Update governance_engine.py

governance_engine.py

@@ -1,9 +1,9 @@
 # ============================================================
-# 🏛️ governance_engine.py (V37.1 - GEM-Architect: Consensus Mod)
+# 🏛️ governance_engine.py (V38.0 - GEM-Architect: Context-Aware Weights)
 # ============================================================
 # Description: 
 #   Evaluates trade quality using 156 INDICATORS.
-#   Update V37.1: Enforced strict unanimity (No Negatives) & >50% Score.
+#   Update V38.0: Dynamic Weighting based on Strategy Type (Bottom vs Momentum).
 # ============================================================
 
 import numpy as np
@@ -16,8 +16,8 @@ from typing import Dict, Any, List
 
 class GovernanceEngine:
     def __init__(self):
-        # ⚖️ Strategic Weights
-        self.WEIGHTS = {
+        # ⚖️ Default Strategic Weights (For Normal/Range Operations)
+        self.DEFAULT_WEIGHTS = {
             "order_book": 0.25,       # 25%
             "market_structure": 0.20, # 20%
             "trend": 0.15,            # 15%
@@ -26,7 +26,7 @@ class GovernanceEngine:
             "volatility": 0.05,       # 5%
             "cycle_math": 0.10        # 10%
         }
-        print("🏛️ [Governance Engine V37.1] Consensus Mode: All Domains Must be Positive (>0) & Score > 50%.")
+        print("🏛️ [Governance Engine V38.0] Context-Aware Protocols Active.")
 
     
     async def evaluate_trade(
@@ -34,13 +34,14 @@ class GovernanceEngine:
         symbol: str,
         ohlcv_data: Dict[str, Any],
         order_book: Dict[str, Any],
+        strategy_type: str = "NORMAL", # ✅ New Parameter
         verbose: bool = True,
         include_details: bool = False,
         use_multi_timeframes: bool = False
     ) -> Dict[str, Any]:
         """
         Main Execution Entry.
-        Now enforces strict checks: No negative domains allowed.
+        Now adapts weights based on 'strategy_type' (SAFE_BOTTOM vs MOMENTUM_LAUNCH).
         """
         try:
             if ta is None:
@@ -71,7 +72,7 @@ class GovernanceEngine:
                         df_map[tf] = d
 
             if verbose:
-                print(f"\n📝 [Gov Audit] Opening Session for {symbol}...")
+                print(f"\n📝 [Gov Audit] Opening Session for {symbol} ({strategy_type})...")
                 print("-" * 80)
 
             # 2) Calculate Domains
@@ -117,9 +118,38 @@ class GovernanceEngine:
             if verbose:
                 print("-" * 80)
 
+            # ============================================================
+            # ⚙️ DYNAMIC WEIGHT SELECTION
+            # ============================================================
+            current_weights = self.DEFAULT_WEIGHTS.copy()
+            
+            if strategy_type == 'SAFE_BOTTOM':
+                # للقاع: نغفر ضعف الترند، ونركز على الرياضيات (الانحراف) والتقلبات والبنية
+                current_weights = {
+                    "order_book": 0.20, 
+                    "market_structure": 0.20, # Hammer/Support important
+                    "trend": 0.05,            # Trend is likely negative, ignore it mostly
+                    "momentum": 0.15,         # Divergence matters
+                    "volume": 0.10, 
+                    "volatility": 0.15,       # Exhaustion/BB Squeeze
+                    "cycle_math": 0.15        # Mean Reversion / Z-Score
+                }
+            elif strategy_type == 'MOMENTUM_LAUNCH':
+                # للانطلاق: الترند والزخم ودفتر الطلبات هم الملوك
+                current_weights = {
+                    "order_book": 0.25,       # Walls needed to push
+                    "market_structure": 0.15, 
+                    "trend": 0.25,            # MUST be uptrending
+                    "momentum": 0.20,         # High RSI is good here
+                    "volume": 0.10,           # Volume backing the move
+                    "volatility": 0.05, 
+                    "cycle_math": 0.00        # Less relevant for breakout
+                }
+
             # ============================================================
             # 🛑 1. STRICT CONSENSUS CHECK (Veto Power)
             # All domains must be non-negative (>= 0).
+            # Exception: For SAFE_BOTTOM, we tolerate negative Trend if other metrics are strong.
             # ============================================================
             domain_scores = {
                 "Trend": s_trend,
@@ -131,8 +161,13 @@ class GovernanceEngine:
                 "OrderBook": s_ob
             }
 
-            # Find any domain with a negative score
-            veto_domains = [name for name, score in domain_scores.items() if score < 0]
+            veto_domains = []
+            for name, score in domain_scores.items():
+                if score < 0:
+                    # Special Exemption for Bottom Fishing
+                    if strategy_type == 'SAFE_BOTTOM' and name == 'Trend':
+                        continue 
+                    veto_domains.append(name)
 
             if veto_domains:
                 reason = f"Vetoed by negative domains: {', '.join(veto_domains)}"
@@ -140,15 +175,15 @@ class GovernanceEngine:
                     print(f"⛔ [Governance VETO] {reason}")
                 return self._create_rejection(reason)
 
-            # 3) Weighted Aggregation (domain scores are in [-1, +1])
+            # 3) Weighted Aggregation using DYNAMIC weights
             raw_weighted_score = (
-                (s_trend * self.WEIGHTS['trend']) +
-                (s_mom * self.WEIGHTS['momentum']) +
-                (s_vol * self.WEIGHTS['volatility']) +
-                (s_volu * self.WEIGHTS['volume']) +
-                (s_cycle * self.WEIGHTS['cycle_math']) +
-                (s_struct * self.WEIGHTS['market_structure']) +
-                (s_ob * self.WEIGHTS['order_book'])
+                (s_trend * current_weights['trend']) +
+                (s_mom * current_weights['momentum']) +
+                (s_vol * current_weights['volatility']) +
+                (s_volu * current_weights['volume']) +
+                (s_cycle * current_weights['cycle_math']) +
+                (s_struct * current_weights['market_structure']) +
+                (s_ob * current_weights['order_book'])
             )
 
             # 4) Final Scoring & Grading