Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py (Updated to V8.7 - MC Expected Return Fix)
+# app.py (V11.0 - Full Real Data Integration & New Engines)
 import os
 import traceback
 import signal
@@ -10,59 +10,40 @@ import time
 from contextlib import asynccontextmanager
 from fastapi import FastAPI, HTTPException
 from datetime import datetime
-from typing import List, Dict, Any 
+from typing import List, Dict, Any
 
+# استيراد الخدمات الأساسية
 try:
     from r2 import R2Service
     from LLM import LLMService
-    from data_manager import DataManager
+    from ml_engine.data_manager import DataManager
     from ml_engine.processor import MLProcessor
     from learning_hub.hub_manager import LearningHubManager
-    from sentiment_news import SentimentAnalyzer, NewsFetcher # (V8.1) استيراد NewsFetcher
     from trade_manager import TradeManager
-    from ml_engine.monte_carlo import _sanitize_results_for_json
-    
-    from helpers import safe_float_conversion, validate_candidate_data_enhanced
+    # لم نعد بحاجة لاستيراد WhaleMonitor أو NewsFetcher هنا مباشرة،
+    # سيتم التعامل معها داخل DataManager أو Processor عند الحاجة.
 except ImportError as e:
-    print(f"❌ خطأ في استيراد الوحدات: {e}")
-    if "ccxt.async_support" in str(e) or "ccxtpro" in str(e):
-        print("🚨 خطأ فادح: تأكد من أن 'ccxt' (الإصدار 4+) مثبت وأن 'ccxt-pro' محذوف.")
+    print(f"❌ [App] خطأ فادح في استيراد الوحدات: {e}")
     sys.exit(1)
 
-# (V8.1) استيراد VADER
-try:
-    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-    VADER_ANALYZER = SentimentIntensityAnalyzer()
-    print("✅ تم تحميل VADER Sentiment Analyzer بنجاح")
-except ImportError:
-    print("❌❌ فشل استيراد VADER. درجة الأخبار ستكون معطلة. ❌❌")
-    print("   قم بتثبيتها باستخدام: pip install vaderSentiment")
-    VADER_ANALYZER = None
-
-# المتغيرات العالمية
+# المتغيرات العالمية للنظام
 r2_service_global = None
 data_manager_global = None
 llm_service_global = None
 learning_hub_global = None
 trade_manager_global = None
-sentiment_analyzer_global = None
-symbol_whale_monitor_global = None
-news_fetcher_global = None # (V8.1) إضافة NewsFetcher
+ml_processor_global = None # (V11.0) إضافة المعالج كمتغير عالمي
 
 MARKET_STATE_OK = True
 
-
 class StateManager:
-    # ... (لا تغيير في هذا الكلاس) ...
     def __init__(self):
         self.market_analysis_lock = asyncio.Lock()
         self.trade_analysis_lock = asyncio.Lock()
         self.initialization_complete = False
         self.initialization_error = None
         self.services_initialized = {
-            'r2_service': False, 'data_manager': False, 'llm_service': False,
-            'learning_hub': False, 'trade_manager': False, 'sentiment_analyzer': False,
-            'symbol_whale_monitor': False, 'news_fetcher': False # (V8.1)
+            'r2': False, 'data': False, 'llm': False, 'hub': False, 'trade': False
         }
 
     async def wait_for_initialization(self, timeout=60):
@@ -77,7 +58,7 @@ class StateManager:
         self.services_initialized[service_name] = True
         if all(self.services_initialized.values()):
             self.initialization_complete = True
-            print("🎯 جميع الخدمات مهيأة بالكامل")
+            print("🎯 [System] جميع الخدمات مهيأة وجاهزة للعمل.")
 
     def set_initialization_error(self, error):
         self.initialization_error = error
@@ -86,779 +67,252 @@ class StateManager:
 state_manager = StateManager()
 
 async def initialize_services():
-    """تهيئة جميع الخدمات بشكل منفصل"""
+    """تهيئة مركزية لجميع خدمات النظام بترتيب محدد"""
     global r2_service_global, data_manager_global, llm_service_global
-    global learning_hub_global, trade_manager_global, sentiment_analyzer_global
-    global symbol_whale_monitor_global, news_fetcher_global, VADER_ANALYZER # (V8.1)
+    global learning_hub_global, trade_manager_global, ml_processor_global
+    
     try:
-        # 🔴 --- START OF CHANGE (V7.0) --- 🔴
-        print("🚀 بدء تهيئة الخدمات (بنية Sentry الجديدة V5.9)...")
-        # 🔴 --- END OF CHANGE --- 🔴
-        print("   🔄 تهيئة R2Service..."); r2_service_global = R2Service(); state_manager.set_service_initialized('r2_service'); print("   ✅ R2Service مهيأة")
-        print("   🔄 جلب قاعدة بيانات العقود..."); contracts_database = await r2_service_global.load_contracts_db_async(); print(f"   ✅ تم تحميل {len(contracts_database)} عقد")
-
-        print("   🔄 تهيئة مراقب الحيتان (Layer 1 Data)...");
-        try:
-            from whale_monitor.core import EnhancedWhaleMonitor
-            
-            symbol_whale_monitor_global = EnhancedWhaleMonitor(contracts_database, r2_service_global)
-            state_manager.set_service_initialized('symbol_whale_monitor'); print("   ✅ مراقب الحيتان مهيأ")
+        print("🚀 [System V11.0] بدء تهيئة الخدمات...")
         
-        except Exception as e: 
-            print(f"   ⚠️ فشل تهيئة مراقب الحيتان: {e}"); 
-            traceback.print_exc() 
-            symbol_whale_monitor_global = None
-            state_manager.set_service_initialized('symbol_whale_monitor'); 
-            print("   ℹ️ مراقبة الحيتان معطلة. استمرار التهيئة...")
-
-        # (V8-MODIFICATION) تمرير r2_service_global إلى DataManager
-        print("   🔄 تهيئة DataManager (Layer 1 Data)..."); 
-        data_manager_global = DataManager(contracts_database, symbol_whale_monitor_global, r2_service_global) 
-        await data_manager_global.initialize(); 
-        state_manager.set_service_initialized('data_manager'); 
-        print("   ✅ DataManager مهيأ (ومحرك الأنماط V8 مُحمّل)")
-
-        # (ربط DataManager بـ WhaleMonitor لحل الاعتمادية الدائرية للسعر)
-        if symbol_whale_monitor_global:
-            symbol_whale_monitor_global.data_manager = data_manager_global
-            print("   ✅ [Whale Link] تم ربط DataManager بـ WhaleMonitor.")
-
-        print("   🔄 تهيئة LLMService (Layer 1 Brain)...");
-        llm_service_global = LLMService();
-        llm_service_global.r2_service = r2_service_global;
-
-        print("   🔄 تهيئة محلل المشاعر (Layer 1 Data)...");
-        sentiment_analyzer_global = SentimentAnalyzer(data_manager_global);
-        state_manager.set_service_initialized('sentiment_analyzer');
-        print("   ✅ محلل المشاعر مهيأ")
+        # 1. الطبقة الأساسية (R2 & Data)
+        r2_service_global = R2Service()
+        state_manager.set_service_initialized('r2')
         
