Update LLM.py

LLM.py

@@ -1,615 +1,461 @@
-# LLM.py (Updated to V5.4 - Fixed Re-Analysis TP/SL Wipe Bug)
-import os, traceback, asyncio, json, time
-import re 
+# LLM.py (V18.1 - 24h Accumulation Analysis)
+import os, traceback, json, time, re
+import httpx
 from datetime import datetime
-from functools import wraps
-from backoff import on_exception, expo
-from openai import OpenAI, RateLimitError, APITimeoutError
-import numpy as np
-from sentiment_news import NewsFetcher
-from helpers import validate_required_fields, format_technical_indicators, format_strategy_scores, format_candle_data_for_pattern_analysis, format_whale_analysis_for_llm, parse_json_from_response
-from ml_engine.processor import safe_json_parse 
-
-NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY")
-PRIMARY_MODEL = "nvidia/llama-3.1-nemotron-ultra-253b-v1"
-
-# (PatternAnalysisEngine - لا تغيير)
-class PatternAnalysisEngine:
-    def __init__(self, llm_service):
-        self.llm = llm_service
-    def _format_chart_data_for_llm(self, ohlcv_data):
-        if not ohlcv_data: return "Insufficient chart data for pattern analysis"
-        try:
-            all_timeframes = []
-            for timeframe, candles in ohlcv_data.items():
-                if candles and len(candles) >= 10:
-                    raw_candle_summary = self._format_raw_candle_data(candles, timeframe)
-                    all_timeframes.append(f"=== {timeframe.upper()} TIMEFRAME ({len(candles)} CANDLES) ===\n{raw_candle_summary}")
-            return "\n\n".join(all_timeframes) if all_timeframes else "No sufficient timeframe data available"
-        except Exception as e: return f"Error formatting chart data: {str(e)}"
-    def _format_raw_candle_data(self, candles, timeframe):
-        try:
-            if len(candles) < 10: return f"Only {len(candles)} candles available - insufficient for deep pattern analysis"
-            analysis_candles = candles[-50:] if len(candles) > 50 else candles
-            summary = []; summary.append(f"Total candles: {len(candles)} (showing last {len(analysis_candles)})"); summary.append("Recent candles (newest to oldest):")
-            for i in range(min(15, len(analysis_candles))):
-                idx = len(analysis_candles) - 1 - i; candle = analysis_candles[idx]
-                try: timestamp = datetime.fromtimestamp(candle[0] / 1000).strftime('%Y-%m-%d %H:%M:%S')
-                except: timestamp = "unknown"
-                open_price, high, low, close, volume = candle[1], candle[2], candle[3], candle[4], candle[5]
-                candle_type = "🟢 BULLISH" if close > open_price else "🔴 BEARISH" if close < open_price else "⚪ NEUTRAL"
-                body_size = abs(close - open_price); body_percent = (body_size / open_price * 100) if open_price > 0 else 0
-                wick_upper = high - max(open_price, close); wick_lower = min(open_price, close) - low; total_range = high - low
-                if total_range > 0: body_ratio = (body_size / total_range) * 100; upper_wick_ratio = (wick_upper / total_range) * 100; lower_wick_ratio = (wick_lower / total_range) * 100
-                else: body_ratio = upper_wick_ratio = lower_wick_ratio = 0
-                summary.append(f"{i+1:2d}. {timestamp} | {candle_type}"); summary.append(f"     O:{open_price:.8f} H:{high:.8f} L:{low:.8f} C:{close:.8f}"); summary.append(f"     Body: {body_percent:.2f}% | Body/Range: {body_ratio:.1f}%"); summary.append(f"     Wicks: Upper {upper_wick_ratio:.1f}% / Lower {lower_wick_ratio:.1f}%"); summary.append(f"     Volume: {volume:,.0f}")
-            if len(analysis_candles) >= 20:
-                stats = self._calculate_candle_statistics(analysis_candles)
-                summary.append(f"\n📊 STATISTICAL ANALYSIS:"); summary.append(f"• Price Change: {stats['price_change']:+.2f}%"); summary.append(f"• Average Body Size: {stats['avg_body']:.4f}%"); summary.append(f"• Volatility (ATR): {stats['atr']:.6f}"); summary.append(f"• Trend: {stats['trend']}"); summary.append(f"• Support: {stats['support']:.6f}"); summary.append(f"• Resistance: {stats['resistance']:.6f}")
-            return "\n".join(summary)
-        except Exception as e: return f"Error formatting raw candle data: {str(e)}"
-    def _calculate_candle_statistics(self, candles):
-        try:
-            closes = [c[4] for c in candles]; opens = [c[1] for c in candles]; highs = [c[2] for c in candles]; lows = [c[3] for c in candles]
-            first_close = closes[0]; last_close = closes[-1]; price_change = ((last_close - first_close) / first_close) * 100
-            body_sizes = [abs(close - open) for open, close in zip(opens, closes)]; avg_body = (sum(body_sizes) / len(body_sizes)) / first_close * 100 if first_close > 0 else 0
-            true_ranges = [];
-            for i in range(1, len(candles)): high, low, prev_close = highs[i], lows[i], closes[i-1]; tr1 = high - low; tr2 = abs(high - prev_close); tr3 = abs(low - prev_close); true_ranges.append(max(tr1, tr2, tr3))
-            atr = sum(true_ranges) / len(true_ranges) if true_ranges else 0
-            if price_change > 3: trend = "STRONG UPTREND"
-            elif price_change > 1: trend = "UPTREND"
-            elif price_change < -3: trend = "STRONG DOWNTREND"
-            elif price_change < -1: trend = "DOWNTREND"
-            else: trend = "SIDEWAYS"
-            support = min(lows); resistance = max(highs)
-            return {'price_change': price_change, 'avg_body': avg_body, 'atr': atr, 'trend': trend, 'support': support, 'resistance': resistance}
-        except Exception as e: return {'price_change': 0, 'avg_body': 0, 'atr': 0, 'trend': 'UNKNOWN', 'support': 0, 'resistance': 0}
-    async def analyze_chart_patterns(self, symbol, ohlcv_data): pass 
-    def _parse_pattern_response(self, response_text): pass 
-
+from typing import List, Dict, Any, Optional
+
+try:
+    from r2 import R2Service
+    from learning_hub.hub_manager import LearningHubManager # (استيراد العقل)
+except ImportError:
+    print("❌ [LLMService] فشل استيراد R2Service أو LearningHubManager")
+    R2Service = None
+    LearningHubManager = None
+
+# (V8.1) استيراد NewsFetcher
+try:
+    from sentiment_news import NewsFetcher
+except ImportError:
+    NewsFetcher = None
+
+# إعدادات النموذج
+LLM_API_URL = os.getenv("LLM_API_URL", "https://api.groq.com/openai/v1/chat/completions")
+LLM_API_KEY = os.getenv("LLM_API_KEY")
+LLM_MODEL = os.getenv("LLM_MODEL", "llama3-70b-8192")
+
+# إعدادات العميل
+# (زيادة المهلة إلى 5 دقائق للتحليلات المعقدة)
+CLIENT_TIMEOUT = 300.0 
 
