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Lower Torch confidence threshold

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Codex
2026-06-24 05:51:55 +03:00
parent a6c94b1555
commit 15a50fb575
6 changed files with 9 additions and 6 deletions
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.env
+1 -1
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@@ -63,7 +63,7 @@ TIME_SERIES_MIN_CANDLES=120
TIME_SERIES_FORECAST_HORIZON=3 TIME_SERIES_FORECAST_HORIZON=3
TIME_SERIES_MIN_EDGE_PERCENT=0.10 TIME_SERIES_MIN_EDGE_PERCENT=0.10
TIME_SERIES_MIN_PROBABILITY_UP=0.52 TIME_SERIES_MIN_PROBABILITY_UP=0.52
TIME_SERIES_MIN_CONFIDENCE=0.72 TIME_SERIES_MIN_CONFIDENCE=0.4
TIME_SERIES_MAX_ADJUSTMENT=0.08 TIME_SERIES_MAX_ADJUSTMENT=0.08
TIME_SERIES_LSTM_ENABLED=true TIME_SERIES_LSTM_ENABLED=true
TIME_SERIES_LSTM_MODEL_PATH=runtime/lstm_forecaster.json TIME_SERIES_LSTM_MODEL_PATH=runtime/lstm_forecaster.json
.env.example
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@@ -63,7 +63,7 @@ TIME_SERIES_MIN_CANDLES=120
TIME_SERIES_FORECAST_HORIZON=3 TIME_SERIES_FORECAST_HORIZON=3
TIME_SERIES_MIN_EDGE_PERCENT=0.08 TIME_SERIES_MIN_EDGE_PERCENT=0.08
TIME_SERIES_MIN_PROBABILITY_UP=0.58 TIME_SERIES_MIN_PROBABILITY_UP=0.58
TIME_SERIES_MIN_CONFIDENCE=0.72 TIME_SERIES_MIN_CONFIDENCE=0.4
TIME_SERIES_MAX_ADJUSTMENT=0.08 TIME_SERIES_MAX_ADJUSTMENT=0.08
TIME_SERIES_LSTM_ENABLED=true TIME_SERIES_LSTM_ENABLED=true
TIME_SERIES_LSTM_MODEL_PATH=runtime/lstm_forecaster.json TIME_SERIES_LSTM_MODEL_PATH=runtime/lstm_forecaster.json
README.md
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@@ -161,7 +161,7 @@ TIME_SERIES_MIN_CANDLES=120
TIME_SERIES_FORECAST_HORIZON=3 TIME_SERIES_FORECAST_HORIZON=3
TIME_SERIES_MIN_EDGE_PERCENT=0.08 TIME_SERIES_MIN_EDGE_PERCENT=0.08
TIME_SERIES_MIN_PROBABILITY_UP=0.58 TIME_SERIES_MIN_PROBABILITY_UP=0.58
TIME_SERIES_MIN_CONFIDENCE=0.72 TIME_SERIES_MIN_CONFIDENCE=0.4
TIME_SERIES_MAX_ADJUSTMENT=0.08 TIME_SERIES_MAX_ADJUSTMENT=0.08
TIME_SERIES_LSTM_ENABLED=true TIME_SERIES_LSTM_ENABLED=true
TIME_SERIES_LSTM_MODEL_PATH=runtime/lstm_forecaster.json TIME_SERIES_LSTM_MODEL_PATH=runtime/lstm_forecaster.json
crypto_spot_bot/config.py
+1 -1
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@@ -262,7 +262,7 @@ def load_settings(env_file: str | Path | None = None) -> Settings:
time_series_forecast_horizon=_int_env("TIME_SERIES_FORECAST_HORIZON", 3), time_series_forecast_horizon=_int_env("TIME_SERIES_FORECAST_HORIZON", 3),
time_series_min_edge_percent=_float_env("TIME_SERIES_MIN_EDGE_PERCENT", 0.08), time_series_min_edge_percent=_float_env("TIME_SERIES_MIN_EDGE_PERCENT", 0.08),
time_series_min_probability_up=_float_env("TIME_SERIES_MIN_PROBABILITY_UP", 0.58), time_series_min_probability_up=_float_env("TIME_SERIES_MIN_PROBABILITY_UP", 0.58),
time_series_min_confidence=_float_env("TIME_SERIES_MIN_CONFIDENCE", 0.72), time_series_min_confidence=_float_env("TIME_SERIES_MIN_CONFIDENCE", 0.4),
time_series_max_adjustment=_float_env("TIME_SERIES_MAX_ADJUSTMENT", 0.08), 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_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_lstm_model_path=Path(os.getenv("TIME_SERIES_LSTM_MODEL_PATH", "runtime/lstm_forecaster.json")),
crypto_spot_bot/dashboard.py
+4 -1
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@@ -698,6 +698,8 @@ HTML = r"""
const forecast = market.forecast || {}; const forecast = market.forecast || {};
const quality = market.quality || {}; const quality = market.quality || {};
const signal = latestSignals[symbol] || {}; const signal = latestSignals[symbol] || {};
const minEdge = state.data.config?.time_series_min_edge_percent ?? 0;
const minConfidence = state.data.config?.time_series_min_confidence ?? 0;
const diagnostics = parseDiagnostics(signal); const diagnostics = parseDiagnostics(signal);
const failed = Object.entries(diagnostics.checks || {}).filter(([, ok]) => !ok).map(([key]) => key); const failed = Object.entries(diagnostics.checks || {}).filter(([, ok]) => !ok).map(([key]) => key);
return panel( return panel(
@@ -712,8 +714,9 @@ HTML = r"""
])} ])}
${kvTable([ ${kvTable([
['Model', modelName(forecast.model)], ['Model', modelName(forecast.model)],
['Expected', signed(forecast.expected_return_percent, 4) + '%'], ['Edge', `${signed(forecast.expected_return_percent, 4)}% / min ${num(minEdge, 3)}%`],
['P(up)', num((forecast.probability_up || 0) * 100, 2) + '%'], ['P(up)', num((forecast.probability_up || 0) * 100, 2) + '%'],
['Confidence', `${num(signal.confidence, 4)} / min ${num(minConfidence, 2)}`],
['Q10/Q50/Q90', `${signed(forecast.quantile_10_percent, 2)} / ${signed(forecast.quantile_50_percent, 2)} / ${signed(forecast.quantile_90_percent, 2)}`], ['Q10/Q50/Q90', `${signed(forecast.quantile_10_percent, 2)} / ${signed(forecast.quantile_50_percent, 2)} / ${signed(forecast.quantile_90_percent, 2)}`],
['Blocked', forecast.block_entry ? 'yes' : 'no'] ['Blocked', forecast.block_entry ? 'yes' : 'no']
])} ])}
tests/conftest.py
+1 -1
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@@ -79,7 +79,7 @@ def make_settings():
time_series_forecast_horizon=3, time_series_forecast_horizon=3,
time_series_min_edge_percent=0.08, time_series_min_edge_percent=0.08,
time_series_min_probability_up=0.58, time_series_min_probability_up=0.58,
time_series_min_confidence=0.72, time_series_min_confidence=0.4,
time_series_max_adjustment=0.08, time_series_max_adjustment=0.08,
time_series_lstm_enabled=True, time_series_lstm_enabled=True,
time_series_lstm_model_path=tmp_path / "lstm_forecaster.json", time_series_lstm_model_path=tmp_path / "lstm_forecaster.json",