Maximize risk-adjusted return (Sharpe/Calmar) using a deep GradientBoostingClassifier with many slow-learning trees and aggressive regularisation (min_samples_leaf=20, subsample=0.75). Feature set deliberately differs from prior RSI+BB+Stoch attempts by adding: ATR-normalised returns, z-score mean-reversion signals, RSI divergence proxy, Williams %R, candle structure ratios, cyclic time encoding, and interaction/lag features to give the model richer multi-timeframe context. S

Maximize risk-adjusted return (Sharpe/Calmar) on USD/CAD 15-min data. GradientBoostingClassifier chosen for strong generalisation on noisy FX price data; moderate depth (4) and learning rate (0.04) with early stopping prevent overfitting. Features: Bollinger Bands (mean-reversion signal via bb_pct and bb_width), ATR/NATR (volatility filter), RSI, MACD, Stochastic, z-score, momentum returns, and candle-body ratios. 2:1 R:R (SL 0.5%, TP 1.0%) with session filter (07-20 UTC) to