BinomialTechnologies/buse-artifacts

BUSE1-TH model artifacts

Released model output for BUSE1-TH, an open US trading-horizon equity factor risk model. Code, methodology and audit suite live in the buse-open repository.

This repository holds derived model quantities only — no raw market data. 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 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_v1.1.0/ — core model

Tree	Partitions	Coverage	Size
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. 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 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, hash_manifest.json, replicated_carrier_exposures.parquet, replicated_carrier_report.json, audit.md.

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 and a hash manifest.

Overlay	Partitions	Coverage
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.
• RETAIL — multiplier from abnormal attention not yet confirmed by 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:

pip install huggingface_hub[cli]

# 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_1_0/*'

Read a single partition without downloading the rest:

import pandas as pd

url = ("hf://datasets/BinomialTechnologies/buse-artifacts/"
       "buse_v1.1.0/exposures_by_month/month=2025-10/part.parquet")
exposures = pd.read_parquet(url)

Apply an overlay to any risk model's specific variances:

from buse1.overlays.loader import apply, load_overlay

mult, provenance = load_overlay("artifacts/imp_v1_1_0", decision_date)
conditioned = apply(base_specific_variance_by_ticker, mult)

Integrity

SHA256SUMS covers all 2,132 artifact files:

cd artifacts && sha256sum -c SHA256SUMS

License

Apache-2.0. These are derived model quantities; see docs/limitations.md for data provenance and reproducibility boundaries.