Strategy evidence
We test whether reported results are reproducible, whether the validation design matches the claim, and whether performance depends on assumptions that would be fragile in live use.
Service · Technical diligence
Quant due diligence for high-stakes decisions.
StatGazer reviews the quantitative evidence behind strategies, models, and data systems when your team needs to decide whether a result is robust enough to trust, fund, buy, integrate, or escalate.
What diligence asks
Is the quantitative story supported by the system underneath it?
Quant due diligence is broader than a single backtest. The strategy may be attractive, but the review needs to understand the research process, data lineage, model assumptions, code quality, validation evidence, monitoring, and the team’s ability to maintain the work after the first impressive result.
Data and infrastructure
We review lineage, vendor dependencies, feature pipelines, versioning, monitoring, and failure points that can turn research quality into operational risk.
Governance readiness
We look for a written record: model purpose, limits, validation evidence, owner, monitoring plan, remediation history, and review cadence.
Scope
Focused diligence, not performative diligence.
A good diligence scope is narrow enough to answer the decision but broad enough to catch the failure modes that matter. We agree the question first: allocation readiness, vendor risk, acquisition screen, technical integration, or internal model challenge. Then we build the work around evidence, not theater.
Typical workstreams
- Backtest and performance reproduction from raw or minimally processed data.
- Model validation: conceptual soundness, out-of-sample behavior, calibration, assumptions, and limits.
- Data and MLOps review: lineage, versioning, monitoring, deployment, drift, and recovery paths.
- Documentation review: what a committee, risk team, or future owner would need to understand.
Deliverables
- A diligence memo written for technical and investment readers.
- An issue register with severity, evidence, owner, and decision impact.
- A reproducibility artifact where the scope and access allow one.
- A final readout that separates confirmed facts, unresolved risks, and recommended next steps.
Decision contexts
Useful before the expensive commitment.
The best time to run diligence is before the decision is locked and before the narrative becomes hard to challenge. The output gives the internal champion a stronger file and gives skeptics a concrete place to focus.
Common triggers
- A strategy backtest is compelling, but the construction is not yet independently reproduced.
- A model or vendor is being evaluated for integration into an investment process.
- A fund, manager, or acquisition target has quantitative claims that need technical review.
- An internal team wants an outside reviewer before presenting to oversight.
Regulatory parallel
Model-risk discipline gives diligence a useful checklist: independent challenge, validation evidence, documented limitations, and ongoing monitoring. StatGazer does not provide regulatory assurance or bank model-risk certification. We provide technical diligence evidence that your organization can use in its own process.
Diligence design
The scope should match the risk, not the size of the deck.
Many diligence processes over-index on interviews and under-index on reproducible evidence. We reverse that. Interviews can explain intent, but the review has to land on artifacts: data lineage, code paths, validation files, monitoring records, model documentation, and exceptions.
The work can be narrow or broad. A narrow scope might focus on one strategy backtest before an allocation meeting. A broader scope might review a model portfolio, research process, data platform, and production controls. In both cases, the output should make the same distinction: what is supported by evidence, what remains unresolved, and which risks should change the decision. That discipline keeps the memo useful after the meeting ends and after new facts arrive during follow-up technical diligence.
For allocators
We focus on whether the quantitative edge is supported by reproducible evidence, whether the process can survive scale, and which claims need more proof before allocation or sizing.
For operators
We examine whether the system can be integrated, monitored, and owned after diligence: dependencies, runbooks, alerting, data contracts, and failure recovery.
For internal champions
The memo is designed to help a sponsor explain both sides: why the opportunity is credible and where the unresolved technical risks still sit.
Professional boundary. Technical model review, validation, research, and engineering consulting — not financial-statement audit, regulatory assurance, investment, legal, or tax advice.
Related note
Apply model-risk discipline without overclaiming.
The governance note explains how non-bank investment teams can use model-risk vocabulary as a practical checklist, not a regulated assurance claim.
Related services
If the diligence question is narrower, use a narrower review.
Use model validation when the concern is an internal model-risk record. Use backtest review when the concern is a specific strategy result.
Delivery model
Diligence review is founder-led.
Scoping, technical review, memo framing, and handoff are handled directly by Evgenii Azarov, PhD, so context does not get lost between intake and delivery.
Next step
Tell us what decision the diligence has to support.
Use the form for the high-level context only. We can sign an NDA before reviewing sensitive materials, source code, holdings, or non-public names.