Maximize risk-adjusted return (Sharpe/Calmar) on USD/CHF 15-min data. Uses Stochastic (14,3), Bollinger Bands (20,2), and RSI-14 as core features with confluence signals, divergence proxies, and EMA crossover context. XGBoost chosen for its strong performance on tabular data with regularization parameters (gamma, alpha, lambda) tuned to reduce overfitting on short date ranges. SL=0.5%/TP=1.0% gives 1:2 R:R ratio. Session filter [7,17] UTC targets London/NY overlap for higher-

Maximize risk-adjusted return (Sharpe) on AUD/USD 15-min bars. GradientBoostingClassifier with shrinkage (lr=0.04), moderate depth (4), subsampling (0.8) and sqrt feature fraction controls overfitting on a noisy FX series. Early stopping (n_iter_no_change=30) prevents over-training. SL=0.5%/TP=1.0% gives 1:2 RR. Threshold=0.55 filters low-confidence signals. EMA 9/21 crossover with RSI 14 confirmation is the primary signal logic, reinforced by MACD, Bollinger Bands, Stochasti

Maximize risk-adjusted return (Sharpe) by combining EMA crossover trend regime with ATR-normalised volatility, RSI, MACD, and Bollinger features. XGBoost hyperparameters tuned for bias-variance balance: moderate depth (4), aggressive shrinkage (lr=0.04), column/row subsampling, and L1/L2 regularisation. SL=0.5% / TP=1.0% targets a 2:1 reward-risk ratio. Session filter [6,20] UTC focuses on the liquid London/NY overlap. min_atr filters dead markets.