Claude-generated EMA 9/21 trend filter with RSI 14 momentum gate on EURUSD 15min. Test holdout: WR 71%, PF 2.36, 77 trades, +1.7% over ~8 days. Lower-trade-count higher-WR variant.

Maximize risk-adjusted return (Sharpe/Calmar) on AUD/USD 15-min. GradientBoostingClassifier with moderate depth and learning rate chosen to balance bias-variance. 2:1 reward-to-risk (SL=0.5%, TP=1.0%). Stochastic crossovers, BB mean-reversion, and RSI regime signals form the core feature set; MACD, volatility, and candle features add context. Early stopping (n_iter_no_change=30) prevents overfitting.

Maximise risk-adjusted return (Sharpe/Calmar). Deeper ensemble (600 trees) with aggressive regularisation (reg_alpha=0.5, reg_lambda=2, gamma=0.2, min_child_weight=5) to prevent overfitting on 15-min USDCAD. Rich feature set adds z-scores, efficiency ratio, session dummies, RSI divergence, candle shape and cross-indicator interactions beyond the prior attempt's plain indicators. 0.5% SL / 1.0% TP gives 1:2 R:R; session filter restricts to liquid London+NY overlap hours.

Maximize risk-adjusted return (Sharpe/Calmar) using a GradientBoostingClassifier with EMA 9/21 crossover as the primary signal source and RSI 14 as confirmation. Deep feature set includes MACD, Bollinger Bands, Stochastic, ATR normalisation, candle structure, lagged features and RSI/EMA interaction terms. Gradient boosting chosen for its ability to capture non-linear interactions between trend, momentum and volatility features without overfitting when regularised via subsampl