Home About Services Portfolio Team Terms & Policies Contact
Axiomix Leadership

Portfolio &
Case Studies

Selected examples of quantitative research, execution analysis, and technical implementation drawn from the experience represented within Axiomix leadership.

Leadership Experience

Selected Case Studies

The examples below present relevant technical and analytical experience in a restrained and anonymised format, without disclosing confidential client or institutional detail.

Quantitative Research Execution Analysis Technical Implementation

Equity Index Futures Research

Design, testing, and refinement of an algorithmic trading workflow for equity index futures, combining quantitative research, execution analysis, and production-grade technical implementation within a single delivery stream.

Market Focus
Equity index futures, with emphasis on execution behaviour, broker connectivity, and systematic research workflow design.
Core Delivery
A connected workflow spanning live platform architecture, event-driven backtesting, and iterative strategy refinement.
Working Style
Close collaboration with the client to move between research, implementation, and practical improvement of live trading logic.
Scope of Work
Built a live algorithmic trading platform architected for integration with APIs from multiple futures brokers.
The implementation was designed to support multi-broker connectivity, stable production deployment, and a clean technical foundation for ongoing quantitative research and execution analysis.
A high-performance event-based backtesting engine was developed to calibrate and optimise the client's trading strategy using a disciplined, repeatable evaluation process.
Close collaboration with the client supported iterative algorithm refinement and optimisation of entry and exit levels, improving how research output translated into live trading behaviour.
Platform Engineering Risk Infrastructure Operational Resilience

Credit Risk Dashboard Modernisation

Modernisation of a fragile risk dashboard used by an institutional credit portfolio, turning a researcher-owned desktop dependency into a resilient production service.

Starting Point
A critical dashboard running from a single desktop machine, with data ingestion tightly coupled to the presentation layer.
Key Intervention
Containerised deployment, service separation, scheduled intermediary processing, and alerting tied to data freshness rather than infrastructure noise.
Operational Outcome
Load times cut from 45 minutes to seconds, self-sufficiency improved, and stale-data exposure removed from a live risk process.
What Changed
The dashboard was containerised and deployed into a managed cloud environment, while ingestion was separated into its own service layer.
Pre-computed intermediary tables were introduced on a scheduled basis so downstream views no longer depended on heavy live recalculation at startup.
Schema validation was added to every ingestion cycle so bad data was rejected rather than silently served into risk views and committee workflows.
Alerting shifted from infrastructure-level noise to the more relevant operational signal: whether data was fresh, usable, and safe to rely on.
Research Automation Release Control Auditability

Research Pipeline Automation

Improvement of a live research pipeline for a systematic fixed income workflow that depended on a human operator each morning, removing manual dependency and introducing a more controlled release process.

Before
Six-hour manual runs, researcher-owned deployment, laptop-based model execution, and no proper audit trail on model parameters.
Engineering Shift
Git-triggered automation, staging deployments on pull requests, tagged production releases, automated regression checks, and versioned parameters.
Outcome
Lead time reduced from hours to minutes, while release control improved through testing, rollback, and research-governance discipline.
What Changed
The pipeline was reworked so a Git push could trigger the full run automatically, removing the researcher as the operational deployment mechanism.
Staging deployments were introduced on every pull request so changes were validated before landing, rather than going straight into production without separation.
Production promotion was moved behind an explicit release-tag flow, giving research leadership clearer control over when changes became live.
Model parameters were versioned in Git and regression tests were run automatically, creating an audit trail, earlier drift detection, and a clean rollback path when required.
Model Validation Self-Service Tooling Release Workflow

Volatility Surface Validation Workflow

Improvement of a derivatives modelling workflow constrained not by mathematics, but by deployment friction, with the solution focused on removing the deployment bottleneck and increasing safe iteration speed.

Initial Constraint
Each iteration required engineering involvement, causing multi-day turnaround and allowing pricing-surface errors to surface too late.
Unlock
Self-service deployment, per-desk parameter overrides, side-by-side validation dashboards, and a controlled GitOps promotion path.
Result
Research iteration moved from a handful of changes each month to a materially faster validation cycle, while pushing pricing errors earlier into UAT.
What Changed
A self-service sandbox was introduced so researchers could deploy their own volatility-builder variants using controlled parameter overrides without waiting on engineering support.
Per-desk parameter overrides allowed independent model tuning without cross-desk interference, improving both iteration freedom and operational separation.
A side-by-side validation dashboard made it possible to compare current and proposed surfaces before promotion, including visibility into Greeks, smile quality, and interpolation errors.
A GitOps approval path then allowed changes to be reviewed in UAT and promoted cleanly, removing deployment friction from the critical path of model validation.

Discuss a Relevant Technical Problem

For enquiries relating to quantitative research, execution analysis, or technical implementation, please get in touch through the Axiomix contact page.

Contact Us