Service · Technical diligence
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
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.
We reproduce reported results from the available evidence, reconcile material differences, and test whether the validation design supports the claim. The review makes clear where performance depends on costs, timing, universe construction, or other assumptions that may be fragile in live use.
We review lineage, vendor dependencies, feature pipelines, versioning, monitoring, and failure points that can turn research quality into operational risk.
We look for a written record: model purpose, limits, validation evidence, owner, monitoring plan, remediation history, and review cadence.
Scope
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.
Decision contexts
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.
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
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.
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.
Evaluating an external manager? Manager technical due diligence has fixed fees, starting with a $2,900 Desk Read. Read a synthetic Desk Read sample.
We examine whether the system can be integrated, monitored, and owned after diligence: dependencies, runbooks, alerting, data contracts, and failure recovery.
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.
Digital assets
The digital-asset review focuses on backtest reconstruction, 24/7 timing, token universes, costs, funding, liquidity, data lineage, and model risk.
Related note
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
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
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
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.
