zwitschi
4b3a15ed15
- Introduced multiple architecture documentation files covering building block view, runtime view, deployment view, concepts, architecture decisions, quality requirements, technical risks, glossary, UI and styling, testing, CI, and development setup. - Migrated existing content from `architecture_overview.md` and `implementation_plan.md` into structured documentation. - Created scripts for checking broken links in documentation and formatting Markdown files for consistency. - Updated quickstart guide to provide clearer setup instructions and usage overview. - Removed outdated MVP features and testing strategy documents to streamline documentation.
21 lines
1.1 KiB
Markdown
21 lines
1.1 KiB
Markdown
# 04 — Solution Strategy
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Status: skeleton
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High-level solution strategy describing major approaches, technology choices, and trade-offs.
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## Monte Carlo engine & persistence
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- **Monte Carlo engine**: `services/simulation.py` will incorporate stochastic sampling (e.g., NumPy, SciPy) to populate `simulation_result` and feed reporting.
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- **Persistence of simulation results**: plan to extend `/api/simulations/run` to persist iterations to `models/simulation_result` and provide a retrieval endpoint for historical runs.
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## Simulation Roadmap
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- Implement stochastic sampling in `services/simulation.py` (e.g., NumPy random draws based on parameter distributions).
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- Store iterations in `models/simulation_result.py` via `/api/simulations/run`.
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- Feed persisted results into reporting for downstream analytics and historical comparisons.
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### Status update (2025-10-21)
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- A scaffolded simulation service (`services/simulation.py`) and `/api/simulations/run` route exist and return in-memory results. Persisting those iterations to `models/simulation_result` is scheduled for a follow-up change.
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