How to Evaluate a Quant Trading Platform: The 10-Point RFP Checklist for Hedge Funds

You have been handed the mandate: evaluate vendors, issue an RFP, and have a recommendation to the investment committee in 90 days. You are looking at three to five vendors, a 6–12 week evaluation cycle, and a decision that will lock your quant research infrastructure in place for the next three to five years.

Most hedge fund technology RFPs fail to produce a good outcome. This framework is designed to prevent that — by separating signal from noise across 10 criteria that map directly to what your quant researchers, CTO, and COO actually need from the platform.

Why Most Quant Platform RFPs Fail

Industry data on enterprise technology RFPs in financial services consistently shows that 60–70% of hedge fund technology procurements result in either no decision within the evaluation window or a decision that is recognized as regrettable within 18 months. Two failure modes account for the majority of bad outcomes.

Failure mode (a): RFP built around vendor feature lists, not actual research workflow requirements. When the evaluation team assembles criteria by reading vendor marketing materials, the RFP ends up measuring what vendors are good at selling rather than what the fund actually needs. A vendor can score 9/10 on a feature-list RFP and still fail to support your team's core research workflow — because the feature list never asked the right operational question.

Failure mode (b): Internal stakeholder misalignment before the RFP is issued. Quant researchers want a native ML pipeline, versioned model registry, and walk-forward backtesting. The CTO wants documented API architecture, vendor support SLAs, and clear exit/data portability terms. The COO wants total cost of ownership transparency and regulatory audit trail compliance. These are not contradictory requirements — but if each constituency defines criteria in isolation, the resulting RFP produces a vendor score that satisfies no one and makes the internal business case impossible to close.

The fix is a structured framework that tests all five layers of the modern quant stack simultaneously. As the Quant Hedge Fund Technology Stack in 2026: The Complete Guide covers in detail, the modern quant stack has five layers — data ingestion, signal generation, backtesting, risk management, and execution integration. Your RFP needs to test all of them. The 10-point checklist below maps directly to each layer.

Before You Send the RFP — The Internal Alignment Meeting

Before a single vendor is invited to respond, your evaluation team needs to answer three questions in a room together. This meeting is worth more than any vendor demo.

1. What research workflows are currently broken or missing? Be specific. Not “we need better ML tooling” — but “our quant researchers cannot run walk-forward backtests on new strategies without three weeks of manual data preparation” or “promoting a model from research to live trading requires a two-week engineering project every time.” These are the requirements that will actually determine whether a vendor delivers value after contract signature.

2. What does “success” look like in 12 months? Define a measurable outcome: time from new strategy concept to first live signal, number of strategies that can be live-tested in parallel, reduction in quant developer time spent on infrastructure maintenance. If you cannot define a success metric before the RFP, you will not be able to evaluate whether any vendor actually delivered after 12 months.

3. Who is the internal decision-maker? Is this decision owned by the quant research lead, the CTO, or the COO? Each has a legitimate veto point — but a procurement process with three co-equal decision-makers and no defined tiebreaker produces no decision. Assign primary ownership before engaging vendors.

Two common mistakes that kill RFPs before they start:

Letting the quant research team define requirements in isolation produces an RFP that evaluates ML pipeline sophistication and backtesting architecture in detail — and fails to include the compliance audit trail, OMS integration requirements, and TCO calculation that the COO's sign-off requires. The system that wins the technical evaluation never passes the operational review.

Letting the COO and legal team define requirements in isolation produces an RFP focused on regulatory documentation, SLA guarantees, and vendor financial stability — and omits the ML tooling assessment that would reveal whether the platform can actually support the research workflows it claims to support. The system that wins the compliance review turns out to have inadequate ML infrastructure.

The output of this alignment meeting should be a one-page requirements brief — three to five specific workflow requirements, one measurable success metric, and the name of the decision-maker — that every RFP criterion maps back to. If a criterion cannot be traced back to a requirement on that brief, it should not be in the RFP.

The 10-Point RFP Checklist

These 10 criteria are organized to test every layer of the quant stack. Each criterion includes the specific question to ask, the threshold that separates production-ready from not-yet-ready, and the evidence you need from vendors to score it honestly.

1. Data layer architecture. Does the platform provide a unified, normalized multi-asset data schema — equities, rates, FX, commodities, crypto, options — or does each asset class require custom ETL work? A platform that normalizes data at ingestion means your quant researchers can write one signal logic layer that runs across all asset classes. A platform that requires per-asset ETL means your team is maintaining integration code indefinitely. The test: ask for a live demo of ingesting a new third-party data feed during the evaluation. How long does it take? Who does it require?

2. Signal generation latency. What is the end-to-end latency from market event to generated signal — measured at P99, not average? The institutional threshold for systematic intraday strategies is sub-10ms. Anything above 100ms is not compatible with intraday alpha capture at scale. Request live benchmark data — P50, P95, P99 across representative signal types. Marketing copy that says “real-time” without a numerical SLA is not a benchmark; do not accept it as one.

