Use Torch as the only forecast model

crypto_spot_bot/dashboard.py

@@ -215,9 +215,7 @@ def _safe_config(settings: Settings) -> dict[str, Any]:
         "kelly_max_fraction": settings.kelly_max_fraction,
         "time_series_forecast_enabled": settings.time_series_forecast_enabled,
         "time_series_min_candles": settings.time_series_min_candles,
-        "time_series_validation_window": settings.time_series_validation_window,
         "time_series_forecast_horizon": settings.time_series_forecast_horizon,
-        "time_series_ewma_lambda": settings.time_series_ewma_lambda,
         "time_series_min_edge_percent": settings.time_series_min_edge_percent,
         "time_series_max_adjustment": settings.time_series_max_adjustment,
         "time_series_lstm_enabled": settings.time_series_lstm_enabled,
@@ -257,16 +255,19 @@ def _time_series_model_artifact(settings: Settings) -> dict[str, Any]:
             "symbol_count": 0,
             "models": [],
         }
+    artifact_type = str(data.get("type", "")).strip()
     symbols = data.get("symbols")
     rows = list(symbols.values()) if isinstance(symbols, dict) else []
     models = sorted(
         {
-            _forecast_model_label(str(row.get("model", row.get("architecture", "lstm"))))
+            _forecast_model_label(
+                str(row.get("model", row.get("architecture", "lstm"))),
+                torch_artifact=artifact_type == "pytorch_recurrent_forecaster",
+            )
             for row in rows
             if isinstance(row, dict)
         }
     )
-    artifact_type = str(data.get("type", "")).strip()
     if artifact_type != "pytorch_recurrent_forecaster":
         return {
             "available": False,
@@ -286,16 +287,16 @@ def _time_series_model_artifact(settings: Settings) -> dict[str, Any]:
     }
 
 
-def _forecast_model_label(model: str) -> str:
+def _forecast_model_label(model: str, *, torch_artifact: bool = False) -> str:
     normalized = model.strip().lower()
-    if normalized == "torch_lstm":
+    if normalized in {"torch_lstm", "lstm"} and torch_artifact:
         return "PyTorch LSTM"
-    if normalized == "torch_gru":
+    if normalized in {"torch_gru", "gru"} and torch_artifact:
         return "PyTorch GRU"
     if normalized == "lstm":
-        return "устаревший LSTM"
+        return "устаревший артефакт"
     if normalized == "gru":
-        return "GRU"
+        return "устаревший артефакт"
     return model
 
 
@@ -746,12 +747,6 @@ HTML = r"""
       const names = {
         torch_lstm: 'PyTorch LSTM',
         torch_gru: 'PyTorch GRU',
-        lstm: 'Устаревший LSTM',
-        naive: 'Baseline',
-        drift: 'Drift',
-        ewma: 'EWMA',
-        ar1: 'AR(1)',
-        ar3: 'AR(3)',
         none: '-'
       };
       return names[key] || String(model || '-');
@@ -937,7 +932,6 @@ HTML = r"""
         ['Kelly размер', `${yesNo(config.kelly_sizing_enabled)} · ${num(config.kelly_fraction, 2)}x · max ${num((config.kelly_max_fraction || 0) * 100, 1)}%`],
         ['Прогноз временных рядов', yesNo(config.time_series_forecast_enabled)],
         ['Модельный горизонт', `${config.time_series_forecast_horizon} свечи`],
-        ['Walk-forward окно', `${config.time_series_validation_window} свечей`],
         ['Мин. edge прогноза', `${num(config.time_series_min_edge_percent, 3)}%`],
         ['Нейропрогноз', modelArtifactSummary(config)],
         ['Файл модели', config.time_series_lstm_model_path || '-'],