirmation; avoid double-counting this factor.
Reliability
44
Validation
n/a
Crowding
100
Revenue acceleration leadershipUse only as a warning or confirmation; avoid double-counting this factor.
Reliability
56
Validation
n/a
Crowding
100
No factor currently clears the decision-grade validation, stability, and uniqueness gates.Some factors are highly correlated with peers, so do not add their attributions together mechanically.Some factors are descriptive only until forward validation or coverage improves.
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