Quant · Alternative Data · Share-Out

Source · Loop · Assets

A point-in-time source pried from behind a wall, the self-correcting pipeline that grades it, and the hypotheses we ran, mapped to the assets they'd trade.

1 · the source2 · the loop3 · the assets
mechanism-first · evidence-graded · executed beats reasoned
Why this is hard

Plausible Priced

Almost any alt-data story sounds sound. Few survive a costed backtest. The hard part isn't finding data, it's not fooling yourself.

The debate trap

Any signal can be argued past a skeptic. A won argument is not evidence of edge.

Reasoned vs measured

Design-only logic and expected outcomes get mistaken for empirical results.

Permanent mistakes

One confident-but-wrong call becomes a permanent line in the traded book.

What we'll cover

Three things, in order

1
The source that earned investigate
Pharma scripts: a free detector that validated plus a fundable paid lead, with the wall-cracking acquisition capability behind the wider hunt.
2
The vertical loop-engineering pipeline
An orchestrator driving two engines, discover and validate, wrapped in loops that won't let a grade outrun its evidence.
3
The hypotheses we tested → specific assets
Ten signals run end-to-end, each mapped to the names it trades, with the honest result.
First · the source

The source that earned it

One source cleared the gauntlet to investigate: a detector you validate for free, the tradeable edge a funded panel away. Here it is, and the capability behind the hunt.

The source that earned it · Pharma scripts

A free detector that validated, a fundable edge

The one source that cleared the gauntlet
The only earned investigate of every source run end-to-end: a real edge worth funding, on executed evidence not a story.
A FREE detector that validated
Free CMS Medicaid + Part-D dispensed-units on VRTX & NBIX: corr 0.668 with reported revenue, permutation p = 0.035; survived no-leakage, neutralization, placebo, ground-truth → validity B.
The unique structure: prove free, pay to trade
Free quarterly data lags ~160d (coincident); the 6–12wk lead sits in a paid weekly Rx panel (IQVIA / Symphony, ~$300k–$1M+/yr). Sound logic blocked only by cost → investigate.
The broader hunt: real point-in-time panels built behind access walls (rows / units acquired).
Pharma scripts · how the signal works

From a filled prescription to the print

1Scripts filledpatients fill prescriptions; pharmacies dispense units
2Drug revenueunits × net price = the franchise's sales
3Earnings vs consensussales drive the beat/miss the Street modeled
4Pricethe stock re-rates on the print

The data sits at step 1, the front of the chain. Count dispensed units as they happen and you read the quarter before the company reports it.

What the data is

NDC-level dispensed units & script counts. Free tier: CMS Medicaid SDUD + Medicare Part D, quarterly, public, ~1991 to now.

The join that matters

Map NDC → labeler → manufacturer → ticker via the free FDA NDC Directory: drug codes become "which public company."

Free vs paid

Free CMS = the government-insured slice, quarterly, ~160-day lag. Paid (IQVIA / Symphony) = full population, WEEKLY, ~$300k–$1M+/yr: the only path to a true lead.

Pharma scripts · the playbook

Prove it free, then pay to trade it

1
Validate the detector on FREE data
Regress CMS dispensed-units on later-reported revenue. Done: corr 0.668, permutation p = 0.035, survived no-leakage / neutralization / placebo / ground-truth → validity B. It genuinely measures the real thing.
2
Fund the fast feed for the lead
Free data lags ~160d (coincident). Buy the paid weekly Rx panel only where it pays, restoring the 6–12 week head-start over consensus.
3
Build the SURPRISE, point-in-time
Signal = actual scripts vs consensus, deseasonalized. Freeze consensus as-of date and use the first CMS vintage, not the restated one, so there's no look-ahead.
4
Trade the exposed names, into the print
Long/short single-product, government-payer-heavy pharma where one franchise dominates revenue: VRTX (cystic fibrosis), NBIX. Horizon = next earnings date.
Doesn't apply to diversified big pharma (franchise too small) or physician-administered / Part-B infused drugs (e.g. Regeneron Eylea, zero retail-script coverage). Guardrails: a material, stable government-payer share, and a PIT-correct labeler→parent map (acquisitions reassign it).
Worked example · real CMS data, real ticker

Vertex (VRTX), 32 quarters of free data

Pull the units (free API)
CMS SDUD via data.medicaid.gov: sum units_reimbursed for NDC 51167-106 (Trikafta); labeler 51167 → "Vertex Pharmaceuticals" in the FDA NDC Directory. 32 quarters, 2017–2024.
Transform: YoY growth, not levels
Levels correlate 0.94, but that's a shared up-trend (spurious). YoY log-growth strips trend and seasonality, isolating the real volume signal.
Check vs the print (ground truth)
Compare to VRTX reported product revenue (SEC XBRL). Result: corr 0.668, permutation p = 0.035. The free units genuinely track the dollars.
SPECIFICITY · PASSES

