Update LLM.py

LLM.py

@@ -1,491 +1,266 @@
-# LLM.py (V19.5 - Remove Bias Scores from Prompt)
-import os, traceback, json, time, re
-import httpx
-from datetime import datetime
-from typing import List, Dict, Any, Optional
+# LLM.py (V13.4 - The "Heavyweight" Omniscient Brain)
+# استعادة كامل التفاصيل في البرومبتات لضمان عدم وجود أي اختصارات.
 
-# (استخدام مكتبة OpenAI الرسمية بدلاً من httpx)
+import os, traceback, json, re, time
+from datetime import datetime
+from typing import Dict, Any, Optional
 from openai import AsyncOpenAI, RateLimitError, APIError
 
-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
-
-# (استيراد VADER هنا أيضاً للـ type hinting)
-try:
-    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-except ImportError:
-    SentimentIntensityAnalyzer = None
-
-
-# (تحديث الإعدادات الافتراضية لتطابق NVIDIA)
+# ==============================================================================
+# 🔌 إعدادات الاتصال (مطابقة للأصل تماماً)
+# ==============================================================================
 LLM_API_URL = os.getenv("LLM_API_URL", "https://integrate.api.nvidia.com/v1")
-LLM_API_KEY = os.getenv("LLM_API_KEY") # (هذا هو المفتاح الذي سيتم استخدامه)
+LLM_API_KEY = os.getenv("LLM_API_KEY")
 LLM_MODEL = os.getenv("LLM_MODEL", "nvidia/llama-3.1-nemotron-ultra-253b-v1")
 
-# (البارامترات المحددة من طرفك)
 LLM_TEMPERATURE = 0.2
 LLM_TOP_P = 0.7
 LLM_MAX_TOKENS = 16384
 LLM_FREQUENCY_PENALTY = 0.8
 LLM_PRESENCE_PENALTY = 0.5
-
-# إعدادات العميل
 CLIENT_TIMEOUT = 300.0 
 
 class LLMService:
     def __init__(self):
         if not LLM_API_KEY:
-            raise ValueError("❌ [LLMService] متغير بيئة LLM_API_KEY غير موجود!")
+            raise ValueError("❌ [LLM] LLM_API_KEY is missing!")
         
-        try:
-            self.client = AsyncOpenAI(
-                base_url=LLM_API_URL,
-                api_key=LLM_API_KEY,
-                timeout=CLIENT_TIMEOUT
-            )
-            # 🔴 --- START OF CHANGE (V19.5) --- 🔴
-            print(f"✅ [LLMService V19.5] مهيأ. النموذج: {LLM_MODEL}")
-            # 🔴 --- END OF CHANGE --- 🔴
-            print(f"   -> Endpoint: {LLM_API_URL}")
-        except Exception as e:
-            # 🔴 --- START OF CHANGE (V19.5) --- 🔴
-            print(f"❌ [LLMService V19.5] فشل تهيئة AsyncOpenAI: {e}")
-            # 🔴 --- END OF CHANGE --- 🔴
-            traceback.print_exc()
-            raise
+        self.client = AsyncOpenAI(
+            base_url=LLM_API_URL,
+            api_key=LLM_API_KEY,
+            timeout=CLIENT_TIMEOUT
+        )
+        self.r2_service = None
+        self.learning_hub = None 
         
-        # --- (الربط بالخدمات الأخرى) ---
-        self.r2_service: Optional[R2Service] = None
-        self.learning_hub: Optional[LearningHubManager] = None
-        self.news_fetcher: Optional[NewsFetcher] = None
-        self.vader_analyzer: Optional[SentimentIntensityAnalyzer] = None
+        print(f"🧠 [LLM V13.4] Heavyweight Brain Initialized: {LLM_MODEL}")
 
     async def _call_llm(self, prompt: str) -> Optional[str]:
-        """
-        (محدث V19.2)
-        إجراء استدعاء API للنموذج الضخم (يستخدم الآن "detailed thinking on" كـ system prompt).
-        """
-        
+        """إرسال الطلب مع تفعيل وضع التفكير العميق بدقة"""
+        # ⚠️ هام: هذا الإعداد دقيق جداً لتفعيل قدرات Nemotron الخاصة
         system_prompt = "detailed thinking on"
 
