feat: train forecasts on trade outcomes

tests/test_calibrate_thresholds.py

@@ -7,12 +7,20 @@ from tools.calibrate_torch_thresholds import (
     ForecastRecord,
     _average_selected_predictions,
     _apply_platt_calibration,
+    _build_torch_model,
     _choose_recommendation,
     _full_backtest,
     _fit_platt_calibration,
+    _record_event_target,
     _entry_validation_skill,
 )
-from tools.train_torch_recurrent_forecaster import _ensemble_candidate
+from tools.train_torch_recurrent_forecaster import (
+    OUTPUT_LAYOUT,
+    RecurrentReturnModel,
+    _ensemble_candidate,
+    _export_head_state,
+    _export_recurrent_state,
+)
 
 
 def _result(*, trades: int, average: float, total: float, profit_factor: float) -> CalibrationResult:
@@ -85,6 +93,13 @@ def test_platt_calibration_learns_probability_direction_from_train_records() ->
     assert calibrated[0].probability_up < calibrated[1].probability_up
 
 
+def test_barrier_event_target_takes_precedence_over_terminal_profit() -> None:
+    record = _record(1, 0.8, 3.0)
+    record.take_profit_first = False
+
+    assert _record_event_target(record) == 0.0
+
+
 def test_entry_quality_never_falls_back_to_holdout_skill() -> None:
     entry = {"validation_skill": 0.12, "skill": 0.99, "holdout_skill": 0.99}
 
@@ -107,6 +122,36 @@ def test_batched_ensemble_averages_decoded_predictions() -> None:
     }
 
 
+def test_calibrator_loads_multitask_head() -> None:
+    model = RecurrentReturnModel(
+        architecture="gru",
+        input_size=2,
+        hidden_size=4,
+        num_layers=1,
+        dropout=0.0,
+        output_size=len(OUTPUT_LAYOUT),
+        attention_pooling=False,
+        context_norm=False,
+        multitask_head=True,
+        head_hidden_size=6,
+    )
+    entry = {
+        "input_size": 2,
+        "hidden_size": 4,
+        "num_layers": 1,
+        "output_size": len(OUTPUT_LAYOUT),
+        "multitask_head": True,
+        "head_hidden_size": 6,
+        "state_dict": _export_recurrent_state(model),
+        **_export_head_state(model),
+    }
+
+    loaded = _build_torch_model(entry, "torch_gru")
+
+    assert loaded is not None
+    assert loaded.multitask_head is True
+
+
 def test_multi_seed_export_does_not_duplicate_first_member_weights() -> None:
     members = [
         {