YUCLAW v7.0.0
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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
HPEBULLISH+0.42068
TSLANEUTRAL+0.34456
INTCNEUTRAL+0.32978
XLKNEUTRAL+0.3230
WFCNEUTRAL+0.29955
XLCNEUTRAL+0.2890
DELLNEUTRAL+0.28692
SMHNEUTRAL+0.2780
PSXNEUTRAL+0.27278
XLRENEUTRAL+0.2720
BACNEUTRAL+0.26755
XLFNEUTRAL+0.2570
MSNEUTRAL+0.24971
MRVLNEUTRAL+0.24399
CNEUTRAL+0.24344
GOOGLNEUTRAL+0.24273
XLINEUTRAL+0.2350
COPNEUTRAL+0.23483
EEMNEUTRAL+0.2330
UUPNEUTRAL+0.2300
QQQNEUTRAL+0.2260
XLYNEUTRAL+0.2230
SPYNEUTRAL+0.2210
RKLBNEGATIVE_EVENT-0.22083
CRCLNEUTRAL+0.21593
JPMNEUTRAL+0.21074
TLTNEUTRAL+0.2080
IEFNEUTRAL+0.2010
DIAWATCH+0.1990
AXPWATCH+0.19877
AAPLWATCH+0.19684
XOMWATCH+0.19560
TAILWATCH+0.1940
ARMWATCH+0.19291
XLEWATCH+0.1920
SLVWATCH+0.18344
GSWATCH+0.17192
IWMWATCH+0.1680
MDYWATCH+0.1640
GLDWATCH+0.16043
NVDAWATCH+0.15399
FXIWATCH+0.1510
ABTWEAKENING-0.14285
MUWATCH+0.13987
CVXWATCH+0.13378
SLBWATCH+0.13285
METAWATCH+0.13078
JNJWEAKENING-0.12977
AMDWATCH+0.11794
LLYWEAKENING-0.10777
IBBWATCH+0.1070
VXXWATCH+0.09125
KREWATCH+0.0850
MRKWATCH+0.08281
PFEWATCH+0.08181
PYPLWATCH+0.07981
VIXYWATCH+0.07644
PEPWATCH+0.07654
COSTWATCH+0.07548
XLBWATCH+0.0680
XLUWEAKENING-0.0640
XLPWATCH+0.0510
UNHWEAKENING-0.05065
TMOWEAKENING-0.04785
ABBVWATCH+0.04587
WMTWEAKENING-0.04486
XBIWEAKENING-0.0390
LUNRWEAKENING-0.03888
DHRWEAKENING-0.03574
MSFTWATCH+0.03093
LRCXWATCH+0.01993
AMATWEAKENING-0.01388
MAWATCH+0.01387
XLVWATCH+0.0070
PGWATCH+0.00780
BMYWEAKENING-0.00645
AMZNWEAKENING-0.00587
VWEAKENING-0.00386
KOWEAKENING-0.00080

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 v7.0.0 · 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 2026-09-11 (last completed U.S. trading day) · regenerated daily after market close