 class LLMService:
-    def __init__(self, api_key=NVIDIA_API_KEY, model_name=PRIMARY_MODEL, temperature=0.7):
-        self.api_key = api_key
-        self.model_name = model_name
-        self.temperature = temperature
-        self.client = OpenAI(base_url="https://integrate.api.nvidia.com/v1", api_key=self.api_key)
-        self.news_fetcher = NewsFetcher()
-        self.pattern_engine = PatternAnalysisEngine(self) 
-        self.semaphore = asyncio.Semaphore(5)
-        self.r2_service = None
-        self.learning_hub = None
-
-    def _rate_limit_nvidia_api(func):
-        @wraps(func)
-        @on_exception(expo, RateLimitError, max_tries=5)
-        async def wrapper(*args, **kwargs):
-            return await func(*args, **kwargs)
-        return wrapper
-
-    async def get_trading_decision(self, data_payload: dict):
+    def __init__(self):
+        if not LLM_API_KEY:
+            raise ValueError("❌ [LLMService] متغير بيئة LLM_API_KEY غير موجود!")
+        
+        self.headers = {
+            "Authorization": f"Bearer {LLM_API_KEY}",
+            "Content-Type": "application/json"
+        }
+        self.http_client = httpx.AsyncClient(timeout=CLIENT_TIMEOUT)
+        
+        # --- (الربط بالخدمات الأخرى) ---
+        self.r2_service: Optional[R2Service] = None
+        self.learning_hub: Optional[LearningHubManager] = None
+        
+        # (V8.1) إضافة NewsFetcher (للاستخدام في إعادة التحليل)
+        self.news_fetcher: Optional[NewsFetcher] = None
+        
+        print(f"✅ [LLMService] مهيأ. النموذج: {LLM_MODEL}")
+
+    async def _call_llm(self, prompt: str, temperature: float = 0.2, max_tokens: int = 1500) -> Optional[str]:
+        """
+        (محدث) إجراء استدعاء API للنموذج الضخم (باللغة الإنجليزية).
+        """
+        payload = {
+            "model": LLM_MODEL,
+            "messages": [
+                {"role": "system", "content": "You are an expert AI trading analyst. You MUST respond ONLY with the requested JSON object, with no introductory text, markdown formatting (like ```json), or explanations."},
+                {"role": "user", "content": prompt}
+            ],
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+            "top_p": 1,
+            "stream": False,
+            "response_format": {"type": "json_object"}
+        }
+        
         try:
-            symbol = data_payload.get('symbol', 'unknown')
-            target_strategy = data_payload.get('target_strategy', 'GENERIC')
-
-            ohlcv_data = data_payload.get('raw_ohlcv') or data_payload.get('ohlcv')
-            if not ohlcv_data: return None
-            total_candles = sum(len(data) for data in ohlcv_data.values() if data) if ohlcv_data else 0
-            if total_candles < 30: return None
-
-            news_text = await self.news_fetcher.get_news_for_symbol(symbol)
-            whale_data = data_payload.get('whale_data', {})
+            response = await self.http_client.post(LLM_API_URL, headers=self.headers, json=payload)
+            response.raise_for_status() # Check for HTTP 4xx/5xx errors
+            
+            data = response.json()
+            
+            if "choices" in data and len(data["choices"]) > 0:
+                content = data["choices"][0].get("message", {}).get("content")
+                if content:
+                    return content.strip()
+            
+            print(f"❌ [LLMService] استجابة API غي�� متوقعة: {data}")
+            return None
+            
+        except httpx.HTTPStatusError as http_err:
+            print(f"❌ [LLMService] خطأ HTTP: {http_err} - {http_err.response.text}")
+        except httpx.RequestError as req_err:
+            print(f"❌ [LLMService] خطأ في الطلب: {req_err}")
+        except json.JSONDecodeError:
+            print(f"❌ [LLMService] فشل في تحليل استجابة JSON: {response.text}")
+        except Exception as e:
+            print(f"❌ [LLMService] خطأ غير متوقع في _call_llm: {e}")
+            traceback.print_exc()
+            
+        return None
+
+    def _parse_llm_response_enhanced(self, 
+                                     response_text: str, 
+                                     fallback_strategy: str = "decision", 
+                                     symbol: str = "N/A") -> Optional[Dict[str, Any]]:
+        """
+        (محدث V8) محلل JSON ذكي ومتسامح مع الأخطاء.
+        """
+        if not response_text:
+            print(f"   ⚠️ [LLMParser] الاستجابة فارغة لـ {symbol}.")
+            return self._get_fallback_response(fallback_strategy, "Empty response")
+
+        # 1. محاولة تحليل JSON مباشرة (لأننا طلبنا response_format=json_object)
+        try:
+            return json.loads(response_text)
+        except json.JSONDecodeError:
+            print(f"   ⚠️ [LLMParser] فشل تحليل JSON المباشر لـ {symbol}. محاولة استخراج JSON...")
+            pass # (الانتقال إلى المحاولة 2)
 
-            statistical_feedback = "No statistical learning data yet."
-            active_context_playbook = "No active learning rules available."
+        # 2. محاولة استخراج JSON من داخل نص (Fallback 1)
+        try:
+            # (البحث عن أول { وآخر })
+            json_match = re.search(r'\{.*\}', response_text, re.DOTALL)
+            if json_match:
+                json_string = json_match.group(0)
+                return json.loads(json_string)
+            else:
+                print(f"   ⚠️ [LLMParser] لم يتم العثور على JSON مطابق لـ {symbol}.")
+                raise json.JSONDecodeError("No JSON object found in text", response_text, 0)
+        except json.JSONDecodeError as e:
+            print(f"   ❌ [LLMParser] فشل الاستخراج النهائي لـ {symbol}. نص الاستجابة: {response_text[:200]}...")
+            return self._get_fallback_response(fallback_strategy, f"Final JSON parse fail: {e}")
+        except Exception as e:
+            print(f"   ❌ [LLMParser] خطأ غير متوقع في المحلل لـ {symbol}: {e}")
+            return self._get_fallback_response(fallback_strategy, f"Unexpected parser error: {e}")
+
+    def _get_fallback_response(self, strategy: str, reason: str) -> Optional[Dict[str, Any]]:
+        """
+        (محدث V8) إرجاع استجابة آمنة عند فشل النموذج الضخم.
+        """
+        print(f"   🚨 [LLMService] تفعيل الاستجابة الاحتياطية (Fallback) لاستراتيجية '{strategy}' (السبب: {reason})")
+        
+        if strategy == "decision":
+            # (القرار الآمن: لا تتداول)
+            return {
+                "action": "NO_DECISION",
+                "strategy_to_watch": "GENERIC",
+                "confidence_level": 0,
+                "reasoning": f"LLM analysis failed: {reason}",
+                "exit_profile": "Standard"
+            }
+        elif strategy == "reanalysis":
+             # (القرار الآمن: استمر في الصفقة الحالية)
+            return {
+                "action": "HOLD",
+                "strategy": "MAINTAIN_CURRENT",
+                "reasoning": f"LLM re-analysis failed: {reason}. Maintaining current trade strategy."
+            }
+        elif strategy == "reflection":
+            # (القرار الآمن: لا تقم بإنشاء قاعدة تعلم)
+            return None # (سيمنع Reflector من إنشاء دلتا)
+            
+        elif strategy == "distillation":
+             # (القرار الآمن: لا تقم بإنشاء قواعد مقطرة)
+            return None # (سيمنع Curator من المتابعة)
             
