Quant Trading Systems Consultant
Trading systems engineered for the real world.
I audit, design, and build systematic trading infrastructure, from research and backtesting to execution, risk controls, monitoring, and production handover.
Algorithmic trading · Quant research · Execution systems · Risk architecture
New York & International · Remote consulting.
- Data
- Research
- Validation
- Execution
- Risk
- Monitoring
Why systems fail
A profitable backtest is not a production system.
Most failures do not come from a single indicator or entry rule. They emerge from weak assumptions, fragmented architecture, unrealistic execution, uncontrolled optimisation, missing safeguards, and poor operational visibility.
Research assumptions
Transaction costs, slippage, data quality, and regime dependence can quietly turn a strong backtest into a fragile result.
Architecture
Strategy logic, execution, risk, evaluation rules, and reporting should not collapse into one unmaintainable script.
Live operations
Disconnections, duplicate orders, stale data, position mismatches, and missing alerts are engineering problems, not market opinions.
Validation
Parameter searches need robustness checks, out-of-sample testing, and clear rejection criteria, not a single attractive equity curve.
Engagement model
Start with clarity before committing to development.
Most engagements begin with an audit. When the system needs deeper redesign, the next step is a technical roadmap. Execution begins only after scope, architecture, ownership, and validation criteria are clear.
System Audit
Find structural weaknesses, hidden assumptions, and operational risks.
Start with an auditTechnical Roadmap
Translate findings into a sequenced architecture and implementation plan.
Plan the architectureSelected work
Systems built across trading, research, execution, and financial modelling.
September 2025–May 2026
Modular NinjaTrader 8 Trading Framework
A modular C# framework separating strategy logic, execution, risk, prop-firm rules, connectivity, and reporting.
- C#
- NinjaScript
- NinjaTrader 8
September 2025–May 2026
Systematic Futures Strategies and Live Deployment
Research, backtesting, optimisation, and deployment workflows for automated futures strategies operating on dedicated infrastructure.
- Python
- NinjaTrader 8
- C#
March–September 2025
U.S. Credit Rating Engine for Special Purpose Entities
A full-stack platform for assigning, reviewing, and documenting internal credit ratings for Special Purpose Entities.
- Python
- R
- R Shiny
- Snowflake
- Posit Cloud
January–May 2026
Modular AI Trading Research System
A Python architecture covering the full quantitative research lifecycle from data ingestion to simulated execution and trade journaling.
- Python
- Scikit-learn
Where I contribute
- Strategy architecture
- Backtesting and robustness
- Risk and position sizing
- Execution and order management
- Prop-firm rule engines
- Data and feature pipelines
- Machine-learning research workflows
- Monitoring and logging
- Refactoring and software rescue
- Model validation and data quality
- Technical documentation
- Research-to-production handover
Trading, quantitative finance, and software engineering in one workflow.
My background combines a dual degree in Computer Engineering and Business Administration with quantitative risk and financial-systems work in Frankfurt and New York. Today, I focus on systematic trading research, execution infrastructure, and the engineering required to move from a promising model to a maintainable live system.
- Dual degree: Computer Engineering + Business Administration
- Quantitative risk and systems experience in Frankfurt and New York
- Python, C#, NinjaTrader 8, R, Snowflake, and R Shiny
- SIE, Series 63, and Series 65 examinations passed
- Independent systematic trading research and live-deployment work
Selected recognition includes 1st place in a University of Granada trading competition and a Top 6% finish in the February 2026 CME Group Monthly Trading Challenge.
The CME result was achieved in a five-day competition using equal virtual starting accounts and does not indicate future performance.
Examinations passed; not a statement of current U.S. registration or licensure.
About FranciscoMedia contributions
Quoted by Money.it on SpaceX valuation, the FTSE MIB, and Italian defence equities. View contributions
How I work
Transparent architecture. Explicit assumptions. Reproducible decisions.
- 01Diagnose before rebuilding
- 02Separate research from production
- 03Make risk rules explicit
- 04Test failure modes, not only expected paths
- 05Document for the next developer
- 06Keep client ownership clear
Have a strategy, platform, or codebase that needs a serious technical review?
Describe what exists today, what is failing, and what outcome you need. I will review the context and suggest the most appropriate starting point.