Maximize risk-adjusted return (Sharpe/Calmar) on GBP/USD 15-min data. GradientBoostingClassifier chosen for its strong bias-variance tradeoff on medium-sized tabular datasets without needing GPU. Hyperparameters: moderate depth=4 prevents overfitting, learning_rate=0.04 with 400 estimators balances convergence vs generalisation, subsample=0.75 adds stochasticity to reduce variance, min_samples_leaf=20 enforces statistical significance at each leaf. Early stopping via n_iter_n

Maximise Sharpe ratio via a Gradient Boosting classifier trained on Bollinger Band position (bb_pct), normalised bandwidth (bb_width), ATR/NATR, RSI, MACD histogram, candle structure, and lagged features. GBM chosen for its ability to capture non-linear interactions between volatility (ATR) and mean-reversion (BB) signals. n_iter_no_change acts as early stopping to prevent overfitting on the 15-min USDJPY series. SL=0.5% / TP=1.0% gives a 1:2 risk-reward; threshold=0.55 reduc

Maximize risk-adjusted return on AUD/USD 15-min data using GradientBoostingClassifier. RSI-14 captures momentum extremes and divergence conditions; MACD (12,26,9) provides trend direction and momentum shifts via crossovers and histogram slope. Additional features (BB, ATR, EMA trend, candle structure, session timing) enrich the feature space. GradientBoosting with shallow trees (depth=4), moderate learning rate (0.05), and early stopping via n_iter_no_change prevents overfitt