-            if self.learning_hub and self.learning_hub.initialized:
-                statistical_feedback = await self.learning_hub.get_statistical_feedback_for_llm(target_strategy)
-                active_context_playbook = await self.learning_hub.get_active_context_for_llm(
-                    domain="strategy", query=f"{target_strategy} {symbol}"
+        return None # (Fallback عام)
+
+    async def get_trading_decision(self, candidate_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """
+        (محدث V8.1)
+        يستدعي النموذج الضخم لاتخاذ قرار "WATCH" استراتيجي (Explorer Brain).
+        """
+        symbol = candidate_data.get('symbol', 'UNKNOWN')
+        try:
+            # 1. (العقل) جلب القواعد (Deltas) من محور التعلم
+            # (سيتم جلب القواعد العامة + قواعد الاستراتيجية بناءً على المرشح)
+            learning_context_prompt = "Playbook: No learning context available."
+            if self.learning_hub:
+                # (استعلام عام لجلب أفضل القواعد الشاملة)
+                learning_context_prompt = await self.learning_hub.get_active_context_for_llm(
+                    domain="general", 
+                    query=f"{symbol} strategy decision"
                 )
-
-            prompt = self._create_comprehensive_sentry_prompt(
-                data_payload, news_text, None, whale_data, 
-                statistical_feedback, active_context_playbook
-            )
             
+            # 2. إنشاء الـ Prompt (باللغة الإنجليزية)
+            prompt = self._create_trading_prompt(candidate_data, learning_context_prompt)
+            
+            # (اختياري: حفظ الـ Prompt للتدقيق)
             if self.r2_service:
-                analysis_data = { 'symbol': symbol, 'target_strategy': target_strategy, 'statistical_feedback': statistical_feedback, 'active_context_playbook': active_context_playbook }
-                await self.r2_service.save_llm_prompts_async(
-                    symbol, 'sentry_watchlist_decision_v5', prompt, analysis_data
-                )
+                await self.r2_service.save_llm_prompts_async(symbol, "trading_decision", prompt, candidate_data)
 
-            async with self.semaphore:
-                response = await self._call_llm(prompt)
+            # 3. استدعاء النموذج الضخم (LLM)
+            response_text = await self._call_llm(prompt, temperature=0.1, max_tokens=1000)
 
-            decision_dict = self._parse_llm_response_enhanced(response, target_strategy, symbol)
+            # 4. تحليل الاستجابة (باستخدام المحلل الذكي)
+            decision_json = self._parse_llm_response_enhanced(
+                response_text, 
+                fallback_strategy="decision", 
+                symbol=symbol
+            )
             
-            if decision_dict:
-                if decision_dict.get('action') == 'WATCH' and 'strategy_to_watch' not in decision_dict:
-                     print(f"   ⚠️ LLM {symbol}: Action is WATCH but strategy_to_watch is missing. Forcing HOLD.")
-                     decision_dict['action'] = 'HOLD'
-                
-                decision_dict['model_source'] = self.model_name
-                decision_dict['whale_data_integrated'] = whale_data.get('data_available', False)
-                return decision_dict
-            else:
-                print(f"❌ LLM parsing failed for {symbol} - no fallback decisions")
-                return None
+            return decision_json
 
         except Exception as e:
-            print(f"❌ Error in get_trading_decision for {data_payload.get('symbol', 'unknown')}: {e}"); traceback.print_exc()
-            return None
-
-    def _parse_llm_response_enhanced(self, response_text: str, fallback_strategy: str, symbol: str) -> dict:
+            print(f"❌ [LLMService] فشل فادح في get_trading_decision لـ {symbol}: {e}")
+            traceback.print_exc()
+            return self._get_fallback_response("decision", str(e)) # (إرجاع قرار آمن)
+
+    async def re_analyze_trade_async(self, trade_data: Dict[str, Any], current_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        """
+        (محدث V8.1)
+        يستدعي النموذج الضخم لإعادة تحليل صفقة مفتوحة (Reflector Brain).
+        """
+        symbol = trade_data.get('symbol', 'UNKNOWN')
         try:
-            json_str = parse_json_from_response(response_text)
-            if not json_str:
-                print(f"❌ Failed to extract JSON from LLM response for {symbol}")
-                return None
-
-            decision_data = safe_json_parse(json_str)
-            if not decision_data:
-                 print(f"❌ Failed to parse JSON (safe_json_parse) for {symbol}: {response_text}")
-                 return None
-
-            if fallback_strategy == "reflection" or fallback_strategy == "distillation":
-                 return decision_data
+            # 1. (العقل) جلب القواعد (Deltas) من محور التعلم
+            learning_context_prompt = "Playbook: No learning context available."
+            if self.learning_hub:
+                # (استعلام محدد لإعادة التحليل)
+                learning_context_prompt = await self.learning_hub.get_active_context_for_llm(
+                    domain="strategy", 
+                    query=f"{symbol} re-analysis {trade_data.get('strategy', 'GENERIC')}"
+                )
 
-            required_fields = ['action', 'reasoning', 'confidence_level', 'pattern_identified_by_llm']
-            
-            if decision_data.get('action') == 'WATCH':
-                required_fields.append('strategy_to_watch')
-            elif decision_data.get('action') == 'BUY': # (احتياطي للنظام القديم)
-                 required_fields.extend(['risk_assessment', 'stop_loss', 'take_profit', 'expected_target_minutes', 'exit_profile', 'exit_parameters'])
-
-            if not validate_required_fields(decision_data, required_fields):
-                print(f"❌ Missing required fields in LLM response for {symbol}")
-                missing = [f for f in required_fields if f not in decision_data]
-                print(f"   MIA: {missing}")
-                return None
+            # 2. (V8.1) جلب أحدث الأخبار (باستخدام NewsFetcher المخصص)
+            latest_news_text = "News data unavailable for re-analysis."
+            latest_news_score = 0.0
+            if self.news_fetcher:
+                # (هذا يجلب أحدث الأخبار في الوقت الفعلي)
+                latest_news_text = await self.news_fetcher.get_news_for_symbol(symbol)
+                if self.news_fetcher.vader_analyzer and latest_news_text:
+                     vader_scores = self.news_fetcher.vader_analyzer.polarity_scores(latest_news_text)
+                     latest_news_score = vader_scores.get('compound', 0.0)
             
-            action = decision_data.get('action')
-            if action not in ['WATCH', 'HOLD']:
-                # (السماح بـ 'BUY' كإجراء احتياطي إذا فشل النموذج في فهم 'WATCH')
-                if action == 'BUY':
-                    print(f"⚠️ LLM {symbol} returned 'BUY' instead of 'WATCH'. Converting to 'WATCH'...")
-                    decision_data['action'] = 'WATCH'
-                    decision_data['strategy_to_watch'] = decision_data.get('strategy', fallback_strategy)
-                else:
-                    print(f"⚠️ LLM suggested unsupported action ({action}) for {symbol}. Forcing HOLD.")
-                    decision_data['action'] = 'HOLD'
+            # (إضافة الأخبار المحدثة إلى البيانات الحالية)
+            current_data['latest_news_text'] = latest_news_text
+            current_data['latest_news_score'] = latest_news_score
+
+            # 3. إنشاء الـ Prompt (باللغة الإنجليزية)
+            prompt = self._create_reanalysis_prompt(trade_data, current_data, learning_context_prompt)
             
-            if decision_data.get('action') == 'BUY': # (معالجة إضافية للحالة الاحتياطية)
-                decision_data['trade_type'] = 'LONG'
-            else:
-                decision_data['trade_type'] = None
+            # (اختياري: حفظ الـ Prompt للتدقيق)
+            if self.r2_service:
+                 await self.r2_service.save_llm_prompts_async(symbol, "trade_reanalysis", prompt, current_data)
 
