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README.md

@@ -17,49 +17,71 @@ factor risk model. Code, methodology and audit suite live in the
 [buse-open repository](https://github.com/Binomial-Capital-Management/buse-open).
 
 This repository holds derived model quantities only — no raw market data.
-316 monthly point-in-time partitions spanning **2000-01 to 2026-08**
-(~642 MB, 1,802 parquet files).
+317 monthly point-in-time partitions spanning **2000-01 to 2026-09**
+(~1.27 GB, 1,807 parquet files).
 
 > The dataset viewer is disabled: this is a set of six parallel
-> monthly-partitioned parquet trees, not a single tabular dataset with
+> monthly-partitioned trees, not a single tabular dataset with
 > train/test splits. Load the tree you need directly (see below).
 
+## What changed in v1.1.0
+
+`buse_v1.1.0` is a **survivorship-complete** rebuild of the same model
+specification (`risk_model_version` is unchanged, `buse1_th_v1`). The
+estimation universe now carries delisted names through their full trading
+history, so historical cross-sections are roughly 50% larger than in
+v1.0.0 (a typical 2025 exposures partition is ~60,800 rows over ~2,650
+tickers, against ~40,800 rows over ~1,777 tickers before). The latest
+cross-section covers 2,437 assets as of 2026-09-01. Partition layout,
+column schema and hash-manifest contract are unchanged.
+
+The previous release (`buse_v1.0.0`, `imp_v1_0_0`, `retail_v1_0_0`) remains
+available in this repository's git history.
+
 ## Contents
 
-### `buse_v1.0.0/` — core model
+### `buse_v1.1.0/` — core model
 
 | Tree | Partitions | Coverage | Size |
 |---|---|---|---|
-| `exposures_by_month/` | 316 | 2000-01 .. 2026-08 | 313 MB |
-| `specific_risk_by_month/` | 315 | 2000-06 .. 2026-08 | 217 MB |
-| `proxy_exposures_by_month/` | 312 | 2000-09 .. 2026-08 | 42 MB |
-| `factor_covariance_by_month/` | 312 | 2000-09 .. 2026-08 | 24 MB |
-| `proxy_specific_by_month/` | 312 | 2000-09 .. 2026-08 | 3.2 MB |
-| `scale_diagnostics_by_month/` | 312 | 2000-09 .. 2026-08 | 1.2 MB |
+| `exposures_by_month/` | 317 | 2000-01 .. 2026-09 | 614 MB |
+| `specific_risk_by_month/` | 316 | 2000-06 .. 2026-09 | 496 MB |
+| `proxy_exposures_by_month/` | 312 | 2000-09 .. 2026-09 | 112 MB |
+| `factor_covariance_by_month/` | 313 | 2000-09 .. 2026-09 | 28 MB |
+| `proxy_specific_by_month/` | 312 | 2000-09 .. 2026-09 | 6.3 MB |
+| `scale_diagnostics_by_month/` | 313 | 2000-09 .. 2026-09 | 0.3 MB |
 
 Staggered start dates are estimator warm-up, not missing data: specific
-risk needs a 5-month EWMA burn-in, proxies and diagnostics 8 months.
+risk needs a 5-month EWMA burn-in, proxies and diagnostics 8 months. The
+final `2026-09` partition holds the single session available at build
+time (2026-09-01); it is a valid point-in-time snapshot, not a full month.
 
 Partitions are Hive-style (`month=YYYY-MM/part.parquet`) and hold **daily**
-point-in-time rows — a typical exposures partition is ~40,800 rows
-(~1,777 tickers x ~23 trading days) x 122 columns, plus the `month`
-partition key when read through `hf://`.
+point-in-time rows x 122 columns, plus the `month` partition key when read
+through `hf://`. `scale_diagnostics_by_month/` is one JSON file per month.
 
 Also at the tree root: `factor_returns.parquet` (full daily history),
 `exposures_latest.parquet`, `specific_risk_latest.parquet`,
 `factor_covariance.parquet`, `factor_universe.json`, `metadata.json`,
-`calibration_report.json`, `hash_manifest.json`, `audit.md`.
+`hash_manifest.json`, `replicated_carrier_exposures.parquet`,
+`replicated_carrier_report.json`, `audit.md`.
 
