fix: honor explicit calibration horizon

tests/test_calibrate_thresholds.py

@@ -8,6 +8,7 @@ from tools.calibrate_torch_thresholds import (
     _average_selected_predictions,
     _apply_platt_calibration,
     _build_torch_model,
+    _calibration_horizon,
     _calibration_symbols,
     _choose_recommendation,
     _full_backtest,
@@ -78,6 +79,14 @@ def test_calibration_symbols_reject_malformed_artifact_symbols() -> None:
     assert _calibration_symbols("", (), {"symbols": []}) == []
 
 
+def test_explicit_calibration_horizon_selects_existing_multi_horizon_output() -> None:
+    entry = {"target_horizon": 12, "target_horizons": [3, 6, 12, 24]}
+
+    assert _calibration_horizon(entry, 24, explicit=True) == 24
+    assert _calibration_horizon(entry, 20, explicit=True) == 24
+    assert _calibration_horizon(entry, 24, explicit=False) == 12
+
+
 def test_calibration_does_not_fallback_to_too_few_trades() -> None:
     selected = _choose_recommendation(
         [_result(trades=1, average=2.0, total=2.0, profit_factor=999.0)],

tools/calibrate_torch_thresholds.py

@@ -120,6 +120,7 @@ def main() -> None:
             trend_candles=trend_candles,
             artifact=artifact,
             horizon=horizon,
+            horizon_is_explicit=args.horizon > 0,
             round_trip_cost=round_trip_cost,
             min_candles=max(30, settings.time_series_min_candles),
             calibration_window=args.calibration_window,
@@ -329,6 +330,15 @@ def _calibration_symbols(
     return [str(symbol).strip().upper() for symbol in artifact_symbols if str(symbol).strip()]
 
 
+def _calibration_horizon(entry: dict[str, Any], requested: int, *, explicit: bool) -> int:
+    horizons = _entry_target_horizons(entry)
+    if explicit and requested > 0:
+        if horizons:
+            return min(horizons, key=lambda value: abs(value - requested))
+        return requested
+    return _entry_horizon(entry, requested)
+
+
 def _forecast_records(
     *,
     symbol: str,
@@ -337,6 +347,7 @@ def _forecast_records(
     trend_candles: list[Candle],
     artifact: dict[str, Any],
     horizon: int,
+    horizon_is_explicit: bool,
     round_trip_cost: float,
     min_candles: int,
     calibration_window: int,
@@ -355,7 +366,7 @@ def _forecast_records(
         trend_candles=trend_candles,
     )
     closes = [float(candle.close) for candle in candles]
-    decision_horizon = _entry_horizon(entry, horizon)
+    decision_horizon = _calibration_horizon(entry, horizon, explicit=horizon_is_explicit)
     start = max(min_candles, int(float(entry.get("lookback", 64))))
     end = len(candles) - decision_horizon - 1
     holdout_start_timestamp = int(float(entry.get("holdout_start_timestamp", 0) or 0))