Maximise risk-adjusted return (Sharpe / Calmar) on NZD/USD 15-min. GradientBoostingClassifier selected for its strong generalisation on structured tabular data with noisy financial features. n_estimators=400 with early stopping (n_iter_no_change=30) prevents overfitting. max_depth=4 keeps trees shallow to reduce variance. subsample=0.8 + max_features=sqrt add stochasticity for robustness. SL 0.5% / TP 1.0% gives a minimum 2:1 reward-risk ratio. Session filter 07-20 UTC covers

Maximise risk-adjusted return (Sharpe/Calmar) on USD/CAD 15-min using Bollinger Band mean-reversion signals augmented by ATR, RSI, MACD, and EMA-cross features fed into a regularised XGBoost classifier. SL=0.5% / TP=1.0% gives a 1:2 RR floor. Conservative depth (4) and strong L1/L2 regularisation prevent overfitting on a single year of data.