-### `imp_v1_0_0/` and `retail_v1_0_0/` — specific-risk overlays
+`audit.md` is the build-time audit for this generation as run. It records
+one FAIL (`E.proxies`: the QQQ anchor's projected beta of 1.37 sits outside
+the audit band) that was reviewed and accepted before release; the other
+21 checks pass or flag informationally. The v1.0.0 calibration matrix
+(`calibration_report.json`) has not yet been re-run on this generation.
+
+### `imp_v1_1_0/` and `retail_v1_1_0/` — specific-risk overlays
 
 Base-agnostic monthly tables of `[date, ticker, variance_multiplier]` with
-hard bounds `[0.25, 4.0]`. Each tree carries `metadata.json`,
-`evaluation_report.json` and a hash manifest.
+hard bounds `[0.25, 4.0]`. Each tree carries `metadata.json` and a hash
+manifest.
 
 | Overlay | Partitions | Coverage |
 |---|---|---|
-| `imp_v1_0_0/` | 127 | 2016-01 .. 2026-07 |
-| `retail_v1_0_0/` | 103 | 2018-01 .. 2026-07 |
+| `imp_v1_1_0/` | 128 | 2016-01 .. 2026-08 |
+| `retail_v1_1_0/` | 104 | 2018-01 .. 2026-08 |
 
 - **IMP** — per-ticker multiplier from the cross-sectionally centered
   implied/realized volatility ratio. Zero fitted parameters.
@@ -67,6 +89,13 @@ hard bounds `[0.25, 4.0]`. Each tree carries `metadata.json`,
   volume (Wikipedia attention z-score minus volume z-score), with one
   calibration constant frozen before out-of-sample evaluation.
 
+Both overlays are rebuilt with the same frozen rules as v1.0.0, extended
+by one month. The out-of-sample gate reports published with v1.0.0
+(`evaluation_report.json`) were not re-run for this rebuild and are not
+included here. Overlay inputs are not survivorship-complete: names that
+delisted before the rebuild ride the base model (the documented
+missing-symbol policy).
+
 ## Usage
 
 Download the full tree, or just the part you need:
@@ -74,13 +103,13 @@ Download the full tree, or just the part you need:
 ```bash
 pip install huggingface_hub[cli]
 
-# everything (~642 MB)
+# everything (~1.27 GB)
 hf download BinomialTechnologies/buse-artifacts \
     --repo-type dataset --local-dir artifacts
 
 # one overlay only
 hf download BinomialTechnologies/buse-artifacts --repo-type dataset \
-    --local-dir artifacts --include 'imp_v1_0_0/*'
+    --local-dir artifacts --include 'imp_v1_1_0/*'
 ```
 
 Read a single partition without downloading the rest:
@@ -89,7 +118,7 @@ Read a single partition without downloading the rest:
 import pandas as pd
 
 url = ("hf://datasets/BinomialTechnologies/buse-artifacts/"
-       "buse_v1.0.0/exposures_by_month/month=2025-10/part.parquet")
+       "buse_v1.1.0/exposures_by_month/month=2025-10/part.parquet")
 exposures = pd.read_parquet(url)
 ```
 
@@ -98,13 +127,13 @@ Apply an overlay to any risk model's specific variances:
 ```python
 from buse1.overlays.loader import apply, load_overlay
 
-mult, provenance = load_overlay("artifacts/imp_v1_0_0", decision_date)
+mult, provenance = load_overlay("artifacts/imp_v1_1_0", decision_date)
 conditioned = apply(base_specific_variance_by_ticker, mult)
 ```
 
 ## Integrity
 
-`SHA256SUMS` covers all 2,129 artifact files:
+`SHA256SUMS` covers all 2,132 artifact files:
 
