1# Equal tuning budget across information sets. Hyper-parameters are tuned ONLY on the validation period.2baselines:3 B0: {type: base_rate}4 B1: {type: logistic, inputs: [fundamentals, controls]}5 B2: {type: logistic, inputs: [fundamentals, controls, daily]}6 B3: {type: logistic, inputs: [fundamentals, controls, daily, intraday_30m]}7 B4: {type: logistic, inputs: [fundamentals, controls, daily, intraday_5m]}8 B5: {type: logistic, inputs: [fundamentals, controls, daily, intraday_1m]}9logistic:10 penalty: l211 C_grid: [0.01, 0.03, 0.1, 0.3, 1.0, 3.0]12 standardize: train_only13 impute: train_median14lightgbm:15 information_sets: [B1, B2, B3, B4, B5]16 budget_trials: 24 # identical random-search budget for every information set17 seed: 2026110218 search_space:19 num_leaves: [7, 15, 31, 63]20 learning_rate: [0.01, 0.03, 0.05]21 min_child_samples: [50, 100, 200]22 feature_fraction: [0.5, 0.7, 0.9]23 bagging_fraction: [0.6, 0.8, 1.0]24 lambda_l2: [0.0, 1.0, 10.0]25 n_estimators_max: 200026 early_stopping_rounds: 10027neural: {enabled: false, reason: "owner instruction 2026-11-02: minimise AI usage; B7/B8 not run"}28