Aetherius Risk Intelligence

A deterministic, auditable downside-risk screen over SEC EDGAR filings and GDELT financial news, with a reproducible crisis backtest, an honest timing analysis, and a pre-registered live forward test.

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In one minute

Aetherius watches a list of companies. It reads new headlines and filings, decides conservatively whether each one really mentions a watched company, scores it with fixed, explainable weights, and puts only evidence-backed, adverse items in front of a human reviewer.

Headline result. Replayed on 161 real archived headlines from three crises (SVB 2023, Wirecard 2020, FTX 2022), it flagged 9 of 9 affected names before their formal resolution. The backtest says nothing about false alarms: its control names never appear in the data (§4.6).

The honest qualifier. In 0 of 9 cases did the first flag come before the first headline reporting the stock already falling. The system is fast, auditable triage of a market reaction, not a predictor (working paper §4.5).

What's next. A pre-registered live forward test started on 2026-10-16. A daily GitHub Action scores live data with the frozen model and commits timestamped results.

What this project demonstrates

Area	Where to look
Data ingestion from two public APIs (SEC EDGAR, GDELT DOC 2.0), with retries, rate limits, dedupe keys and idempotent reruns	ingestion/
Precision-first entity resolution: word-boundary regex, acronym stoplist, aliases, counterparty links	entity_mapping/
Deterministic scoring: one shared adverse-language list and formula used by the API, backtest and forward test	scoring/
Backend engineering: FastAPI, PostgreSQL + Alembic, JWT auth with per-client operator scoping, Celery workers and schedules	aetherius/
Quality gates before anything reaches a reader: banned hype language, required disclaimer, evidence for every high-severity item, retry with backoff, dead-lettering	delivery/
Reproducible research: frozen fixtures, bit-identical metrics, tests that pin the published numbers	simulations/backtest/, tests/
Research honesty: confidence intervals, a market-timing check that weakens the headline claim, documented false-positive classes

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Results

Event	Mechanism	Affected names flagged before resolution	Median lead to resolution	First flag before market reaction
SVB-2023	Regional-bank contagion	5 / 5	2.34 days	0 / 5
Wirecard-2020	Accounting fraud	1 / 1	6.60 days	0 / 1
FTX-2022	Crypto counterparty contagion	3 / 3	7.12 days	0 / 3
All		9 / 9 (95% CI 66–100%)	2.48 days	0 / 9

"Resolution" means the formal event: receivership, insolvency filing or Chapter 11. The "market reaction" is the first headline in the corpus reporting the stock already falling (market_reaction_check.py).

Limitations

• Small sample, chosen with hindsight. There are three famous failures and nine affected names, and both the events and the adverse-term list were chosen knowing the outcomes. The confidence intervals above are wide.
• Triage, not anticipation. Flags arrive together with the press reporting a sell-off.
• False alarms are untested. Each event has a control company, but none appears in its headlines, so "no control was flagged" proves nothing. The live forward test measures false alarms instead.
• Known false positives. Tickers that are English words match ordinary text ("coin prices fall" matches COIN). Adverse terms match as substrings ("fall" fires inside "shortfall").
• EDGAR adds provenance, not signal. Filings are scored on metadata (form and date), never on the filing text.
• Whitelisted sources only. Coverage is a fixed list of financial-news domains, which is thin for non-U.S. events.

All of these are written up in §5 of the working paper. The forward test is the start of answering them.

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How it works

EDGAR filings ─┐
               ├─▶ ingest + dedupe ─▶ entity mapping ─▶ deterministic score ─▶ adverse gate ─▶ signal
GDELT news ────┘   (idempotent)       (word-boundary)    (fixed weights)       (≥1 downside     │
                                                                                term required)  ▼
                                      operator review ◀─ daily brief / urgent alert drafts ◀────┘
                                             │
                                             ▼
                               quality gates ─▶ email + PDF delivery (retry, dead-letter)

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Quick start

git clone https://github.com/zariffromlatif/Aetherius-Risk-Intelligence.git
cd Aetherius-Risk-Intelligence
pip install -r requirements.txt

pytest aetherius/tests -q                                          # 129 tests
python simulations/backtest/run_backtest.py --event svb-2023       # or wirecard-2020, ftx-2022
PYTHONPATH=aetherius python simulations/backtest/market_reaction_check.py --markdown

Generate a per-company stress-test deck (HTML, print to PDF) from the frozen SVB data:

python scripts/build_deck.py \
  --ticker SIVB --name "SVB Financial Group" --sector "Regional Banks" \
  --aliases "Silicon Valley Bank,SVB" \
  --thesis "Concentrated regional-bank position with rate-sensitive HTM book." \
  --counterparty "FRC:First Republic Bank:peer:0.6:First Republic" \
  --window 2023-03-06:2023-03-12 \
  --fixture-jsonl simulations/backtest/events/svb-2023/observations.jsonl \
  --out sivb-deck.html

To run the full backend (API, worker, scheduler, Postgres, Redis, Mailpit), see aetherius/README.md.

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Project structure

aetherius/
  app/
    api/                 # FastAPI routers, JWT auth, per-client scoping
    services/
      ingestion/         # SEC EDGAR and GDELT adapters
      entity_mapping/    # Word-boundary matching, acronym stoplist
      scoring/           # Shared adverse-language list and deterministic formulas
      signals/           # Evidence -> scored, typed risk signal
      pipeline/          # Observation -> evidence -> signal flow (API and workers)
      drafting/ review/  # Brief drafting and operator review workflow
      delivery/          # Quality gates, email/PDF, retries, dead-letter
      reporting/         # Weekly summaries, stress-test deck assembly
    workers/             # Celery beat: hourly ingestion, briefs, retries, retention
  alembic/ migrations/   # Schema
  tests/                 # 129 tests
simulations/
  backtest/              # Frozen crisis fixtures, replay harness, market-timing check
  forward_test/          # Pre-registered live forward test + daily summarizer
docs/
  working_paper/         # Methodology, results and limitations (Revision 2)
site/                    # Static project page (Cloudflare Pages build output directory)

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Author

Built by Zarif Latif. I'm looking for roles in data engineering, risk analytics or financial data. If your team works on problems like this, I'd be glad to talk: [email protected].

Contributing

See CONTRIBUTING.md. PRs must keep scoring deterministic, include tests, and keep the frozen fixtures bit-identical.

License

Apache License 2.0. See LICENSE.

Disclaimer

Research software. It does not provide investment advice, fiduciary services or return forecasts.