 ```bash
 cd artifacts && sha256sum -c SHA256SUMS

buse_v1.1.0/audit.md

@@ -0,0 +1,27 @@
+# Risk model audit — buse1_th_v1
+generated by scripts/audit_risk_model.py
+
+- **PASS** `S.schema` — 107 non-market factors match manifest, stock, returns and 312 proxy months; covariance omissions explicitly inactive: ['industry:industry_agricultural_machinery', 'industry:industry_conglomerates', 'industry:industry_consumer_electronics', 'industry:industry_diversified_utilities', 'industry:industry_insurance_diversified', 'industry:industry_luxury_goods', 'industry:industry_tobacco', 'theme:theme_memory']
+- **PASS** `A.factor_returns` — 108 factors x 6735 dates loaded
+- **PASS** `A.factor_returns` — no market/style zero-gaps in the PIT era (>= 2006)
+- **FLAG** `A.factor_returns` — 7 category factor-years with zeros (dummies can be legitimately absent) e.g. ['industry:industry_semiconductors:2000(6%)', 'theme:theme_semis:2000(6%)', 'industry:industry_semiconductors:2001(5%)', 'theme:theme_semis:2001(5%)', 'industry:industry_semiconductors:2002(3%)', 'industry:industry_semiconductors:2003(3%)']
+- **PASS** `A.factor_returns` — no degenerate blowups
+- **FLAG** `A.factor_returns` — 98 large-but-significant daily factor returns (crisis days, informational)
+- **PASS** `A.factor_returns` — all non-dormant factor vols inside type bands
+- **PASS** `B.exposures` — post-activation style coverage above the estimator floor across 30 sampled months x 8 styles
+- **FLAG** `B.exposures` — 62 pre-activation or below era-target month-styles (informational breadth): ['2000-01:style_beta(0%,pre-2000-06)', '2000-01:style_size(100%,pre-2000-06)', '2000-01:style_value(40%,pre-2000-06)', '2000-01:style_momentum(0%,pre-2000-06)', '2000-01:style_quality(40%,pre-2000-06)', '2000-01:style_volatility(0%,pre-2000-06)', '2000-01:style_leverage(40%,pre-2000-06)', '2000-01:style_growth(40%,pre-2000-06)']
+- **FLAG** `B.exposures` — z-score shape anomalies: ['2000-06:style_size(sd=1.00,max=3.5)', '2002-06:style_value(sd=1.00,max=4.0)', '2002-06:style_volatility(sd=1.00,max=3.5)', '2002-06:style_growth(sd=1.00,max=3.7)', '2003-06:style_leverage(sd=1.00,max=3.6)', '2003-06:style_growth(sd=1.00,max=3.6)', '2004-06:style_value(sd=1.00,max=5.4)', '2004-06:style_growth(sd=1.00,max=3.7)', '2005-06:style_value(sd=1.00,max=4.4)', '2005-06:style_leverage(sd=1.00,max=3.9)', '2005-06:style_growth(sd=1.00,max=4.3)', '2006-06:style_value(sd=1.00,max=4.4)']
+- **PASS** `C.covariance` — PSD (min eig 1.03e-04, asym 0.00e+00, cond 1.6e+03)
+- **PASS** `C.covariance` — all non-dormant covariance-implied vols in band (0 dormant excluded)
+- **PASS** `C.covariance` — max |factor corr| 0.900
+- **PASS** `D.specific` — specific vol p01/p50/p99 = 0.03/0.32/1.22 ann, 1.3% at structural bounds, all finite
+- **FAIL** `E.proxies` — anchor ETF projected betas off: ['QQQ(projected=1.37)']
+- **PASS** `I.construction` — tradability map: 97 eligible / 104 reviewed-ineligible
+- **PASS** `I.construction` — 4 anchor instruments within 0.5 of realized beta
+- **PASS** `I.construction` — carrier-method provenance complete across 312 months
+- **PASS** `F.pit` — 135 component partitions across 27 sampled months re-hash to the manifest
+- **PASS** `F.pit` — no partition dates beyond their asof
+- **PASS** `G.realized` — 6 era checks @20d — 2011-08[R]: pred=30.7% real=29.5% ratio=1.04; 2017-12[O]: pred=6.3% real=8.9% ratio=0.71; 2020-03[R]: pred=62.9% real=43.3% ratio=1.45; 2022-06[R]: pred=24.8% real=17.7% ratio=1.40; 2024-04[O]: pred=12.0% real=10.0% ratio=1.20; 2026-06[O]: pred=11.8% real=8.4% ratio=1.42 | gating: 3 reaction era(s) vs absolute band [0.45, 2.2] | informational mean|log ratio| 0.251 vs baseline 0.258; onset eras informational
+- **PASS** `H.delta` — largest factor-vol moves vs baseline: ['theme:theme_uranium: 19.1%->21.6%', 'industry:industry_computer_hardware: 18.7%->16.5%', 'sector:BASIC MATERIALS: 20.1%->17.9%', 'industry:industry_marine_shipping: 19.9%->21.9%', 'industry:industry_construction_materials: 18.0%->16.2%', 'style_beta: 14.5%->12.7%', 'industry:industry_software_application: 15.0%->13.4%', 'industry:industry_information_technology_services: 16.9%->15.3%']
+
+**Summary: 1 FAIL / 4 FLAG / 17 PASS**

buse_v1.1.0/factor_universe.json

@@ -0,0 +1,125 @@
+{
+  "artifact_model_id": "BUSE1-TH",
+  "factor_universe": {
+    "country": [],
+    "industry": [
+      "industry_aerospace_defense",
+      "industry_agricultural_machinery",
+      "industry_airlines_airports_air_services",
+      "industry_apparel_retail",
+      "industry_asset_management",
+      "industry_auto_manufacturers",
+      "industry_auto_parts",
+      "industry_banks_diversified",
+      "industry_banks_regional",
+      "industry_beverages_non_alcoholic",
+      "industry_biotechnology",
+      "industry_chemicals",
+      "industry_chemicals_specialty",
+      "industry_communication_equipment",
+      "industry_computer_hardware",
+      "industry_conglomerates",
+      "industry_construction_materials",
+      "industry_consumer_electronics",
+      "industry_discount_stores",
+      "industry_diversified_utilities",
+      "industry_drug_manufacturers_general",
+      "industry_drug_manufacturers_specialty_generic",
+      "industry_electrical_equipment_parts",
+      "industry_engineering_construction",
+      "industry_entertainment",
+      "industry_financial_capital_markets",
+      "industry_financial_credit_services",
+      "industry_financial_data_stock_exchanges",
+      "industry_gold",
+      "industry_hardware_equipment_parts",
+      "industry_household_personal_products",
+      "industry_industrial_distribution",
+      "industry_industrial_machinery",
+      "industry_industrial_materials",
+      "industry_information_technology_services",
+      "industry_insurance_diversified",
+      "industry_insurance_life",
+      "industry_insurance_property_casualty",
+      "industry_integrated_freight_logistics",
+      "industry_internet_content_information",
+      "industry_luxury_goods",
+      "industry_marine_shipping",
+      "industry_medical_care_facilities",
+      "industry_medical_devices",
+      "industry_medical_diagnostics_research",
+      "industry_medical_healthcare_plans",
+      "industry_medical_instruments_supplies",
+      "industry_oil_gas_equipment_services",
+      "industry_oil_gas_exploration_production",
+      "industry_oil_gas_integrated",
+      "industry_oil_gas_midstream",
+      "industry_oil_gas_refining_marketing",
+      "industry_packaged_foods",
+      "industry_regulated_electric",
+      "industry_regulated_gas",
+      "industry_reit_industrial",
+      "industry_reit_mortgage",
+      "industry_reit_other",
+      "industry_reit_residential",
+      "industry_reit_retail",
+      "industry_residential_construction",
+      "industry_restaurants",
+      "industry_semiconductors",
+      "industry_software_application",
+      "industry_software_infrastructure",
+      "industry_specialty_business_services",
+      "industry_specialty_retail",
+      "industry_steel",
+      "industry_telecommunications_services",
+      "industry_tobacco",
+      "industry_travel_services"
+    ],
+    "sector": [
+      "FINANCIAL SERVICES",
+      "INDUSTRIALS",
+      "TECHNOLOGY",
+      "CONSUMER CYCLICAL",
+      "HEALTHCARE",
+      "BASIC MATERIALS",
+      "CONSUMER DEFENSIVE",
+      "REAL ESTATE",
+      "ENERGY",
+      "UTILITIES",
+      "COMMUNICATION SERVICES"
+    ],
+    "style": [
+      "style_beta",
+      "style_size",
+      "style_value",
+      "style_momentum",
+      "style_quality",
+      "style_volatility",
+      "style_leverage",
+      "style_growth"
+    ],
+    "theme": [
+      "theme_semis",
+      "theme_software",
+      "theme_biotech",
+      "theme_pharma",
+      "theme_medical_devices",
+      "theme_oil_gas_ep",
+      "theme_oil_services",
+      "theme_oil_midstream",
+      "theme_aerospace_defense",
+      "theme_airlines",
+      "theme_regional_banks",
+      "theme_gold_miners",
+      "theme_uranium",
+      "theme_solar",
+      "theme_renewables",
+      "theme_specialty_retail",
+      "theme_memory"
+    ]
+  },
+  "generated_at_utc": "2026-09-02T20:00:17.463485+00:00",
+  "model_family": "BUSE1",
+  "model_name": "BUSE1-TH",
+  "risk_model_version": "buse1_th_v1"
+}

