Update governance_engine.py

governance_engine.py

@@ -1,10 +1,9 @@
 # ============================================================
-# 🏛️ governance_engine.py (V1.3.1 - TrendErr Fix)
+# 🏛️ governance_engine.py (V37.1 - GEM-Architect: Consensus Mod)
 # ============================================================
 # Description: 
 #   Evaluates trade quality using 156 INDICATORS.
-#   Fixes: Solved "The truth value of a Series is ambiguous" error.
-#   Update: Enhanced error logging to show real causes.
+#   Update V37.1: Enforced strict unanimity (No Negatives) & >50% Score.
 # ============================================================
 
 import numpy as np
@@ -27,7 +26,7 @@ class GovernanceEngine:
             "volatility": 0.05,       # 5%
             "cycle_math": 0.10        # 10%
         }
-        print("🏛️ [Governance Engine V1.3] Stability Patch Applied. Ready.")
+        print("🏛️ [Governance Engine V37.1] Consensus Mode: All Domains Must be Positive (>0) & Score > 50%.")
 
     
     async def evaluate_trade(
@@ -41,11 +40,7 @@ class GovernanceEngine:
     ) -> Dict[str, Any]:
         """
         Main Execution Entry.
-
-        Backwards compatible:
-        - Requires '15m' data (same as before)
-        - Output schema unchanged unless include_details=True
-        - Multi-timeframe aggregation is opt-in (use_multi_timeframes=True)
+        Now enforces strict checks: No negative domains allowed.
         """
         try:
             if ta is None:
@@ -79,7 +74,7 @@ class GovernanceEngine:
                 print(f"\n📝 [Gov Audit] Opening Session for {symbol}...")
                 print("-" * 80)
 
-            # 2) Calculate Domains (single TF by default for compatibility)
+            # 2) Calculate Domains
             details_pack = {}  # only filled when include_details=True
 
             if not use_multi_timeframes:
@@ -122,6 +117,29 @@ class GovernanceEngine:
             if verbose:
                 print("-" * 80)
 
+            # ============================================================
+            # 🛑 1. STRICT CONSENSUS CHECK (Veto Power)
+            # All domains must be non-negative (>= 0).
+            # ============================================================
+            domain_scores = {
+                "Trend": s_trend,
+                "Momentum": s_mom,
+                "Volatility": s_vol,
+                "Volume": s_volu,
+                "Math": s_cycle,
+                "Structure": s_struct,
+                "OrderBook": s_ob
+            }
+
+            # Find any domain with a negative score
+            veto_domains = [name for name, score in domain_scores.items() if score < 0]
+
+            if veto_domains:
+                reason = f"Vetoed by negative domains: {', '.join(veto_domains)}"
+                if verbose:
+                    print(f"⛔ [Governance VETO] {reason}")
+                return self._create_rejection(reason)
+
             # 3) Weighted Aggregation (domain scores are in [-1, +1])
             raw_weighted_score = (
                 (s_trend * self.WEIGHTS['trend']) +
@@ -135,6 +153,15 @@ class GovernanceEngine:
 
             # 4) Final Scoring & Grading
             final_score = max(0.0, min(100.0, ((raw_weighted_score + 1) / 2) * 100))
+            
+            # ============================================================
+            # 🛑 2. SCORE THRESHOLD CHECK (> 50%)
+            # ============================================================
+            if final_score <= 50.0:
+                if verbose:
+                    print(f"⛔ [Governance FAIL] Score {final_score:.2f}% is too low (Must be > 50%).")
+                return self._create_rejection(f"Low Score: {final_score:.2f}% (Threshold > 50%)")
+
             grade = self._get_grade(final_score)
 
             result = {
@@ -149,7 +176,7 @@ class GovernanceEngine:
                     "structure": round(float(s_struct), 3),
                     "order_book": round(float(s_ob), 3),
                 },
-                "status": "APPROVED" if grade != "REJECT" else "REJECTED",
+                "status": "APPROVED",
             }
 
             if include_details:
@@ -961,8 +988,7 @@ class GovernanceEngine:
     def _get_grade(self, score: float) -> str:
         if score >= 85: return "ULTRA"
         if score >= 70: return "STRONG"
-        if score >= 50: return "NORMAL"
-        if score >= 35: return "WEAK"
+        if score > 50: return "NORMAL"
         return "REJECT"
 
     def _create_rejection(self, reason: str):