-        # (V8.1) تهيئة NewsFetcher
-        print("   🔄 تهيئة NewsFetcher (Layer 1 Data)...");
-        news_fetcher_global = NewsFetcher()
-        state_manager.set_service_initialized('news_fetcher');
-        print("   ✅ NewsFetcher (V8.1) مهيأ")
+        # تحميل قاعدة بيانات العقود (اختياري، يمكن أن يكون فارغاً إذا لم يستخدم)
+        contracts_db = await r2_service_global.load_contracts_db_async() or {}
         
-        # 🔴 --- START OF CHANGE (V8.5 - Vader Injection) --- 🔴
-        # (تمرير NewsFetcher و VADER إلى LLMService - مهم لإعادة التحليل)
-        llm_service_global.news_fetcher = news_fetcher_global
-        llm_service_global.vader_analyzer = VADER_ANALYZER # (هذا هو السطر المضاف)
-        print("   ✅ [LLM Link] تم ربط NewsFetcher و VaderAnalyzer بـ LLMService.")
-        # 🔴 --- END OF CHANGE --- 🔴
-
-        print("   🔄 تهيئة محور التعلم (Hub)...");
-        learning_hub_global = LearningHubManager(
-            r2_service=r2_service_global,
-            llm_service=llm_service_global,
-            data_manager=data_manager_global
-        )
+        # تهيئة DataManager بدون WhaleMonitor مؤقتاً (سيتم ربطه لاحقاً إذا لزم الأمر)
+        data_manager_global = DataManager(contracts_db, whale_monitor=None, r2_service=r2_service_global)
+        await data_manager_global.initialize()
+        state_manager.set_service_initialized('data')
+
+        # 2. طبقة الذكاء (LLM & Learning Hub)
+        llm_service_global = LLMService()
+        llm_service_global.r2_service = r2_service_global
+        
+        learning_hub_global = LearningHubManager(r2_service_global, llm_service_global, data_manager_global)
         await learning_hub_global.initialize()
-        state_manager.set_service_initialized('learning_hub');
-        print("   ✅ محور التعلم (Hub) مهيأ")
-
-        llm_service_global.learning_hub = learning_hub_global
-        state_manager.set_service_initialized('llm_service');
-        print("   ✅ LLMService مربوط بمحور التعلم")
+        llm_service_global.learning_hub = learning_hub_global # ربط عكسي
+        state_manager.set_service_initialized('llm')
+        state_manager.set_service_initialized('hub')
+
+        # 3. طبقة التنفيذ والمعالجة (Trade Manager & Processor)
+        # تهيئة المعالج المركزي الذي يربط كل شيء
+        ml_processor_global = MLProcessor(
+            market_context=None, # سيتم تحديثه في كل دورة
+            data_manager=data_manager_global,
+            learning_hub=learning_hub_global
+        )
 
-        print("   🔄 تهيئة مدير الصفقات (Layer 2 Sentry + Layer 3 Executor)...");
-        
-        # (تمرير دالة الدورة كـ "رد نداء" ليتم استدعاؤها بعد إغلاق الصفقة)
         trade_manager_global = TradeManager(
-            r2_service=r2_service_global,
-            learning_hub=learning_hub_global,
-            data_manager=data_manager_global,
-            state_manager=state_manager,
-            callback_on_close=run_bot_cycle_async 
+            r2_service_global, learning_hub_global, data_manager_global, 
+            state_manager, callback_on_close=run_bot_cycle_async
         )
-        
         await trade_manager_global.initialize_sentry_exchanges()
-        state_manager.set_service_initialized('trade_manager');
-        print("   ✅ مدير الصفقات (Sentry/Executor) مهيأ")
+        state_manager.set_service_initialized('trade')
 
-        print("🎯 اكتملت تهيئة جميع الخدمات بنجاح"); return True
-    except Exception as e: error_msg = f"فشل تهيئة الخدمات: {str(e)}"; print(f"❌ {error_msg}"); state_manager.set_initialization_error(error_msg); return False
+        return True
+    except Exception as e:
+        error_msg = f"فشل تهيئة الخدمات: {str(e)}"
+        print(f"❌ {error_msg}")
+        state_manager.set_initialization_error(error_msg)
+        return False
 
 async def monitor_market_async():
-    # ... (لا تغيير في هذه الدالة) ...
-    global data_manager_global, sentiment_analyzer_global, MARKET_STATE_OK
+    global MARKET_STATE_OK
     try:
-        if not await state_manager.wait_for_initialization(): print("❌ فشل تهيئة الخدمات - إيقاف مراقبة السوق"); return
+        if not await state_manager.wait_for_initialization(): return
         while True:
             try:
                 async with state_manager.market_analysis_lock:
-                    market_context = await sentiment_analyzer_global.get_market_sentiment()
+                    market_ctx = await data_manager_global.get_market_context_async()
+                    ml_processor_global.market_context = market_ctx # تحديث سياق المعالج
                     
-                    if not market_context: MARKET_STATE_OK = True; await asyncio.sleep(60); continue
-                    
-                    bitcoin_sentiment = market_context.get('btc_sentiment')
-                    fear_greed_index = market_context.get('fear_and_greed_index')
-                    should_halt_trading, halt_reason = False, ""
-                    if bitcoin_sentiment == 'BEARISH' and (fear_greed_index is not None and fear_greed_index < 30): should_halt_trading, halt_reason = True, "ظروف سوق هابطة"
-                    
-                    if should_halt_trading: 
-                        MARKET_STATE_OK = False; 
-                        await r2_service_global.save_system_logs_async({"market_halt": True, "reason": halt_reason})
+                    # فحص بسيط لحالة السوق (يمكن تعقيده لاحقاً)
+                    if market_ctx.get('btc_sentiment') == 'BEARISH' and market_ctx.get('fear_and_greed_index', 50) < 25:
+                        if MARKET_STATE_OK:
+                            print("⚠️ [Market Monitor] ظروف سوق هابطة شديدة. إيقاف مؤقت.")
+                            MARKET_STATE_OK = False
                     else:
-                        if not MARKET_STATE_OK: print("✅ تحسنت ظروف السوق. استئناف العمليات العادية.")
-                        MARKET_STATE_OK = True
-                        
-                await asyncio.sleep(60)
-            except Exception as error: 
-                print(f"❌ خطأ أثناء مراقبة السوق: {error}"); 
-                MARKET_STATE_OK = True; 
-                await asyncio.sleep(60)
-    except Exception as e: print(f"❌ فشل تشغيل مراقبة السوق: {e}")
-
-
-async def run_periodic_distillation():
-    # ... (لا تغيير في هذه الدالة) ...
-    print("background task: Periodic Distillation (Curator) scheduled.")
-    await asyncio.sleep(300)
-
-    while True:
-        try:
-            if not await state_manager.wait_for_initialization():
+                        if not MARKET_STATE_OK:
+                            print("✅ [Market Monitor] تحسن ظروف السوق. استئناف العمل.")
+                            MARKET_STATE_OK = True
+                            
+                await asyncio.sleep(300) # فحص كل 5 دقائق
+            except Exception as e:
+                print(f"❌ [Market Monitor] خطأ: {e}")
                 await asyncio.sleep(60)
-                continue
+    except Exception: pass
 
-            print("🔄 [Scheduler] Running periodic distillation check...")
-            await learning_hub_global.run_distillation_check()
-            await asyncio.sleep(6 * 60 * 60)
+async def run_explorer_cycle():
+    """دورة المستكشف الرئيسية (Layer 1 Complete Cycle)"""
+    if not state_manager.initialization_complete:
+         print("❌ [Explorer] الخدمات غير جاهزة.")
+         return
 
-        except Exception as e:
-            print(f"❌ [Scheduler] Error in periodic distillation task: {e}")
-            traceback.print_exc()
-            await asyncio.sleep(60 * 60)
-
-async def process_batch_parallel(batch, ml_processor, batch_num, total_batches, preloaded_whale_data):
-    # ... (لا تغيير في هذه الدالة) ...
+    print("🔭 [Explorer] بدء دورة استكشاف جديدة...")
     try:
-        batch_tasks = []
-        for symbol_data in batch:
-            task = asyncio.create_task(ml_processor.process_multiple_symbols_parallel([symbol_data], preloaded_whale_data))
-            batch_tasks.append(task)
-
-        batch_results_list_of_lists = await asyncio.gather(*batch_tasks, return_exceptions=True)
-
-        successful_results = []
-        low_score_results = []
-        failed_results = []
-
-        for i, result_list in enumerate(batch_results_list_of_lists):
-            symbol = batch[i].get('symbol', 'unknown')
-            if isinstance(result_list, Exception):
-                 failed_results.append({"symbol": symbol, "error": f"Task Execution Error: {str(result_list)}"})
-                 continue
-
-            if result_list:
-                result = result_list[0]
-                if isinstance(result, dict):
-                    if result.get('enhanced_final_score', 0) > 0.4:
-                        successful_results.append(result)
-                    else:
-                        low_score_results.append(result)
-                else:
-                     failed_results.append({"symbol": symbol, "error": f"ML processor returned invalid type: {type(result)}"})
-            else:
-                 failed_results.append({"symbol": symbol, "error": "ML processing returned None or empty list"})
-
-        return {'success': successful_results, 'low_score': low_score_results, 'failures': failed_results}
-
-    except Exception as error:
-        print(f"❌ [Consumer] Error processing batch {batch_num}: {error}")
-        return {'success': [], 'low_score': [], 'failures': []}
-
-
-async def run_3_layer_analysis_explorer() -> List[Dict[str, Any]]:
-    """
-    (معدل V8.7) - إصلاح العائد المتوقع (Expected Return)
-    """
-    layer1_candidates = []
-    layer2_candidates = []
-    final_layer2_candidates = []
-    watchlist_candidates = []
-    
-    preloaded_whale_data_dict = {}
-
-    try:
-        print("🎯 Starting Explorer Analysis (Layer 1)...")
-
-        if not await state_manager.wait_for_initialization():
-            print("❌ Services not fully initialized (Explorer)"); return []
-            
-        # (V8.1) التأكد من تهيئة VADER و NewsFetcher
-        if not VADER_ANALYZER or not news_fetcher_global:
-            print("❌ VADER or NewsFetcher not initialized! News analysis will be skipped.")
-
-        print("\n🔍 Layer 1.1: Rapid Screening (data_manager V7.3)...")
-        layer1_candidates = await data_manager_global.layer1_rapid_screening()
-        if not layer1_candidates: print("❌ No candidates found in Layer 1.1"); return []
-        print(f"✅ Selected {len(layer1_candidates)} symbols for Layer 1.2")
-        
-        print(f"\n📊 Layer 1.2: Fetching OHLCV data for {len(layer1_candidates)} symbols (Streaming)...")
-        DATA_QUEUE_MAX_SIZE = 2
-        ohlcv_data_queue = asyncio.Queue(maxsize=DATA_QUEUE_MAX_SIZE)
-        ml_results_list = []
-        market_context = await data_manager_global.get_market_context_async()
-
-        ml_processor = MLProcessor(market_context, data_manager_global, learning_hub_global)
-
-        batch_size = 15
-        total_batches = (len(layer1_candidates) + batch_size - 1) // batch_size
-
-        async def ml_consumer_task(queue: asyncio.Queue, results_list: list, whale_data_store: dict):
-            # ... (لا تغيير في هذه الدالة الداخلية) ...
-            batch_num = 0
-            while True:
-                try:
-                    batch_data = await queue.get()
-                    if batch_data is None: 
-                        queue.task_done()
-                        break
-                    batch_num += 1
-                    batch_results_dict = await process_batch_parallel(
-                        batch_data, ml_processor, batch_num, total_batches, whale_data_store
-                    )
-                    results_list.append(batch_results_dict)
-                    queue.task_done()
-                except Exception as e: 
-                    print(f"❌ [ML Consumer] Fatal Error: {e}"); 
-                    traceback.print_exc(); 
-                    queue.task_done()
-
-        consumer_task = asyncio.create_task(ml_consumer_task(ohlcv_data_queue, ml_results_list, preloaded_whale_data_dict))
+        if not MARKET_STATE_OK:
+            print("⏸️ [Explorer] السوق في حالة توقف. تخطي الدورة.")
+            return
+
+        # 1. الغربلة الأولية السريعة (Layer 1.1)
+        # تستخدم الآن (Ranker + XGB 1h + MC) داخلياً
+        candidates_l1 = await data_manager_global.layer1_rapid_screening()
+        if not candidates_l1:
+            print("😴 [Explorer] لم يتم العثور على مرشحين في الغربلة الأولية.")
+            # تحديث الحارس بقائمة فارغة إذا لم نجد شيئاً
+            await trade_manager_global.update_sentry_watchlist([])
+            return
+
+        # 2. التحليل العميق المتوازي (Layer 1.2 & 1.3)
+        print(f"🔬 [Explorer] تحليل عميق لـ {len(candidates_l1)} عملة...")
+        analyzed_candidates = []
         
-        producer_task = asyncio.create_task(data_manager_global.stream_ohlcv_data(layer1_candidates, ohlcv_data_queue))
+        # نستخدم Queue لتدفق البيانات بين DataManager و Processor
+        data_queue = asyncio.Queue(maxsize=5)
+        # بدء منتج البيانات في الخلفية
+        producer_task = asyncio.create_task(data_manager_global.stream_ohlcv_data(candidates_l1, data_queue))
         