-            # (تعديل: استخدام 'strategy_to_watch' بدلاً من 'strategy')
-            strategy_value = decision_data.get('strategy_to_watch') if decision_data.get('action') == 'WATCH' else decision_data.get('strategy')
-            if not strategy_value or strategy_value == 'unknown':
-                decision_data['strategy'] = fallback_strategy
-                if decision_data.get('action') == 'WATCH':
-                    decision_data['strategy_to_watch'] = fallback_strategy
+            # 4. استدعاء النموذج الضخم (LLM)
+            response_text = await self._call_llm(prompt, temperature=0.0, max_tokens=1000) # (درجة حرارة 0.0 للحسم)
 
-            return decision_data
-        except Exception as e:
-            print(f"❌ Error parsing LLM response for {symbol}: {e}")
-            return None
+            # 5. تحليل الاستجابة (باستخدام المحلل الذكي)
+            decision_json = self._parse_llm_response_enhanced(
+                response_text, 
+                fallback_strategy="reanalysis", 
+                symbol=symbol
+            )
+            
+            return decision_json
 
-    async def _get_pattern_analysis(self, data_payload):
-        try:
-            symbol = data_payload['symbol']
-            ohlcv_data = data_payload.get('raw_ohlcv') or data_payload.get('ohlcv')
-            if ohlcv_data: return None 
-            return None
         except Exception as e:
-            print(f"❌ Pattern analysis failed for {data_payload.get('symbol')}: {e}")
-            return None
-
-    def _create_comprehensive_sentry_prompt(
-        self, 
-        payload: dict, 
-        news_text: str, 
-        pattern_analysis: dict, 
-        whale_data: dict, 
-        statistical_feedback: str, 
-        active_context_playbook: str
-    ) -> str:
+            print(f"❌ [LLMService] فشل فادح في re_analyze_trade_async لـ {symbol}: {e}")
+            traceback.print_exc()
+            return self._get_fallback_response("reanalysis", str(e)) # (إرجاع قرار آمن)
+            
+    # --- (دوال إنشاء الـ Prompts) ---
+    # (ملاحظة: هذه الدوال يجب أن تكون دائماً باللغة الإنجليزية)
+
+    def _create_trading_prompt(self, 
+                               candidate_data: Dict[str, Any], 
+                               learning_context: str) -> str:
+        """
+        (معدل V18.1)
+        إنشاء الـ Prompt (باللغة الإنجليزية) لاتخاذ قرار التداول الأولي (Explorer).
+        """
+        
+        symbol = candidate_data.get('symbol', 'N/A')
         
-        symbol = payload.get('symbol', 'N/A')
-        current_price = payload.get('current_price', 'N/A')
+        # --- 1. استخراج بيانات ML (الطبقة 1) ---
+        l1_score = candidate_data.get('enhanced_final_score', 0)
+        l1_reasons = candidate_data.get('reasons_for_candidacy', [])
+        pattern_data = candidate_data.get('pattern_analysis', {})
+        mc_data = candidate_data.get('monte_carlo_distribution', {})
         
-        price_change_24h_raw = payload.get('price_change_24h', 0)
-        price_change_24h_display = f"{price_change_24h_raw:+.2f}%" if isinstance(price_change_24h_raw, (int, float)) else "N/A"
+        # --- 2. استخراج بيانات المشاعر والأخبار (الطبقة 1) ---
+        # (V8.2) جلب بيانات VADER الإحصائية (المتعلمة)
+        news_text = candidate_data.get('news_text', 'No news text provided.')
+        news_score_raw = candidate_data.get('news_score_raw', 0.0) # (درجة VADER الخام)
+        statistical_news_pnl = candidate_data.get('statistical_news_pnl', 0.0) # (الدرجة الإحصائية المتعلمة)
         
-        reasons = payload.get('reasons_for_candidacy', [])
-        sentiment_data = payload.get('sentiment_data', {})
-        advanced_indicators = payload.get('advanced_indicators', {})
-        strategy_scores = payload.get('strategy_scores', {})
-        recommended_strategy = payload.get('recommended_strategy', 'N/A')
-        target_strategy = payload.get('target_strategy', 'GENERIC')
-        enhanced_final_score = payload.get('enhanced_final_score', 0)
-        enhanced_score_display = f"{enhanced_final_score:.3f}" if isinstance(enhanced_final_score, (int, float)) else str(enhanced_final_score)
-
-        indicators_summary = format_technical_indicators(advanced_indicators)
-        strategies_summary = format_strategy_scores(strategy_scores, recommended_strategy)
-        whale_analysis_section = format_whale_analysis_for_llm(whale_data)
-        ohlcv_data = payload.get('raw_ohlcv') or payload.get('ohlcv', {})
-        candle_data_section = self._format_candle_data_comprehensive(ohlcv_data) 
-        market_context_section = self._format_market_context(sentiment_data)
+        # --- 3. استخراج بيانات الحيتان (الطبقة 1) ---
+        # (ملاحظة: هذا هو القسم الذي سنقوم بتحديثه)
+        whale_data = candidate_data.get('whale_data', {})
+        whale_summary = whale_data.get('llm_friendly_summary', {})
+        exchange_flows = whale_data.get('exchange_flows', {})
         
-        statistical_feedback_section = f"🧠 STATISTICAL FEEDBACK (Slow-Learner):\n{statistical_feedback}"
-        playbook_section = f"📚 LEARNING PLAYBOOK (Fast-Learner Active Rules):\n{active_context_playbook}"
+        whale_signal = whale_summary.get('recommended_action', 'HOLD')
+        whale_confidence = whale_summary.get('confidence', 0.3)
+        whale_reason = whale_summary.get('whale_activity_summary', 'No whale data.')
+        
+        # (البيانات قصيرة المدى - نفترض أنها من أفضل نافذة متعلمة، e.g., 1h)
+        net_flow_usd = exchange_flows.get('net_flow_usd', 0.0)
+
+        # 🔴 --- START OF CHANGE (V18.1) --- 🔴
+        # (البيانات طويلة المدى - من تحليل 24 ساعة الجديد)
+        accumulation_data_24h = whale_data.get('accumulation_analysis_24h', {})
+        net_flow_24h_usd = accumulation_data_24h.get('net_flow_usd', 0.0)
+        total_inflow_24h_usd = accumulation_data_24h.get('to_exchanges_usd', 0.0)
+        total_outflow_24h_usd = accumulation_data_24h.get('from_exchanges_usd', 0.0)
+        relative_net_flow_24h_percent = accumulation_data_24h.get('relative_net_flow_percent', 0.0)
+        # 🔴 --- END OF CHANGE --- 🔴
 
