Harden trading, training, and monitoring

crypto_spot_bot/strategy.py

@@ -678,7 +678,20 @@ def _torch_forecast_entry_signal(
     spread_ok = ticker.spread_percent <= settings.max_spread_percent
     liquidity_ok = ticker.turnover_24h >= settings.min_24h_turnover_usdt
     model_ok = _is_torch_forecast(forecast)
-    quality_gate_ok = forecast.get("quality_gate_passed") is not False
+    manual_quality_override = settings.time_series_manual_quality_override
+    quality_gate_ok = bool(
+        manual_quality_override
+        or (
+            forecast.get("quality_gate_passed") is True
+            if settings.time_series_require_quality_gate
+            else forecast.get("quality_gate_passed") is not False
+        )
+    )
+    model_fresh_ok = (
+        forecast.get("model_fresh") is True
+        if settings.time_series_require_fresh_model
+        else True
+    )
     rebound = _torch_rebound_overlay(
         settings=settings,
         candles=candles or [],
@@ -697,6 +710,7 @@ def _torch_forecast_entry_signal(
         rebound.get("active")
         and model_ok
         and quality_gate_ok
+        and model_fresh_ok
         and bool(forecast.get("usable", False))
         and not bool(forecast.get("block_entry", False))
         and expected_return >= 0.0
@@ -709,6 +723,7 @@ def _torch_forecast_entry_signal(
         and rebound.get("active")
         and missing_torch_model
         and quality_gate_ok
+        and model_fresh_ok
         and not bool(forecast.get("block_entry", False))
         and confidence >= settings.time_series_min_confidence
     )
@@ -733,6 +748,7 @@ def _torch_forecast_entry_signal(
     checks = {
         "torch_model_ok": model_ok,
         "quality_gate_ok": quality_gate_ok,
+        "model_fresh_ok": model_fresh_ok,
         "forecast_usable": bool(forecast.get("usable", False)),
         "forecast_not_blocked": not bool(forecast.get("block_entry", False)),
         "expected_edge_ok": full_edge_ok or probe_edge_ok,
@@ -770,6 +786,10 @@ def _torch_forecast_entry_signal(
         "skill": skill,
         "quality_gate": forecast.get("quality_gate", {}),
         "quality_gate_passed": forecast.get("quality_gate_passed"),
+        "manual_quality_override": manual_quality_override,
+        "model_created_at": forecast.get("model_created_at", ""),
+        "model_age_hours": forecast.get("model_age_hours"),
+        "model_fresh": forecast.get("model_fresh", False),
         "spread_percent": round(ticker.spread_percent, 5),
         "turnover_24h": ticker.turnover_24h,
         "checks": checks,
@@ -926,6 +946,28 @@ def _torch_forecast_exit_signal(
                 diagnostics,
             )
         return Signal(position.symbol, "SELL", 0.94, "torch_forecast: ATR trailing stop hit", diagnostics)
+    if (
+        settings.time_series_require_quality_gate
+        and not settings.time_series_manual_quality_override
+        and forecast.get("quality_gate_passed") is not True
+    ):
+        diagnostics["forecast_exit_blocked_by_quality_gate"] = True
+        return Signal(
+            position.symbol,
+            "HOLD",
+            0.42,
+            "torch_forecast: hold uses only risk exits while quality gate is unavailable",
+            diagnostics,
+        )
+    if settings.time_series_require_fresh_model and forecast.get("model_fresh") is not True:
+        diagnostics["forecast_exit_blocked_by_model_age"] = True
+        return Signal(
+            position.symbol,
+            "HOLD",
+            0.42,
+            "torch_forecast: hold uses only risk exits while model is stale",
+            diagnostics,
+        )
     if not _is_torch_forecast(forecast):
         if rebound_fallback_position:
             hold_seconds = (utc_now() - position.opened_at).total_seconds()