Update learning_hub/adaptive_hub.py

learning_hub/adaptive_hub.py

@@ -1,6 +1,6 @@
 # ==============================================================================
 # 🧠 learning_hub/adaptive_hub.py 
-# (V61.0 - GEM-Architect: Per-Asset Context Injection)
+# (V61.1 - GEM-Architect: Persistence Fix)
 # ==============================================================================
 
 import json
@@ -70,7 +70,9 @@ class StrategyDNA:
             "model_weights": self.model_weights,
             "ob_settings": self.ob_settings,
             "backtest_performance": self.backtest_performance, 
-            "stats": self.stats
+            "stats": self.stats,
+            # ✅ FIX: Save the trade buffer to R2
+            "trade_buffer": self.trade_buffer
         }
 
 class AdaptiveHub:
@@ -79,7 +81,7 @@ class AdaptiveHub:
         self.dna_file_key = "learning/strategic_dna_v61_context.json" 
         self.current_market_regime = "RANGE"
         self.strategies: Dict[str, StrategyDNA] = {}
-        print("🧠 [AdaptiveHub V61.0] Per-Asset Context Engine Ready.")
+        print("🧠 [AdaptiveHub V61.1] Persistence & Context Engine Ready.")
 
     def get_learning_progress(self) -> str:
         """يعيد نسبة التقدم في التعلم السريع"""
@@ -169,6 +171,10 @@ class AdaptiveHub:
             dna.delta_weekly = val.get("delta_weekly", dna.delta_weekly)
             dna.delta_monthly = val.get("delta_monthly", dna.delta_monthly)
             dna.stats = val.get("stats", {"wins":0, "losses":0})
+            
+            # ✅ FIX: Load the trade buffer so progress is not lost (0/100)
+            dna.trade_buffer = val.get("trade_buffer", [])
+            
             self.strategies[key] = dna
         self.current_market_regime = data.get("current_regime", "RANGE")
 
@@ -240,12 +246,18 @@ class AdaptiveHub:
         dna = self.strategies[self.current_market_regime]
         dna.trade_buffer.append(trade_data)
         
+        # حفظ التقدم في كل مرة تضاف صفقة (اختياري للأمان) أو الاعتماد على الحفظ الدوري
+        # هنا سنعتمد على الحفظ عند الاكتمال أو عند الخروج الآمن
+        
         if len(dna.trade_buffer) >= 100:
             print(f"🎓 [Fast Learner] Batch of 100 trades reached. Analyzing...")
             self._process_fast_learning_batch(dna)
             dna.trade_buffer.clear()
             await self._save_state_to_r2()
             self._inject_current_parameters()
+        else:
+            # حفظ جزئي كل صفقة لضمان عدم الضياع عند الريستارت
+            asyncio.create_task(self._save_state_to_r2())
 
     def _process_fast_learning_batch(self, dna: StrategyDNA):
         models = ['l3_oracle_thresh', 'l4_sniper_thresh']