buse_v1.1.0/metadata.json

@@ -0,0 +1,308 @@
+{
+  "artifact_generation_id": "buse_v1.1.0",
+  "artifact_model_id": "BUSE1-TH",
+  "artifact_schema_version": 1,
+  "artifacts": {
+    "exposures": "exposures_by_month",
+    "exposures_latest": "exposures_latest.parquet",
+    "factor_covariance": "factor_covariance.parquet",
+    "factor_returns": "factor_returns.parquet",
+    "factor_universe": "factor_universe.json",
+    "specific_risk": "specific_risk_by_month",
+    "specific_risk_latest": "specific_risk_latest.parquet"
+  },
+  "asset_class": "equity",
+  "available_theme_factors": [
+    "theme_semis",
+    "theme_software",
+    "theme_biotech",
+    "theme_pharma",
+    "theme_medical_devices",
+    "theme_oil_gas_ep",
+    "theme_oil_services",
+    "theme_oil_midstream",
+    "theme_aerospace_defense",
+    "theme_airlines",
+    "theme_regional_banks",
+    "theme_gold_miners",
+    "theme_uranium",
+    "theme_solar",
+    "theme_renewables",
+    "theme_specialty_retail",
+    "theme_memory"
+  ],
+  "build_start_date": "2022-11-17",
+  "category_factors": {
+    "country": [],
+    "industry": [
+      "industry_aerospace_defense",
+      "industry_agricultural_machinery",
+      "industry_airlines_airports_air_services",
+      "industry_apparel_retail",
+      "industry_asset_management",
+      "industry_auto_manufacturers",
+      "industry_auto_parts",
+      "industry_banks_diversified",
+      "industry_banks_regional",
+      "industry_beverages_non_alcoholic",
+      "industry_biotechnology",
+      "industry_chemicals",
+      "industry_chemicals_specialty",
+      "industry_communication_equipment",
+      "industry_computer_hardware",
+      "industry_conglomerates",
+      "industry_construction_materials",
+      "industry_consumer_electronics",
+      "industry_discount_stores",
+      "industry_diversified_utilities",
+      "industry_drug_manufacturers_general",
+      "industry_drug_manufacturers_specialty_generic",
+      "industry_electrical_equipment_parts",
+      "industry_engineering_construction",
+      "industry_entertainment",
+      "industry_financial_capital_markets",
+      "industry_financial_credit_services",
+      "industry_financial_data_stock_exchanges",
+      "industry_gold",
+      "industry_hardware_equipment_parts",
+      "industry_household_personal_products",
+      "industry_industrial_distribution",
+      "industry_industrial_machinery",
+      "industry_industrial_materials",
+      "industry_information_technology_services",
+      "industry_insurance_diversified",
+      "industry_insurance_life",
+      "industry_insurance_property_casualty",
+      "industry_integrated_freight_logistics",
+      "industry_internet_content_information",
+      "industry_luxury_goods",
+      "industry_marine_shipping",
+      "industry_medical_care_facilities",
+      "industry_medical_devices",
+      "industry_medical_diagnostics_research",
+      "industry_medical_healthcare_plans",
+      "industry_medical_instruments_supplies",
+      "industry_oil_gas_equipment_services",
+      "industry_oil_gas_exploration_production",
+      "industry_oil_gas_integrated",
+      "industry_oil_gas_midstream",
+      "industry_oil_gas_refining_marketing",
+      "industry_packaged_foods",
+      "industry_regulated_electric",
+      "industry_regulated_gas",
+      "industry_reit_industrial",
+      "industry_reit_mortgage",
+      "industry_reit_other",
+      "industry_reit_residential",
+      "industry_reit_retail",
+      "industry_residential_construction",
+      "industry_restaurants",
+      "industry_semiconductors",
+      "industry_software_application",
+      "industry_software_infrastructure",
+      "industry_specialty_business_services",
+      "industry_specialty_retail",
+      "industry_steel",
+      "industry_telecommunications_services",
+      "industry_tobacco",
+      "industry_travel_services"
+    ],
+    "sector": [
+      "FINANCIAL SERVICES",
+      "INDUSTRIALS",
+      "TECHNOLOGY",
+      "CONSUMER CYCLICAL",
+      "HEALTHCARE",
+      "BASIC MATERIALS",
+      "CONSUMER DEFENSIVE",
+      "REAL ESTATE",
+      "ENERGY",
+      "UTILITIES",
+      "COMMUNICATION SERVICES"
+    ],
+    "style": [
+      "style_beta",
+      "style_size",
+      "style_value",
+      "style_momentum",
+      "style_quality",
+      "style_volatility",
+      "style_leverage",
+      "style_growth"
+    ],
+    "theme": [
+      "theme_semis",
+      "theme_software",
+      "theme_biotech",
+      "theme_pharma",
+      "theme_medical_devices",
+      "theme_oil_gas_ep",
+      "theme_oil_services",
+      "theme_oil_midstream",
+      "theme_aerospace_defense",
+      "theme_airlines",
+      "theme_regional_banks",
+      "theme_gold_miners",
+      "theme_uranium",
+      "theme_solar",
+      "theme_renewables",
+      "theme_specialty_retail",
+      "theme_memory"
+    ]
+  },
+  "coverage": {
+    "latest_assets": 2437,
+    "latest_assets_with_all_style_factors": 2408,
+    "latest_assets_with_market_cap": 2437,
+    "latest_mean_style_coverage": 0.9972814917564392
+  },
+  "estimation_stats": {
+    "correlation_noise_floor": 0.07970838986179911,
+    "correlation_recent_effective_obs": 160.39536076853804,
+    "correlation_recent_weight": 0.0,
