Tech & Data
Infrastructure First. Alpha Always. You drive the strategy — we build the production-grade engine underneath it.
See the 7 layers →QuantDesigned Role
You drive the strategy. We build the engine. QuantDesigned sits at the data-platform, strategy and backtesting layers that turn raw market access into a production-grade quantitative operation.
Fragile foundations compound at the worst moment
More than half of a quant's or data scientist's time is lost to data wrangling rather than analysis, by many estimates. — NYT / critical review
$400bn → $847bn — the managed-services market (2025 → 2033, ~9.9% CAGR); firms increasingly rent specialist capability rather than build it in-house. — Grand View Research (directional, SEO-aggregate)
The 7 foundation layers
A production-grade quant stack is built from the ground up. Each layer depends on the one beneath it. Weakness at any level propagates upward — into your data quality, your analytics, and ultimately your alpha.
IT Infrastructure
Network architecture, security policy, identity and access management, endpoint controls, and monitoring. The foundation everything else depends on.
Cloud Infrastructure
AWS, Azure, Snowflake, Databricks — containerised environments, Kubernetes orchestration, CI/CD pipelines, scheduling, and code hosting at scale.
Database Infrastructure
Relational and time-series databases, multi-asset historical data stores, and internal data management — structured for query performance and auditability.
Data Sources
Market data connectivity: OTC, market microstructure, alternative data, internal systems. Clean, normalised, vendor-neutral data feeds via standardised APIs.
Coding / Quant Infrastructure
Python, C++, and R environments with curated libraries, version control (Git), reproducible workflows, and scheduled batch processes — built for quants, not just developers.
Risk Management Infrastructure
Portfolio and performance calculation, factor models, limit management, scenario analytics, and volatility regime detection. Risk by design, not by exception.
Trade Execution Infrastructure
OMS/EMS integration, smart order routing, FIX connectivity, TCA analytics, and execution slippage reporting. The full execution loop, connected and monitored.
The infrastructure trap
Firms that build alpha strategies on fragile foundations spend most of their time managing the infrastructure, not the alpha. Manual data pulls, brittle pipelines, ad-hoc scripts, and disconnected systems create technical debt that compounds at exactly the wrong moment.
The Typical Pattern
- PMs and quants spend 40–60% of their time on data wrangling and system maintenance
- Key-person risk: one person knows how the pipeline works
- New strategies take months to on-board to data and execution systems
- Risk is an add-on — caught late, managed manually
- Higher cost and more headcount than the quantitative work justifies
- Growth is constrained and opportunities are missed
The QD Approach
- Specialists focus on alpha generation — the infrastructure runs itself
- Fast on-boarding of new strategies, markets, and data sources
- Lower cost through automation, standardisation, and reuse
- Risk embedded at every layer — not bolted on at the end
- Scalable by design: the same stack supports one portfolio or ten
- Sustained performance and resilient operations across market cycles
Weak foundations don't just cost time — they cost outcomes
80.3% of enterprise AI projects deliver no business value — the root causes are organisational (scope, data ownership, sponsorship), rarely the algorithms. — RAND 2025 / Gartner
77% of APAC employers can't find the skilled talent they need — up from 45% in 2014; IT & Data is the hardest skill set to fill. Afterthought infrastructure is hard to staff your way out of. — ManpowerGroup 2025
Governance and validation are no longer optional
Regional regulators — HKMA, MAS, ASIC/APRA — are actively pushing governance, validation and human-in-the-loop controls at every layer of the stack. — HKMA
5,000+ funds reviewed and 300+ operational-failure events catalogued — the risk a stack without embedded controls is exposed to. — Castle Hall (2017 white paper — illustrative base rate)
Risk management by design
Risk is not a dashboard you add at the end. In a properly constructed stack, risk controls are embedded at every layer — from access permissions in the IT layer through to position limits at the execution layer. This is what we mean by risk management by design.
Embedded, Not Bolted On
Controls sit within each infrastructure layer. A misconfigured data source or an unapproved library import is caught before it reaches production — not after a loss event.
Consistent Across Personas
Portfolio managers, strategists, quants, and traders all operate within the same controlled environment. Risk visibility is uniform — there are no information gaps between teams.
Automated and Resilient
Limit monitoring, scenario generation, and exposure reporting run automatically. Human review is reserved for decision-making, not for running reports.
What we build
We design, build, and implement infrastructure stacks for quantitative finance teams. Engagements range from a targeted assessment of your current stack to a full infrastructure build from the ground up.
- Quant environment setup: Python / C++ / R, libraries, version control, schedulers
- Market data pipeline design and vendor-neutral connectivity
- Time-series and relational database architecture
- Cloud deployment on AWS, Azure, Snowflake, or Databricks
- OMS / EMS integration and FIX connectivity
- Portfolio and risk analytics frameworks
- TCA and execution quality analytics
- CI/CD pipelines and automated testing for quant code
- Infrastructure audit: identify bottlenecks and single points of failure
- Documentation and knowledge transfer to internal teams