Retire completed GTL tuning harnesses

app/services/recommendation_service.py

@@ -93,7 +93,6 @@ def _zone_representative_levels(
     entry_price: float,
     *,
     strength_mode: str = "sum",
-    tolerance: float = _SR_ZONE_TOLERANCE,
 ) -> list[Any]:
     """Collapse near-duplicate S/R levels into one representative per zone.
 
@@ -125,7 +124,7 @@ def _zone_representative_levels(
     zones = cluster_sr_zones(
         level_dicts,
         entry_price,
-        tolerance=tolerance,
+        tolerance=_SR_ZONE_TOLERANCE,
         strength_mode=strength_mode,
     )
 
@@ -316,16 +315,9 @@ class TargetGenerator:
         stop_loss: float,
         sr_levels: list[SRLevel],
         atr_value: float,
-        *,
-        max_targets: int | None = 5,
-        max_atr_multiple_override: float | None = None,
     ) -> list[dict[str, Any]]:
         if atr_value <= 0:
             return []
-        if max_targets is not None and max_targets < 1:
-            raise ValueError("max_targets must be positive or None")
-        if max_atr_multiple_override is not None and max_atr_multiple_override <= 0:
-            raise ValueError("max_atr_multiple_override must be positive or None")
 
         risk = abs(entry_price - stop_loss)
         if risk <= 0:
@@ -334,12 +326,11 @@ class TargetGenerator:
         candidates: list[dict[str, Any]] = []
         atr_pct = atr_value / entry_price if entry_price > 0 else 0.0
 
-        max_atr_multiple: float | None = max_atr_multiple_override
-        if max_atr_multiple is None:
-            if atr_pct > 0.05:
-                max_atr_multiple = 10.0
-            elif atr_pct < 0.02:
-                max_atr_multiple = 3.0
+        max_atr_multiple: float | None = None
+        if atr_pct > 0.05:
+            max_atr_multiple = 10.0
+        elif atr_pct < 0.02:
+            max_atr_multiple = 3.0
 
         for level in sr_levels:
             is_candidate = False
@@ -392,12 +383,6 @@ class TargetGenerator:
         if not candidates:
             return []
 
-        if max_targets is None:
-            candidates.sort(key=lambda row: row["distance_from_entry"])
-            for target in candidates:
-                target.pop("quality", None)
-            return candidates
-
         # Select up to 5 targets that SPAN the distance range, instead of the
         # top-5 by quality (which biases toward far, high-R:R levels and buries
         # every nearby target). Guarantees the nearest level plus a
@@ -411,8 +396,6 @@ class TargetGenerator:
         selected_ids: set[int] = set()
 
         def _add(candidate: dict[str, Any] | None) -> None:
-            if len(selected) >= max_targets:
-                return
             if candidate is not None and candidate["sr_level_id"] not in selected_ids:
                 selected.append(candidate)
                 selected_ids.add(candidate["sr_level_id"])
@@ -425,7 +408,7 @@ class TargetGenerator:
                 _add(max(bucket, key=lambda c: c["quality"]))
         # Fill remaining slots with the next-best by quality
         for candidate in sorted(candidates, key=lambda c: c["quality"], reverse=True):
-            if len(selected) >= max_targets:
+            if len(selected) >= 5:
                 break
             _add(candidate)