Update trade_manager.py

trade_manager.py

@@ -1,10 +1,6 @@
 # trade_manager.py
-# (V28.1 - GEM-Architect: Clear Hydra Logging Edition)
-# Fixes:
-# 1. Full wording for Hydra Stats (Crash, Giveback, Stagnation).
-# 2. Handles "Cold Start" zeros by showing "Gathering Data" status.
-# 3. Improved formatting to percentages for better readability.
-# 4. [GEM-FIX] Added 'highest_price' tracking for accurate Giveback detection.
+# (V33.0 - GEM-Architect: Adaptive Feedback Loop Edition)
+# Linked with AdaptiveHub via register_trade_outcome
 
 import asyncio
 import uuid
@@ -18,7 +14,7 @@ class TradeManager:
         self.r2 = r2_service
         self.data_manager = data_manager
         self.processor = processor 
-        self.learning_hub = None
+        self.learning_hub = None # سيتم حقن AdaptiveHub هنا من app.py
         
         self.open_positions = {}
         self.watchlist = {}
@@ -34,17 +30,16 @@ class TradeManager:
         # إعدادات المراجعة الاستراتيجية (Oracle Re-check)
         self.ORACLE_CHECK_INTERVAL = 900  # كل 15 دقيقة
         
-        # إحصائيات AI (محدثة لتعكس رؤوس Hydra)
+        # إحصائيات AI
         self.ai_stats = {
             "hybrid": {"total": 0, "good": 0, "saved": 0.0, "missed": 0.0},
-            # رؤوس الهيدرا الثلاثة
             "crash":  {"total": 0, "good": 0, "saved": 0.0, "missed": 0.0},
             "giveback": {"total": 0, "good": 0, "saved": 0.0, "missed": 0.0},
             "stagnation": {"total": 0, "good": 0, "saved": 0.0, "missed": 0.0}
         }
         
         self.execution_lock = asyncio.Lock()
-        print(f"🛡️ [TradeManager V28.1] Hydra Policy Engine Online (Verbose Mode).")
+        print(f"🛡️ [TradeManager V33.0] Adaptive Feedback Engine Online.")
 
     async def initialize_sentry_exchanges(self):
         print("🛡️ [TradeManager] Syncing state with R2...")
@@ -178,7 +173,6 @@ class TradeManager:
             oracle_strength = float(signal_data.get('strength', 0.5))
             oracle_class = signal_data.get('target_class', 'TP2')
             
-            # Map target class to int for Hydra if needed (TP1=1, TP2=2...)
             target_class_int = 3
             if isinstance(oracle_class, str) and oracle_class.startswith('TP'):
                 try: target_class_int = int(oracle_class[-1])
@@ -204,13 +198,11 @@ class TradeManager:
                 'entry_capital': current_capital,
                 'entry_fee_usd': entry_fee_usd,
                 'l1_score': float(signal_data.get('enhanced_final_score', 0.0)),
-                # نحفظ target_class كـ int لسهولة استخدامه في Hydra لاحقاً
                 'target_class_int': target_class_int,
                 'decision_data': {
                     'components': signal_data.get('components', {}),
                     'oracle_conf': signal_data.get('confidence', 0)
                 },
-                # [GEM-FIX] Initialize highest_price at entry
                 'highest_price': current_price
             }
             
@@ -249,18 +241,16 @@ class TradeManager:
                 trade = self.open_positions.get(symbol)
                 if not trade:
                     break
-                # 1. تحديث السعر الفوري
+                
                 current_ticker_price = await self.data_manager.get_latest_price_async(symbol)
 
-                # [GEM-FIX] Track Highest Price for Giveback Logic (Dynamic Update)
                 if 'highest_price' not in trade:
                     trade['highest_price'] = float(trade['entry_price'])
                 
                 if current_ticker_price > float(trade['highest_price']):
                     trade['highest_price'] = current_ticker_price
-                    # Note: We update memory instantly. Persistence happens on next save event.
 
-                # 2. فحص الحدود الصلبة (Hard Limits)
+                # Hard Limits
                 if current_ticker_price >= trade['tp_price']:
                     print(f"🎯 [TP HIT] Price {current_ticker_price} hit Target {trade['tp_price']}")
                     async with self.execution_lock:
@@ -273,9 +263,9 @@ class TradeManager:
                         await self._execute_exit(symbol, trade['sl_price'], "SL_HIT")
                     break 
 
-                # 3. الحارس الذكي (Hydra AI Check) - كل دقيقة
+                # Hydra AI Check (Processor determines Policy)
                 if time.time() - last_ai_check_time > 60:
-                    t1 = self.data_manager.get_latest_ohlcv(symbol, '1m', 200) # نحتاج تاريخ أطول للميزات
+                    t1 = self.data_manager.get_latest_ohlcv(symbol, '1m', 200)
                     t5 = self.data_manager.get_latest_ohlcv(symbol, '5m', 100)
                     t15 = self.data_manager.get_latest_ohlcv(symbol, '15m', 100)
                     
