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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/strategy.py
+3 -2
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@@ -644,7 +644,7 @@ def _torch_forecast_entry_signal(
"expected_edge_ok": expected_return >= min_edge,
"probability_ok": probability_up >= min_probability,
"skill_ok": skill > 0.0,
"confidence_ok": confidence >= settings.min_signal_confidence,
"confidence_ok": confidence >= settings.time_series_min_confidence,
"spread_ok": spread_ok,
"liquidity_ok": liquidity_ok,
"risk_size_ok": position_notional >= settings.min_position_usdt,
@@ -661,6 +661,7 @@ def _torch_forecast_entry_signal(
"min_edge_percent": min_edge,
"probability_up": probability_up,
"min_probability_up": min_probability,
"min_confidence": settings.time_series_min_confidence,
"skill": skill,
"spread_percent": round(ticker.spread_percent, 5),
"turnover_24h": ticker.turnover_24h,
@@ -766,7 +767,7 @@ def _is_torch_forecast(forecast: dict) -> bool:
def _torch_min_probability(settings: Settings) -> float:
return round(_clamp(settings.min_signal_confidence - 0.08, 0.52, 0.68), 4)
return round(_clamp(settings.time_series_min_probability_up, 0.45, 0.75), 4)
def _torch_forecast_confidence(settings: Settings, forecast: dict) -> float: