Implement walk-forward cross-validation orchestrator for time-series model training

cli/training.py

@@ -926,6 +926,7 @@ def _train_ensemble_models(
         regime_lookback=regime_lookback,
         regime_lookahead=regime_lookahead,
         console=console,
+        cfg=cfg,
     )
     all_metrics['direction'] = dir_metrics
 
@@ -1345,6 +1346,7 @@ def _train_direction_or_regime_model(
     regime_lookback,
     regime_lookahead,
     console,
+    cfg=None,
 ):
     """Train either the regime or direction model. Returns (dir_data, dir_metrics, dir_model_path, dir_trainer)."""
     from src.training.modular_trainers import (
@@ -1388,34 +1390,80 @@ def _train_direction_or_regime_model(
     _absl_logger = _logging.getLogger('absl')
     _absl_logger.setLevel(_logging.ERROR)
 
-    if use_transformer:
-        dir_trainer = TransformerDirectionTrainer(trainer_config)
-        dir_metrics = dir_trainer.train(
-            dir_data['X_train'], dir_data['y_train'],
-            dir_data['X_val'], dir_data['y_val'],
+    # Check if Walk-Forward Cross-Validation is enabled
+    wf_config = None
+    if cfg and isinstance(cfg, dict):
+        wf_config = cfg.get('walkforward', {})
+
+    wf_enabled = wf_config and wf_config.get('enabled', False)
+
+    if wf_enabled:
+        # Use WalkForwardOrchestrator for training
+        from src.training.walkforward_orchestrator import WalkForwardOrchestrator
+
+        trainer_class = TransformerDirectionTrainer if use_transformer else TCNTrainer
+
+        orchestrator = WalkForwardOrchestrator(
+            trainer_class=trainer_class,
+            trainer_config=trainer_config,
+            wf_config=wf_config,
+            console=console,
+        )
+
+        # Train with walk-forward validation
+        dir_trainer, dir_metrics = orchestrator.train(
+            X_train=dir_data['X_train'],
+            y_train=dir_data['y_train'],
+            X_val=dir_data['X_val'],
+            y_val=dir_data['y_val'],
             feature_names=dir_data['feature_names'],
+            instrument=training_instrument,
             w_train=dir_data.get('w_train'),
             w_val=dir_data.get('w_val'),
             warm_start_path=str(warm_start_path) if warm_start_path else None,
-            instrument=training_instrument,
         )
+
+        # Save the best model from WFCV
         dir_trainer.save(str(pair_paths['direction']), instrument=training_instrument)
         if training_instrument != "GENERIC":
-            dir_trainer.save(str(model_dir / _TRANSFORMER_KERAS_FILE), instrument=training_instrument)
+            model_filename = _TRANSFORMER_KERAS_FILE if use_transformer else "tcn_direction.keras"
+            dir_trainer.save(str(model_dir / model_filename), instrument=training_instrument)
         dir_model_path = str(pair_paths['direction'])
-        console.print(f"[cyan]💾 Direction model saved to: {pair_paths['direction']}[/cyan]")
+
+        # Save WFCV summary
+        orchestrator.save_summary(model_dir)
+
+        console.print(f"[cyan]💾 Direction model (WFCV best) saved to: {pair_paths['direction']}[/cyan]")
     else:
-        dir_trainer = TCNTrainer(trainer_config)
-        dir_metrics = dir_trainer.train(
-            dir_data['X_train'], dir_data['y_train'],
-            dir_data['X_val'], dir_data['y_val'],
-            feature_names=dir_data['feature_names']
-        )
-        dir_trainer.save(str(pair_paths['direction']))
-        if training_instrument != "GENERIC":
-            dir_trainer.save(str(model_dir / "tcn_direction.keras"))
-        dir_model_path = str(pair_paths['direction'])
-        console.print(f"[cyan]💾 TCN model saved to: {pair_paths['direction']}[/cyan]")
+        # Standard training (no WFCV)
+        if use_transformer:
+            dir_trainer = TransformerDirectionTrainer(trainer_config)
+            dir_metrics = dir_trainer.train(
+                dir_data['X_train'], dir_data['y_train'],
+                dir_data['X_val'], dir_data['y_val'],
+                feature_names=dir_data['feature_names'],
+                w_train=dir_data.get('w_train'),
+                w_val=dir_data.get('w_val'),
+                warm_start_path=str(warm_start_path) if warm_start_path else None,
+                instrument=training_instrument,
+            )
+            dir_trainer.save(str(pair_paths['direction']), instrument=training_instrument)
+            if training_instrument != "GENERIC":
+                dir_trainer.save(str(model_dir / _TRANSFORMER_KERAS_FILE), instrument=training_instrument)
+            dir_model_path = str(pair_paths['direction'])
+            console.print(f"[cyan]💾 Direction model saved to: {pair_paths['direction']}[/cyan]")
+        else:
+            dir_trainer = TCNTrainer(trainer_config)
+            dir_metrics = dir_trainer.train(
+                dir_data['X_train'], dir_data['y_train'],
+                dir_data['X_val'], dir_data['y_val'],
+                feature_names=dir_data['feature_names']
+            )
+            dir_trainer.save(str(pair_paths['direction']))
+            if training_instrument != "GENERIC":
+                dir_trainer.save(str(model_dir / "tcn_direction.keras"))
+            dir_model_path = str(pair_paths['direction'])
+            console.print(f"[cyan]💾 TCN model saved to: {pair_paths['direction']}[/cyan]")
 
     if 'val_balanced_accuracy' in dir_metrics:
         console.print(f"[green]✓ Directional predictor complete: Validation accuracy={dir_metrics['val_accuracy']:.1%} • Balanced={dir_metrics['val_balanced_accuracy']:.1%}[/green]")
@@ -2586,6 +2634,26 @@ def _generate_training_report(
 - Validation Accuracy: {dir_metrics['val_accuracy']:.2%}
 - Balanced Accuracy: {dir_metrics.get('val_balanced_accuracy', dir_metrics['val_accuracy']):.2%}
 """
+
+    # Add Walk-Forward CV metrics if available
+    if 'wfcv_mean_test_accuracy' in dir_metrics:
+        wfcv_mean = dir_metrics['wfcv_mean_test_accuracy']
+        wfcv_std = dir_metrics.get('wfcv_std_test_accuracy', 0.0)
+        wfcv_n_folds = dir_metrics.get('wfcv_n_folds', 0)
+        wfcv_best_fold = dir_metrics.get('wfcv_best_fold', 0)
+
+        report_content += f"""
+#### Walk-Forward Cross-Validation
+- **Test Accuracy (Mean ± Std)**: {wfcv_mean:.2%} ± {wfcv_std:.2%}
+- **Number of Folds**: {wfcv_n_folds}
+- **Best Fold Selected**: {wfcv_best_fold}
+- **Stability (CV)**: {(wfcv_std / wfcv_mean if wfcv_mean > 0 else 0):.4f}
+
+> **Note**: Walk-forward validation provides robust out-of-sample performance estimates.
+> The reported model is the best-performing fold from time-series cross-validation.
+
+"""
+
     if 'bootstrap_ci_lower' in dir_metrics:
         report_content += f"- Bootstrap 95% CI: [{dir_metrics['bootstrap_ci_lower']:.2%}, {dir_metrics['bootstrap_ci_upper']:.2%}]\n"