app.py

# app.py (Fully updated to V8.2 - Statistical VADER Scoring)
import os
import traceback
import signal
import sys
import uvicorn
import asyncio
import json
import time
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException
from datetime import datetime
from typing import List, Dict, Any 

try:
    from r2 import R2Service
    from LLM import LLMService
    from 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
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.")
    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
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

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)
        }

    async def wait_for_initialization(self, timeout=60):
        start_time = time.time()
        while not self.initialization_complete and (time.time() - start_time) < timeout:
            if self.initialization_error: raise Exception(f"فشل التهيئة: {self.initialization_error}")
            await asyncio.sleep(2)
        if not self.initialization_complete: raise Exception(f"انتهت مهلة التهيئة ({timeout} ثانية)")
        return self.initialization_complete

    def set_service_initialized(self, service_name):
        self.services_initialized[service_name] = True
        if all(self.services_initialized.values()):
            self.initialization_complete = True
            print("🎯 جميع الخدمات مهيأة بالكامل")

    def set_initialization_error(self, error):
        self.initialization_error = error
        print(f"❌ خطأ في التهيئة: {error}")

state_manager = StateManager()

async def initialize_services():
    """تهيئة جميع الخدمات بشكل منفصل"""
    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 # (V8.1)
    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")
        
        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)")

        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")
        
        # (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")
        # (تمرير NewsFetcher إلى LLMService - مهم لإعادة التحليل)
        llm_service_global.news_fetcher = news_fetcher_global

        print("   🔄 تهيئة محور التعلم (Hub)...");
        learning_hub_global = LearningHubManager(
            r2_service=r2_service_global,
            llm_service=llm_service_global,
            data_manager=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")

        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 
        )
        
        await trade_manager_global.initialize_sentry_exchanges()
        state_manager.set_service_initialized('trade_manager');
        print("   ✅ مدير الصفقات (Sentry/Executor) مهيأ")

        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

async def monitor_market_async():
    # ... (لا تغيير في هذه الدالة) ...
    global data_manager_global, sentiment_analyzer_global, MARKET_STATE_OK
    try:
        if not await state_manager.wait_for_initialization(): print("❌ فشل تهيئة الخدمات - إيقاف مراقبة السوق"); return
        while True:
            try:
                async with state_manager.market_analysis_lock:
                    market_context = await sentiment_analyzer_global.get_market_sentiment()
                    
                    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})
                    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():
                await asyncio.sleep(60)
                continue

            print("🔄 [Scheduler] Running periodic distillation check...")
            await learning_hub_global.run_distillation_check()
            await asyncio.sleep(6 * 60 * 60)

        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) ...
    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 V8.2) - استخدام درجة الأخبار الإحصائية
    """
    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))
        
        producer_task = asyncio.create_task(data_manager_global.stream_ohlcv_data(layer1_candidates, ohlcv_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
                
                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)
        
        # (V8.1) نأخذ أفضل 10 هنا (بدلاً من 5) لإضافة الحيتان والأخبار
        target_count = min(10, len(layer2_candidates))
        
        final_layer2_candidates = layer2_candidates[:target_count]

        # 🔴 --- START OF CHANGE (V8.2) --- 🔴
        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')
            try:
                data = await data_manager_global.get_whale_data_for_symbol(symbol)
                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:
                # 1. جلب نص الأخبار (باستخدام NewsFetcher المطور)
                news_text = await news_fetcher_global.get_news_for_symbol(symbol)
                candidate['news_text'] = news_text
                
                # 2. حساب درجة VADER الخام
                if "No specific news found" in news_text or not news_text:
                    candidate['news_score_raw'] = 0.0 # محايد
                else:
                    vader_score = VADER_ANALYZER.polarity_scores(news_text)
                    candidate['news_score_raw'] = vader_score.get('compound', 0.0) # النتيجة (-1 إلى +1)
                
            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:
                # (V8.2) جلب درجة VADER الخام
                raw_vader_score = candidate.get('news_score_raw', 0.0)
                
                # (V8.2) جلب الربح/الخسارة الإحصائي المرتبط بهذه الدرجة من محور التعلم
                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 # (الوضع الآمن)
                
                # (هذه الدالة في processor.py تم تعديلها لتقبل statistical_news_pnl)
                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.")
        # 🔴 --- END OF CHANGE (V8.2) --- 🔴


        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':
                    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)...")
        # (V8.1) نختار أفضل 5 الآن *بعد* اكتمال كل التحليلات (حيتان + أخبار + MC متقدم)
        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()

                # (V8.2) النموذج الضخم سيحصل على 'news_text' (النص الخام)
                # ولن يرى 'statistical_news_pnl' (الدرجة المتعلمة)
                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
        
        # --- (V8-MODIFICATION) إنشاء وحفظ سجل التدقيق ---
        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", # (من النموذج الناجح)
            "scaler_key": "scaler_combined.pkl", # (من النموذج الناجH)
            "model_accuracy": 0.5870, # (من ملف evaluation_results.txt)
            "predictions": []
        }

        # (اجمع كل التوقعات التي حدثت في هذه الدورة)
        for candidate in final_layer2_candidates: # (استخدام المرشحين النهائيين)
            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" # (نفترض عدم وجود خطأ، وإلا لما كان مرشحاً)
            }
            audit_log["predictions"].append(audit_entry)

        # (حفظ السجل في R2)
        if r2_service_global:
            await r2_service_global.save_analysis_audit_log_async(audit_log)
        # --- (نهاية الإضافة) ---
        
        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):
    """(V8.2) إضافة جلب الأخبار ودرجة VADER لـ Reflector"""
    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()
            
            await data_manager_global.stream_ohlcv_data(
                [{'symbol': symbol, 'layer1_score': 0, 'reasons_for_candidacy': ['re-analysis']}], 
                temp_queue
            )
            
            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]

            re_analysis_whale_data = await data_manager_global.get_whale_data_for_symbol(symbol)

            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:
                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
            
            # 🔴 --- START OF CHANGE (V8.2) --- 🔴
            # (جلب الأخبار ودرجة VADER الخام لتمريرها إلى Reflector لاحقاً)
            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
            else:
                processed_data['news_text'] = "News analysis disabled."
                processed_data['news_score'] = 0.0
            # 🔴 --- END OF CHANGE --- 🔴

            # (LLMService سيستخدم NewsFetcher الخاص به لجلب أحدث الأخبار لتحليلها)
            re_analysis_decision = await llm_service_global.re_analyze_trade_async(trade_data, processed_data)

            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


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

        # 🔴 --- START OF CHANGE (V6.9) --- 🔴
        # (إضافة: تأخير بسيط لضمان تحرير القفل من الدورة السابقة)
        await asyncio.sleep(1.0) 
        # 🔴 --- END OF CHANGE --- 🔴
        
        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([])

        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")

    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()

@asynccontextmanager
async def lifespan(application: FastAPI):
    # ... (لا تغيير في هذه الدالة) ...
    """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

        asyncio.create_task(monitor_market_async())
        asyncio.create_task(trade_manager_global.start_sentry_and_monitoring_loops())
        asyncio.create_task(run_periodic_distillation())

        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")
        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)

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 )