Quantitative strategy engineering
Turn a research question into a testable set of rules.
- Strategy specifications and reproducible backtests
- Transaction-cost and parameter-sensitivity analysis
- Walk-forward evaluation and integration planning
For teams
From research question
to a working specification.
Quantitative research and engineering engagements for proprietary trading desks, fintech platforms, and research teams.
Define the work. Then build it.
These are available engagement areas. Deliverables, timelines, data rights, and support are agreed in a written scope.
Risk analytics infrastructure
Make portfolio assumptions and scenario exposures easier to inspect.
- Scenario and stress-testing workflows
- Exposure dashboards and monitoring requirements
- Documented model assumptions and limitations
Market data engineering
Build a research workflow around consistent, usable data.
- Data cleaning and validation pipelines
- Options analytics and historical research datasets
- API specifications and integration documentation
A clear engagement process.
Begin with goals and constraints. Establish the evidence needed to judge the work before implementation starts.
- 01
Discovery: objectives, data access, and constraints
- 02
Written scope: deliverables, milestones, and acceptance criteria
- 03
Build and review: reproducible results and limitations
- 04
Handover: documentation, integration, and agreed support
What a backtest handover should explain
The research question
Does a defined screening rule behave consistently across the chosen historical periods?
The evidence package
Data provenance, reproducible code, baseline comparisons, costs, out-of-sample evaluation, and failure cases.
The limits
Historical results depend on assumptions. A backtest is not proof of future returns or live execution quality.
The review
Check the work against agreed criteria. Ask which findings support the idea and which findings challenge it.
Verified case studies
Evidence before publication.
Client work will appear here only after written permission and a review of the source material.
No public case study has been verified yet
Each published case study must identify the problem, process, data period, assumptions, transaction-cost model, risk limits, educational outcome, and important failure cases.
Profit screenshots and unsupported performance claims are not accepted.Bring a problem worth examining.
Describe the objective, current tools, and required output. Avoid sending confidential datasets in the initial inquiry.
Looking for individual courses?