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Calibrate Torch forecast thresholds

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Codex
2026-06-23 16:35:24 +03:00
parent 12f470e0a3
commit 13de641fe3
8 changed files with 778 additions and 9 deletions
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crypto_spot_bot/config.py
+7 -2
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@@ -108,6 +108,8 @@ class Settings:
time_series_min_candles: int
time_series_forecast_horizon: int
time_series_min_edge_percent: float
time_series_min_probability_up: float
time_series_min_confidence: float
time_series_max_adjustment: float
time_series_lstm_enabled: bool
time_series_lstm_model_path: Path
@@ -185,6 +187,7 @@ def load_settings(env_file: str | Path | None = None) -> Settings:
top_symbols_count = len(FIXED_SPOT_SYMBOLS)
symbols = FIXED_SPOT_SYMBOLS
forecast_enabled_default = strategy_mode == "torch_forecast"
min_signal_confidence = _float_env("MIN_SIGNAL_CONFIDENCE", 0.64)
settings = Settings(
trading_mode=mode,
host=os.getenv("HOST", "127.0.0.1"),
@@ -207,7 +210,7 @@ def load_settings(env_file: str | Path | None = None) -> Settings:
fast_entry_cooldown_seconds=_int_env("FAST_ENTRY_COOLDOWN_SECONDS", 20),
max_entries_per_minute=_int_env("MAX_ENTRIES_PER_MINUTE", 12),
websocket_enabled=_bool_env("WEBSOCKET_ENABLED", True),
min_signal_confidence=_float_env("MIN_SIGNAL_CONFIDENCE", 0.64),
min_signal_confidence=min_signal_confidence,
max_spread_percent=_float_env("MAX_SPREAD_PERCENT", 0.18),
min_24h_turnover_usdt=_float_env("MIN_24H_TURNOVER_USDT", 1000000.0),
pattern_analysis_enabled=_bool_env("PATTERN_ANALYSIS_ENABLED", False),
@@ -247,7 +250,9 @@ def load_settings(env_file: str | Path | None = None) -> Settings:
time_series_forecast_enabled=_bool_env("TIME_SERIES_FORECAST_ENABLED", forecast_enabled_default),
time_series_min_candles=_int_env("TIME_SERIES_MIN_CANDLES", 120),
time_series_forecast_horizon=_int_env("TIME_SERIES_FORECAST_HORIZON", 3),
time_series_min_edge_percent=_float_env("TIME_SERIES_MIN_EDGE_PERCENT", 0.04),
time_series_min_edge_percent=_float_env("TIME_SERIES_MIN_EDGE_PERCENT", 0.10),
time_series_min_probability_up=_float_env("TIME_SERIES_MIN_PROBABILITY_UP", 0.64),
time_series_min_confidence=_float_env("TIME_SERIES_MIN_CONFIDENCE", 0.72),
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")),