3. Backtesting integrity. Does the backtesting engine use true point-in-time data, or is it susceptible to survivorship bias and look-ahead contamination? Is walk-forward testing with expanding or rolling training windows native to the platform, or does it require manual loop construction by your quant developers? Ask for methodology documentation: what is the training window / test window convention? How are corporate actions applied? How is universe construction handled? The backtesting methodology framework covers the specific tests to apply; platforms that cannot pass point-in-time correctness verification should be eliminated regardless of their other scores.

4. ML model registry and governance. Does the platform have a native model registry with semantic versioning, A/B testing, champion/challenger comparison, shadow mode deployment, and one-click rollback? Is SHAP or equivalent feature attribution built in — not just global feature importance, but per-signal attribution exportable for compliance review? For any fund operating under a systematic trading mandate, model governance is now a regulatory requirement, not a best practice. SEC and FCA model risk frameworks require documentation of which model version generated which signal, what features were live at generation time, and what validation evidence existed for the model at deployment. ML in quantitative finance covers the full model governance infrastructure requirements.

5. Risk dashboard and compliance. Does the platform generate real-time portfolio-level VaR, CVaR, and factor exposure attribution — updated continuously, not at end-of-day? Is the full audit trail exportable in a format acceptable to SEC, FCA, and ESMA reviewers? For funds subject to FRTB SA/IMA requirements, ask specifically about sensitivity-based measures and whether the platform supports the 250-trading-day backtesting validation. This is a binary checkpoint: the audit trail either exists in a format your compliance team can use or it does not.

6. Execution layer integration. Does the platform connect to your OMS/EMS — Fidessa, FlexTrade, ION, Charles River — via FIX protocol natively, or does the integration require custom development? Can signals pass directly to the execution layer, or is there a manual handoff? The signal pipeline is only as valuable as the execution it enables. Any “integration” that requires a custom build puts the maintenance burden on your internal team permanently.

7. API-first architecture. Is the platform truly API-first — every function accessible programmatically — or is it primarily a desktop application with an API bolt-on? The diagnostic test is simple: ask for the full API documentation on day one of vendor engagement, before any NDA or commercial discussion. A vendor that delivers complete, well-maintained API documentation immediately is API-first. A vendor that shares documentation only after NDA signature, or provides documentation that covers only a subset of platform functionality, has a desktop-first architecture with an API layer added after the fact. The CTO's guide to evaluating quant platforms covers API architecture evaluation in detail, including the specific red flags that indicate a bolt-on rather than native API layer.

8. Alt data and third-party data connectors. How many third-party data providers are natively integrated without custom ETL work — satellite imagery, credit card transaction panels, NLP earnings call feeds, ESG scoring? What is the onboarding time for a new data provider not currently in the connector catalog? The alt data landscape is moving fast; a platform that requires three months of engineering work to onboard a new data source will always be behind the market. The answer to this question is a direct proxy for the platform's data layer architecture maturity.

9. Total cost of ownership (not sticker price). Fully-loaded TCO must include seat and license fees, data costs (including any add-on feeds for point-in-time correction, alt data, or asset class coverage gaps), integration development time (internal quant developer hours plus any external contractors), and ongoing maintenance burden. The sticker price is the least informative number in a quant platform evaluation. The Quant Fund Operations: The CFO/COO Guide to Technology Cost provides a complete TCO model with AUM-tier benchmarks; any legacy stack running above 5 bps of AUM in technology costs has a structural problem, and the TCO comparison against that baseline is the number the investment committee will actually respond to.

10. Vendor support and roadmap. What is the support model — 24/7 with a dedicated customer success manager, or business hours with a ticket queue? What is the P1 SLA for production incidents (signal pipeline down, data feed failure) during market hours? Is the product roadmap publicly visible, and do roadmap commitments map to actual delivery cycles that clients can verify? Ask for three customer references at your AUM tier. References at a different scale — a $50M fund testifying on behalf of a platform you intend to run at $2B — are not representative evidence.

Red Flags to Eliminate Vendors Early

Five vendor behaviors that should end the evaluation immediately — before the scoring matrix, before the pilot, and before any commercial negotiation begins.

Before applying these red flags, confirm you are evaluating platforms designed for the same use case. A direct comparison of QuantConnect, Kensho, and Two Sigma Venn against a full-stack platform covers the architectural distinctions — research tool vs. specialized signal layer vs. allocator analytics vs. production platform — that determine whether a vendor belongs in your RFP at all.

“We'll build that for you.” Any vendor who responds to a core feature question — latency SLA, walk-forward backtesting, ML model registry — with a custom development promise is offering to make you the product manager of their feature roadmap. You will own that tech debt forever. Core platform capabilities are either built or they are not. A roadmap commitment for a core feature that does not exist today is not a feature evaluation — it is a vendor commitment to deliver something in an unknown timeline with your contract as the funding mechanism.

No live benchmark data on signal latency. Any vendor that cannot provide live P99 latency benchmarks — measured, not estimated, across representative signal types — is not production-ready for systematic strategies. The inability to produce this data in a demo means it has never been measured, which means the platform has never been engineered to a latency requirement.