-        await producer_task; 
-        await ohlcv_data_queue.join()
-        await consumer_task; 
-
-        print("🔄 Aggregating all ML (Layer 1.3) results...")
-        for batch_result in ml_results_list:
-            for success_item in batch_result['success']:
-                symbol = success_item['symbol']
-                l1_data = success_item
-                
-                if l1_data:
-                    success_item['reasons_for_candidacy'] = l1_data.get('reasons_for_candidacy', [])
-                    success_item['layer1_score'] = l1_data.get('layer1_score', 0)
-                
-                success_item['whale_data'] = {'data_available': False, 'reason': 'Not fetched yet'}
-                # (V8.2) إضافة قيم افتراضية للأخبار
-                success_item['news_text'] = ""
-                success_item['news_score_raw'] = 0.0 # درجة VADER الخام
-                success_item['statistical_news_pnl'] = 0.0 # الدرجة المتعلمة
-                
-                # (V8.3) تمرير البيانات التي يحتاجها تحليل الحيتان
-                original_l1_data = next((c for c in layer1_candidates if c['symbol'] == symbol), None)
-                if original_l1_data:
-                    success_item['dollar_volume'] = original_l1_data.get('dollar_volume', 0.0)
-                
-                layer2_candidates.append(success_item)
-
-        if not layer2_candidates: print("❌ No candidates found in Layer 1.3"); return []
-
-        layer2_candidates.sort(key=lambda x: x.get('enhanced_final_score', 0), reverse=True)
-        
-        target_count = min(10, len(layer2_candidates))
-        
-        final_layer2_candidates = layer2_candidates[:target_count]
-
-        print(f"\n🐋📰 Layer 1.4 (Optimized): Fetching Whale Data & News for top {len(final_layer2_candidates)} candidates...")
-        
-        # (دالة مساعدة لجلب الحيتان)
-        async def get_whale_data_for_candidate(candidate):
-            symbol = candidate.get('symbol', 'UNKNOWN')
-            symbol_daily_volume = candidate.get('dollar_volume', 0.0)
+        while True:
+            # انتظار دفعة من البيانات
+            batch = await data_queue.get()
+            if batch is None: # إشارة نهاية التدفق
+                data_queue.task_done()
+                break 
             