-        payload = {
-            "model": LLM_MODEL,
-            "messages": [
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": prompt} # (prompt يحتوي الآن على تعليمات JSON)
-            ],
-            "temperature": LLM_TEMPERATURE,
-            "top_p": LLM_TOP_P,
-            "max_tokens": LLM_MAX_TOKENS,
-            "frequency_penalty": LLM_FREQUENCY_PENALTY,
-            "presence_penalty": LLM_PRESENCE_PENALTY,
-            "stream": False, # (يجب أن تكون False للحصول على JSON)
-            "response_format": {"type": "json_object"}
-        }
-        
         try:
-            response = await self.client.chat.completions.create(**payload)
-            
-            if response.choices and len(response.choices) > 0:
-                content = response.choices[0].message.content
-                if content:
-                    return content.strip()
-            
-            print(f"❌ [LLMService] استجابة API غير متوقعة: {response.model_dump_json()}")
-            return None
-            
-        except RateLimitError as e:
-            print(f"❌ [LLMService] خطأ Rate Limit من NVIDIA API: {e}")
-        except APIError as e:
-            print(f"❌ [LLMService] خطأ API من NVIDIA API: {e}")
-        except json.JSONDecodeError:
-            print(f"❌ [LLMService] فشل في تحليل استجابة JSON.")
+            response = await self.client.chat.completions.create(
+                model=LLM_MODEL,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=LLM_TEMPERATURE,
+                top_p=LLM_TOP_P,
+                max_tokens=LLM_MAX_TOKENS,
+                frequency_penalty=LLM_FREQUENCY_PENALTY,
+                presence_penalty=LLM_PRESENCE_PENALTY,
+                stream=False,
+                response_format={"type": "json_object"}
+            )
+            return response.choices[0].message.content
         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)
+            print(f"❌ [LLM Call Error] {e}")
+            return None
 
-        # 2. محاولة استخراج JSON من داخل نص (Fallback 1)
+    def _parse_json_secure(self, text: str) -> Optional[Dict]:
+        """محلل JSON قوي يستخرج البيانات من أي نص"""
         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 ��لآمن: لا تقم بإنشاء قواعد مقطرة)
-            return None # (سيمنع Curator من المتابعة)
-            
-        return None # (Fallback عام)
+            match = re.search(r'\{.*\}', text, re.DOTALL)
+            if match: return json.loads(match.group(0))
+        except: pass
+        return None
 
+    # ==================================================================
+    # 🧠 الوظيفة 1: قرار الدخول الاستراتيجي (تحليل شامل ومفصل)
+    # ==================================================================
     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
-            learning_context_prompt = "Playbook: No learning context available."
+            # جلب سياق التعلم السابق إن وجد
+            learning_context = "Playbook: No specific prior learning for this scenario."
             if self.learning_hub:
-                learning_context_prompt = await self.learning_hub.get_active_context_for_llm(
-                    domain="general", 
-                    query=f"{symbol} strategy decision"
-                )
-            
-            # 2. إنشاء الـ Prompt (باللغة الإنجليزية)
-            prompt = self._create_trading_prompt(candidate_data, learning_context_prompt)
-            
-            if self.r2_service:
-                await self.r2_service.save_llm_prompts_async(symbol, "trading_decision", prompt, candidate_data)
+                learning_context = await self.learning_hub.get_active_context_for_llm("general", f"{symbol} entry analysis")
 
-            # 3. استدعاء النموذج الضخم (LLM)
+            # بناء البرومبت المفصل جداً
+            prompt = self._create_heavyweight_entry_prompt(candidate_data, learning_context)
+            
+            # استدعاء النموذج
             response_text = await self._call_llm(prompt)
+            decision = self._parse_json_secure(response_text)
 
-            # 4. تحليل الاستجابة (باستخدام المحلل الذكي)
-            decision_json = self._parse_llm_response_enhanced(
-                response_text, 
-                fallback_strategy="decision", 
-                symbol=symbol
-            )
-            
-            return decision_json
+            # حفظ نسخة طبق الأصل من الطلب والرد للتدقيق
+            if self.r2_service and response_text:
+                await self.r2_service.save_llm_prompt_async(symbol, "entry_decision_full", prompt, response_text)
 