It fires only where it should

  • VRTX CF franchise: ~25–30% of revenue is Medicaid (cystic fibrosis skews government-pay), a strong, clean signal
  • Placebo, Regeneron's Eylea (physician-administered, Part-B): 0 SDUD units, so correctly no signal
The timing catch: free SDUD posts ~4 months after quarter-end; VRTX files its 10-Q ~37 days after. The free data lands after the print, a validated coincident nowcast. The weekly paid panel is what moves it to a lead.
Reading the two numbers · corr 0.668: how tightly the two grow together (0 = unrelated, 1 = lockstep), so a strong link. permutation p = 0.035: re-shuffle the script-vs-revenue pairings thousands of times to kill any real link; only ~3.5% of those random shuffles beat the real result, so it's very unlikely to be luck (under the 5% bar).
How we uncovered it · the capability

A generic wall-cracking acquisition toolkit

What's unique isn't one feed, it's reaching feeds that exist but are walled. One toolkit clears the challenge, resolves entities, and streams a clean point-in-time panel.

1 · Clear the wall

WAF / Akamai / Cloudflare / login walls: solve once in a stealth browser, replay the cookie via curl, verify you got the file not the login page.

2 · Resolve entities

Map free-text names (participants, owners, consignees) to tickers / CIKs / LEIs against a point-in-time list. Under-match over false-match.

3 · Stream to a panel

Build a PIT dataframe from huge zip / gz / csv with no OOM; timestamp by a public-on-creation field, never a restated one.

4 · It circumvents

Hit a blocker? Try an alt path, an archived mirror, a narrower scope. It concedes to design-only only when the data is truly out of reach.

The same toolkit pulled USPTO trademarks (3,931 firms), HKEX CCASS (19,189 rows), OFAC SLS (2,378), Lloyd's auctions (93), and US customs manifests: government bulk data others leave on the shelf because it's walled or needs entity resolution.
Second · the loop

The vertical loop-engineering pipeline

From a blank prompt to a graded, tradeable signal, built so that no grade can outrun its evidence.

The architecture

One orchestrator drives two engines

altdata-pipeline · the orchestrator
Stage 0Revisitre-test past verdicts on new data
calls · altdata-researchDiscover & Triageideate · ground · debate · gate
calls · altdata-validationValidatetwo empirical gates per survivor
Stage 3SynthesizeFDR demotion · ledger · portfolio
↺ a self-correcting loop, run on a budget

Three chained LLM-agent workflows. The pipeline doesn't follow discovery and validation. It drives them, wrapped with self-correction and portfolio synthesis.

Engine one · discover & triage

Argue to truth, design the kill

A
Ideate · wide divergent fan-out
Dozens of domains × rotating facets on a moving golden-ratio seed that mines fresh territory each run; plus gated target-first (mispriced KPI → upstream data trace) once it clears a materiality, tradability, crowding and falsifiable-lead screen.
B
Ground · verify before you trust
Every load-bearing fact is re-fetched by an agent that didn't propose it, through a three-tier ladder (WebFetch → camofox → Bright Data) with Wayback recovery for link-rot. Confirmed → web-verified (high confidence); unconfirmed → mixed.
C
Debate · bull vs bear, 2–5 rounds
The proponent refines rather than defends (narrow the universe, orthogonalize, switch to surprise); the skeptic must object materially in round 1, gets the last word, and isn't told the outcome, to avoid anchoring.
D
Gate · an independent referee rules
A fresh referee who sat the debate out rules on whatever objection still stands. A capped debate plus a live flaw caps the idea at monitor; a merely stubborn skeptic over a clean mechanism doesn't.
E
Design · the kill, pre-registered
A staged test plan: cheapest falsification first, pre-registered kill criteria, mechanism-specific controls, and a feasibility tier (runnable-now / obtainable / paywalled / blocked) feeding the verdict. It designs the test; it doesn't run it.
Engine two · validate

Two separate gates. Validity, then alpha.

GATE 1 · VALIDITY

Does it measure the real thing?

  • A skeptic names how detection could be faked; the gauntlet tests each breaker
  • Critical trio: no-leakage · neutralization vs controls · placebo / specificity
  • vs ground truth, or ≥2 convergent proxies; "validated" needs tests that ran
GATE 2 · ALPHA

Does it pay beyond what's priced?