-            try:
-                data = await data_manager_global.get_whale_data_for_symbol(
-                    symbol, 
-                    daily_volume_usd=symbol_daily_volume
-                )
-                if data:
-                    candidate['whale_data'] = data
-                else:
-                    candidate['whale_data'] = {'data_available': False, 'reason': 'No data returned'}
-            except Exception as e:
-                print(f"   ❌ [Whale Fetch] {symbol} - Error: {e}")
-                candidate['whale_data'] = {'data_available': False, 'error': str(e)}
-
-        # (دالة مساعدة لجلب الأخبار وتحليل VADER)
-        async def get_news_data_for_candidate(candidate):
-            symbol = candidate.get('symbol', 'UNKNOWN')
-            if not news_fetcher_global or not VADER_ANALYZER:
-                candidate['news_text'] = "News analysis disabled."
-                candidate['news_score_raw'] = 0.0
-                return
-
-            try:
-                news_text = await news_fetcher_global.get_news_for_symbol(symbol)
-                candidate['news_text'] = news_text
-                
-                if "No specific news found" in news_text or not news_text:
-                    candidate['news_score_raw'] = 0.0
-                else:
-                    vader_score = VADER_ANALYZER.polarity_scores(news_text)
-                    candidate['news_score_raw'] = vader_score.get('compound', 0.0)
-                
-            except Exception as e:
-                print(f"   ❌ [News Fetch] {symbol} - Error: {e}")
-                candidate['news_text'] = f"Error fetching news: {e}"
-                candidate['news_score_raw'] = 0.0
-
-        # (تنفيذ المهام بالتوازي)
-        tasks = []
-        for candidate in final_layer2_candidates:
-            tasks.append(asyncio.create_task(get_whale_data_for_candidate(candidate)))
-            tasks.append(asyncio.create_task(get_news_data_for_candidate(candidate)))
-        
-        await asyncio.gather(*tasks)
-        print("   ✅ Whale data and News data fetched for top candidates.")
-
-        print("   🔄 Re-calculating enhanced scores with new Whale & Statistical News data...")
-        for candidate in final_layer2_candidates:
-            try:
-                raw_vader_score = candidate.get('news_score_raw', 0.0)
-                
-                if learning_hub_global:
-                    statistical_pnl = await learning_hub_global.get_statistical_news_score(raw_vader_score)
-                    candidate['statistical_news_pnl'] = statistical_pnl
-                else:
-                    candidate['statistical_news_pnl'] = 0.0
-                
-                new_score = ml_processor._calculate_enhanced_final_score(candidate)
-                candidate['enhanced_final_score'] = new_score
-            except Exception as e:
-                print(f"   ❌ [Score Recalc] {candidate.get('symbol')} - Error: {e}")
-        
-        final_layer2_candidates.sort(key=lambda x: x.get('enhanced_final_score', 0), reverse=True)
-        print("   ✅ Top scores updated (with Stat. News + Whale) and re-sorted.")
-
-
-        print(f"\n🔬 Layer 1.5: Running Advanced MC (GARCH+LGBM) on top {len(final_layer2_candidates)} candidates...")
-        advanced_mc_analyzer = ml_processor.monte_carlo_analyzer
-        updated_candidates_for_llm = []
-        for candidate in final_layer2_candidates:
-            symbol = candidate.get('symbol', 'UNKNOWN')
-            try:
-                advanced_mc_results = await advanced_mc_analyzer.generate_1h_distribution_advanced(
-                    candidate.get('ohlcv')
-                )
-                if advanced_mc_results and advanced_mc_results.get('simulation_model') == 'Phase2_GARCH_LGBM':
-                    
-                    # 🔴 --- START OF CHANGE (V8.7 - MC Expected Return Fix) --- 🔴
-                    # (إصلاح: حساب العائد المتوقع يدوياً إذا كان مفقوداً)
-                    if 'expected_return_pct' not in advanced_mc_results:
-                        try:
-                            mean_price = advanced_mc_results.get('distribution_summary', {}).get('mean_price', 0)
-                            current_price = advanced_mc_results.get('current_price', 0)
-                            if mean_price > 0 and current_price > 0:
-                                expected_return_pct = (mean_price - current_price) / current_price
-                                advanced_mc_results['expected_return_pct'] = expected_return_pct
-                                print(f"      [MC Patch] {symbol}: Calculated Expected Return: {expected_return_pct:+.2%}")
-                            else:
-                                advanced_mc_results['expected_return_pct'] = 0.0
-                        except Exception:
-                            advanced_mc_results['expected_return_pct'] = 0.0
-                    # 🔴 --- END OF CHANGE --- 🔴
-
-                    candidate['monte_carlo_distribution'] = advanced_mc_results
-                    candidate['monte_carlo_probability'] = advanced_mc_results.get('probability_of_gain', 0)
-                    candidate['advanced_mc_run'] = True
-                else:
-                    candidate['advanced_mc_run'] = False
-                updated_candidates_for_llm.append(candidate)
-            except Exception as e:
-                print(f"   ❌ [Advanced MC] {symbol} - Error: {e}. Using Phase 1 results.")
-                candidate['advanced_mc_run'] = False
-                updated_candidates_for_llm.append(candidate)
-        
-        print("   🔄 Sanitizing final candidates for JSON serialization...")
-        sanitized_candidates = []
-        for cand in updated_candidates_for_llm:
-            sanitized_candidates.append(_sanitize_results_for_json(cand))
-        
-        final_layer2_candidates = sanitized_candidates
-        
-        await r2_service_global.save_candidates_async(final_layer2_candidates)
-
-        print("\n🧠 Layer 1.6: LLM Strategic Analysis (Explorer Brain)...")
-        top_5_for_llm = final_layer2_candidates[:5]
-        print(f"   (Sending Top {len(top_5_for_llm)} candidates to LLM)")
-        
-        for candidate in top_5_for_llm:
-            try:
-                symbol = candidate['symbol']
-                ohlcv_data = candidate.get('ohlcv'); 
-                if not ohlcv_data: continue
-                
-                candidate['raw_ohlcv'] = ohlcv_data
-                total_candles = sum(len(data) for data in ohlcv_data.values()) if ohlcv_data else 0
-                if total_candles < 30: continue
-
-                candidate['sentiment_data'] = await data_manager_global.get_market_context_async()
-
-                llm_analysis = await llm_service_global.get_trading_decision(candidate) 
-
-                if llm_analysis and llm_analysis.get('action') in ['WATCH']:
-                    strategy_to_watch = llm_analysis.get('strategy_to_watch', 'GENERIC')
-                    confidence = llm_analysis.get('confidence_level', 0)
-
-                    watchlist_entry = {
-                        'symbol': symbol,
-                        'strategy_hint': strategy_to_watch,
-                        'explorer_score': candidate.get('enhanced_final_score', 0),
-                        'llm_confidence': confidence,
-                        'analysis_timestamp': datetime.now().isoformat(),
-                        'llm_decision_context': {
-                            'decision': llm_analysis,
-                            'full_candidate_data': candidate 
-                        }
-                    }
-                    watchlist_candidates.append(watchlist_entry)
-                    print(f"   ✅ {symbol}: Added to Sentry Watchlist (Strategy: {strategy_to_watch} | Conf: {confidence:.2f})")
-                else:
-                    action = llm_analysis.get('action', 'NO_DECISION') if llm_analysis else 'NO_RESPONSE';
-                    print(f"   ⚠️ {symbol}: Not recommended by LLM for watching ({action})")
-            except Exception as e: print(f"❌ Error in LLM analysis for {candidate.get('symbol')}: {e}"); traceback.print_exc(); continue
-
-        if watchlist_candidates:
-            watchlist_candidates.sort(key=lambda x: (x['llm_confidence'] + x['explorer_score']) / 2, reverse=True)
-
-        if not watchlist_candidates:
-            print("❌ Explorer analysis complete: No suitable candidates for Sentry Watchlist.")
-            return []
-
-        top_watchlist = watchlist_candidates
-        
-        print("📊 إنشاء سجل تدقيق لمحرك الأنماط V8...")
-        audit_log = {
-            "log_id": f"audit_{int(datetime.now().timestamp())}",
-            "timestamp": datetime.now().isoformat(),
-            "model_key": "lgbm_pattern_model_combined.pkl", 
-            "scaler_key": "scaler_combined.pkl",
-            "model_accuracy": 0.5870, 
-            "predictions": []
-        }
-
-        for candidate in final_layer2_candidates: 
-            pattern_analysis = candidate.get('pattern_analysis', {})
-            audit_entry = {
-                "symbol": candidate.get('symbol', 'N/A'),
-                "timeframe": pattern_analysis.get('timeframe', 'N/A'),
-                "pattern_detected": pattern_analysis.get('pattern_detected', 'N/A'),
-                "confidence": pattern_analysis.get('pattern_confidence', 0),
-                "predicted_direction": pattern_analysis.get('predicted_direction', 'neutral'),
-                "error": "None" 
-            }
-            audit_log["predictions"].append(audit_entry)
-
-        if r2_service_global:
-            await r2_service_global.save_analysis_audit_log_async(audit_log)
-        
-        print(f"✅ Explorer analysis complete. Sending {len(top_watchlist)} candidates to Sentry.")
-        return top_watchlist
-
-    except Exception as error:
-        print(f"❌ Fatal error in Explorer (Layer 1) system: {error}"); traceback.print_exc()
-        return []
-
-
-async def re_analyze_open_trade_async(trade_data):
-    """(V8.7) إضافة إصلاح ndarray + إصلاح العائد المتوقع"""
-    symbol = trade_data.get('symbol')
-    try:
-        async with state_manager.trade_analysis_lock:
-            print(f"🔄 [Re-Analyze] Starting strategic analysis for {symbol}...")
-            market_context = await data_manager_global.get_market_context_async()
-            ohlcv_data_list = []
-            temp_queue = asyncio.Queue()
+            # معالجة الدفعة بالتوازي باستخدام Processor المحدث
+            tasks = [ml_processor_global.process_and_score_symbol_enhanced(c) for c in batch]
+            results = await asyncio.gather(*tasks, return_exceptions=True)
             
-            await data_manager_global.stream_ohlcv_data(
-                [{'symbol': symbol, 'layer1_score': 0, 'reasons_for_candidacy': ['re-analysis']}], 
-                temp_queue
-            )
+            for res in results:
+                # قبول العملات التي تجاوزت درجة 60% فقط
+                if res and isinstance(res, dict) and res.get('enhanced_final_score', 0) >= 0.60:
+                    analyzed_candidates.append(res)
             
-            while True:
-                try:
-                    batch = await asyncio.wait_for(temp_queue.get(), timeout=1.0)
-                    if batch is None: temp_queue.task_done(); break
-                    ohlcv_data_list.extend(batch)
-                    temp_queue.task_done()
-                except asyncio.TimeoutError:
-                    if temp_queue.empty(): break
-                except Exception: break
-
-            if not ohlcv_data_list: print(f"⚠️ Failed to get re-analysis data for {symbol}"); return None
-            ohlcv_data = ohlcv_data_list[0]
-
-            print(f"   🔄 [Re-Analyze] Fetching current daily volume for {symbol}...")
-            symbol_daily_volume = await data_manager_global.get_symbol_daily_volume(symbol)
+            data_queue.task_done()
+        
+        # التأكد من انتهاء مهمة المنتج
+        await producer_task
+
+        # 3. الترشيح النهائي والاختيار (Layer 1.4 LLM)
+        if analyzed_candidates:
+            # ترتيب حسب الدرجة النهائية الجديدة
+            analyzed_candidates.sort(key=lambda x: x['enhanced_final_score'], reverse=True)
+            top_5 = analyzed_candidates[:5]
             
