YUCLAW v8.0.0
Validation LabSMH Evidence LensXLK Evidence LensCanada Resources EvidenceForward TrackingSignal ReviewExplorerSectorsEvidence ScoreboardGitHubPyPILedgerMethodologyHome

Evidence-First Financial AI

Every signal traces to a real SEC filing.

Agent Research API · MCP · LangChain/LlamaIndex · pip install yuclaw

About YUCLAW

Disclaimer — Research & education only. Not investment advice. Signal labels are research classifications, not buy/sell recommendations.

Built in Canada — from Lake Ontario to Lake Louise and Kananaskis Lake — with gratitude to the country whose land and light frame this work.

How we work
  • We don’t ask you to trust us. We give you the hash.
  • We don’t predict. We register, compute once, and disclose.
  • We don’t hide the days we were wrong. We chain them.
What you get
  • Analysts The evidence behind every label, and the label’s limits.
  • Builders Machine-readable receipts — passports, endpoints, and a registry you can walk line by line.
  • Institutions A record that can be audited without asking us.
Current signals — Forward Tracking Ledger
Current research classifications — not recommendations
TickerSignal label Score Evidence coverage
INTCBULLISH+0.43773
DELLBULLISH+0.41294
ARMBULLISH+0.410100
WFCNEUTRAL+0.34950
SMHNEUTRAL+0.3250
XLKNEUTRAL+0.3160
HPENEUTRAL+0.28789
MRVLNEUTRAL+0.26398
SLVNEUTRAL+0.25840
JPMNEUTRAL+0.25782
CNEUTRAL+0.25340
MRKNEUTRAL+0.25176
UUPNEUTRAL+0.2490
AXPNEUTRAL+0.24585
BACNEUTRAL+0.24551
MSNEUTRAL+0.24468
QQQNEUTRAL+0.2430
TSLANEUTRAL+0.23872
XLFNEUTRAL+0.2280
METANEUTRAL+0.22687
TLTNEUTRAL+0.2260
RKLBNEGATIVE_EVENT-0.22485
KRENEUTRAL+0.2230
SPYNEUTRAL+0.2220
XLINEUTRAL+0.2180
CRCLNEUTRAL+0.21582
EEMNEUTRAL+0.2140
PFENEUTRAL+0.21473
AAPLNEUTRAL+0.20787
ABBVNEUTRAL+0.20377
XLVNEUTRAL+0.2010
IEFWATCH+0.1960
DHRWATCH+0.19467
AMDWATCH+0.18299
DIAWATCH+0.1810
GLDWATCH+0.18140
GSWATCH+0.17983
FXIWATCH+0.1710
XLEWATCH+0.1710
TAILWATCH+0.1690
TMOWATCH+0.16577
PSXWATCH+0.16287
XLYWATCH+0.1510
MDYWATCH+0.1460
IWMWATCH+0.1450
CVXWATCH+0.13672
COPWATCH+0.13376
WMTWEAKENING-0.12487
MUWATCH+0.12480
UNHWATCH+0.11658
NVDAWATCH+0.109100
MAWATCH+0.10678
PYPLWATCH+0.09887
AMZNWATCH+0.09584
MSFTWATCH+0.09491
BMYWATCH+0.08866
VXXWATCH+0.08725
XLUWEAKENING-0.0860
VIXYWATCH+0.08340
XOMWATCH+0.07453
LLYWATCH+0.06871
IBBWATCH+0.0670
JNJWATCH+0.06278
COSTWATCH+0.04947
PGWATCH+0.04573
SLBWATCH+0.04574
LRCXWEAKENING-0.04288
AMATWATCH+0.04188
XBIWATCH+0.0390
KOWATCH+0.03473
VWATCH+0.03382
XLBWEAKENING-0.0320
GOOGLWATCH+0.02969
XLREWATCH+0.0250
ABTWATCH+0.01878
LUNRWEAKENING-0.01588
PEPWATCH+0.01274
XLCWATCH+0.0040
XLPWATCH+0.0010

Evidence coverage = how much evidence stands under this classification — coverage, not prediction (Evidence Coverage v1, registered protocol). Score = composite research score. It is not an expected return, a probability, a price target, or a recommendation.

Public signal vocabulary

Labels are research classifications, not buy/sell recommendations:

STRONG_BULLISH · BULLISH · NEUTRAL · WATCH · WEAKENING · NEGATIVE_EVENT · BEARISH_WATCH · RISK_ALERT (each label links to its locked threshold definition)

There is no SELL or SHORT label. The SDK's _validate_label() is invoked on every signal-bearing return.

How it works

1 · Evidence layer

SEC EDGAR filings (Form 4, 8-K, 10-Q, 10-K, 6-K, 40-F) are extracted with a local Llama 3.1 70B model. A deterministic SourceLock Guard validates every extraction against the source text before any signal sees it.

2 · Composite scoring

Nine components combine into a confidence-weighted composite. C6 event impact carries the highest weight (0.18) — by design, the evidence layer leads.

3 · Time-machine replay

Any signal can be recomputed as of a past date. Point-in-time filtering (available_as_of <= as_of) is leak-audited; reproducible via the yuclaw replay CLI or REST API.

