Filter priced market opportunity noise

tests/test_ops_market_opportunities.py

@@ -233,6 +233,57 @@ def test_build_market_opportunities_filters_late_priced_no_when_models_are_in_bu
     assert rows == []
 
 
+def test_build_market_opportunities_filters_priced_no_when_models_support_market_bucket():
+    event = {
+        "slug": "highest-temperature-in-buenos-aires-on-july-3-2026",
+        "markets": [
+            {
+                "question": "Will the highest temperature in Buenos Aires be 10°C on July 3?",
+                "slug": "highest-temperature-in-buenos-aires-on-july-3-2026-10c",
+                "active": True,
+                "closed": False,
+                "enableOrderBook": True,
+                "liquidity": "141",
+                "volume": "7749",
+                "outcomes": '["Yes", "No"]',
+                "clobTokenIds": '["yes-10", "no-10"]',
+            }
+        ],
+    }
+    row = _row("buenos aires")
+    row["local_time"] = "15:21"
+    row["current_max_so_far"] = None
+    row["deb_prediction"] = 8.7
+    row["model_cluster_sources"] = {
+        "AI-GFS": 8.4,
+        "ECMWF": 8.3,
+        "ECMWF AIFS": 9.2,
+        "GFS": 9.5,
+        "ICON": 9.1,
+        "GEM": 9.0,
+        "GDPS": 8.9,
+        "JMA": 8.8,
+        "AROME HD": 9.5,
+    }
+    row["distribution_full"] = [
+        {"value": 9, "probability": 0.27},
+        {"value": 10, "probability": 0.73},
+    ]
+
+    rows = build_market_opportunity_rows(
+        [row],
+        {"buenos aires": event},
+        {"yes-10": 0.94, "no-10": 0.08},
+        max_price=0.20,
+        side="both",
+        positive_edge_only=True,
+        min_edge=0.0,
+        limit=20,
+    )
+
+    assert rows == []
+
+
 def test_build_market_opportunities_keeps_late_no_when_bucket_conflicts_with_anchors():
     event = {
         "slug": "highest-temperature-in-jeddah-on-july-3-2026",

web/services/ops/market_opportunities.py

@@ -299,7 +299,17 @@ def _option_near_value(option: Mapping[str, Any], value: Any) -> bool:
     return False
 
 
-def _is_late_priced_no_noise(
+def _option_effective_bounds(option: Mapping[str, Any]) -> Tuple[Optional[float], Optional[float]]:
+    lower = _finite_number(option.get("lower"))
+    upper = _finite_number(option.get("upper"))
+    unit = str(option.get("unit") or "").upper()
+    half_width = 1.0 if "F" in unit and lower != upper else 0.5
+    effective_lower = lower - half_width if lower is not None else None
+    effective_upper = upper + half_width if upper is not None else None
+    return effective_lower, effective_upper
+
+
+def _is_priced_no_noise(
     row: Mapping[str, Any],
     option: Mapping[str, Any],
     ask_prices_by_token: Mapping[str, Optional[float]],
@@ -309,26 +319,30 @@ def _is_late_priced_no_noise(
     model_median: Optional[float],
     model_relation: Optional[Mapping[str, Any]] = None,
 ) -> bool:
-    if no_ask > 0.05:
-        return False
-    hour = _local_hour(row)
-    if hour is None or hour < 17:
+    if no_ask > 0.20:
         return False
     yes_token = tokens.get("yes")
     yes_ask = _finite_number(ask_prices_by_token.get(yes_token)) if yes_token else None
     if yes_ask is not None and yes_ask < 0.80:
         return False
     relation = model_relation or _model_option_relation(row, option)
-    outside = int(relation.get("models_above_bucket") or 0) + int(relation.get("models_below_bucket") or 0)
+    above = int(relation.get("models_above_bucket") or 0)
+    below = int(relation.get("models_below_bucket") or 0)
     inside = int(relation.get("models_in_bucket") or 0)
-    if outside > inside:
+    if above > max(inside, below):
         return False
     anchors = (
         row.get("current_max_so_far"),
         row.get("deb_prediction"),
         model_median,
     )
-    return any(_option_near_value(option, anchor) for anchor in anchors)
+    if any(_option_near_value(option, anchor) for anchor in anchors):
+        return True
+    effective_lower, _effective_upper = _option_effective_bounds(option)
+    model_max = _finite_number(relation.get("model_max"))
+    if above == 0 and inside > 0 and effective_lower is not None and model_max is not None:
+        return model_max >= effective_lower
+    return False
 
 
 def _market_tokens(market: Mapping[str, Any]) -> Dict[str, Optional[str]]:
@@ -418,7 +432,7 @@ def build_market_opportunity_rows(
                 ask_number = _finite_number(ask)
                 if ask_number is None or ask_number <= 0 or ask_number > float(max_price):
                     continue
-                if option_side == "no" and _is_late_priced_no_noise(
+                if option_side == "no" and _is_priced_no_noise(
                     scan_row,
                     option,
                     ask_prices_by_token,