LightGBM vs XGBoost for Options Signals

Both gradient-boosted trees work for Nifty/Bank Nifty signals. XGBoost is the conservative default: stable, well-understood, slightly slower. LightGBM uses histogram-based splits, trains 3-5x faster on larger strike/date grids, and often ties XGBoost on accuracy. For index data (more rows, more strikes), LightGBM's speed enables more walk-forward folds. Code: import lightgbm as lgb; lgb.LGBMClassifier(n_estimators=400, learning_rate=0.05, max_depth=5). Verdict: use LightGBM for iteration speed, XGBoost for production conservatism; ensemble only if out-of-sample improves. Neither replaces the feature work -- a bad feature set makes both useless.