Add multifeature direct horizon Torch forecaster

tests/test_dashboard.py

@@ -46,4 +46,7 @@ def test_safe_config_summarizes_torch_forecast_artifact(make_settings, tmp_path)
         "created_at": "2026-06-20T18:15:05+00:00",
         "symbol_count": 2,
         "models": ["PyTorch GRU", "PyTorch LSTM"],
+        "feature_count": 1,
+        "target_horizon": 0,
+        "direct_horizon": False,
     }

tests/test_time_series.py

@@ -77,6 +77,53 @@ def _write_torch_gru_artifact(
     )
 
 
+def _write_multifeature_torch_gru_artifact(path, *, head_bias: float) -> None:
+    hidden_size = 2
+    input_size = 2
+    path.write_text(
+        json.dumps(
+            {
+                "version": 3,
+                "type": "pytorch_recurrent_forecaster",
+                "target_horizon": 3,
+                "direct_horizon": True,
+                "feature_count": input_size,
+                "feature_names": ["return_1", "range_percent"],
+                "symbols": {
+                    "BTCUSDT": {
+                        "model": "torch_gru",
+                        "architecture": "gru",
+                        "lookback": 8,
+                        "target_horizon": 3,
+                        "direct_horizon": True,
+                        "input_size": input_size,
+                        "feature_names": ["return_1", "range_percent"],
+                        "feature_means": [0.0, 0.0],
+                        "feature_scales": [0.001, 0.001],
+                        "target_mean": 0.0,
+                        "target_scale": 0.001,
+                        "hidden_size": hidden_size,
+                        "num_layers": 1,
+                        "clip": 8.0,
+                        "validation_mae_percent": 0.01,
+                        "baseline_mae_percent": 0.08,
+                        "skill": 0.2,
+                        "state_dict": {
+                            "weight_ih_l0": [[0.0, 0.0] for _ in range(3 * hidden_size)],
+                            "weight_hh_l0": [[0.0, 0.0] for _ in range(3 * hidden_size)],
+                            "bias_ih_l0": [0.0 for _ in range(3 * hidden_size)],
+                            "bias_hh_l0": [0.0 for _ in range(3 * hidden_size)],
+                        },
+                        "head_weight": [0.0, 0.0],
+                        "head_bias": head_bias,
+                    },
+                },
+            }
+        ),
+        encoding="utf-8",
+    )
+
+
 def test_time_series_forecaster_requires_torch_artifact(make_settings, tmp_path) -> None:
     settings = make_settings(
         tmp_path,
@@ -166,3 +213,23 @@ def test_time_series_forecaster_reads_torch_gru_artifact(make_settings, tmp_path
     assert forecast.candidates == [{"model": "torch_gru", "mae_percent": 0.02}]
     assert forecast.expected_return_percent > 0
     assert forecast.probability_up > 0.5
+
+
+def test_time_series_forecaster_reads_multifeature_direct_horizon_artifact(make_settings, tmp_path) -> None:
+    artifact_path = tmp_path / "lstm_forecaster.json"
+    _write_multifeature_torch_gru_artifact(artifact_path, head_bias=0.2)
+    settings = make_settings(
+        tmp_path,
+        time_series_min_candles=80,
+        time_series_forecast_horizon=3,
+        time_series_lstm_model_path=artifact_path,
+    )
+    returns = [0.00015 if index % 4 else -0.00005 for index in range(140)]
+
+    forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns), symbol="BTCUSDT")
+
+    assert forecast.usable is True
+    assert forecast.model == "torch_gru"
+    assert forecast.horizon == 3
+    assert 0.015 <= forecast.expected_return_percent <= 0.025
+    assert forecast.volatility_model == "direct horizon validation MAE"