+    "correlation_regime_shift": 0.05641129402275354,
+    "eigen_simulations": 0,
+    "factor_volatility_adjustment": "none",
+    "factor_vra_applied": false,
+    "forecast_horizon_days": 63,
+    "market_volatility_anchor": "none",
+    "market_volatility_anchor_inputs": {
+      "active": true,
+      "anchor_volatility": 0.13107392563895773,
+      "median_volatility": 0.1369064736464421,
+      "pre_reversion_volatility": 0.12910435509920962,
+      "reversion_kappa": 0.7089197218304122,
+      "target_variance": 0.017259495182908077,
+      "target_volatility": 0.13137539793625014,
+      "vix_variance_ratio": 0.9701729731058852,
+      "vol_half_life": 20.0
+    },
+    "market_volatility_anchor_scale": 0.9801193421820612,
+    "market_volatility_forecast": "causal-vix-anchored-oudecay",
+    "market_volatility_propagation": "beta-propagated-systematic",
+    "market_volatility_target": 0.13137539793625014,
+    "optimization_bias_adjustment": "none",
+    "pooled_forward_vol_ratio": null,
+    "risk_horizon": "buse-trading",
+    "risk_horizon_profile": {
+      "corr_half_life": 125.0,
+      "corr_lookback": 1008,
+      "corr_nw_lags": 3,
+      "cov_lookback": 504,
+      "factor_vra_half_life": 21.0,
+      "forecast_horizon_days": 20,
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+  "history_start_date": "2000-01-03",
+  "input_identity": {
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+    "sha256": "c7625eab5e0836a017afd068a02b43e54d400956b6b43ecad36f72125d74fa77"
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+  "market_scope": "US",
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+    "factor_returns": "daily cross-sectional WLS (sqrt-cap weights, Huber IRLS robustness) of close-close return D on each ticker's immediately previous available style, sector, theme (industry-membership), industry (reviewed consolidation-map membership), country, market-cap and liquidity snapshot; categorical factors require both raw and effective breadth of at least MIN_CATEGORY_MEMBERS and receive a zero-mean empirical-Bayes ridge prior equivalent to MIN_CATEGORY_MEMBERS effective stocks, mapped through the cap-weighted sum-to-zero restrictions. Penalized-factor standard errors use the ridge sandwich covariance (X'WX+P)^-1 X'WX (X'WX+P)^-1. Return cross-sections are filtered for abs(return) <= 200% and unstable fits with abs(factor_return) > 75% are dropped. Cap-weighted sum-to-zero restrictions on the sector, theme, industry, and country blocks identify the market factor. Style/sector/theme/industry/country blocks are individually selectable; themes and industries are nested in sectors and are sector-orthogonal only when sectors are excluded.",
+    "incremental_write": "rebuild overlap window, merge by date, write temp files, then atomic rename",
+    "market_exposure": "implicit unit loading for stocks",
+    "specific_risk": "EWMA residual variance + monthly structural ln-vol fallback (gamma blend), Bayesian shrinkage toward cap-weighted size-decile mean vol (adaptive intensity, q), per-date specific VRA, 5% vol floor; annualized",
+    "style_exposure": "mean of available normalized descriptors, then re-standardized to cap-weighted mean 0 / equal-weighted std 1",
+    "weights": "previous-observation sqrt market cap, clipped at daily 99th percentile, times Huber robustness weights"
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+    "style_growth": [],
+    "style_leverage": [],
+    "style_momentum": [],
+    "style_quality": [],
+    "style_size": [],
+    "style_value": [],
+    "style_volatility": []
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+  "model_family": "BUSE1",
+  "model_name": "BUSE1-TH",
+  "parameters": {
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+    "baseline_universe": null,
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+    "industries": "all",
+    "market_volatility_anchor": "none",
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+    "market_volatility_propagation": "beta-propagated-systematic",
+    "max_countries": 0,
+    "max_sectors": 0,
+    "min_cross_section": 100,
+    "native_market_beta_dir": null,
+    "native_market_beta_horizon": null,
+    "optimization_bias_adjustment": "none",
+    "returns_timing": "none",
+    "ridge_lambda": 1e-05,
+    "risk_horizon": "buse-trading",
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+    },
+    "sectors_included": true,
+    "specific_gamma_method": "count-only",
+    "specific_lookback": 504,
+    "specific_shrinkage": 0.05,
+    "styles": "style_beta,style_size,style_value,style_momentum,style_quality,style_volatility,style_leverage,style_growth",
+    "themes": "all",
+    "universe_region": "US"
+  },
+  "replace_start_date": "2025-11-16",
+  "risk_model_version": "buse1_th_v1",
+  "row_counts": {
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+    "exposures_latest": 2437,
+    "factor_covariance": 10000,
+    "factor_returns": 727380,
+    "specific_risk": 20105303,
+    "specific_risk_latest": 2437
+  },
+  "style_factors": [
+    "style_beta",
+    "style_size",
+    "style_value",
+    "style_momentum",
+    "style_quality",
+    "style_volatility",
+    "style_leverage",
+    "style_growth"
+  ],
+  "supported_view_types": [
+    "artifact"
+  ]
+}

