Complete documentation for building a production-grade mean-reversion analytics platform.
- 00-implementation-guide.md - Step-by-step implementation checklist
- ../QUICKSTART.md - Get started in 15 minutes
- 01-product-requirements.md - Mathematical foundation & metrics
- 02-api-specification.md - Complete REST API contract
- 03-system-architecture.md - System design & deployment
- 04-ux-design.md - User experience philosophy
- 05-wireframe-flows.md - UI/CLI interaction flows
- 06-starter-templates.md - Project structures & boilerplate
- 07-validation-testing.md - Testing & validation guide
- 08-faq.md - Frequently asked questions
- Developer: Start with 00 → 01 → 02 → 06
- Architect: Read 03 → 02 → 01
- Data Scientist: Focus on 01 → 07
- Designer: See 04 → 05
- Math & Statistics: 01-product-requirements.md
- API Design: 02-api-specification.md
- Infrastructure: 03-system-architecture.md
- Testing: 07-validation-testing.md
- Getting Started: 00-implementation-guide.md, QUICKSTART.md
| Doc | Topic | Pages | Key Content |
|---|---|---|---|
| 00 | Implementation | 3 | Phase-by-phase checklist, tips, pitfalls |
| 01 | Product Reqs | 15 | Z-scores, walk-forward, FDR, OU process |
| 02 | API Spec | 10 | Endpoints, schemas, errors, examples |
| 03 | Architecture | 8 | Data flow, storage, compute, deployment |
| 04 | UX Design | 7 | Personas, journeys, design principles |
| 05 | Wireframes | 8 | CLI/Web layouts, interaction flows |
| 06 | Templates | 10 | Project structures, starter code |
| 07 | Testing | 6 | Test scenarios, validation, benchmarks |
| 08 | FAQ | 5 | Common questions & troubleshooting |
- Z-Score: Normalized price deviation from mean
- Walk-Forward: Anti-overfitting validation technique
- FDR: False Discovery Rate correction for multiple testing
- Hurst Exponent: Measure of mean-reversion strength
- OU Process: Ornstein-Uhlenbeck mean-reversion model
- Provenance: Data hash + version for reproducibility
- Idempotency: Safe request retries
- Async Jobs: Long-running analysis handling
- Parquet: Columnar storage format
- Read 00, 01, 06
- Implement z-score calculation
- Test on synthetic data
- Build minimal API
- Read 02, 03, 07
- Add walk-forward validation
- Implement statistical tests
- Deploy with Docker
- Read 04, 05
- Build CLI/Web UI
- Add webhooks & monitoring
- Optimize performance
- Polygon API: https://polygon.io/docs
- Statsmodels: https://www.statsmodels.org/
- FastAPI: https://fastapi.tiangolo.com/
- Parquet: https://arrow.apache.org/docs/python/parquet.html
Ready to build? Start with 00-implementation-guide.md! 🚀