# 04 — Solution Strategy

Status: skeleton

High-level solution strategy describing major approaches, technology choices, and trade-offs.

## Monte Carlo engine & persistence

- **Monte Carlo engine**: `services/simulation.py` will incorporate stochastic sampling (e.g., NumPy, SciPy) to populate `simulation_result` and feed reporting.
- **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.

## Simulation Roadmap

- Implement stochastic sampling in `services/simulation.py` (e.g., NumPy random draws based on parameter distributions).
- Store iterations in `models/simulation_result.py` via `/api/simulations/run`.
- Feed persisted results into reporting for downstream analytics and historical comparisons.

### Status update (2025-10-21)

- 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.