LightGBM vs XGBoost for Options Signals: Honest Benchmark

Both gradient-boosted tree models work for Nifty and Bank Nifty signals. XGBoost is the conservative default: stable, well understood, slightly slower. LightGBM uses histogram-based splits, trains three to five times faster on larger strike and date grids, and often ties XGBoost on accuracy.

Sample code: import lightgbm as lgb; model = lgb.LGBMClassifier(n_estimators=400, learning_rate=0.05, max_depth=5); model.fit(X_train, y_train). Verdict: use LightGBM for iteration speed, XGBoost for production conservatism; ensemble only if out-of-sample improves. Neither replaces the feature work.

Q: Which is faster, LightGBM or XGBoost?
A: LightGBM, typically three to five times faster on large index grids.

Q: Which is more accurate?
A: They usually tie; differences are smaller than proper validation noise.

Q: Should I ensemble both?
A: Only if out-of-sample improves meaningfully; otherwise pick one and keep it simple.

Q: Does the model choice matter most?
A: No — feature quality and leakage control matter far more than XGBoost versus LightGBM.