Interview Prompt: Quantitative Model for Market Data

Provide a concrete example of a quantitative model you built to analyze market data. Cover the following:

1. Purpose and scope
   • What decision or business outcome did the model support?
   • Target variable and prediction horizon.
2. Key assumptions
   • Market, data, and modeling assumptions (e.g., stationarity, tradability, no look-ahead).
3. Data inputs
   • Sources, frequency, features engineered, and how you handled lags/corporate actions.
4. Modeling approach
   • Algorithm(s), target definition, feature engineering, and any constraints.
5. Validation methodology
   • How you split data (time-aware), metrics you used, and how you ensured no leakage.
6. Overfitting controls
   • Regularization, hyperparameter search strategy, feature selection, and simplicity constraints.
7. Drift monitoring and maintenance
   • How you monitored data/model drift, retrained, and used guardrails in production.

Be specific: include formulas, small numeric examples if helpful, and discuss pitfalls and mitigations.