Illustrative article · Sample editorial content and publication date, prepared for this website. Not a disclosure of proprietary research.
Data first
Price, volume and open interest arrive with gaps, revisions and timestamps that do not always mean what they appear to. Cleaning and aligning them, so that every value was genuinely available at the time it is used, is the least visible and most important step.
Features carry the idea
A feature turns a hypothesis into a number: a return over a window, a deviation from a moving average, a change in open interest. Good features are few, explainable and stable across periods. Adding more rarely helps as much as choosing better ones.
Validation decides
A model is fitted on one period and judged on another it has never seen, rolling forward through time. Regularised regression and sequence models are both useful; what matters more is that the evaluation mirrors how the model would be used, including costs and delays.