-        exhaustion_warning = ""
-        try:
-            rsi_1d = advanced_indicators.get('1d', {}).get('rsi', 50)
-            rsi_4h = advanced_indicators.get('4h', {}).get('rsi', 50)
-            if price_change_24h_raw > 40 and (rsi_1d > 75 or rsi_4h > 75):
-                exhaustion_warning = (
-                    "🚩 **تنبيه استراتيجي: تم رصد زخم مرتفع (احتمال إرهاق)** 🚩\n"
-                    f"الأصل مرتفع {price_change_24h_display} خلال 24 ساعة ومؤشر RSI على 1D/4H في منطقة تشبع شرائي.\n"
-                    "هذا ليس أمر 'إيقاف'، بل هو 'تحدي تحليلي'. مهمتك هي التحقيق وتحديد ما إذا كان هذا:\n"
-                    "1.  **فخ إرهاق (Exhaustion Trap):** (يجب 'HOLD')\n"
-                    "2.  **اختراق استمراري حقيقي (Sustainable Continuation):** (يمكن 'WATCH')\n"
-                    "------------------------------------------------------------------"
-                )
-        except Exception:
-            pass 
-
-
-        prompt = f"""
-COMPREHENSIVE STRATEGIC ANALYSIS FOR {symbol} (FOR SENTRY WATCHLIST)
-
-🚨 IMPORTANT: You are a STRATEGIC EXPLORER. Your job is NOT to execute a trade. Your job is to decide if this asset is interesting enough to be passed to the "SENTRY" (a high-speed tactical agent) for real-time monitoring and execution.
-
-{exhaustion_warning}
-
-🎯 STRATEGY CONTEXT:
-* Target Strategy: {target_strategy}
-* Recommended Strategy (from ML): {recommended_strategy}
-* Current Price: ${current_price}
-* 24H Price Change: {price_change_24h_display}
-* Enhanced System Score: {enhanced_score_display}
-
---- LEARNING HUB INPUT (CRITICAL) ---
-{playbook_section}
-{statistical_feedback_section}
---- END OF LEARNING INPUT ---
-
-📊 TECHNICAL INDICATORS (ALL TIMEFRAMES):
-{indicators_summary}
-
-📈 RAW CANDLE DATA SUMMARY & STATISTICS (FOR YOUR PATTERN ANALYSIS):
-{candle_data_section}
-{chr(10)}--- END OF CANDLE DATA ---{chr(10)}
-
-🎯 STRATEGY ANALYSIS (System's recommendation based on various factors):
-{strategies_summary}
-
-🐋 WHALE ACTIVITY ANALYSIS:
-{whale_analysis_section}
-
-🌍 MARKET CONTEXT:
-{market_context_section if market_context_section and "No market context" not in market_context_section else "Market context data not available for this analysis."}
-
----
-🎯 SENTRY DECISION INSTRUCTIONS (WATCH or HOLD):
-
-1.  **PERFORM CHART PATTERN ANALYSIS:** Based *ONLY* on the provided 'RAW CANDLE DATA SUMMARY & STATISTICS', identify relevant patterns.
-2.  **[CRITICAL] INVESTIGATE THE STRATEGIC ALERT (if present):**
-    * ** للتحقق من الاستمرارية (Continuation):** هل الارتفاع مدعوم بـ 'volume_ratio' عالي (موجود في المؤشرات)؟ هل هو اختراق واضح لنمط تجميعي (مثل 'Bull Flag' أو 'Consolidation Breakout')؟ هل الشموع قوية (أجسام كبيرة)؟
-    * ** للتحقق من الإرهاق (Exhaustion):** هل ترى 'Bearish Divergence' (السعر يصنع قمة جديدة بينما RSI/MACD لا يفعل)؟ هل يضعف 'volume_ratio' مع الصعود؟ هل تظهر شموع انعكاسية (Doji, Shooting Star) على 4H/1D؟
-3.  **INTEGRATE ALL DATA:** ادمج 'تحقيقك' مع باقي البيانات (Learning Hub, Whale Activity).
-4.  **DECIDE ACTION (WATCH or HOLD):**
-    * **WATCH:** فقط إذا أكد تحقيقك أنها 'Sustainable Continuation' ولديك ثقة عالية (>= 0.75).
-    * **HOLD:** إذا أظهر تحقيقك أنها 'Exhaustion Trap'، أو إذا كان الوضع غير واضح ومحفوف بالمخاطر.
-5.  **DEFINE STRATEGY:** If (and only if) action is 'WATCH', you MUST specify which strategy the Sentry should use (e.g., 'breakout_momentum', 'mean_reversion').
-6.  **SELF-CRITIQUE:** Justify your decision. Why is this strong enough for the Sentry?
-7.  **[CRITICAL]** If you recommend 'WATCH', you MUST also provide the *original strategic* stop_loss and take_profit, as the Sentry will use these as hard boundaries.
-
-OUTPUT FORMAT (JSON - SENTRY DECISION):
-{{
-  "action": "WATCH/HOLD",
-  "reasoning": "Detailed explanation integrating ALL data sources. If the Strategic Alert was present, *explicitly state your investigation findings* (e.g., 'I confirmed this is continuation because volume is increasing and a bull flag is forming on 1H...')",
-  "pattern_identified_by_llm": "Name of the primary pattern(s) identified (e.g., 'Bull Flag on 1H', 'No Clear Pattern')",
-  
-  "confidence_level": 0.85,
-  "strategy_to_watch": "breakout_momentum",
-  
-  "stop_loss": 0.000000,
-  "take_profit": 0.000000,
-  "exit_profile": "ATR_TRAILING",
-  "exit_parameters": {{ "atr_multiplier": 2.0, "atr_period": 14, "break_even_trigger_percent": 1.5 }},
-  
-  "self_critique": {{
-      "failure_modes": [
-          "What is the first reason this 'WATCH' decision could fail? (e.g., 'The identified pattern is a false breakout.')",
-          "What is the second reason? (e.g., 'The Sentry might enter too late.')"
-      ],
-      "confidence_adjustment_reason": "Brief reason if confidence was adjusted post-critique."
-  }}
-}}
+        # --- 4. استخراج بيانات السوق (الطبقة 0) ---
+        market_context = candidate_data.get('sentiment_data', {})
+        market_trend = market_context.get('market_trend', 'UNKNOWN')
+        btc_sentiment = market_context.get('btc_sentiment', 'UNKNOWN')
+        
+        # --- 5. بناء أقسام الـ Prompt (الإنجليزية) ---
+        
+        # (التعلم)
+        playbook_prompt = f"""
+--- START OF LEARNING PLAYBOOK ---
+{learning_context}
+--- END OF PLAYBOOK ---
+"""
+        # (ملخص ML)
+        ml_summary_prompt = f"""
+1.  **ML Analysis (Score: {l1_score:.3f}):**
+    * Reasons: {', '.join(l1_reasons)}
+    * Chart Pattern: {pattern_data.get('pattern_detected', 'None')} (Conf: {pattern_data.get('pattern_confidence', 0):.2f})
+    * Monte Carlo (1h): {mc_data.get('probability_of_gain', 0):.1f}% chance of profit (Expected: {mc_data.get('expected_return_pct', 0):.2f}%)
+"""
+        # (ملخص الأخبار)
+        news_prompt = f"""
+2.  **News & Sentiment Analysis:**
+    * Market Trend: {market_trend} (BTC: {btc_sentiment})
+    * VADER (Raw): {news_score_raw:.3f}
+    * Statistical PnL (Learned): {statistical_news_pnl:+.2f}%
+    * News Text: {news_text[:300]}...
+"""
+        # (ملخص الحيتان - محدث)
+        whale_activity_prompt = f"""
+3.  **Whale Activity (Real-time Flow - Optimized Window):**
+    * Signal: {whale_signal} (Confidence: {whale_confidence:.2f})
+    * Reason: {whale_reason}
+    * Net Flow (to/from Exchanges): ${net_flow_usd:,.2f}
+
+4.  **Whale Activity (24h Accumulation):**
+    * 24h Net Flow (Accumulation): ${net_flow_24h_usd:,.2f}
+    * 24h Total Deposits: ${total_inflow_24h_usd:,.2f}
+    * 24h Total Withdrawals: ${total_outflow_24h_usd:,.2f}
+    * Relative 24h Net Flow (vs Daily Volume): {relative_net_flow_24h_percent:+.2f}%
 """
-        return prompt
-
-    def _format_candle_data_comprehensive(self, ohlcv_data):
-        if not ohlcv_data: return "No raw candle data available for analysis"
-        try:
-            timeframes_available = []; total_candles = 0
-            for timeframe, candles in ohlcv_data.items():
-                if candles and len(candles) >= 5: timeframes_available.append(f"{timeframe.upper()} ({len(candles)} candles)"); total_candles += len(candles)
-            if not timeframes_available: return "Insufficient candle data across all timeframes"
-            summary = f"📊 Available Timeframes: {', '.join(timeframes_available)}\n"
-            summary += f"📈 Total Candles Available: {total_candles}\n\n"
-            raw_candle_analysis_text = self.pattern_engine._format_chart_data_for_llm(ohlcv_data)
-            summary += raw_candle_analysis_text
-            return summary
-        except Exception as e: return f"Error formatting raw candle data: {str(e)}"
         