-            re_analysis_whale_data = await data_manager_global.get_whale_data_for_symbol(
-                symbol,
-                daily_volume_usd=symbol_daily_volume
-            )
-
-            ml_processor = MLProcessor(market_context, data_manager_global, learning_hub_global)
-
-            print(f"🔄 [Re-Analyze] Using Advanced MC (Phase 2+3) for {symbol}...")
-            advanced_mc_results = await ml_processor.monte_carlo_analyzer.generate_1h_distribution_advanced(
-                ohlcv_data.get('ohlcv')
-            )
-
-            processed_data = await ml_processor.process_and_score_symbol_enhanced(ohlcv_data, {symbol: re_analysis_whale_data} if re_analysis_whale_data else {})
-            if not processed_data: return None
-
-            if advanced_mc_results:
-                
-                # 🔴 --- START OF CHANGE (V8.7 - MC Expected Return Fix) --- 🔴
-                # (إصلاح: حساب العائد المتوقع يدوياً إذا كان مفقوداً)
-                if 'expected_return_pct' not in advanced_mc_results:
-                    try:
-                        mean_price = advanced_mc_results.get('distribution_summary', {}).get('mean_price', 0)
-                        current_price = advanced_mc_results.get('current_price', 0)
-                        if mean_price > 0 and current_price > 0:
-                            expected_return_pct = (mean_price - current_price) / current_price
-                            advanced_mc_results['expected_return_pct'] = expected_return_pct
-                            print(f"      [MC Patch] {symbol}: Calculated Expected Return: {expected_return_pct:+.2%}")
-                        else:
-                            advanced_mc_results['expected_return_pct'] = 0.0
-                    except Exception:
-                        advanced_mc_results['expected_return_pct'] = 0.0
-                # 🔴 --- END OF CHANGE --- 🔴
-
-                processed_data['monte_carlo_distribution'] = advanced_mc_results
-                processed_data['monte_carlo_probability'] = advanced_mc_results.get('probability_of_gain', 0)
-
-            processed_data['raw_ohlcv'] = ohlcv_data.get('raw_ohlcv') or ohlcv_data.get('ohlcv')
-            processed_data['ohlcv'] = processed_data['raw_ohlcv']
-            processed_data['sentiment_data'] = market_context
+            print(f"🧠 [Explorer] إرسال أفضل {len(top_5)} عملة إلى LLM للقرار النهائي...")
+            watchlist = []
+            for cand in top_5:
+                # استشارة النموذج اللغوي لاتخاذ قرار استراتيجي
+                decision = await llm_service_global.get_trading_decision(cand)
+                if decision and decision.get('action') == 'WATCH':
+                     watchlist.append({
+                         'symbol': cand['symbol'],
+                         'strategy_hint': decision.get('strategy_to_watch', 'GENERIC'),
+                         # نحتفظ بالسياق الكامل للقرار لاستخدامه لاحقاً في الحارس
+                         'llm_decision_context': {'decision': decision}
+                     })
             
-            if news_fetcher_global and VADER_ANALYZER:
-                try:
-                    news_text = await news_fetcher_global.get_news_for_symbol(symbol)
-                    processed_data['news_text'] = news_text
-                    
-                    vader_score = VADER_ANALYZER.polarity_scores(news_text)
-                    processed_data['news_score'] = vader_score.get('compound', 0.0)
-                except Exception as e:
-                    print(f"   ❌ [Re-Analyze News] {symbol} - Error: {e}")
-                    processed_data['news_text'] = "News analysis failed."
-                    processed_data['news_score'] = 0.0
+            # تحديث قائمة مراقبة الحارس بالنتائج النهائية
+            if watchlist:
+                print(f"✅ [Explorer] تم اختيار {len(watchlist)} عملة للمراقبة.")
+                await trade_manager_global.update_sentry_watchlist(watchlist)
             else:
-                processed_data['news_text'] = "News analysis disabled."
-                processed_data['news_score'] = 0.0
-            
-            # 🔴 --- START OF CHANGE (V8.6 - ndarray Fix) --- 🔴
-            # (يجب تنقية البيانات قبل إرسالها إلى LLMService لتجنب خطأ ndarray)
-            print(f"   🔄 [Re-Analyze] Sanitizing data for {symbol} before LLM log...")
-            sanitized_processed_data = _sanitize_results_for_json(processed_data)
-            
-            re_analysis_decision = await llm_service_global.re_analyze_trade_async(trade_data, sanitized_processed_data)
-            # 🔴 --- END OF CHANGE --- 🔴
-
-            if re_analysis_decision:
-                await r2_service_global.save_system_logs_async({ "trade_reanalyzed": True, "symbol": symbol, "action": re_analysis_decision.get('action'), 'strategy': re_analysis_decision.get('strategy', 'GENERIC') })
-                print(f"✅ [Re-Analyze] Strategic analysis complete for {symbol}. Decision: {re_analysis_decision.get('action')}")
-                return {"symbol": symbol, "decision": re_analysis_decision, "current_price": processed_data.get('current_price')}
-            else: return None
-    except Exception as error: await r2_service_global.save_system_logs_async({ "reanalysis_error": True, "symbol": symbol, "error": str(error) }); print(f"❌ Error in re_analyze_open_trade_async for {symbol}: {error}"); traceback.print_exc(); return None
+                print("⚠️ [Explorer] رفض LLM جميع المرشحين.")
+                await trade_manager_global.update_sentry_watchlist([])
+        else:
+             print("📉 [Explorer] لم تنجح أي عملة في التحليل العميق (Score < 60%).")
+             await trade_manager_global.update_sentry_watchlist([])
 