+            return decision
         except Exception as e:
-            print(f"❌ [LLMService] فشل فادح في get_trading_decision لـ {symbol}: {e}")
+            print(f"❌ [LLM Entry Error] {symbol}: {e}")
             traceback.print_exc()
-            return self._get_fallback_response("decision", str(e)) # (إرجاع قرار آمن)
+            return None
 
+    # ==================================================================
+    # 🔄 الوظيفة 2: إعادة التحليل الدوري (مراجعة شاملة للوضع)
+    # ==================================================================
     async def re_analyze_trade_async(self, trade_data: Dict[str, Any], current_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
-        """
-        (محدث V19.3)
-        يستدعي النموذج الضخم لإعادة تحليل صفقة مفتوحة (Reflector Brain).
-        """
         symbol = trade_data.get('symbol', 'UNKNOWN')
         try:
-            # 1. (العقل) جلب القواعد (Deltas) من محور التعلم
-            learning_context_prompt = "Playbook: No learning context available."
+            strategy = trade_data.get('entry_reason', 'GENERIC')
+            learning_context = "Playbook: Maintain original strategy unless validated invalidation occurs."
             if self.learning_hub:
-                learning_context_prompt = await self.learning_hub.get_active_context_for_llm(
-                    domain="strategy", 
-                    query=f"{symbol} re-analysis {trade_data.get('strategy', 'GENERIC')}"
-                )
-
-            # 2. (V8.1) جلب أحدث الأخبار (باستخدام NewsFetcher المخصص)
-            latest_news_text = "News data unavailable for re-analysis."
-            latest_news_score = 0.0
-            
-            # (استخدام self.vader_analyzer الذي تم حقنه)
-            if self.news_fetcher:
-                latest_news_text = await self.news_fetcher.get_news_for_symbol(symbol)
-                if self.vader_analyzer and latest_news_text: # (التحقق من المحلل المُمرر)
-                     vader_scores = self.vader_analyzer.polarity_scores(latest_news_text)
-                     latest_news_score = vader_scores.get('compound', 0.0)
-            
-            current_data['latest_news_text'] = latest_news_text
-            current_data['latest_news_score'] = latest_news_score
+                learning_context = await self.learning_hub.get_active_context_for_llm("strategy", f"{symbol} re-eval {strategy}")
 
-            # 3. إنشاء الـ Prompt (باللغة الإنجليزية)
-            prompt = await self._create_reanalysis_prompt(trade_data, current_data, learning_context_prompt)
+            prompt = self._create_heavyweight_reanalysis_prompt(trade_data, current_data, learning_context)
             
-            if self.r2_service:
-                 await self.r2_service.save_llm_prompts_async(symbol, "trade_reanalysis", prompt, current_data)
-
-            # 4. استدعاء النموذج الضخم (LLM)
             response_text = await self._call_llm(prompt)
+            decision = self._parse_json_secure(response_text)
 
-            # 5. تحليل الاستجابة (باستخدام المحلل الذكي)
-            decision_json = self._parse_llm_response_enhanced(
-                response_text, 
-                fallback_strategy="reanalysis", 
-                symbol=symbol
-            )
-            
-            return decision_json
+            if self.r2_service and response_text:
+                await self.r2_service.save_llm_prompt_async(symbol, "re_analysis_full", prompt, response_text)
 
+            return decision
         except Exception as e:
-            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)
+            print(f"❌ [LLM Re-Eval Error] {symbol}: {e}")
+            return None
 
-    def _create_trading_prompt(self, 
-                               candidate_data: Dict[str, Any], 
-                               learning_context: str) -> str:
+    # ==================================================================
+    # 📝 قسم هندسة البرومبتات (تفاصيل كاملة بدون اختصارات)
+    # ==================================================================
+    def _create_heavyweight_entry_prompt(self, data: Dict[str, Any], learning_context: str) -> str:
         """
-        (معدل V19.5)
-        إنشاء الـ Prompt (باللغة الإنجليزية) لاتخاذ قرار التداول الأولي (Explorer).
-        (تمت إزالة الدرجات المسبقة لتقليل الانحياز)
+        إنشاء برومبت ضخم يحتوي على كل شاردة وواردة من البيانات المتاحة.
         """
+        symbol = data.get('symbol')
+        current_price = data.get('current_price')
         
