Nifty Data Engine: From Tick to Feature

Every model is only as good as its data engine. Pipeline: (1) pull underlying 1-min + daily from a retail feed, (2) pull full Option Chain per expiry, (3) align by timestamp, compute atm_iv/pcr/oi_buildup/max_pain, (4) lag all features by 1 bar vs label (next close direction), (5) emit a tidy parquet daily. Pitfalls: mixing expiries, using settlement price as feature, 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.