+    except Exception as e:
+        print(f"❌ [Explorer Error] {e}")
+        traceback.print_exc()
 
 async def run_bot_cycle_async():
-    # ... (لا تغيير في هذه الدالة) ...
-    """
-    (محدث V5.9) - دورة البوت الرئيسية (المستكشف)
-    """
-    try:
-        if not await state_manager.wait_for_initialization():
-            print("❌ Services not fully initialized - skipping cycle"); return
-
-        await asyncio.sleep(1.0) 
-        
-        print("🔄 Starting Explorer cycle (Layer 1)...");
-        await r2_service_global.save_system_logs_async({"explorer_cycle_started": True})
-
-        if not r2_service_global.acquire_lock():
-            print("❌ Failed to acquire lock - skipping cycle (another cycle likely running)"); return
-
-        open_trades = []
-        try:
-            open_trades = await trade_manager_global.get_open_trades();
-            print(f"📋 Open trades: {len(open_trades)}")
-
-            if open_trades:
-                now = datetime.now()
-                trades_to_reanalyze = [t for t in open_trades if now >= datetime.fromisoformat(t.get('expected_target_time', now.isoformat()))]
-                
-                if trades_to_reanalyze:
-                    print(f"🔄 (Explorer) Re-analyzing {len(trades_to_reanalyze)} trades strategically...")
-                    reanalysis_results = await asyncio.gather(*[re_analyze_open_trade_async(trade) for trade in trades_to_reanalyze], return_exceptions=True)
-                    
-                    for i, result in enumerate(reanalysis_results):
-                         trade = trades_to_reanalyze[i]
-                         if isinstance(result, Exception): 
-                             print(f"   ❌ Re-analysis failed for {trade.get('symbol')}: {result}")
-                             
-                         elif result and result['decision'].get('action') == "UPDATE_TRADE":
-                             print(f"   ✅ (Explorer) Updating strategy for {trade.get('symbol')}.");
-                             await trade_manager_global.update_trade_strategy(trade, result['decision'])
-                             
-                         elif result and result['decision'].get('action') == "HOLD":
-                             print(f"   ℹ️ (Explorer) Holding {trade.get('symbol')}. Resetting 15-min timer.")
-                             await trade_manager_global.update_trade_strategy(trade, result['decision'])
-                         
-                         elif result and result['decision'].get('action') == "CLOSE_TRADE":
-                             print(f"   🛑 (Explorer) LLM Re-analysis ordered CLOSE_TRADE for {trade.get('symbol')}. Executing...")
-                             await trade_manager_global.immediate_close_trade(
-                                 trade.get('symbol'),
-                                 result['current_price'], 
-                                 f"Strategic Exit: LLM Re-analysis ({result['decision'].get('reasoning', 'N/A')[:50]}...)"
-                             )
-                             
-                         elif result: 
-                             print(f"   ℹ️ (Explorer) Re-analysis returned unhandled action '{result['decision'].get('action')}' for {trade.get('symbol')}.")
-                             
-                         else: 
-                             print(f"   ⚠️ Re-analysis for {trade.get('symbol')} yielded no decision.")
-
-            current_open_trades_count = len(await trade_manager_global.get_open_trades())
-            should_look_for_new_trade = current_open_trades_count == 0
-
-            if should_look_for_new_trade:
-                portfolio_state = await r2_service_global.get_portfolio_state_async();
-                current_capital = portfolio_state.get("current_capital_usd", 0)
-                if current_capital > 1:
-                    print("🎯 (Explorer) Looking for new trading opportunities...")
-
-                    sentry_watchlist = await run_3_layer_analysis_explorer()
-
-                    if sentry_watchlist:
-                        print(f"✅ (Explorer) Found {len(sentry_watchlist)} candidates. Sending to Sentry (Layer 2)...")
-                        await trade_manager_global.update_sentry_watchlist(sentry_watchlist)
-                    else:
-                        print("❌ (Explorer) No suitable trading opportunities found for Sentry.")
-                        await trade_manager_global.update_sentry_watchlist([])
-                else:
-                    print("❌ Insufficient capital to open new trades")
-            else:
-                print("ℹ️ A trade is already open, skipping new trade search.")
-                await trade_manager_global.update_sentry_watchlist([])
+    """دورة البوت الرئيسية (تستدعى دورياً أو عند الطلب)"""
+    if not state_manager.initialization_complete: return
+    
+    # استخدام قفل R2 لمنع تداخل الدورات
+    if not r2_service_global.acquire_lock():
+        print("⚠️ [System] دورة أخرى قيد التشغيل. تخطي.")
+        return
 
-        finally:
-            if r2_service_global.lock_acquired: r2_service_global.release_lock()
-            await r2_service_global.save_system_logs_async({ "explorer_cycle_completed": True, "open_trades": len(open_trades)})
-            print("✅ Explorer cycle complete")
+    try:
+        # 1. إدارة الصفقات المفتوحة (إعادة التحليل إذا لزم الأمر)
+        open_trades = await trade_manager_global.get_open_trades()
+        if open_trades:
+            now = datetime.now()
+            # إعادة تحليل الصفقات التي تجاوزت وقت هدفها المتوقع
+            trades_to_reanalyze = [t for t in open_trades if now >= datetime.fromisoformat(t.get('expected_target_time', now.isoformat()))]
+            for trade in trades_to_reanalyze:
+                print(f"🔄 [Re-Analyze] إعادة تحليل {trade['symbol']}...")
+                # (يمكن إضافة منطق إعادة التحليل هنا باستخدام LLM و Processor)
+                # حالياً سنقوم فقط بتمديد الوقت لتجنب التكرار المستمر
+                trade['expected_target_time'] = (now + timedelta(minutes=15)).isoformat()
+                await trade_manager_global._update_trade_in_db(trade)
+
+        # 2. تشغيل دورة المستكشف للبحث عن فرص جديدة
+        # (نبحث فقط إذا كان لدينا رأس مال متاح أو عدد صفقات قليل)
+        if len(open_trades) < 3: 
+            await run_explorer_cycle()
+        else:
+            print("ℹ️ [System] الحد الأقصى للصفقات مفتوح. تخطي الاستكشاف.")
 
-    except Exception as error:
-        print(f"❌ Unhandled error in main cycle: {error}"); traceback.print_exc()
-        await r2_service_global.save_system_logs_async({ "cycle_error": True, "error": str(error) });
-        if r2_service_global and r2_service_global.lock_acquired: r2_service_global.release_lock()
+    finally:
+        if r2_service_global.lock_acquired:
+            r2_service_global.release_lock()
 
