Add Torch probe entries and Pi artifact sync

crypto_spot_bot/config.py

@@ -118,6 +118,10 @@ class Settings:
     time_series_max_adjustment: float
     time_series_lstm_enabled: bool
     time_series_lstm_model_path: Path
+    time_series_probe_enabled: bool
+    time_series_probe_min_edge_percent: float
+    time_series_probe_min_probability_up: float
+    time_series_probe_size_multiplier: float
     stop_loss_percent: float
     take_profit_percent: float
     trailing_stop_percent: float
@@ -266,6 +270,10 @@ def load_settings(env_file: str | Path | None = None) -> Settings:
         time_series_max_adjustment=_float_env("TIME_SERIES_MAX_ADJUSTMENT", 0.08),
         time_series_lstm_enabled=_bool_env("TIME_SERIES_LSTM_ENABLED", True),
         time_series_lstm_model_path=Path(os.getenv("TIME_SERIES_LSTM_MODEL_PATH", "runtime/lstm_forecaster.json")),
+        time_series_probe_enabled=_bool_env("TIME_SERIES_PROBE_ENABLED", True),
+        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),
         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),