Quantitative solutions for financial markets
We design, build, validate, and operationalise quantitative analytics — from pricing models and risk frameworks to execution algorithms and data infrastructure. Every solution is tailored to your asset class, data environment, and operational requirements.
QD designs, builds, validates, and operationalises quant solutions across three practice areas — Quant, Tech & Data, and Training. Every engagement is tailored to your asset class, data environment, and operational requirements.
Quants on Demand
Analytics, models, and validation across every investment process — for discretionary, fundamental, and systematic managers, banks, asset managers, and trading desks.
Tech & Data — Infrastructure First. Alpha Always.
You drive the strategy — we build the production-grade engine underneath it, from data pipelines and quant tooling through to risk management and trade execution.
Practitioner-led training for financial markets
Face-to-face or live-online programmes built by practitioners with decades of markets experience, using finance-authentic datasets and real desk workflows, and structured to meet CPT and CPD requirements.
Additional Services
Independent, senior-led reviews where you need an opinion, not an implementation — covering model validation, due diligence on complex models and platforms, and quant candidate vetting. Discrete, fixed-fee engagements.
Typical client challenges
Different organisations come to us at different stages. Tap the option that sounds most like you to see how we'd approach it.
You know there are opportunities — better analytics, automated workflows, smarter use of your data — but you haven't mapped them yet. The right starting point is a structured diagnostics engagement: a short, focused assessment that clarifies your current state and produces a prioritised roadmap of where quant adds the most leverage.
Example scenarios
- A discretionary portfolio manager who relies on Excel-based models wants to understand which parts of their process — screening, position sizing, risk monitoring — would benefit most from quantitative tooling, before committing to a build.
- A family office managing a multi-asset portfolio is aware that peers are using more sophisticated infrastructure, but hasn't yet determined which investments in data or analytics would have the clearest return.
- A trading desk that has accumulated years of execution data and wants to know what an independent specialist would prioritise before they commission anything.
- A firm that has inherited or purchased a quantitative model and wants an independent assessment of its assumptions and reliability before putting it into production.
You might be here if
- You've discussed 'doing something with data or quant' internally but haven't taken the first step.
- You're unsure which workflows would benefit most from quantitative improvement.
- You want an independent expert view before committing budget to a larger initiative.
- You've seen what competitors or peers are doing and want to understand what's realistic for your firm.
Recommended starting point: Quantitative Diagnostics engagement (Assess). Discuss your requirements →
You have a defined requirement — a pricing model, a backtesting framework, an execution analytics pipeline, a risk dashboard — but lack the in-house quant resource to deliver it to a professional standard. We scope, build, and deliver in time-boxed sprints with clear milestones, documented output, and code your team can maintain.
Example scenarios
- An asset manager needs independent pricing and risk analytics for interest rate derivatives but does not have a dedicated quant developer on staff.
- A trading desk whose analysts spend two or more hours each day on manual data pulls and report assembly — and needs that process automated into a reproducible, shared pipeline.
- A hedge fund wants a robust backtesting engine that controls for overfitting and produces reliable out-of-sample performance metrics before allocating to a new strategy.
- A firm that has tried a general-purpose software vendor and found the output didn't address the specifics of their workflow or market.
You might be here if
- You can describe what you need in business terms but don't have the quant or development resource to build it.
- You need something built to a professional standard — documented, tested, and production-ready.
- You have a clear timeline and budget and want deliverables, not open-ended consulting.
- The work is too specialised or too short-duration to justify a full-time hire.
Recommended starting point: Targeted Analytics Delivery (Build). Discuss your requirements →
You require continuous quantitative development, validation, and support — but hiring a full in-house quant team is impractical or premature. We provide an embedded engagement that scales with your needs, integrates with your existing processes, and progressively transfers capability to your internal staff.
Example scenarios
- A bank's trading desk needs localised pricing models, execution tools, and ongoing R&D support but cannot justify the headcount for a permanent quant team.
- A systematic trading firm wants a dedicated quant resource for model development, code review, and governance — fully integrated into their sprint cycles.
- A mid-sized bank that needs ongoing model governance, documentation, and regulatory-ready validation across a suite of inherited models, without the cost or lead time of building a model-risk function from scratch.
- A data or analytics vendor that needs an independent quant partner to validate their platform, support client engagements, and produce technical content.
You might be here if
- You need quant capability on an ongoing basis, not just for a single project.
- Hiring full-time quants is too expensive, too slow, or the local market is too competitive.
- You want knowledge transferred to your team over time — specific people gaining specific skills, not a dependency that stays with us.
- You need governance, documentation, and audit-ready standards applied to your quant work.
Recommended starting point: Embedded Quant Engagement (Embed). Discuss your requirements →
Your traders, portfolio managers, analysts, or operations staff need to move from spreadsheet-based workflows to Python-based analytics — but standard coding courses don't address the financial markets context. Our training programmes are built by practitioners, delivered on real market data and real desk workflows, and can be structured to count towards CPT and CPD requirements.
Example scenarios
- A sell-side desk where sales traders want to automate client reporting and pull data via APIs — but have never written a line of code.
- A buy-side firm whose portfolio managers want to build and test their own signals in Python rather than queuing requests through the quant team.
- A compliance or middle-office team that needs enough quantitative literacy to understand, challenge, and audit the models their desk relies on.
- A recruiter or hiring panel that assesses quant candidates but lacks the technical background to evaluate them independently.
You might be here if
- Your team depends on spreadsheets for analytics and reporting that should be automated.
- Generic Python or data science courses feel too abstract — your people need financial markets context to apply what they learn.
- You have CPT or CPD requirements to meet and want training that counts towards them.
- You want your team to become self-sufficient with analytics, reducing reliance on external providers for routine work.
Recommended starting point: Practitioner-Led Training Programme (Train). See training programmes →
Why the right starting point matters
80.3% of enterprise AI projects deliver no business value — the root causes are organisational (scope, ownership, sponsorship), rarely the algorithms. — RAND 2025 / Gartner
60–70% less cost and a ~2-week ramp vs 3–6 months — the economics of a senior specialist on demand. — fractional-market data (fractional-CFO analogue)
20–30% salary premium on APAC quant / AI / ML / data roles, after a decade-long supply gap. — Selby Jennings
Regulators now tell banks to upskill existing staff over salary wars. — HKMA 2025