Desktop-first architecture. Any platform that requires a Windows desktop client as the primary workflow interface is not compatible with cloud-native systematic research. The signal pipeline, model development environment, and backtesting infrastructure need to be accessible via API and browser — not a locally installed application that cannot be automated, containerized, or integrated into a CI/CD workflow.

“Our compliance team will work with your legal” for model governance. Model governance is a platform feature, not a negotiation between compliance departments. If the platform does not have a native audit trail, model versioning, and SHAP attribution layer — if the vendor's answer to the model governance question is a process rather than a product — the feature does not exist.

API documentation available only after NDA. Restricting API documentation to post-NDA access is not a confidentiality practice — it is an acknowledgment that the documentation reveals complexity, gaps, or architectural limitations that would end the evaluation if visible before the commercial relationship begins. Require full API documentation on day one or end the evaluation.

Scoring the Vendors — A Simple Decision Matrix

Assign ownership of the 10 criteria to the three internal constituencies who will use the platform:

Quant research lead owns criteria 1, 2, 3, 4, 8 — data layer architecture, signal generation latency, backtesting integrity, ML model registry and governance, and alt data connectors. These are the criteria that determine whether the platform can support the research workflows it claims to support.

CTO owns criteria 6, 7, 10 — execution layer integration, API-first architecture, and vendor support and roadmap. These are the criteria that determine whether the platform can be integrated, maintained, and depended on at institutional scale.

COO/compliance owns criteria 5, 9 — risk dashboard and compliance, and total cost of ownership. These are the criteria that determine whether the platform will survive an LP or regulatory review and whether it is financially defensible against the alternatives.

Weight the scores by your fund's current bottleneck. If research throughput is the binding constraint — strategies take too long to validate, the research-to-live pipeline is too slow — over-weight criteria 2 (latency), 3 (backtesting integrity), and 4 (ML model registry). If compliance is the binding constraint — model governance gaps, regulatory review exposure, audit trail deficiencies — over-weight criteria 5 (risk dashboard and compliance) and 9 (total cost of ownership, including compliance build cost).

Hard floor: eliminate any vendor scoring below 6/10 on criterion 2 (signal generation latency) or criterion 3 (backtesting integrity). These are table-stakes. A vendor that cannot demonstrate production-grade latency benchmarks or point-in-time backtesting integrity is not a production-ready platform regardless of how it scores on other criteria.

One final note on scoring: a four-week parallel pilot — running your existing research pipeline on the new platform alongside your current stack — is worth more than any RFP score. Require this of your finalists. The RFP score tells you what the vendor demonstrated under controlled conditions; the parallel pilot tells you whether the platform actually integrates with how your team works. For the parallel pilot methodology and what to measure, see the Quant Hedge Fund Technology Stack in 2026: The Complete Guide, which covers the five-layer evaluation framework in detail.

The Internal Business Case — Closing the Investment Committee

The investment committee will approve technology spend when it understands the cost of not acting. Three numbers close the conversation.

Number 1: Current fully-loaded legacy stack TCO. For a typical five-quant desk at $500M–$2B AUM, the legacy stack — Bloomberg terminals plus FactSet plus Refinitiv plus the quant developer time to maintain the ETL glue layer — runs $970K–$1.82M per year. That is 97–182 basis points on $1B AUM, before a single signal is generated. The tech stack guide breaks this down by line item; the Bloomberg, Eikon, and FactSet teardowns provide per-vendor TCO with line-item documentation.

Number 2: Fully-loaded TCO of the modern alternative. AlphaEdge AI Enterprise at $2,999/month is 0.36 basis points on $1B AUM. Even accounting for retained Bloomberg or FactSet subscriptions for workflows that are genuinely better on those platforms — fundamentals, consensus estimates, fixed income pricing — the total modern stack TCO is a fraction of the legacy equivalent. The gap is not marginal; it is structural.

Number 3: Research throughput improvement. On a legacy Bloomberg/FactSet stack, the time from new strategy concept to first live signal is typically 4–6 months: weeks of data preparation, manual backtest construction, in-sample-only validation (which requires a re-validation cycle when the out-of-sample results inevitably differ), and a manual engineering project to promote the model to live. On an API-first platform with native walk-forward backtesting and a model registry, the same cycle is 4–6 weeks. The investment committee math: at 3–4 strategy cycles per year saved, what is the value of three to four additional live strategies?

Frame it this way: “We are not replacing a terminal. We are removing the research bottleneck that is costing us 3–4 strategies per year and consuming 97–182 bps of AUM in infrastructure overhead that generates zero alpha.”

AlphaEdge AI meets all 10 RFP criteria out of the box — live latency benchmarks, point-in-time backtesting, ML model registry with SHAP attribution, real-time VaR/CVaR with SEC/FCA/ESMA audit trail, FIX execution integration, full API-first architecture with documentation available from day one, native alt data connectors, and dedicated enterprise support with market-hours P1 SLA. For evaluating the vendors behind your quant infrastructure — data, execution, and risk systems — see our guide to quant fund technology vendor due diligence.