@@ -284,9 +274,7 @@ class TradeManager:
                     except:
                         continue 
                     
-                    # [MODIFIED] Clear Check: Only run if data is sufficient
                     if d1 and d5 and d15 and len(d1) >= 100:
-                        # ✅ تجهيز سياق الصفقة لـ Hydra
                         context_data = {
                             'entry_price': trade['entry_price'],
                             'tp_price': trade['tp_price'],
@@ -295,11 +283,10 @@ class TradeManager:
                             '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),
-                            # [GEM-FIX] Pass the highest price reached so far
                             'highest_price': float(trade['highest_price'])
                         }
 
-                        # استدعاء الحارس عبر Processor
+                        # Processor consults Guardian AND applies SystemLimits overrides
                         decision = self.processor.consult_guardian(symbol, d1, d5, d15, context_data)
                         
                         action = decision.get('action', 'HOLD')
@@ -308,7 +295,6 @@ class TradeManager:
                         p_giveback = probs.get('giveback', 0.0)
                         p_stag = probs.get('stagnation', 0.0)
                         
-                        # [UPDATED LOG] Full words + Percentage display
                         log_msg = (f"🛡️ {action} | "
                                    f"Crash: {p_crash:.0%} | "
                                    f"Giveback: {p_giveback:.0%} | "
@@ -316,7 +302,6 @@ class TradeManager:
                         
                         self.latest_guardian_log = log_msg
                         
-                        # تنفيذ القرار
                         if action in ['EXIT_HARD', 'EXIT_SOFT']:
                             print(f"🐲 [Hydra] {action}: {decision.get('reason')}")
                             async with self.execution_lock:
@@ -344,13 +329,12 @@ class TradeManager:
                                     self.open_positions[symbol]['last_update'] = datetime.now().isoformat()
                                     await self.r2.save_open_trades_async(list(self.open_positions.values()))
                     else:
-                        # [ADDED] Inform user why zeros/waiting exists
                         self.latest_guardian_log = f"📡 Hydra: Gathering Data (Need 100 candles)..."
 
                     last_ai_check_time = time.time()
                     self.open_positions[symbol]['last_update'] = datetime.now().isoformat()
                 
-                # 4. الطبقة الاستراتيجية (Oracle Re-Check)
+                # 4. Oracle Re-Check
                 last_oracle_check = datetime.fromisoformat(trade.get('last_oracle_check', datetime.now().isoformat()))
                 if (datetime.now() - last_oracle_check).total_seconds() > self.ORACLE_CHECK_INTERVAL:
                     self.open_positions[symbol]['last_oracle_check'] = datetime.now().isoformat()
@@ -425,23 +409,21 @@ class TradeManager:
             
             self._update_specific_stat("hybrid", is_good_exit, usd_impact)
             
-            # تحديث إحصائيات Hydra
             if ai_scores:
-                # إذا كان الاحتمال عالياً وتصرفنا بناءً عليه، هل كان التصرف صحيحاً؟
                 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:
-                    # للركود، الخروج الجيد يعني السعر لم يصعد بقوة بعدنا
                     self._update_specific_stat("stagnation", is_good_exit, usd_impact)
 
             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)
 
+            # ✅ [Adaptive Loop] Feed results back to AdaptiveHub
             if self.learning_hub and trade_obj:
                 trade_obj['pnl_percent'] = trade_obj.get('profit_pct', 0.0)
-                await self.learning_hub.analyze_trade_and_learn(trade_obj, trade_obj.get('exit_reason', 'UNKNOWN'))
+                await self.learning_hub.register_trade_outcome(trade_obj)
 
         except Exception as e:
             print(f"⚠️ [Ghost/Learning Error] {e}")
@@ -458,7 +440,6 @@ class TradeManager:
             entry_capital = float(trade.get('entry_capital', 100.0))
             entry_fee_usd = float(trade.get('entry_fee_usd', 0.0))
             
-            # Spot PnL
             exit_value_gross = (exit_price / entry_price) * entry_capital
             exit_fee_usd = exit_value_gross * self.FEE_RATE
             net_exit_value = exit_value_gross - exit_fee_usd
@@ -498,7 +479,6 @@ class TradeManager:
 
             self._launch_post_exit_analysis(symbol, exit_price, trade.get('exit_time'), entry_capital, ai_scores, trade)
 
-            # Update UI Log
             self.latest_guardian_log = f"✅ Closed {symbol} ({reason})"
 
             if symbol in self.sentry_tasks: