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QuantLab is not a single fixed recipe. Depending on what you ask for, the agent chains different steps. Think of it as a few common paths rather than one mandatory sequence. For dates: without ML you usually name only the sample start and end. The part you evaluate is that whole window except the first covariance_window days used as warmup. With ML/DL you still think in start + end of the sample — not a three-ended train / mid / test calendar.

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Common paths

Quick backtest — you care about weights and historical performance, not machine learning:
Forecast-driven research — you want models to estimate returns before building the book:
Stress and “what if” — you already have a portfolio (or just tickers and weights) and want scenario losses:
Often that sits on top of a backtest path: after you have a constructed portfolio, you ask for charts, tearsheets, factor stories, or multi-model stress in the same conversation. You do not have to name these steps yourself. Describe the goal; the agent picks the sequence.

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Mixing and matching

A few patterns people use often:
  • Same data, many portfolios — download prices once, then try equal weight, HRP, max Sharpe, DCC, and so on side by side.
  • Backtest, then dig in — after a run, ask for an equity curve, weight paths, or a risk report without starting from scratch.
  • Your book, QuantLab analytics — paste holdings and ask how bad tomorrow looks under a shock, or whether something looks overweight versus optimized alternatives.
  • With or without ML — skip models when you only need classical portfolio math; add XGBoost, LightGBM, CatBoost, or a neural net when forecasts matter.
Independent asks (several optimizers, or charts + stress + risk after one portfolio) can run in parallel. Steps that truly depend on each other still wait their turn.

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Keeping results

Each finished step leaves a result link you can reuse later — so you can change one assumption, compare methods, or come back tomorrow without re-downloading everything. Example prompts show how that sounds in chat.