Evidence-First Financial AI

Accepted filing events link to SEC sources. Composite classifications also use market inputs; some names have no direct filing evidence in our corpus.

Agent Research API · MCP · LangChain/LlamaIndex · pip install 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
LLYBULLISH+0.48890
XLVBULLISH+0.4690
UNHBULLISH+0.45150
XLREBULLISH+0.4430
PFEBULLISH+0.43064
TMOBULLISH+0.42881
ABBVBULLISH+0.42175
XLUBULLISH+0.4090
COPNEUTRAL+0.38962
ABTNEUTRAL+0.38075
XLFNEUTRAL+0.3750
PSXNEUTRAL+0.37393
KONEUTRAL+0.37359
TSLANEUTRAL+0.37369
COSTNEUTRAL+0.36888
PYPLNEUTRAL+0.34086
AXPNEUTRAL+0.32983
KRENEUTRAL+0.3280
PEPNEUTRAL+0.32789
XLBNEUTRAL+0.3240
XOMNEUTRAL+0.32249
XLPNEUTRAL+0.3150
SLBNEUTRAL+0.314100
BMYNEUTRAL+0.30872
TLTNEUTRAL+0.3030
JPMNEUTRAL+0.30072
WFCNEUTRAL+0.29777
JNJNEUTRAL+0.29682
VNEUTRAL+0.28586
XLYNEUTRAL+0.2820
MDYNEUTRAL+0.2760
XLENEUTRAL+0.2760
IEFNEUTRAL+0.2680
DIANEUTRAL+0.2660
TAILNEUTRAL+0.2650
IWMNEUTRAL+0.2640
UUPNEUTRAL+0.2510
SPYNEUTRAL+0.2450
XLINEUTRAL+0.2440
MSNEUTRAL+0.24352
WMTNEUTRAL+0.23583
VXXNEUTRAL+0.23442
MRKNEUTRAL+0.23193
VIXYNEUTRAL+0.22936
GOOGLNEGATIVE_EVENT-0.22562
IBBNEUTRAL+0.2210
BACNEUTRAL+0.20384
LRCXNEGATIVE_EVENT-0.20391
CNEUTRAL+0.20363
MAWATCH+0.19887
DHRWATCH+0.19087
MSFTWATCH+0.19099
CVXWATCH+0.18988
XLCWEAKENING-0.1850
QQQWATCH+0.1840
MUWEAKENING-0.18387
PGWATCH+0.17385
DELLWEAKENING-0.16898
FXIWATCH+0.1480
MRVLWEAKENING-0.14591
AAPLWEAKENING-0.14081
AMDWEAKENING-0.13793
INTCWEAKENING-0.12378
ARMWEAKENING-0.11767
GSWATCH+0.11194
SMHWEAKENING-0.1090
GLDWATCH+0.10436
XLKWEAKENING-0.1020
HPEWEAKENING-0.09883
AMZNWATCH+0.09387
AMATWEAKENING-0.07285
METAWEAKENING-0.07093
RKLBWEAKENING-0.05778
LUNRWEAKENING-0.05376
EEMWATCH+0.0480
SLVWEAKENING-0.02436
XBIWATCH+0.0120
NVDAWEAKENING-0.01283
CRCLWATCH+0.00289

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 = core tables reproduced and bundled ledger consistency checked; external provenance is separate. 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 →

v9 English · v9 Français
📖 User Guide / Guide utilisateur (v8 · 8.0.1): English (PDF) · Français (PDF) · 📖 Workbench foundation guide (v8, English) — install, workspaces, roles, the local workbench, the four modules, export and verification; the same text ships in the package (yuclaw workbench guide). · 5-minute command-line tour · Earlier PDF guides — historical: written for the version 5 command line, 12 pages, not updated for v8: English · Français

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 checks the core expected tables and bundled ledger consistency; external provenance requires a separately acquired ledger
  • ✓ 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.

Research briefs (v9), built on the local workbench (v8)

Version 9 adds sentence inspection, brief composition, tracked edits and translations, imported provenance records, portable verification and operation counts. It does not include live model-generation or watermark-detection connectors.

This website is the research-content site: evidence, labs and records you can read and re-compute. The v8 workbench is a different thing: an application that runs on your own computer (it binds 127.0.0.1 only; your data stays in the folder you name; there is no hosted service and no account). It traces one financial commitment from an exact source passage through a typed, frozen claim, its revisions, exact calculation, history and review to an export that a fresh workspace re-verifies. Four modules work on the same records: SHD Distillation Shield (protected evidence intake), EVO Evolution Evidence Audit (what changed in an AI system, and which evidence still applies), COM Research Commons Guard (a fair, bounded review queue) and PRC Independent Practice (attempt first, then compare).

pip install yuclaw
yuclaw workbench selftest                                  # checks this installation in a temporary fictional workspace
yuclaw workbench principals init --workspace ~/yuclaw-ws   # optional: roles for the four modules; prints the first administrator's credential once
yuclaw workbench serve --workspace ~/yuclaw-ws             # then open http://127.0.0.1:8765  (Ctrl-C stops it)
yuclaw workbench guide                                     # the full guide, from the package

In the browser: load a fictional example on the Workspace page, follow its seven steps, build the export, then verify it in a second, fresh workspace (current guide). Status: experimental, local, owner-operated. Including the modules activates nothing — no role, approval, budget or study exists until you set one up. Protected SHD admission needs Linux with Landlock; on macOS, Windows or a kernel without it that one route stays closed and everything else works. No independent security review has been performed, and no user study: human benefit is PENDING. What has and has not been demonstrated is on the evidence scoreboard, under its own as-of time.

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.

Data through 2026-10-29 (last completed U.S. trading day); generated at 2026-10-29 23:01 UTC.