Train Torch model for 12 spot pairs

crypto_spot_bot/config.py

@@ -122,6 +122,7 @@ class Settings:
     time_series_probe_min_edge_percent: float
     time_series_probe_min_probability_up: float
     time_series_probe_size_multiplier: float
+    time_series_rebound_fallback_enabled: bool
     stop_loss_percent: float
     take_profit_percent: float
     trailing_stop_percent: float
@@ -190,11 +191,8 @@ def load_settings(env_file: str | Path | None = None) -> Settings:
         raise ValueError("STRATEGY_MODE must be legacy, trend_macd or torch_forecast")
     auto_select_symbols = _bool_env("AUTO_SELECT_SYMBOLS", False)
     top_symbols_count = _int_env("TOP_SYMBOLS_COUNT", len(FIXED_SPOT_SYMBOLS))
-    symbols = _symbols_env("SYMBOLS") or FIXED_SPOT_SYMBOLS
-    if strategy_mode == "torch_forecast":
-        auto_select_symbols = False
-        top_symbols_count = len(FIXED_SPOT_SYMBOLS)
-        symbols = FIXED_SPOT_SYMBOLS
+    requested_symbols = _symbols_env("SYMBOLS")
+    symbols = requested_symbols if requested_symbols else (() if auto_select_symbols else FIXED_SPOT_SYMBOLS)
     forecast_enabled_default = strategy_mode == "torch_forecast"
     min_signal_confidence = _float_env("MIN_SIGNAL_CONFIDENCE", 0.64)
     settings = Settings(
@@ -274,6 +272,7 @@ def load_settings(env_file: str | Path | None = None) -> Settings:
         time_series_probe_min_edge_percent=_float_env("TIME_SERIES_PROBE_MIN_EDGE_PERCENT", 0.02),
         time_series_probe_min_probability_up=_float_env("TIME_SERIES_PROBE_MIN_PROBABILITY_UP", 0.55),
         time_series_probe_size_multiplier=_float_env("TIME_SERIES_PROBE_SIZE_MULTIPLIER", 0.40),
+        time_series_rebound_fallback_enabled=_bool_env("TIME_SERIES_REBOUND_FALLBACK_ENABLED", True),
         stop_loss_percent=_float_env("STOP_LOSS_PERCENT", 0.04),
         take_profit_percent=_float_env("TAKE_PROFIT_PERCENT", 0.035),
         trailing_stop_percent=_float_env("TRAILING_STOP_PERCENT", 0.015),