XGBoost Feature Engineering for Nifty: Full Pipeline

Building XGBoost features for Nifty options starts with clean data. Pull the underlying 1-minute and daily bars plus the full Option Chain per expiry from a retail feed. Compute atm_iv, pcr_strikes, oi_buildup, vix_india, theta_decay_rate and max_pain_distance.

The single most important rule: lag every feature by one bar relative to the label, which is next-day direction. Same-bar features leak future information and produce fake accuracy. Train with LightGBM or XGBoost using max_depth 4-6, subsample 0.8, and validate via walk-forward folds. The feature work, not the model choice, determines whether the system ranks Nifty direction better than chance.

Q: Which features matter most for Nifty XGBoost?
A: OI buildup, PCR across strikes, ATM IV and VIX z-score consistently rank highest because they capture sentiment and regime.

Q: How do I avoid leakage in labels?
A: Lag all features by one bar versus the next-day direction label; never use same-bar close or settlement price as a feature.

Q: Why walk-forward not train/test split?
A: Walk-forward rolls the training window forward and tests on unseen future data, exposing overfitting that a random split hides.

Q: Is LightGBM better than XGBoost here?
A: They tie on accuracy; LightGBM trains faster on larger strike/date grids, aiding more folds.