Case study
AEM / Strategy Research OS
A research operating system concept for organizing strategy ideas, experiment history, validation notes, and decision records.
- Research workflow
- AI-assisted analysis
- Strategy validation
Screenshot / demo TODO
Add project screenshots, architecture diagrams, backtest reports, or repository previews here.
Overview
A structured workspace for moving trading ideas from rough hypotheses into tracked research artifacts, validation workflows, and implementation-ready notes.
Problem
Strategy research can become scattered across notebooks, chats, spreadsheets, platform scripts, and screenshots, making it hard to preserve context or compare experiments honestly.
What I Built
- A central project structure for strategy notes, test plans, assumptions, and review checkpoints.
- A workflow model for documenting hypotheses, dataset choices, validation windows, and rejection criteria.
- Placeholders for AI-assisted research summaries while keeping final decisions auditable by a human reviewer.
Technologies Used
- Python
- pandas
- NumPy
- Market data workflows
- AI-assisted research tooling
- TODO: add exact storage and UI details
Key Technical Challenges
- Keeping research notes useful without turning the system into busywork.
- Separating exploratory analysis from claims that are ready to drive implementation.
- Designing metadata that supports comparison without inventing false precision.
What It Demonstrates
- Research system design
- Quant workflow discipline
- Practical AI-assisted engineering
- Experiment traceability
TODO
- Add screenshots or diagrams.
- Add repository, demo, or write-up links when they are public.
- Add measured results only when they can be accurately sourced.