Update trade_manager.py

trade_manager.py

@@ -1,5 +1,5 @@
 # trade_manager.py
-# (V33.0 - GEM-Architect: Adaptive Feedback Loop Edition)
+# (V34.0 - GEM-Architect: Dual-Core Guardian Edition)
 # Linked with AdaptiveHub via register_trade_outcome
 
 import asyncio
@@ -22,7 +22,7 @@ class TradeManager:
         self.running = True
         
         # متغير لتمرير الرسائل إلى الواجهة (UI Log Bridge)
-        self.latest_guardian_log = "🛡️ Guardian Hydra Initialized."
+        self.latest_guardian_log = "🛡️ Guardian Dual-Core Initialized."
         
         # إعدادات الرسوم
         self.FEE_RATE = 0.001 
@@ -39,7 +39,7 @@ class TradeManager:
         }
         
         self.execution_lock = asyncio.Lock()
-        print(f"🛡️ [TradeManager V33.0] Adaptive Feedback Engine Online.")
+        print(f"🛡️ [TradeManager V34.0] Dual-Core Engine Online.")
 
     async def initialize_sentry_exchanges(self):
         print("🛡️ [TradeManager] Syncing state with R2...")
@@ -226,10 +226,10 @@ class TradeManager:
             traceback.print_exc()
 
     # ==============================================================================
-    # 🛡️ Hydra Guardian Loop (The Active Manager)
+    # 🛡️ Dual-Core Guardian Loop (The Active Manager)
     # ==============================================================================
     async def _guardian_loop(self, symbol: str):
-        print(f"🛡️ [Hydra] STARTING WATCH for {symbol}...")
+        print(f"🛡️ [Dual-Core] STARTING WATCH for {symbol}...")
         last_ai_check_time = 0
         
         while self.running:
@@ -263,9 +263,10 @@ class TradeManager:
                         await self._execute_exit(symbol, trade['sl_price'], "SL_HIT")
                     break 
 
-                # Hydra AI Check (Processor determines Policy)
+                # Dual AI Check (Processor determines Policy)
                 if time.time() - last_ai_check_time > 60:
-                    t1 = self.data_manager.get_latest_ohlcv(symbol, '1m', 200)
+                    # ✅ زيادة الحد الأدنى للشمعات لدعم الحارس القديم (يحتاج EMA200)
+                    t1 = self.data_manager.get_latest_ohlcv(symbol, '1m', 300) 
                     t5 = self.data_manager.get_latest_ohlcv(symbol, '5m', 100)
                     t15 = self.data_manager.get_latest_ohlcv(symbol, '15m', 100)
                     
@@ -274,7 +275,8 @@ class TradeManager:
                     except:
                         continue 
                     
-                    if d1 and d5 and d15 and len(d1) >= 100:
+                    # التحقق من كفاية البيانات للحارس القديم
+                    if d1 and d5 and d15 and len(d1) >= 200:
                         context_data = {
                             'entry_price': trade['entry_price'],
                             'tp_price': trade['tp_price'],
@@ -282,37 +284,34 @@ class TradeManager:
                             'entry_time': trade['entry_time'],
                             'oracle_conf': trade.get('decision_data', {}).get('oracle_conf', 0.8),
                             'l2_score': trade.get('l1_score', 0.7),
-                            'target_class': trade.get('target_class_int', 3),
-                            'highest_price': float(trade['highest_price'])
+                            'target_class_int': trade.get('target_class_int', 3),
+                            'highest_price': float(trade['highest_price']),
+                            'time_in_trade_mins': (datetime.now() - datetime.fromisoformat(trade['entry_time'])).total_seconds() / 60
                         }
 
-                        # Processor consults Guardian AND applies SystemLimits overrides
-                        decision = self.processor.consult_guardian(symbol, d1, d5, d15, context_data)
+                        # ✅ استشارة الحارس المزدوج (Dual Guardians)
+                        decision = self.processor.consult_dual_guardians(symbol, d1, d5, d15, context_data)
                         