buse_v1.1.0/scale_diagnostics_by_month/2000-12.json

@@ -0,0 +1,26 @@
+{
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+}

buse_v1.1.0/scale_diagnostics_by_month/2001-05.json

@@ -0,0 +1,26 @@
+{
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+}

buse_v1.1.0/scale_diagnostics_by_month/2001-09.json

@@ -0,0 +1,26 @@
+{
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buse_v1.1.0/scale_diagnostics_by_month/2002-03.json

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+{
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buse_v1.1.0/scale_diagnostics_by_month/2003-02.json

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+{
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buse_v1.1.0/scale_diagnostics_by_month/2004-10.json

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+{
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buse_v1.1.0/scale_diagnostics_by_month/2005-07.json

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+{
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buse_v1.1.0/scale_diagnostics_by_month/2005-11.json

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+{
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+}

buse_v1.1.0/scale_diagnostics_by_month/2006-01.json

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+{
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+}

buse_v1.1.0/scale_diagnostics_by_month/2008-05.json

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+{
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+}

buse_v1.1.0/scale_diagnostics_by_month/2008-09.json

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+{
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+}

buse_v1.1.0/scale_diagnostics_by_month/2009-04.json

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+{
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buse_v1.1.0/scale_diagnostics_by_month/2009-12.json

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+{
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+}

buse_v1.1.0/scale_diagnostics_by_month/2010-04.json

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+{
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buse_v1.1.0/scale_diagnostics_by_month/2010-08.json

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buse_v1.1.0/scale_diagnostics_by_month/2010-12.json

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buse_v1.1.0/scale_diagnostics_by_month/2011-05.json

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buse_v1.1.0/scale_diagnostics_by_month/2011-09.json

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buse_v1.1.0/scale_diagnostics_by_month/2012-03.json

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buse_v1.1.0/scale_diagnostics_by_month/2013-02.json

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buse_v1.1.0/scale_diagnostics_by_month/2014-10.json

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buse_v1.1.0/scale_diagnostics_by_month/2015-07.json