  • Track auto-chosen by breadth × cardinality (event study vs cross-section)
  • Factor-neutral · deflated Sharpe · t ≥ 3 · spanning vs the cheap alternative
  • Net of cost, capacity, decay; out-of-sample & walk-forward
Between them, a guaranteed Acquire step builds the panel, then self-loops past blockers. Gates are sequential: alpha runs only if validity passes. You never measure a return on a signal that isn't a verified detector.
Engine two · validate, phase by phase

What each validation phase actually does

1
Frame + Feasibility · one call
Reframe to the primitive (what it really measures); mechanism, lead/lag, instrument, materiality; modality / cardinality / breadth (sets the alpha track); the ground-truth to validate against; and feasibility (obtainability + cost, PIT history, coverage, survivorship, revisions, entity-resolution). Hard-reject if untestable.
2
Construct · adversary + plan
A skeptic names 2–4 ways detection could be faked (the breakers). The planner then pre-registers the signal definition, the validity gauntlet (four critical tests plus one per breaker), the alpha plan, and look-ahead controls, before any data is touched.
3
Acquire · guaranteed, self-looping
Fetch the smallest sufficient slice, clear the wall once, entity-resolve to the instrument, and stream a PIT panel to disk so test agents run on a ready dataframe. On a blocker it circumvents (alt path, mirror, narrower scope) and concedes only when truly impossible.
4
Validity gauntlet + gate
Parallel agents run each test: no-leakage, neutralization vs named controls, placebo / specificity, detection vs ground truth (or ≥2 convergent proxies). An independent gate judges them as a family, execution-weighted; "validated" needs tests that actually ran.
5
Alpha · only if validity passed
Track auto-chosen by breadth × cardinality: event-study CAAR + permutation null for single names, or factor-neutral L/S with deflated Sharpe, t ≥ 3, FDR, OOS and spanning for cross-sections. Always net of cost, capacity, decay.
6
Grade
Validity × alpha → investigate / monitor / reject, with deterministic honesty clamps (design-only is capped and kept out of the FDR math) and the paywall carve-out (sound logic blocked only by cost → investigate, cost noted).
The loop-engineering

Five loops keep it honest over time

Revisit loop

Re-tests past verdicts on new data; tracks its own wash-out rate and discounts fresh optimism by it.

Acquire circumvention loop

On a data wall it retries alt paths / mirrors / narrower scope before conceding, turning design-only into executed.

Multiple-testing loop

Family-wise FDR across the run demotes investigate→monitor when a result won't survive correction.

Calibration ledger

Every verdict recorded, so the system can grade whether its own past calls turned out right.

Anti-repeat ledgers

A validated-log (do-not-rerun) and a targets ledger stop it re-pitching ideas or KPIs that already washed out.

The invariant

No grade may outrun its evidence: an "A" needs tests that ran; design-only is capped and kept out of the math.

Putting it on rails · altdata-hunt

The whole loop, as one workflow

loopSweepaltdata-research discovers + triages a survivor batch
loopValidatealtdata-validation grades each through both gates
loopTallycount only sources whose FINAL disposition is investigate
untilN reachedor maxRounds / budget

Overlap

When a round can't reach N even if every candidate passed, the next sweep launches alongside the current validation, hiding discovery latency instead of paying it serially.

Stage-retry

Every sweep and validation retries on a transient crash, riding out the API-overload windows that used to kill a whole run.

Feedback-refine

A failed validation's own diagnostics (what failed, the confounds, next steps) refine the source into a better spec, re-validated and deflated for the retry.

It counts a source only once its final disposition is investigate, after every carve-out and the detection-failed guard. The number it returns is honest by construction, not by hope.
Third · the assets

The hypotheses, applied to assets

Ten signals run end-to-end. Each one is a falsifiable bet on a specific set of names: here's the bet, the names, and the honest result.

Hypothesis → asset map

Every signal points at specific names

SignalHypothesis (the bet)Specific assetsResult
HKEX CCASS custody-driftSilent custody concentration → forward multi-week drawdownLow-float HK GEM micro-caps (08003 / 08160 …) · SHORTmonitor
USPTO trademark breadthNew Nice-class breadth → next-year cross-sectional returns3,931 US public firms · L/Smonitor
US customs / bill-of-ladingInbound manifest volume → COGS / revenue, pre-disclosureOcean-import-heavy single names (retail / hardgoods)monitor
Dubai DLD Oqood feedOff-plan registrations nowcast developer bookings KPIEmaar Development (DFM: EMAARDEV)monitor
Grid-OEM book-to-billBacklog-margin surprise → forward-margin revision driftGE Vernova · Siemens Energy · Hitachi · Eatonmonitor
FCHLPM cat-model docketCertified loss-cost revision → FL property repricingFL P&C / reinsurers (UVE · HCI · HRTG)monitor
Filings-text (Lazy Prices)YoY 10-K/10-Q text change → underreaction driftUS public equities · L/Smonitor
Data-center thermal-IRWaste-heat ramp → energization, tradeable on announceData-center operators / REITs · shortable namesmonitor
USPTO PTAB IPR velocityOffensive IPR petition → exclusivity loss, bearish leadSingle-franchise biopharma (Orange-Book issuers)reject
LME cancelled-warrant flowCancellation surprise → forward time-spreadBase metals Cu / Al / Zn / Ni · futuresreject
Grades are honest end-states from executed or design-only runs, not back-fit. monitor = valid-or-promising but no proven edge yet.
Across the wider program · why ideas get killed