-    def _analyze_timeframe_candles(self, candles, timeframe):
-        # (دالة مساعدة - لا تغيير)
-        return "" # (تم اختصارها)
-
-    def _format_market_context(self, sentiment_data):
-        if not sentiment_data or sentiment_data.get('data_quality', 'LOW') == 'LOW': return "Market context data not available or incomplete."
-        btc_sentiment = sentiment_data.get('btc_sentiment', 'N/A'); fear_greed = sentiment_data.get('fear_and_greed_index', 'N/A'); market_trend = sentiment_data.get('market_trend', 'N/A') 
-        lines = [f"• Bitcoin Sentiment: {btc_sentiment}", f"• Fear & Greed Index: {fear_greed} ({sentiment_data.get('sentiment_class', 'Neutral')})", f"• Overall Market Trend: {market_trend.replace('_', ' ').title() if isinstance(market_trend, str) else 'N/A'}"]
-        general_whale = sentiment_data.get('general_whale_activity', {});
-        if general_whale and general_whale.get('sentiment') != 'NEUTRAL': 
-             whale_sentiment = general_whale.get('sentiment', 'N/A'); critical_alert = general_whale.get('critical_alert', False)
-             lines.append(f"• General Whale Sentiment: {whale_sentiment.replace('_', ' ').title() if isinstance(whale_sentiment, str) else 'N/A'}");
-             if critical_alert: lines.append("  ⚠️ CRITICAL WHALE ALERT ACTIVE")
-        return "\n".join(lines)
-
-
-    async def re_analyze_trade_async(self, trade_data: dict, processed_data: dict):
-        try:
-            symbol = trade_data['symbol']; original_strategy = trade_data.get('strategy', 'GENERIC')
-            ohlcv_data = processed_data.get('raw_ohlcv') or processed_data.get('ohlcv')
-            if not ohlcv_data: return None
-            news_text = await self.news_fetcher.get_news_for_symbol(symbol)
-            pattern_analysis = await self._get_pattern_analysis(processed_data)
-            whale_data = processed_data.get('whale_data', {})
-
-            statistical_feedback = "No statistical learning data yet."
-            active_context_playbook = "No active learning rules available."
-            if self.learning_hub and self.learning_hub.initialized:
-                statistical_feedback = await self.learning_hub.get_statistical_feedback_for_llm(original_strategy)
-                active_context_playbook = await self.learning_hub.get_active_context_for_llm(
-                    domain="strategy", query=f"{original_strategy} {symbol} re-analysis"
-                )
-
-            prompt = self._create_re_analysis_prompt(
-                trade_data, processed_data, news_text, pattern_analysis, 
-                whale_data, statistical_feedback, active_context_playbook
-            )
-
-            if self.r2_service:
-                analysis_data = { 'symbol': symbol, 'original_strategy': original_strategy }
-                await self.r2_service.save_llm_prompts_async(
-                    symbol, 'trade_reanalysis_v5_hub', prompt, analysis_data
-                )
-
-            async with self.semaphore:
-                response = await self._call_llm(prompt)
+        prompt_sections = [
+            f"SYSTEM: You are an expert AI trading analyst (Explorer Brain). Analyze the provided data for {symbol} and decide if it's a high-potential candidate to 'WATCH'.",
+            playbook_prompt,
+            "--- START OF CANDIDATE DATA ---",
+            ml_summary_prompt,
+            news_prompt,
+            whale_activity_prompt,
+            "--- END OF CANDIDATE DATA ---",
+            """
+TASK:
+1.  Synthesize all data points (ML, News, Whale Flow, 24h Accumulation).
+2.  Consult the "Playbook" for learned rules.
+3.  Decide the final action: 'WATCH' (if high confidence) or 'NO_DECISION' (if low confidence or conflicting signals).
+4.  If 'WATCH', define the 'strategy_to_watch' (e.g., 'Breakout', 'MeanReversion', 'WhaleAccumulation') and an appropriate 'exit_profile' (e.g., 'Aggressive', 'Standard', 'Patient').
+
+OUTPUT (JSON Object ONLY):
+{
+  "action": "WATCH" or "NO_DECISION",
+  "strategy_to_watch": "STRATEGY_NAME",
+  "confidence_level": 0.0_to_1.0,
+  "reasoning": "Brief justification (max 40 words) synthesizing all data points.",
+  "exit_profile": "Aggressive" or "Standard" or "Patient"
+}
+"""
+        ]
+        
+        return "\n".join(prompt_sections)
 
-            # 🔴 --- START OF CHANGE (V5.4) --- 🔴
-            # (تمرير كائن الصفقة الأصلي بالكامل لنسخ القيم القديمة إذا لزم الأمر)
-            re_analysis_dict = self._parse_re_analysis_response(response, original_strategy, symbol, trade_data)
-            # 🔴 --- END OF CHANGE --- 🔴
-            
-            if re_analysis_dict:
-                re_analysis_dict['model_source'] = self.model_name
-                return re_analysis_dict
-            else:
-                print(f"❌ LLM re-analysis parsing failed for {symbol}")
-                return None
-        except Exception as e:
-            print(f"❌ Error in LLM re-analysis: {e}"); traceback.print_exc()
-            return None
 