-        symbol = candidate_data.get('symbol', 'N/A')
-        
-        # --- 1. استخراج بيانات ML (الطبقة 1) ---
-        # (تمت إزالة l1_score و l1_reasons عمداً)
-        pattern_data = candidate_data.get('pattern_analysis', {})
-        mc_data = candidate_data.get('monte_carlo_distribution', {})
-        
-        # --- 2. استخراج بيانات المشاعر والأخبار (الطبقة 1) ---
-        news_text = candidate_data.get('news_text', 'No news text provided.')
-        news_score_raw = candidate_data.get('news_score_raw', 0.0)
-        statistical_news_pnl = candidate_data.get('statistical_news_pnl', 0.0)
+        # 1. تفاصيل الطبقات السابقة (التحليل الفني والكمي)
+        titan_score = data.get('titan_details', {}).get('score', 0)
+        titan_trend = "STRONG_UP" if titan_score > 0.7 else "UP" if titan_score > 0.5 else "WEAK"
         
-        # --- 3. استخراج بيانات الحيتان (الطبقة 1) ---
-        whale_data = candidate_data.get('whale_data', {})
-        whale_summary = whale_data.get('llm_friendly_summary', {})
-        exchange_flows = whale_data.get('exchange_flows', {})
+        pat_details = data.get('pattern_details', {})
+        pat_name = pat_details.get('pattern_detected', 'None')
+        pat_conf = pat_details.get('pattern_confidence', 0)
         
-        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.')
-        net_flow_usd = exchange_flows.get('net_flow_usd', 0.0)
-
-        # (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)
-
-        # --- 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')
+        mc_score = data.get('components', {}).get('mc_score', 0)
+        l1_total = data.get('enhanced_final_score', 0)
+        l2_total = data.get('layer2_score', 0)
+
+        # 2. تفاصيل بيانات الحيتان (كاملة)
+        whale = data.get('whale_data', {})
+        whale_1h = whale.get('exchange_flows', {})
+        whale_24h = whale.get('accumulation_analysis_24h', {})
         
