ai-asset-screener

General info

ai-asset-screener is a CLI tool for fast, fundamentals-driven equity screening with LLM-assisted reading of 10-K filings. It pulls core metrics from Yahoo Finance, enriches them with fair-value EV adjustments and insurance float extracted from 10-Ks, and builds a segment-level SOTP model. The tool issues BUY/SELL/UNSURE signals per metric and per peer group using robust, outlier-aware rules, then aggregates them into a final verdict. Configuration is done via environment variables (.env supported): LLM_MODEL (required), plus either an OpenAI-compatible LLM_ENDPOINT or LLM_OPENAI_API_KEY. Results are printed as readable console reports with group details and a summary table; intermediate data and 10-K parses are cached under cache/. Logging goes to asset.log.

Features list

• Data foundation

  • Yahoo Finance fetch (market cap, EV, debt, cash, FCF TTM, EBIT/EBITDA TTM, Forward P/E) with local JSON caching per ticker.
  • EDGAR 10-K ingestion (via edgartools) to analyze notes and segment disclosures; large-file chunking and caching.

• Fair-value EV adjustments (10-K notes => EV)

  • LLM extracts candidate items with signed deltas (USD millions): e.g., lease liabilities, pension deficits, TRA payables, contingent consideration, AROs, environmental liabilities, FV uplifts/discounts.
  • Canonicalization + whitelist/blacklist filters (drops obligations without recognized liabilities, operating-lease obligations schedules, VIE “exposures”, deferred taxes, etc.).
  • Sanity caps by category and as a fraction of EV; duplicates merged; EV and EV/EBITDA recomputed.

• Insurance float model (insurers only)

  • LLM reads balance/notes to assemble float components (USD millions):

    • Unpaid losses & LAE (net), Unearned premiums (net), Future policy benefits, Policyholders’ account balances, Other insurance liabilities; − Reinsurance recoverable, (Re)insurance balances receivable, Deferred charge assets for retroactive reinsurance.

  • Strict netting rules (prefer NET where disclosed), scale sanity checks, and Float/EV ratio computed.

• SOTP (Sum-of-the-Parts) from operating segments

  • LLM extracts operating segments (ASC 280) for the latest year and parses a clean table: Segment | Revenue | Operating income.
  • Segment valuation via peer median EV/EBIT; fallback: EV/EBITDA × 1.25 when EV/EBIT is sparse.
  • Loss-making segments: insurers => EV≈0 (conservative); others => fallback EV/Sales ≈ 1× if revenue is available.
  • Outputs per-segment implied EVs, peer bucket used, multiples applied, and a premium/discount vs current EV.

• Signals & robustness

  • Metric signals (IQR rule):

    • Forward P/E, P/FCF, EV/EBITDA => BUY if value < Q1, SELL if > Q3, else UNSURE (with caps for outliers).
    • Float/EV signal for insurers uses peer quartiles within subsector (P&C / Life / Reins / Conglom).

  • SOTP signal: discount/premium threshold ±10% => BUY/SELL; otherwise UNSURE.

  • Majority vote across available signals with tie-break priority: EV/EBITDA > P/FCF > Forward P/E.

• Peer groups & auto-scoping

  • Built-in universes (e.g., BIG_TECH, SEMI, CLOUD_SOFTWARE, INTERNET_ADS, ECOMMERCE, AUTO_EV, ASSET_MANAGERS, CONGLOMERATES, INSURANCE, CRYPTO miners/exchanges).
  • Auto-selection of relevant groups for a ticker or explicit filtering via --group.

• DX & ops

  • .env support; clear stdout reports (per group + consolidated table).
  • Persistent caching for Yahoo snapshots and 10-K-derived artifacts.
  • Structured logging to asset.log.

How to run:

pip install ai-asset-screener
ai-asset-screener --ticker=ADBE --group=BIG_TECH_CORE  --use-cache

.env file example:

LLM_ENDPOINT = "http://localhost:1234/v1"
LLM_MODEL = "openai/gpt-oss-20b"

Add LLM_OPENAI_API_KEY if you use commercial OpenAI API and remove LLM_ENDPOINT