-    # 🔴 --- START OF CHANGE (V5.4) --- 🔴
-    def _parse_re_analysis_response(self, response_text: str, fallback_strategy: str, symbol: str, original_trade: dict) -> dict:
-        """(محدث V5.4) يضمن عدم مسح قيم SL/TP أبداً."""
-        try:
-            json_str = parse_json_from_response(response_text)
-            if not json_str: return None
-            decision_data = safe_json_parse(json_str)
-            if not decision_data: print(f"❌ Failed to parse JSON (safe_json_parse) for re-analysis of {symbol}: {response_text}"); return None
-
-            action = decision_data.get('action')
-            if action not in ['HOLD', 'CLOSE_TRADE', 'UPDATE_TRADE']:
-                 print(f"⚠️ LLM suggested unsupported re-analysis action ({action}) for {symbol}. Forcing HOLD.")
-                 decision_data['action'] = 'HOLD'
-            
-            # (منطق التحقق الجديد V5.4)
-            if action == 'UPDATE_TRADE':
-                required_update_fields = ['new_stop_loss', 'new_take_profit', 'new_exit_profile', 'new_exit_parameters']
-                if not validate_required_fields(decision_data, required_update_fields):
-                    print(f"❌ Missing required fields for UPDATE_TRADE for {symbol}"); decision_data['action'] = 'HOLD' 
-                elif not isinstance(decision_data['new_exit_parameters'], dict):
-                    print(f"❌ 'new_exit_parameters' is not a valid dict for {symbol}"); decision_data['action'] = 'HOLD'
-            
-            # (آلية الحماية من المسح)
-            if action in ['HOLD', 'UPDATE_TRADE']:
-                # التحقق من new_stop_loss
-                new_sl = decision_data.get('new_stop_loss')
-                if not isinstance(new_sl, (int, float)) or new_sl <= 0:
-                    print(f"⚠️ LLM Re-Analysis {symbol}: new_stop_loss is invalid ({new_sl}). Reverting to original SL.")
-                    decision_data['new_stop_loss'] = original_trade.get('stop_loss')
-                
-                # التحقق من new_take_profit
-                new_tp = decision_data.get('new_take_profit')
-                if not isinstance(new_tp, (int, float)) or new_tp <= 0:
-                    print(f"⚠️ LLM Re-Analysis {symbol}: new_take_profit is invalid ({new_tp}). Reverting to original TP.")
-                    decision_data['new_take_profit'] = original_trade.get('take_profit')
-            
-            strategy_value = decision_data.get('strategy')
-            if not strategy_value or strategy_value == 'unknown':
-                decision_data['strategy'] = fallback_strategy
-            
-            return decision_data
-        except Exception as e:
-            print(f"Error parsing re-analysis response for {symbol}: {e}")
-            return None
-    # 🔴 --- END OF CHANGE --- 🔴
-
-    def _create_re_analysis_prompt(
-        self, 
-        trade_data: dict, processed_data: dict, news_text: str, 
-        pattern_analysis: dict, whale_data: dict, 
-        statistical_feedback: str, active_context_playbook: str
-    ) -> str:
+    def _create_reanalysis_prompt(self, 
+                                    trade_data: Dict[str, Any], 
+                                    current_data: Dict[str, Any], 
+                                    learning_context: str) -> str:
+        """
+        (معدل V8.1)
+        إنشاء الـ Prompt (باللغة الإنجليزية) لإعادة تحليل صفقة مفتوحة (Reflector Brain).
+        """
         
-        symbol = trade_data.get('symbol', 'N/A'); entry_price = trade_data.get('entry_price', 'N/A'); current_price = processed_data.get('current_price', 'N/A'); strategy = trade_data.get('strategy', 'GENERIC'); current_exit_profile = trade_data.get('decision_data', {}).get('exit_profile', 'N/A'); current_exit_params = json.dumps(trade_data.get('decision_data', {}).get('exit_parameters', {}))
+        symbol = trade_data.get('symbol', 'N/A')
         
-        # 🔴 --- START OF CHANGE (V5.4) --- 🔴
-        # (تمرير الأهداف الحالية إلى النموذج)
-        current_sl = trade_data.get('stop_loss', 'N/A')
-        current_tp = trade_data.get('take_profit', 'N/A')
-        # 🔴 --- END OF CHANGE --- 🔴
-
-        statistical_feedback_section = f"🧠 STATISTICAL FEEDBACK (Slow-Learner):\n{statistical_feedback}"; playbook_section = f"📚 LEARNING PLAYBOOK (Fast-Learner Active Rules):\n{active_context_playbook}"
-        try: price_change = ((current_price - entry_price) / entry_price) * 100 if entry_price else 0; price_change_display = f"{price_change:+.2f}%"
-        except (TypeError, ZeroDivisionError): price_change_display = "N/A"
+        # --- 1. بيانات الصفقة الأصلية (القديمة) ---
+        original_strategy = trade_data.get('strategy', 'N/A')
+        original_reasoning = trade_data.get('decision_data', {}).get('reasoning', 'N/A')
+        entry_price = trade_data.get('entry_price', 0)
+        current_pnl = trade_data.get('pnl_percent', 0)
+        current_sl = trade_data.get('stop_loss', 0)
+        current_tp = trade_data.get('take_profit', 0)
+        
+        # --- 2. البيانات الفنية المحدثة (الحالية) ---
+        current_price = current_data.get('current_price', 0)
+        mc_data = current_data.get('monte_carlo_distribution', {})
+        mc_prob = mc_data.get('probability_of_gain', 0)
+        mc_expected_return = mc_data.get('expected_return_pct', 0)
         
-        price_change_24h_raw = processed_data.get('price_change_24h', 0)
-        price_change_24h_display = f"{price_change_24h_raw:+.2f}%" if isinstance(price_change_24h_raw, (int, float)) else "N/A"
+        # --- 3. (V8.1) بيانات الأخبار المحدثة (الحالية) ---
+        latest_news_text = current_data.get('latest_news_text', 'No news.')
+        latest_news_score = current_data.get('latest_news_score', 0.0)
 
-        indicators_summary = format_technical_indicators(processed_data.get('advanced_indicators', {})); pattern_summary = self._format_pattern_analysis(pattern_analysis) if pattern_analysis else "Pattern analysis data not available for re-analysis."; whale_analysis_section = format_whale_analysis_for_llm(whale_data); market_context_section = self._format_market_context(processed_data.get('sentiment_data', {})); ohlcv_data = processed_data.get('raw_ohlcv') or processed_data.get('ohlcv', {}); candle_data_section = self._format_candle_data_comprehensive(ohlcv_data)
+        # --- 4. (العقل) بيانات التعلم الإحصائي ---
+        statistical_feedback = ""
+        if self.learning_hub:
+            statistical_feedback = await self.learning_hub.get_statistical_feedback_for_llm(original_strategy)
 