                         action = decision.get('action', 'HOLD')
-                        probs = decision.get('probs', {})
-                        p_crash = probs.get('crash', 0.0)
-                        p_giveback = probs.get('giveback', 0.0)
-                        p_stag = probs.get('stagnation', 0.0)
-                        
-                        log_msg = (f"🛡️ {action} | "
-                                   f"Crash: {p_crash:.0%} | "
-                                   f"Giveback: {p_giveback:.0%} | "
-                                   f"Stagnation: {p_stag:.0%}")
+                        reason = decision.get('reason', '')
                         
+                        # دمج النتائج (Probs من الهيدرا أو Scores من القديم)
+                        ai_metrics = decision.get('probs') or decision.get('scores') or {}
+
+                        log_msg = f"🛡️ {action} | {reason}"
                         self.latest_guardian_log = log_msg
                         
                         if action in ['EXIT_HARD', 'EXIT_SOFT']:
-                            print(f"🐲 [Hydra] {action}: {decision.get('reason')}")
+                            print(f"🐲 [Dual-Core Trigger] {action}: {reason}")
                             async with self.execution_lock:
-                                await self._execute_exit(symbol, current_ticker_price, f"Hydra_{action}", ai_scores=probs)
+                                await self._execute_exit(symbol, current_ticker_price, f"DualGuard_{action}", ai_scores=ai_metrics)
                             break
                         
                         elif action == 'TIGHTEN_SL':
                             entry_p = float(trade['entry_price'])
                             curr_sl = float(trade['sl_price'])
                             if curr_sl < entry_p:
-                                print(f"🐲 [Hydra] TIGHTEN_SL: Moving Stop to Entry {entry_p}")
+                                print(f"🛡️ [Dual-Core] TIGHTEN_SL: Moving Stop to Entry {entry_p}")
                                 self.open_positions[symbol]['sl_price'] = entry_p
                                 self.open_positions[symbol]['last_update'] = datetime.now().isoformat()
                                 await self.r2.save_open_trades_async(list(self.open_positions.values()))
@@ -324,12 +323,12 @@ class TradeManager:
                                 potential_sl = entry_p + ((curr_p - entry_p) * 0.5)
                                 curr_sl = float(trade['sl_price'])
                                 if potential_sl > curr_sl:
-                                    print(f"🐲 [Hydra] TRAIL_SL: Moving Stop to {potential_sl:.4f}")
+                                    print(f"🛡️ [Dual-Core] TRAIL_SL: Moving Stop to {potential_sl:.4f}")
                                     self.open_positions[symbol]['sl_price'] = potential_sl
                                     self.open_positions[symbol]['last_update'] = datetime.now().isoformat()
                                     await self.r2.save_open_trades_async(list(self.open_positions.values()))
                     else:
-                        self.latest_guardian_log = f"📡 Hydra: Gathering Data (Need 100 candles)..."
+                        self.latest_guardian_log = f"📡 Gathering Deep Data (Need 200+ candles)..."
 
                     last_ai_check_time = time.time()
                     self.open_positions[symbol]['last_update'] = datetime.now().isoformat()
@@ -407,15 +406,20 @@ class TradeManager:
             usd_impact = change_pct * position_size_usd
             is_good_exit = change_pct < 0 
             
+            # تحديث الإحصائيات العامة
             self._update_specific_stat("hybrid", is_good_exit, usd_impact)
             
+            # تحديث الإحصائيات التفصيلية (سواء جاءت من Hydra probs أو Legacy scores)
             if ai_scores:
+                # Hydra Keys
                 if ai_scores.get('crash', 0) >= 0.60: 
                     self._update_specific_stat("crash", is_good_exit, usd_impact)
                 if ai_scores.get('giveback', 0) >= 0.70: 
                     self._update_specific_stat("giveback", is_good_exit, usd_impact)
                 if ai_scores.get('stagnation', 0) >= 0.50:
                     self._update_specific_stat("stagnation", is_good_exit, usd_impact)
+                
+                # Legacy Keys (ممكن تحويلها إذا لزم الأمر، لكن حالياً نتركها ضمن hybrid)
 
             record = {"symbol": symbol, "exit_price": exit_price, "price_15m": curr, "usd_impact": usd_impact, "verdict": "SUCCESS" if is_good_exit else "MISS"}
             await self.r2.append_deep_steward_audit(record)