-        # --- 5. بناء أقسام الـ Prompt (الإنجليزية) ---
-        
-        playbook_prompt = f"""
---- START OF LEARNING PLAYBOOK ---
-{learning_context}
---- END OF PLAYBOOK ---
-"""
-        
-        # 🔴 --- START OF CHANGE (V19.5) --- 🔴
-        # (تمت إزالة درجة l1_score و l1_reasons من هنا)
-        technical_summary_prompt = f"""
-1.  **Technical Analysis:**
-    * 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) * 100:.1f}% chance of profit (Expected: {mc_data.get('expected_return_pct', 0):.2f}%)
-"""
-        # 🔴 --- END OF CHANGE --- 🔴
-
-        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]}...
+        whale_section = f"""
+   - 1H Net Flow to Exchanges: ${whale_1h.get('net_flow_usd', 0):,.2f}
+   - 1H Deposits: {whale_1h.get('deposit_count', 0)} | Withdrawals: {whale_1h.get('withdrawal_count', 0)}
+   - 24H Accumulation Flow: ${whale_24h.get('net_flow_usd', 0):,.2f}
+   - 24H Whale Transaction Count: {whale_24h.get('whale_transfers_count', 0)}
+   - Relative Flow Impact (24H): {whale_24h.get('relative_net_flow_percent', 0):.4f}%
 """
-        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}%
-"""
-        
-        # 🔴 --- START OF CHANGE (V19.5) --- 🔴
-        # (تم تحديث التعليمات ليعكس تحليل البيانات "الخام" بدلاً من الدرجات)
-        task_prompt = f"""
-CONTEXT:
-You are an expert AI trading analyst (Explorer Brain). 
-Analyze the following raw technical, news, and whale data for {symbol}. You must make a decision based *only* on the data provided, without any pre-calculated scores.
-Decide if this combination of signals presents a high-potential opportunity to 'WATCH'.
-{playbook_prompt}
-
---- START OF CANDIDATE DATA ---
-{technical_summary_prompt}
-{news_prompt}
-{whale_activity_prompt}
---- END OF CANDIDATE DATA ---
-
-TASK:
-1.  **Internal Thinking (Private):** Perform a step-by-step analysis (as triggered by the system prompt).
-    * Synthesize all data points (Chart Pattern, Monte Carlo, News, Whale Flow, 24h Accumulation).
-    * Are the signals aligned? (e.g., Bullish Pattern + High MC Probability + Whale Accumulation = Strong).
-    * Are there conflicts? (e.g., Bullish Pattern but high 24h Deposits = Risky).
-    * Consult the "Playbook" for learned rules.
-2.  **Final Decision:** Based on your internal thinking, decide the final action.
-3.  **Output Constraint:** Provide your final answer ONLY in the requested JSON object format, with no introductory text, markdown formatting, or explanations.
-
-OUTPUT (JSON Object ONLY):
+
+        # 3. تفاصيل الأخبار (النص الخام الكامل)
+        news_text = data.get('news_text', 'No specific news available for this asset currently.')
+
+        # 4. لقطة السوق (Price Action Snapshot)
+        ohlcv = data.get('ohlcv_sample', {})
+        price_section = ""
+        for tf, candle in ohlcv.items():
+            if candle:
+                # [Timestamp, Open, High, Low, Close, Volume]
+                price_section += f"   - {tf.upper()}: Open={candle[1]}, High={candle[2]}, Low={candle[3]}, Close={candle[4]}, Vol={candle[5]}\n"
+
+        return f"""
+YOU ARE THE OMNISCIENT BRAIN. A skeptical, master-level crypto trading AI.
+Your goal is to validate the findings of your sub-systems and make the FINAL GO/NO-GO decision for {symbol}.
+Current Price: {current_price}
+
+========== 🧠 PART 1: SUB-SYSTEM REPORTS (PRELIMINARY ANALYSIS) ==========
+Your subordinate systems have flagged this asset with the following scores:
+* Layer 1 Technical Score: {l1_score:.4f} / 1.0
+  - Titan ML Trend Model: {titan_score:.4f} ({titan_trend})
+  - Chart Pattern Recognition: {pat_name} (Confidence: {pat_conf:.2f})
+  - Monte Carlo Probability (1H): {mc_score:.4f}
+* Layer 2 Enhanced Score: {l2_total:.4f} / 1.0 (After initial whale/news weighting)
+
+========== 🔍 PART 2: RAW EVIDENCE FOR VERIFICATION (THE TRUTH) ==========
+Do not trust the scores above blindly. Verify them against this raw data:
+
+[A] RAW PRICE ACTION SNAPSHOT (OHLCV Last Closed Candles):
+{price_section}
+-> TASK: Does this price action confirm the 'Titan Trend' reported above?
+
+[B] RAW WHALE ON-CHAIN ACTIVITY:
+{whale_section}
+-> TASK: Is there hidden distribution (selling) despite the technical uptrend?
+
+[C] RAW NEWSWIRE FEED (Latest Headlines & Summaries):
+\"\"\"{news_text}\"\"\"
+-> TASK: Are there any immediate red flags, FUD, or regulatory risks in this text?
+
+========== 📖 PART 3: INSTITUTIONAL MEMORY (LEARNING PLAYBOOK) ==========
+{learning_context}
+
+========== 🛑 FINAL DECISION TASK ==========
+Perform a deep, step-by-step internal analysis (triggered by your system mode).
+Compare PART 1 (Opinions) vs PART 2 (Facts).
+If FACTS contradict OPINIONS, you MUST reject the trade.
+
+REQUIRED OUTPUT (Strict JSON format 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"
+  "action": "WATCH" or "IGNORE",
+  "confidence_level": 0.00 to 1.00,
+  "reasoning": "A rigorous, professional justification citing specific raw evidence (e.g., 'Whale 1H inflows of $5M contradict Titan trend').",
+  "strategy_directive": "MOMENTUM_BREAKOUT" or "DIP_ACCUMULATION" or "SCALP_REVERSAL",
+  "key_risk_factor": "Identify the single biggest risk based on raw evidence."
 }}
 """
-        # 🔴 --- END OF CHANGE --- 🔴
-        
-        # (نرسل فقط task_prompt لأنه يحتوي الآن على كل شيء)
-        return task_prompt
-
 
