Examples¶
The examples use public Persistra APIs and explicit intermediate objects.
| Topic | Use it for |
|---|---|
| Monte Carlo research | Generate reproducible paths and inspect convergence |
| Data and features | Normalize data and build point-in-time inputs |
| Factor models | Fit regressions and build risk and forecast objects |
| Portfolio optimization | Express objectives, constraints, costs, and backtests |
| Analysis and visualization | Inspect research and portfolio results |
For a factor workflow, move from data and features to factor models, then portfolio optimization. For distributional research, begin with Monte Carlo. Use the Trading Engine guide when a completed scenario needs deterministic execution replay.
Keep these distinctions visible in application code:
- retrieval time is provenance, not market availability;
- a feature is not a forward label;
- a forecast is not a target portfolio;
- a vectorized backtest is not an order-level replay.