 @asynccontextmanager
 async def lifespan(application: FastAPI):
-    # (V8.4 - Whale Learning Loop)
-    """Application lifecycle management"""
-    print("🚀 Starting application initialization (Explorer/Sentry/Executor)...")
-    try:
-        success = await initialize_services()
-        if not success: print("❌ Application initialization failed - shutting down..."); yield; return
-
-        # (تشغيل المهام الخلفية)
+    """إدارة دورة حياة التطبيق"""
+    print("🏁 [System] بدء تشغيل التطبيق...")
+    success = await initialize_services()
+    if success:
+        # تشغيل المهام الخلفية
         asyncio.create_task(monitor_market_async())
         asyncio.create_task(trade_manager_global.start_sentry_and_monitoring_loops())
-        asyncio.create_task(run_periodic_distillation())
-        
-        # (جديد: تشغيل حلقة تعلم الحيتان التي أضفناها إلى hub_manager)
-        if learning_hub_global and hasattr(learning_hub_global, 'run_whale_learning_check'):
-            asyncio.create_task(learning_hub_global.run_whale_learning_check())
-            print("   -> 🐋 Whale Learning Loop (V3) is scheduled")
-        else:
-            print("   -> ⚠️ Whale Learning Loop is NOT scheduled (function not found)")
-
-        await r2_service_global.save_system_logs_async({"application_started": True})
-        print("🎯 Application ready - Explorer-Sentry-Executor Architecture is active")
-        print("   -> 📈 Sentry (Layer 2) & Executor (Layer 3) are active")
-        print("   -> 🧠 Periodic Distillation (Curator) is scheduled")
+        # تشغيل دورة استكشاف أولية بعد وقت قصير
+        asyncio.get_event_loop().call_later(10, lambda: asyncio.create_task(run_bot_cycle_async()))
+        yield
+    else:
+        print("❌ [System] فشل بدء التشغيل. الخروج...")
         yield
-    except Exception as error:
-        print(f"❌ Application startup failed: {error}");
-        traceback.print_exc()
-        if r2_service_global:
-            await r2_service_global.save_system_logs_async({ "application_startup_failed": True, "error": str(error) })
-        raise
-    finally:
-        await cleanup_on_shutdown()
-
-
-application = FastAPI(lifespan=lifespan, title="AI Trading Bot", description="Explorer-Sentry-Executor Architecture (V5.9)", version="5.9.0")
-
-@application.get("/")
-# ... (لا تغيير في نقاط النهاية (Endpoints)) ...
-async def root(): return {"message": "Welcome to the AI Trading System", "system": "Explorer-Sentry-Executor", "status": "running" if state_manager.initialization_complete else "initializing", "timestamp": datetime.now().isoformat()}
-
-@application.get("/run-cycle")
-async def run_cycle_api():
-    if not state_manager.initialization_complete: raise HTTPException(status_code=503, detail="Services not fully initialized")
-    asyncio.create_task(run_bot_cycle_async())
-    return {"message": "Explorer (Layer 1) cycle initiated", "system": "Explorer-Sentry-Executor"}
-
-@application.get("/health")
-async def health_check(): return {"status": "healthy" if state_manager.initialization_complete else "initializing", "initialization_complete": state_manager.initialization_complete, "services_initialized": state_manager.services_initialized, "initialization_error": state_manager.initialization_error, "timestamp": datetime.now().isoformat(), "system_architecture": "Explorer-Sentry-Executor (V5.9)"}
-
-@application.get("/analyze-market")
-async def analyze_market_api():
-    if not state_manager.initialization_complete: raise HTTPException(status_code=503, detail="Services not fully initialized")
-    result = await run_3_layer_analysis_explorer()
-    if result: return {"watchlist_generated": True, "count": len(result), "top_candidate": result[0]}
-    else: return {"watchlist_generated": False, "message": "No suitable candidates found for Sentry"}
-
-@application.get("/portfolio")
-async def get_portfolio_api():
-    if not state_manager.initialization_complete: raise HTTPException(status_code=503, detail="Services not fully initialized")
-    try: portfolio_state = await r2_service_global.get_portfolio_state_async(); open_trades = await trade_manager_global.get_open_trades(); return {"portfolio": portfolio_state, "open_trades": open_trades, "timestamp": datetime.now().isoformat()}
-    except Exception as e: raise HTTPException(status_code=500, detail=f"Error getting portfolio: {str(e)}")
-
-@application.get("/system-status")
-async def get_system_status():
-    monitoring_status = trade_manager_global.get_sentry_status() if trade_manager_global else {};
     
-    return {"initialization_complete": state_manager.initialization_complete, "services_initialized": state_manager.services_initialized, "initialization_error": state_manager.initialization_error, "market_state_ok": MARKET_STATE_OK, "sentry_status": monitoring_status, "timestamp": datetime.now().isoformat()}
-
-async def cleanup_on_shutdown():
-    # ... (لا تغيير في هذه الدالة) ...
-    global r2_service_global, data_manager_global, trade_manager_global, learning_hub_global, symbol_whale_monitor_global
-
-    print("🛑 Shutdown signal received. Cleaning up...")
-
-    if trade_manager_global:
-        await trade_manager_global.stop_sentry_loops()
-        print("✅ Sentry/Executor loops stopped")
-
-    if learning_hub_global and learning_hub_global.initialized:
-        try:
-            await learning_hub_global.shutdown()
-            print("✅ Learning hub data saved")
-        except Exception as e: print(f"❌ Failed to save learning hub data: {e}")
-
-    if symbol_whale_monitor_global:
-        try:
-            await symbol_whale_monitor_global.cleanup()
-            print("✅ Whale monitor cleanup complete.")
-        except Exception as e:
-            print(f"❌ Failed to cleanup whale monitor: {e}")
-
-    if data_manager_global: await data_manager_global.close(); print("✅ Data manager closed")
-    if r2_service_global:
-        try: await r2_service_global.save_system_logs_async({"application_shutdown": True}); print("✅ Shutdown log saved")
-        except Exception as e: print(f"❌ Failed to save shutdown log: {e}")
-        if r2_service_global.lock_acquired: r2_service_global.release_lock(); print("✅ R2 lock released")
-
-def signal_handler(signum, frame): print(f"🛑 Received signal {signum}. Initiating shutdown..."); asyncio.create_task(cleanup_on_shutdown()); sys.exit(0)
-signal.signal(signal.SIGINT, signal_handler); signal.signal(signal.SIGTERM, signal_handler)
+    # تنظيف عند الإغلاق
+    print("🛑 [System] إيقاف التشغيل...")
+    if trade_manager_global: await trade_manager_global.stop_sentry_loops()
+    if data_manager_global: await data_manager_global.close()
+    if learning_hub_global: await learning_hub_global.shutdown()
+
+app = FastAPI(lifespan=lifespan, title="AI Trading Bot V11.0")
+
+@app.get("/")
+async def root():
+    return {
+        "status": "running" if state_manager.initialization_complete else "initializing",
+        "system": "V11.0 (Real Data + XGBoost Integrated)",
+        "timestamp": datetime.now().isoformat()
+    }
+
+@app.get("/force-cycle")
+async def force_cycle():
+    """نقطة نهاية لتشغيل دورة استكشاف يدوياً"""
+    if not state_manager.initialization_complete:
+        raise HTTPException(status_code=503, detail="System not ready")
+    asyncio.create_task(run_bot_cycle_async())
+    return {"message": "Explorer cycle initiated manually"}
+
+@app.get("/status")
+async def system_status():
+    """عرض حالة النظام والحارس"""
+    sentry_status = trade_manager_global.get_sentry_status() if trade_manager_global else {}
+    return {
+        "initialization": state_manager.initialization_complete,
+        "market_ok": MARKET_STATE_OK,
+        "sentry": sentry_status,
+        "timestamp": datetime.now().isoformat()
+    }
 
 if __name__ == "__main__":
-    print("🚀 Starting AI Trading Bot (Explorer-Sentry-Executor V5.9)...")
-    uvicorn.run( application, host="0.0.0.0", port=7860, log_level="info", access_log=True )
+    uvicorn.run(app, host="0.0.0.0", port=8000)