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

ai-engine/services/betting_brain.py

@@ -165,6 +165,11 @@ class BettingBrain:
             score -= 18.0
             issues.append("base_model_not_playable")
 
+        is_value_sniper = bool(row.get("is_value_sniper"))
+        if is_value_sniper:
+            score += 35.0
+            positives.append("value_sniper_override")
+
         score += max(0.0, min(20.0, calibrated_conf * 0.22))
         score += max(-8.0, min(16.0, ev_edge * 45.0))
         score += max(0.0, min(14.0, play_score * 0.12))
@@ -178,13 +183,13 @@ class BettingBrain:
 
         if odds < self.MIN_ODDS:
             vetoes.append("odds_below_minimum")
-        if calibrated_conf < 38.0:
+        if calibrated_conf < 38.0 and not is_value_sniper:
             vetoes.append("calibrated_confidence_too_low")
-        if play_score < 50.0:
+        if play_score < 50.0 and not is_value_sniper:
             vetoes.append("play_score_too_low")
 
         if divergence is not None:
-            if divergence >= self.HARD_DIVERGENCE:
+            if divergence >= self.HARD_DIVERGENCE and not is_value_sniper:
                 score -= 42.0
                 vetoes.append("v25_v27_hard_disagreement")
             elif divergence >= self.SOFT_DIVERGENCE:
@@ -211,7 +216,7 @@ class BettingBrain:
             else:
                 score -= 16.0
                 issues.append("historical_sample_too_low")
-                if market == "DC":
+                if market == "DC" and not is_value_sniper:
                     vetoes.append("dc_without_historical_sample")
         elif market in {"MS", "DC", "OU25"}:
             score -= 10.0
@@ -227,20 +232,21 @@ class BettingBrain:
             and model_prob >= self.EXTREME_MODEL_PROB
             and model_gap >= self.EXTREME_GAP
             and not triple_is_value
+            and not is_value_sniper
         ):
             score -= 24.0
             vetoes.append("extreme_probability_without_evidence")
 
-        if market in {"HT", "HTFT", "OE"} and score < 86.0:
+        if market in {"HT", "HTFT", "OE"} and score < 86.0 and not is_value_sniper:
             vetoes.append("volatile_market_requires_exceptional_evidence")
 
         score = max(0.0, min(100.0, score))
         action = "BET"
         if vetoes:
             action = "REJECT"
-        elif score < self.MIN_WATCH_SCORE:
+        elif score < self.MIN_WATCH_SCORE and not is_value_sniper:
             action = "REJECT"
-        elif score < self.MIN_BET_SCORE:
+        elif score < self.MIN_BET_SCORE and not is_value_sniper:
             action = "WATCH"
 
         row["betting_brain"] = {
@@ -276,6 +282,7 @@ class BettingBrain:
         for source in ("main_pick", "value_pick"):
             item = package.get(source)
             if isinstance(item, dict) and item.get("market"):
+                # print(f"DEBUG: {source} is_value_sniper: {item.get('is_value_sniper')}")
                 rows[self._row_key(item)] = dict(item)
 
         for source in ("supporting_picks", "bet_summary"):
@@ -283,6 +290,7 @@ class BettingBrain:
                 if isinstance(item, dict) and item.get("market"):
                     key = self._row_key(item)
                     rows[key] = self._merge_row(rows.get(key), item)
+        
         return list(rows.values())
 
     @staticmethod