4 · Verified Research Ledger

Each day's published signals have their content hashes committed to a public git repo (yuclaw-trust). Anyone can call yuclaw verify to confirm a signal hasn't been edited since publication.

Full disclaimer & methodology

Open-source equity research where every composite signal traces back to a verifiable SEC filing or deterministic supply-chain cascade. Replayable point-in-time. Tamper-evidenced via a public git-anchored Verified Research Ledger. Research and education only.

Disclaimer — YUCLAW research output. Not investment advice. Past performance does not guarantee future results. Signal labels are research classifications, not buy/sell recommendations. YUCLAW is not a registered investment adviser. Past results — in-sample or forward-tracked — do not predict future performance.
About YUCLAW — mission and vision

YUCLAW

Evidence-First Financial AI
The Science Trust Layer for Financial AI.

Evidence before answers.

Financial AI normally gives you an answer.

YUCLAW gives you the evidence — what was known, when it was known, what it can support, what it cannot, and whether the conclusion survived.

Mission

Make financial AI accountable to evidence.

A public, hash-linked record, built to be recomputed by anyone.

Vision

Become the Science Trust Layer for Financial AI.

The evidence infrastructure that AI systems, researchers, and institutions use to decide what deserves to be believed.

How we work

Principle Practice
We don’t ask you to trust us. We give you the hash.
We don’t predict. We register, compute once, and disclose.
We don’t hide the days we were wrong. We chain them.

What you get

For What you get
Analysts The evidence behind every label, and the label’s limits.
Builders Machine-readable receipts — passports, endpoints, and a registry you can walk line by line.
Institutions A record that can be audited without asking us.

Statistics is one instrument. Evidence is the foundation. Science is the discipline.

AI is the market. Trust is the product. Accountability is the mission.

🍁 Built in Canada

Use YUCLAW in your research
1 · Verify the record

pip install yuclaw then yuclaw replay-lab.
No install: tools/replay_lab.py (stdlib only) against the published bundle.
Exit 0 = every statistic and evidence-ledger root reproduced. How to report a replication →

2 · Inspect one evidence trace

One real Suncor 6-K, end to end:
filing → exhibit → extracted prose → event type → grade → C6 posture.
Open the trace → · example evidence memo (Suncor) →

3 · Cite a research lens

Every evidence packet ships a ready citation snippet
(version, data-through, build date, source commit).
Get the citation →

📖 User Guide (PDF) — from pip install to full verification, six pages. · 📖 Guide de l'utilisateur (FR)

Status — proven · not proven · accruing

Rendered from one shared source (v3/web/useful_blocks.py) on every page that shows it, so the copies cannot drift. Statuses are measured, not aspirational.

Proven (verifiable today)
  • ✓ Replay works — one command reproduces every Lab statistic and evidence-ledger root from published data
  • ✓ Ledger anchored daily — sha-256 daily roots committed to a public git repository before pages update
  • ✓ Evidence traces to filings — every accepted event carries a source URL, accession number, and verified excerpt
  • ✓ Coverage measured — SEC-filer weight per lens is stated as measured, never rounded up
  • ✓ Snapshots are point-in-time — daily as-of writes, zero retroactive edits (outage window disclosed, not repaired)
  • ✓ Evidence-tier names are never scored — enforced by positive gating and a standing negative check
Not proven
  • ✗ Forward alpha — no spread, IC, or alpha significant at 5% with adequate power
  • ✗ C6 risk-gate sign — rareness confirmed OOS 2026-07-06 (22% fire rate, n=9 held-out); sign confirmation pending (elevated arm n=2; accrual live from 2026-07-16)
  • ✗ Peer-model CAR lead — event-study lead over peer models is not established; live-era sample remains small
Accruing
  • · Forward out-of-sample record — one period per trading day, accruing daily
  • · Matured CAR events — each accepted event matures into the event study after its forward window completes
  • · C6 elevated arm — live Form-4 ingestion since 2026-07-16 restores the insider stream to production inputs
  • · External replications — the replication log accrues as independent runs are reported
For AI agents & researchers

YUCLAW is the open evidence layer underneath AI research tools. Start with llms.txt and the machine-readable evidence_index.json (every page, packet, and protocol with stable URLs and data-through dates). Packets carry derived statistics, event CSVs, engine run JSONs, and citation snippets; yuclaw replay-lab re-computes the published statistics from the public bundle. Derived data only — preserve the disclaimers when quoting; nothing here is advice or a recommendation.

Install + try it
pip install yuclaw
yuclaw demo                         # 3-minute guided "Why AMD?" journey
yuclaw why AMD --as-of 2026-05-20   # bundled offline signal
yuclaw verify AMD --date 2026-05-20 # check the ledger record
# all tickers/dates: connect the local backend — see README

SDK + REST API + MCP server documented at github.com/YuClawLab/yuclaw-brain. REST API terms at /API_TERMS.md.

YUCLAW v7.0.1 · YuClawLab · Full Disclaimer · 🚀 5-minute tour · 🤖 For AI builders · 📐 EvidenceBench · 📖 User Guide (PDF) · 📖 Guide de l'utilisateur (FR) · Methodology · Replication · Today's Evidence Changes · YUCLAW's Lane · Weekly Note · Apache-2.0 Licensed
Data through 2026-09-18 (last completed U.S. trading day); generated at 2026-09-18 23:02 UTC.