Keep bot operational when forecast model is unavailable

tests/test_time_series.py

@@ -334,6 +334,29 @@ def test_time_series_forecaster_uses_symbol_calibration(make_settings, tmp_path)
     assert forecast.calibrated_min_confidence == 0.45
 
 
+def test_time_series_forecaster_blocks_symbol_outside_train_only_allowlist(make_settings, tmp_path) -> None:
+    artifact_path = tmp_path / "lstm_forecaster.json"
+    _write_torch_gru_artifact(artifact_path, head_bias=0.2)
+    (tmp_path / "torch_threshold_calibration.json").write_text(
+        json.dumps(
+            {
+                "validation": {"status": "pass", "passed": True},
+                "eligible_symbols": ["ETHUSDT"],
+            }
+        ),
+        encoding="utf-8",
+    )
+    settings = make_settings(tmp_path, time_series_lstm_model_path=artifact_path)
+
+    forecast = TimeSeriesForecaster(settings).forecast(
+        _candles_from_returns([0.0001] * 140), symbol="BTCUSDT"
+    )
+
+    assert forecast.usable is True
+    assert forecast.block_entry is True
+    assert forecast.reason == "symbol excluded by train-only calibration"
+
+
 def test_time_series_forecaster_averages_ensemble_members(make_settings, tmp_path) -> None:
     artifact_path = tmp_path / "lstm_forecaster.json"
     _write_torch_gru_artifact(artifact_path, head_bias=0.9)
@@ -343,6 +366,9 @@ def test_time_series_forecaster_averages_ensemble_members(make_settings, tmp_pat
         {"state_dict": entry["state_dict"], "head_weight": [0.0, 0.0], "head_bias": bias}
         for bias in (0.1, 0.3)
     ]
+    entry.pop("state_dict")
+    entry.pop("head_weight")
+    entry.pop("head_bias")
     artifact_path.write_text(json.dumps(artifact), encoding="utf-8")
     settings = make_settings(
         tmp_path,