feat(ai-engine): value sniper thresholds and logic relaxed

ai-engine/models/v27_predictor.py

@@ -29,7 +29,7 @@ class V27Predictor:
     82-feature odds-free vector.
     """
 
-    MARKETS = ["ms", "ou25"]
+    MARKETS = ['ms', 'ou25', 'btts']
 
     def __init__(self):
         self.models: Dict[str, Dict[str, object]] = {}
@@ -56,7 +56,7 @@ class V27Predictor:
             return False
 
         # Load models per market
-        model_types = {"xgb": "xgb", "lgb": "lgb", "cb": "cb"}
+        model_types = {"xgb": "xgb", "lgb": "lgb"}
 
         for market in self.MARKETS:
             self.models[market] = {}
@@ -227,11 +227,63 @@ class V27Predictor:
             "over": float(avg[1]),
         }
 
+    def predict_btts(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Both Teams To Score probabilities.
+        Returns dict with keys: no, yes.
+        """
+        if not self._loaded or 'btts' not in self.models or not self.models['btts']:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models['btts'].items():
+            proba = self._predict_with_model(model, X, f'BTTS/{label}', expected_classes=2)
+            if proba is not None and len(proba) == 2:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        avg = np.mean(probs_list, axis=0)
+        return {
+            'no': float(avg[0]),
+            'yes': float(avg[1]),
+        }
+
+    def predict_dc(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Double Chance probabilities.
+
+        DC is algebraically derived from MS predictions:
+          1X = home + draw
+          X2 = draw + away
+          12 = home + away
+
+        This gives an odds-free DC estimate for divergence detection.
+        """
+        ms_probs = self.predict_ms(features)
+        if not ms_probs:
+            return None
+
+        home = ms_probs['home']
+        draw = ms_probs['draw']
+        away = ms_probs['away']
+
+        return {
+            '1x': round(home + draw, 4),
+            'x2': round(draw + away, 4),
+            '12': round(home + away, 4),
+        }
+
     def predict_all(self, features: Dict[str, float]) -> Dict[str, Optional[Dict[str, float]]]:
         """Run predictions for all supported markets."""
         return {
-            "ms": self.predict_ms(features),
-            "ou25": self.predict_ou25(features),
+            'ms': self.predict_ms(features),
+            'ou25': self.predict_ou25(features),
+            'btts': self.predict_btts(features),
+            'dc': self.predict_dc(features),
         }