COUNTERPHASE / RESEARCH VALIDATION
CONSTRUCTED EXAMPLE  ·  NO REAL CLIENT DATA
A WORKING RESEARCH CHALLENGE BUILT FOR QUANTITATIVE TEAMS

Your backtest looks convincing.
What did it overlook?

Counterphase audits how research models were tested—what they knew at the time, which outcomes went missing, and whether their decisions relied on data they were never meant to use.

Try this small, constructed experiment: a model trains on four outcomes. Remove one of the sixteen it didn't choose. Should that change the model's decision?

Try the real audit
01 / CHOOSE WHICH MODEL TO CHALLENGE
20 ORIGINAL T0 SLOTS
2 BLOCKS · 2 SELECTED PER BLOCK
02 / TAKE AWAY DATA THE MODEL DIDN'T CHOOSE SELECTED    UNSELECTED    HIDDEN
Each outlined square is an outcome the model did not choose. Click one to hide it.
ORIGINAL OPPORTUNITIES20 / 20unchanged in every challenge
OUTCOMES ACTUALLY CHOSEN4 / 20two per policy block
UNUSED OUTCOMES HIDDEN0 / 16counterfactual intervention
03 / OBSERVE THE DECISION
Hide an unchosen outcome.
Nothing about the four selected outcomes changes. Will the research decision stay the same?
BASELINEMODEL CAN LEARNAFTER INTERVENTIONMODEL CAN LEARNDECISION CONTRADICTIONNONE
NOT JUST A VISUAL TRICKINSPECT THE ACTUAL TEST OUTPUT

If a model changes its decision when we remove data it claims not to use, its research process has a problem worth investigating.

The screen above teaches the idea. The reports below come from a separate, locally tested Python audit—not a live fund or a return backtest.
WHAT THIS INSTRUMENT CAN — AND CANNOT — ESTABLISH

The examples are synthetic. Passing a set of tests does not prove that a model never leaked information. These reports cannot independently certify customer timestamps, trading performance, or compliance. The current local runner is for trusted, customer-approved Python code; it does not safely isolate arbitrary programs.

02 / YOUR EVIDENCE · YOUR DEVICE

Don't take our word for it.
Test your own records.

Bring a binary forecast ledger and its expected opportunity schedule. Counterphase will count every expected slot, challenge your model against a fixed rival, and expose how unknown outcomes can change the conclusion.

◈LOCAL ANALYSISNo uploads. No account.
No live market connection.
CHOOSE A RESEARCH COHORT
Files are read by your browser. They are not transmitted, stored on the site, or sent to Counterphase. No external connections are enabled by this page.
LOCAL AUDIT / QUARANTINENOT RUN
?
What does your evidence really support?

Load the synthetic example to see the instrument, or select your own two local files. Neither is uploaded.

Supported: a strict, time-stamped binary-outcome CSV with paired candidate/rival probabilities and a separate manifest. Not supported: P&L, continuous labels, full model-code execution, externally attested issue times, confidential uploads, or statistical significance claims. All mathematical signs remain research-only.
PRIVATE PILOT / PREPARATION

Bring one research result.
Let us challenge it.

NO UPLOAD
NO TRADING
NO LIVE CONNECTION

If your team tests quantitative models or automated research strategies, we can help examine one result you are considering trusting. Run the tools in your own environment, keep proprietary data under your control, and independently reproduce any reported failure.

01Start with the original recordsWhat the model predicted, when it predicted it, the simple benchmark, and which outcomes were missing.
02Run the local auditCheck missing outcomes, whether future information slipped in, and whether a simple baseline still holds up.
03Review what changedReproduce the findings and decide whether the evidence supports the research conclusion.
PILOT INQUIRY

Pilot inquiries are open by email. Please describe your research question without including confidential forecasts, strategy code, or account information.

CONTACT THE RESEARCH TEAM
Research-validation prototype · Pilot contact: [email protected] · No investment recommendations or customer performance claims