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buse_v1.1.0/scale_diagnostics_by_month/2015-11.json

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buse_v1.1.0/scale_diagnostics_by_month/2016-01.json

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buse_v1.1.0/scale_diagnostics_by_month/2018-05.json

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buse_v1.1.0/scale_diagnostics_by_month/2018-09.json

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buse_v1.1.0/scale_diagnostics_by_month/2019-04.json

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buse_v1.1.0/scale_diagnostics_by_month/2019-12.json

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buse_v1.1.0/scale_diagnostics_by_month/2020-01.json

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buse_v1.1.0/scale_diagnostics_by_month/2022-06.json

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buse_v1.1.0/scale_diagnostics_by_month/2022-10.json

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buse_v1.1.0/scale_diagnostics_by_month/2023-07.json

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buse_v1.1.0/scale_diagnostics_by_month/2023-11.json

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buse_v1.1.0/scale_diagnostics_by_month/2024-03.json

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buse_v1.1.0/scale_diagnostics_by_month/2025-02.json

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buse_v1.1.0/scale_diagnostics_by_month/2026-04.json

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+{
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buse_v1.1.0/scale_diagnostics_by_month/2026-08.json

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imp_v1_1_0/metadata.json

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+{
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+}

imp_v1_1_0/specific_risk_overlay_by_month/asof_dates.json

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imp_v1_1_0/specific_risk_overlay_hash_manifest.json

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retail_v1_1_0/metadata.json

@@ -0,0 +1,11 @@
+{
+  "artifact_model_id": "BUSE1-TH-RETAIL",
+  "overlay_contract": {
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+    "min_variance_multiplier": 0.25,
+    "missing_symbol_policy": "base"
+  },
+  "supported_view_types": [
+    "retail_overlay"
+  ]
+}

retail_v1_1_0/specific_risk_overlay_by_month/asof_dates.json

@@ -0,0 +1,106 @@
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+  "2022-03": "2022-03-31",
+  "2022-04": "2022-04-29",
+  "2022-05": "2022-05-31",
+  "2022-06": "2022-06-30",
+  "2022-07": "2022-07-29",
+  "2022-08": "2022-08-31",
+  "2022-09": "2022-09-30",
+  "2022-10": "2022-10-31",
+  "2022-11": "2022-11-30",
+  "2022-12": "2022-12-30",
+  "2023-01": "2023-01-31",
+  "2023-02": "2023-02-28",
+  "2023-03": "2023-03-31",
+  "2023-04": "2023-04-28",
+  "2023-05": "2023-05-31",
+  "2023-06": "2023-06-30",
+  "2023-07": "2023-07-31",
+  "2023-08": "2023-08-31",
+  "2023-09": "2023-09-29",
+  "2023-10": "2023-10-31",
+  "2023-11": "2023-11-30",
+  "2023-12": "2023-12-29",
+  "2024-01": "2024-01-31",
+  "2024-02": "2024-02-29",
+  "2024-03": "2024-03-29",
+  "2024-04": "2024-04-30",
+  "2024-05": "2024-05-31",
+  "2024-06": "2024-06-28",
+  "2024-07": "2024-07-31",
+  "2024-08": "2024-08-30",
+  "2024-09": "2024-09-30",
+  "2024-10": "2024-10-31",
+  "2024-11": "2024-11-29",
+  "2024-12": "2024-12-31",
+  "2025-01": "2025-01-31",
+  "2025-02": "2025-02-28",
+  "2025-03": "2025-03-31",
+  "2025-04": "2025-04-30",
+  "2025-05": "2025-05-30",
+  "2025-06": "2025-06-30",
+  "2025-07": "2025-07-31",
+  "2025-08": "2025-08-29",
+  "2025-09": "2025-09-30",
+  "2025-10": "2025-10-31",
+  "2025-11": "2025-11-28",
+  "2025-12": "2025-12-31",
+  "2026-01": "2026-01-30",
+  "2026-02": "2026-02-27",
+  "2026-03": "2026-03-31",
+  "2026-04": "2026-04-30",
+  "2026-05": "2026-05-29",
+  "2026-06": "2026-06-30",
+  "2026-07": "2026-07-31",
+  "2026-08": "2026-08-31"
+}

retail_v1_1_0/specific_risk_overlay_hash_manifest.json

@@ -0,0 +1,420 @@
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