Update learning_hub/adaptive_hub.py

learning_hub/adaptive_hub.py

@@ -1,6 +1,6 @@
 # ==============================================================================
 # 🧠 learning_hub/adaptive_hub.py 
-# (V68.0 - GEM-Architect: Aggressive Entry / Lowered Gates)
+# (V68.1 - GEM-Architect: Realistic Oracle Calibration [0.60])
 # ==============================================================================
 
 import json
@@ -55,21 +55,28 @@ class AdaptiveHub:
         self.dna_file_key = "learning/coin_type_dna_v2.json"
         self.strategies: Dict[str, StrategyDNA] = {}
         self.tuner = None 
-        print("🧠 [AdaptiveHub V68.0] Aggressive/Loosened Profiles Loaded.")
+        print("🧠 [AdaptiveHub V68.1] Realistic Oracle Calibration (0.60) Loaded.")
 
     async def initialize(self):
         try:
-            # ⚠️ هام: حتى لو وجدنا ملف قديم، سنقوم بتحديث القيم الافتراضية إذا كانت "شديدة" جداً
-            # لكن للأمان، سنحاول التحميل، وإذا فشل ننشئ الجديد.
-            # نصيحة: بعد هذا التحديث، يفضل عمل "Reset" من الواجهة لمسح الذاكرة القديمة الصارمة.
+            # ⚠️ هام: التحديث الإجباري لتطبيق العتبات الجديدة الواقعية
+            # سنقوم بإنشاء القيم الافتراضية الجديدة وتجاهل القديمة الصارمة جداً لهذه الجلسة
             if self.r2:
+                # محاولة قراءة الملف، لكن إذا كانت العتبات قديمة (0.90) يفضل استبدالها
+                # هنا سأقوم بتحميلها، والموالف (Tuner) سيقوم بالتعديل، أو يمكنك عمل Reset من الواجهة
                 data = await self.r2.get_file_json_async(self.dna_file_key)
                 if data: 
                     self._load_from_dict(data)
                     print("   🧠 Loaded existing DNA from R2.")
+                    
+                    # 🔥 Auto-Correction: If Oracle thresh is too high, force lower it
+                    for k, strat in self.strategies.items():
+                        if strat.filters.get("l3_oracle_thresh", 0.90) > 0.70:
+                            print(f"   ⚠️ [Correction] Lowering Oracle for {k} to 0.60")
+                            strat.filters["l3_oracle_thresh"] = 0.60
                 else: 
                     self._create_coin_type_defaults()
-                    print("   🧠 Created NEW Aggressive Defaults.")
+                    print("   🧠 Created NEW Realistic Defaults.")
             else:
                 self._create_coin_type_defaults()
         except Exception:
@@ -77,21 +84,19 @@ class AdaptiveHub:
 
     def _create_coin_type_defaults(self):
         """
-        Implements the 'Aggressive/Loosened' Profile (-10 points).
-        Target: Increase Trade Flow immediately.
+        Implements the 'Realistic' Profile based on user diagnostics.
+        Oracle Max: 0.75 -> Set Threshold: 0.60
+        L2 Gates: Lowered to allow flow.
         """
-        # 1. SAFE_BOTTOM (Aggressive)
-        # Old: Titan 0.70 -> New: 0.60
-        # Old: Pattern 0.60 -> New: 0.50
-        # Old: Min Score 0.75 -> New: 0.65
+        # 1. SAFE_BOTTOM (Realistic)
         self.strategies["SAFE_BOTTOM"] = StrategyDNA(
             "SAFE_BOTTOM",
             model_weights={"titan": 0.40, "patt": 0.40, "mc": 0.20}, 
-            ob_settings={"wall_ratio_limit": 0.40}, # Relaxed from 0.50
+            ob_settings={"wall_ratio_limit": 0.40}, 
             filters={
                 "l2_gate_titan": 0.60, "l2_gate_pattern": 0.50, "l2_gate_mc": 0.50,
                 "l2_min_score": 0.65,
-                "l3_oracle_thresh": 0.85, # Relaxed from 0.90
+                "l3_oracle_thresh": 0.60, # ✅ Corrected: Realistic (>55, <75)
                 "l4_sniper_thresh": 0.40
             },
             guards={
@@ -99,7 +104,7 @@ class AdaptiveHub:
             }
         )
         
-        # 2. MOMENTUM_LAUNCH (Aggressive)
+        # 2. MOMENTUM_LAUNCH (Realistic)
         self.strategies["MOMENTUM_LAUNCH"] = StrategyDNA(
             "MOMENTUM_LAUNCH",
             model_weights={"titan": 0.40, "patt": 0.40, "mc": 0.20},
@@ -107,7 +112,7 @@ class AdaptiveHub:
             filters={
                 "l2_gate_titan": 0.60, "l2_gate_pattern": 0.50, "l2_gate_mc": 0.50,
                 "l2_min_score": 0.65,
-                "l3_oracle_thresh": 0.85, 
+                "l3_oracle_thresh": 0.60, # ✅ Corrected
                 "l4_sniper_thresh": 0.40
             },
             guards={
@@ -115,7 +120,7 @@ class AdaptiveHub:
             }
         )
         
-        # 3. ACCUMULATION_SQUEEZE (Aggressive)
+        # 3. ACCUMULATION_SQUEEZE (Realistic)
         self.strategies["ACCUMULATION_SQUEEZE"] = StrategyDNA(
             "ACCUMULATION_SQUEEZE",
             model_weights={"titan": 0.40, "patt": 0.40, "mc": 0.20},
@@ -123,7 +128,7 @@ class AdaptiveHub:
             filters={
                 "l2_gate_titan": 0.60, "l2_gate_pattern": 0.50, "l2_gate_mc": 0.50,
                 "l2_min_score": 0.65,
-                "l3_oracle_thresh": 0.85, 
+                "l3_oracle_thresh": 0.60, # ✅ Corrected
                 "l4_sniper_thresh": 0.40
             },
             guards={
@@ -156,14 +161,14 @@ class AdaptiveHub:
             "w_patt": mw.get("patt", 0.40),
             "w_mc": mw.get("mc", 0.20),
 
-            # Gates & Scores (Lowered)
+            # Gates & Scores
             "l2_gate_titan": f.get("l2_gate_titan", 0.60),
             "l2_gate_pattern": f.get("l2_gate_pattern", 0.50),
             "l2_gate_mc": f.get("l2_gate_mc", 0.50),
             "l2_min_score": f.get("l2_min_score", 0.65),
 
             # Oracle & Sniper
-            "l3_oracle_thresh": f.get("l3_oracle_thresh", 0.85),
+            "l3_oracle_thresh": f.get("l3_oracle_thresh", 0.60), # ✅ Ensure Value is Passed
             "l4_sniper_thresh": f.get("l4_sniper_thresh", 0.40),
             "l4_ob_wall_ratio": dna.ob_settings.get("wall_ratio_limit", 0.35),
             
@@ -181,4 +186,4 @@ class AdaptiveHub:
     def get_status(self) -> str:
         dna = self.strategies.get("SAFE_BOTTOM")
         if not dna: return "Initializing..."
-        return f"Aggressive Mode | SB: {dna.trade_count}/30 | ML: {self.strategies['MOMENTUM_LAUNCH'].trade_count}/30"
+        return f"Realistic Mode (Oracle>0.60) | SB: {dna.trade_count}/30 | ML: {self.strategies['MOMENTUM_LAUNCH'].trade_count}/30"