-        exhaustion_warning = ""
-        try:
-            rsi_1d = processed_data.get('advanced_indicators', {}).get('1d', {}).get('rsi', 50)
-            if price_change_24h_raw > 40 and rsi_1d > 75:
-                exhaustion_warning = (
-                    "🚩 **تنبيه استراتيجي: تم رصد زخم مرتفع (احتمال إرهاق)** 🚩\n"
-                    f"الأصل مرتفع {price_change_24h_display} خلال 24 ساعة ومؤشر RSI على 1D/4H في منطقة تشبع شرائي.\n"
-                    "هذا يزيد من خطورة الاستمرار في الصفقة. قم بالتحقيق في قوة الاتجاه الحالية.\n"
-                    "هل بدأ الحجم (Volume) يضعف؟ هل تظهر إشارات انعكاس على 1H/4H؟\n"
-                    "------------------------------------------------------------------"
-                )
-        except Exception:
-            pass
-            
-        # 🔴 --- START OF CHANGE (V5.4) --- 🔴
-        prompt = f"""
-TRADE RE-ANALYSIS FOR {symbol} (SPOT ONLY - Currently Open LONG Position)
-
-{exhaustion_warning}
-
-📊 CURRENT TRADE CONTEXT:
-* Strategy: {strategy}
-* Entry Price: {entry_price} (LONG position)
-* Current Price: {current_price}
-* 24H Price Change: {price_change_24h_display}
-* Current Performance: {price_change_display}
-* Current Exit Profile: {current_exit_profile}
-* CURRENT Stop Loss: {current_sl}
-* CURRENT Take Profit: {current_tp}
-
---- LEARNING HUB INPUT (CRITICAL) ---
-{playbook_section}
-{statistical_feedback_section}
---- END OF LEARNING INPUT ---
-
-🔄 UPDATED TECHNICAL ANALYSIS:
-{indicators_summary}
-
-📈 UPDATED RAW CANDLE DATA SUMMARY & STATISTICS:
-{candle_data_section}
-{chr(10)}--- END OF CANDLE DATA ---{chr(10)}
-
-🐋 UPDATED WHALE ACTIVITY:
-{whale_analysis_section}
-
-🌍 UPDATED MARKET CONTEXT:
-{market_context_section if market_context_section and "No market context" not in market_context_section else "Market context data not available for this re-analysis."}
-
----
-🎯 RE-ANALYSIS INSTRUCTIONS (SPOT - LONG POSITION):
-
-1.  **Analyze the Data:** Review all new data (Indicators, Candles, Whale, Market).
-2.  **Evaluate Current State:** Is the original reason for entry still valid? Has the risk changed?
-3.  **Investigate Alert (if present):** If the 'Exhaustion Alert' is active, determine if the trend is weakening (exit) or just consolidating (hold).
-4.  **Decide Action (HOLD, CLOSE_TRADE, or UPDATE_TRADE):**
-    * **HOLD:** The trade is still valid. The current strategy/targets are fine.
-    * **CLOSE_TRADE:** The trade is invalidated (e.g., trend reversal, risk too high).
-    * **UPDATE_TRADE:** The trade is valid, but the exit parameters need adjustment (e.g., raise stop loss to lock profit, or change exit profile).
-
-**[CRITICAL OUTPUT RULES - YOU MUST FOLLOW THESE]:**
-1.  You **MUST** return one of three actions: `HOLD`, `CLOSE_TRADE`, or `UPDATE_TRADE`.
-2.  If `action` is `HOLD` or `UPDATE_TRADE`, you **MUST** provide valid (non-zero) numeric values for `new_stop_loss` and `new_take_profit`.
-3.  **If `action` is `HOLD` and you do not want to change the targets,** you **MUST** return the *CURRENT* values (Current SL: {current_sl}, Current TP: {current_tp}) in the `new_stop_loss` and `new_take_profit` fields.
-4.  If `action` is `CLOSE_TRADE`, the values for targets/exit profile are irrelevant.
-
-OUTPUT FORMAT (JSON - SPOT RE-ANALYSIS):
-{{
-    "action": "HOLD/CLOSE_TRADE/UPDATE_TRADE",
-    "reasoning": "Comprehensive justification. If 'HOLD' or 'UPDATE', justify the new (or existing) SL/TP values.",
-    "new_stop_loss": 0.000000,
-    "new_take_profit": 0.000000,
-    "new_exit_profile": "The new exit profile (or the existing one if HOLD)",
-    "new_exit_parameters": {{ "example_key": "example_value" }},
-    "new_expected_minutes": 15,
-    "confidence_level": 0.85,
-    "strategy": "{strategy}",
-    "self_critique": {{
-        "failure_modes": ["Primary risk of this new decision?", "Second risk?"],
-        "confidence_adjustment_reason": "Brief reason if confidence was adjusted."
-    }}
-}}
-"""
-        # 🔴 --- END OF CHANGE --- 🔴
-        return prompt
+        # --- 5. بناء أقسام الـ Prompt (الإنجليزية) ---
         
-    def _format_pattern_analysis(self, pattern_analysis):
-        if not pattern_analysis or not pattern_analysis.get('pattern_detected') or pattern_analysis.get('pattern_detected') == 'no_clear_pattern': return "No clear chart pattern detected by the system."
-        pattern = pattern_analysis.get('pattern_detected', 'N/A'); confidence = pattern_analysis.get('pattern_confidence', 0); direction = pattern_analysis.get('predicted_direction', 'N/A'); timeframe = pattern_analysis.get('timeframe', 'N/A'); tf_display = f"on {timeframe} timeframe" if timeframe != 'N/A' else ""
-        return f"System Pattern Analysis: Detected '{pattern}' {tf_display} with {confidence:.2f} confidence. Predicted direction: {direction}."
+        playbook_prompt = f"""
+--- START OF LEARNING PLAYBOOK ---
+{learning_context}
+{statistical_feedback}
+--- END OF PLAYBOOK ---
+"""
 
-    @_rate_limit_nvidia_api
-    async def _call_llm(self, prompt: str) -> str:
-        try:
-            for attempt in range(2): 
-                try:
-                    response = self.client.chat.completions.create(
-                        model=self.model_name,
-                        messages=[{"role": "user", "content": prompt}],
-                        temperature=self.temperature,
-                        seed=int(time.time()), 
-                        max_tokens=4000 
-                    )
-                    
-                    content = None
-                    if response.choices and response.choices[0].message:
-                        content = response.choices[0].message.content
-                    
-                    if content and '{' in content and '}' in content:
-                        return content
-                    else:
-                        if content is None:
-                            print(f"⚠️ LLM returned NO content (None) (attempt {attempt+1}). Check content filters or API status.")
-                        else:
-                            print(f"⚠️ LLM returned invalid content (not JSON) (attempt {attempt+1}): {content[:100]}...")
-                    
-                    if attempt == 0: await asyncio.sleep(1) 
-
-                except (RateLimitError, APITimeoutError) as e:
-                    print(f"❌ LLM API Error (Rate Limit/Timeout): {e}. Retrying via backoff...")
-                    raise 
-                except Exception as e:
-                    print(f"❌ Unexpected LLM API error (attempt {attempt+1}): {e}")
-                    if attempt == 0: await asyncio.sleep(2) 
-                    elif attempt == 1: raise 
-            
-            print("❌ LLM failed to return valid content after retries.")
-            return "" 
-            
-        except Exception as e:
-            print(f"❌ Final failure in _call_llm after backoff retries: {e}")
-            raise 
+        trade_status_prompt = f"""
+1.  **Open Trade Status ({symbol}):**
+    * Current PnL: {current_pnl:+.2f}%
+    * Original Strategy: {original_strategy}
+    * Original Reasoning: {original_reasoning}
+    * Entry Price: {entry_price}
+    * Current Price: {current_price}
+    * Current StopLoss: {current_sl}
+    * Current TakeProfit: {current_tp}
+"""
 
-print("✅ LLM Service loaded - V5.4 (Fixed Re-Analysis TP/SL Wipe Bug)")
+        current_analysis_prompt = f"""
+2.  **Current Real-time Analysis:**
+    * Monte Carlo (1h): {mc_prob:.1f}% chance of profit (Expected: {mc_expected_return:.2f}%)
+    * Latest News (VADER: {latest_news_score:.3f}): {latest_news_text[:300]}...
+"""
+        
+        prompt_sections = [
+            f"SYSTEM: You are an expert AI trading analyst (Reflector Brain). An open trade for {symbol} has triggered a mandatory re-analysis. Analyze the new data and decide the next action.",
+            playbook_prompt,
+            "--- START OF TRADE DATA ---",
+            trade_status_prompt,
+            current_analysis_prompt,
+            "--- END OF TRADE DATA ---",
+            """
+TASK:
+1.  Analyze the "Open Trade Status" against the "Current Real-time Analysis".
+2.  Consult the "Playbook" for learned rules and statistical feedback.
+3.  Decide the best course of action:
+    * 'HOLD': Maintain the current strategy (if analysis is still valid or neutral).
+    * 'UPDATE_TRADE': Adjust StopLoss/TakeProfit (if risk/reward changed).
+    * 'CLOSE_TRADE': Immediately close the trade (if analysis is invalidated or high risk).
+4.  You MUST provide new SL/TP targets if action is 'UPDATE_TRADE'.
+
+OUTPUT (JSON Object ONLY):
+{
+  "action": "HOLD" or "UPDATE_TRADE" or "CLOSE_TRADE",
+  "strategy": "MAINTAIN_CURRENT" or "ADAPTIVE_EXIT" or "IMMEDIATE_EXIT",
+  "reasoning": "Brief justification (max 40 words) for the decision.",
+  "new_stop_loss": (float or null, required if action is 'UPDATE_TRADE'),
+  "new_take_profit": (float or null, required if action is 'UPDATE_TRADE')
+}
+"""
+        ]
+        
+        return "\n".join(prompt_sections)