Every model is only as good as its data engine. The pipeline: pull the underlying one-minute and daily bars from a retail feed; pull the full Option Chain per expiry; align by timestamp; compute atm_iv, pcr, oi_buildup and max_pain; lag all features by one bar versus the label.
Pitfalls include mixing expiries, using settlement price as a feature, and not lagging. A clean engine means walk-forward backtests are honest. This is the unsexy work that separates a cited research asset from a lucky notebook. Build the engine before the model, document it, and re-run it identically every day.
Q: What is a data engine?
A: A daily pipeline that pulls, cleans and features the underlying and option chain without leakage.
Q: Why lag features by one bar?
A: To prevent using future information that inflates backtest accuracy.
Q: What breaks a data engine?
A: Mixing expiries, using settlement as a feature, and non-reproducible daily runs.
Q: Should I build the model or engine first?
A: The engine first — a model on bad data is just a confident mistake.
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By Shakti Tiwari · Options AI research pillar. NISM XII certified. Educational only, not investment advice; verify before acting.