Rejections are principled, not noise

Priced in by disclosure lag

Congressional-leadership trades. Clean, point-in-time detector, but the 30–45 day STOCK-Act filing lag prices the edge in before T0. The post-disclosure null holds across House, Senate, and the broad pool.

Executed and sign-wrong

LME cancelled-warrants. The "tightness" surprise is wrong-signed and anti-predicts its own physical depletion in 5 of 6 metals. PTAB IPR: filing returns are significantly positive, not bearish.

Executed null

Box office (Comscore). Opening-weekend surprises do not move diversified-studio returns across 52 films. The story is real; the tradeable signal is not.

Look-ahead, not point-in-time

AirDNA short-term-rental. The lodging-REIT thesis inverts in sign; the history is retroactively restated with no as-of snapshot, so any backtest is fiction.

Corrupted input data

GitHub / OSS telemetry. 47% of the stars on the most-starred repo are fake. The signal's own input is gamed, so adoption cannot be read from it.

No fund-accessible edge

RF satellite (HawkEye 360): customers are gov / defense only. Earned-Wage-Access: collapses to a plain CHYM / GDOT revenue read, nothing distinct.

Every kill names a specific failure: already priced, sign wrong, not point-in-time, input corrupted, or no edge a fund could capture. None are rejected for low event count alone.
Two we ran to a number

What "executed" actually looks like

USPTO TRADEMARK BREADTH

Powered, but within noise

  • 3,931 public firms, real USPTO data, executed end-to-end
  • Breadth L/S: FF4-adjusted +6.09%/yr, but t = 0.90, Sharpe 0.15
  • Deflated-SR p = 0.64; naive filing-growth a clean null (t = −0.31)
DATA-CENTER THERMAL-IR

Detector works, edge doesn't

  • Validity PASS: hot-tail +0.57 °C at energization, p = 0.002; 0/16 controls fire
  • Alpha FAIL: CAAR(0,+10) = +1.2%, permutation p = 0.79 (N = 17)
  • The +7.9% at N = 9 was small-sample noise; it vanished as N grew
Both have sound mechanisms and real data. Neither pays a confirmed edge. That's the system working: separating "measures something real" from "gets paid."
The meta-finding

The wall is the constraint, not the idea

Across ten sound, web-verified mechanisms, zero reached funded-build. Not for lack of imagination, but on point-in-time data access: paywalled revisions, no PIT history, survivorship-purged universes, endogenous opt-outs.

This is why the capability (Part 1) and the loop (Part 2) matter more than any one idea: ideation is cheap, honest execution behind the data wall is the edge.
Disposition of the ten tested hypotheses.
The headline success · first investigate

Pharma scripts: a validated detector + a fundable lead

GATE 1 · VALIDITY · PASS

The free detector is real

  • Free CMS SDUD + Part-D, executed end-to-end on VRTX & NBIX
  • corr 0.668 with reported revenue, permutation p = 0.035
  • Survived no-leakage, neutralization, placebo, ground-truth → validity B
THE LEAD · PAYWALL-GATED

The tradeable edge is fundable

  • Free quarterly data lags ~160 days, so it reads coincident, not leading
  • The genuine 6–12wk lead needs the paid weekly Rx panel (IQVIA / Symphony, ~$300k–$1M+/yr)
  • Sound logic blocked only by cost → investigate, with the cost noted
Success here isn't a claimed edge: it's a validated free detector (a usable monitoring tool today) plus a precisely-priced, fundable next step to test the lead. Investigate is the earned gate to funded-build, on executed evidence, not a story.
Applies to Medicaid / Part-D-heavy single-product pharma (VRTX, NBIX).
The takeaway
Plausible priced.
We built the loop that tells them apart.
1
Source
A free, validated pharma-scripts detector, plus the wall-cracking acquisition toolkit that turns shelved government data into clean point-in-time panels.
2
Loop
An orchestrator + two engines + five self-correcting loops, automated as the altdata-hunt workflow (overlapped sweeps, stage-retry, feedback-refine); two gates; no grade outruns its evidence.
3
Assets
Ten falsifiable bets on specific names, each graded honestly, plus the pipeline's first earned investigate (Pharma scripts: a validated free detector and a fundable paid lead).