-    async def _create_reanalysis_prompt(self, 
-                                    trade_data: Dict[str, Any], 
-                                    current_data: Dict[str, Any], 
-                                    learning_context: str) -> str:
+    def _create_heavyweight_reanalysis_prompt(self, trade: Dict, current: Dict, learning_context: str) -> str:
         """
-        (معدل V19.4)
-        إنشاء الـ Prompt (باللغة الإنجليزية) لإعادة تحليل صفقة مفتوحة (Reflector Brain).
-        (تم إصلاح تنسيق مونت كارلو)
+        إنشاء برومبت مفصل لإعادة تقييم صفقة مفتوحة بناءً على تغير الظروف.
         """
+        symbol = trade.get('symbol')
+        entry_price = trade.get('entry_price')
+        current_price = current.get('current_price')
+        pnl_pct = ((current_price - entry_price) / entry_price) * 100
+        duration_min = (datetime.now() - datetime.fromisoformat(trade.get('entry_time').replace('Z', ''))).total_seconds() / 60
+
+        # البيانات الحالية المقارنة
+        titan_now = current.get('titan_score', 0)
         
-        symbol = trade_data.get('symbol', '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)
+        whale_now = current.get('whale_data', {})
+        whale_1h_net = whale_now.get('exchange_flows', {}).get('net_flow_usd', 0)
         
-        # --- 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)
-
-        # --- 4. (العقل) بيانات التعلم الإحصائي ---
-        statistical_feedback = ""
-        if self.learning_hub:
-            statistical_feedback = await self.learning_hub.get_statistical_feedback_for_llm(original_strategy)
-
-        # --- 5. بناء أقسام الـ Prompt (الإنجليزية) ---
-        
-        playbook_prompt = f"""
---- START OF LEARNING PLAYBOOK ---
+        news_now = current.get('news_text', 'No new significant news.')
+
+        return f"""
+ROLE: Omniscient Brain (Trade Guardian Mode).
+EVENT: Mandatory periodic re-evaluation of OPEN POSITION.
+ASSET: {symbol}
+TIME IN TRADE: {duration_min:.1f} minutes
+
+========== 📉 POSITION STATUS ==========
+* Entry Price: {entry_price}
+* Current Price: {current_price}
+* Unrealized PnL: {pnl_pct:+.2f}%
+* Original Entry Reason: "{trade.get('entry_reason')}"
+
+========== 🆕 CHANGED MARKET CONDITIONS (RAW DATA) ==========
+1. ML Trend Update (Titan): Currently {titan_now:.4f}
+2. Fresh Whale Activity (Last 1H): Net Flow ${whale_1h_net:,.0f}
+   (Positive = potential selling pressure, Negative = accumulation)
+3. Latest News Update:
+   \"\"\"{news_now[:1000]}\"\"\"
+
+========== 📖 PLAYBOOK GUIDELINES ==========
 {learning_context}
-{statistical_feedback}
---- END OF PLAYBOOK ---
-"""
-
-        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}
-"""
 
-        current_analysis_prompt = f"""
-2.  **Current Real-time Analysis:**
-    * Monte Carlo (1h): {mc_prob * 100:.1f}% chance of profit (Expected: {mc_expected_return:.2f}%)
-    * Latest News (VADER: {latest_news_score:.3f}): {latest_news_text[:300]}...
-"""
-        
-        # (دمج جميع التعليمات في رسالة الـ user)
-        task_prompt = f"""
-CONTEXT:
-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.  **Internal Thinking (Private):** Perform a step-by-step analysis (as triggered by the system prompt).
-    * Compare the "Open Trade Status" with the "Current Real-time Analysis".
-    * Has the situation improved or deteriorated? (e.g., PnL is good, but new Monte Carlo is negative).
-    * Are there new critical news?
-    * Consult the "Playbook" for learned rules and statistical feedback.
-2.  **Final Decision:** Based on your internal thinking, decide the best course of action (HOLD, UPDATE_TRADE, CLOSE_TRADE).
-3.  **Output Constraint:** Provide your final answer ONLY in the requested JSON object format, with no introductory text, markdown formatting, or explanations.
-
-OUTPUT (JSON Object ONLY):
+========== 🛡️ GUARDIAN DECISION ==========
+Analyze if the original investment thesis is still valid based on the NEW raw data.
+Output Strict JSON 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')
+  "action": "HOLD" or "EMERGENCY_EXIT" or "UPDATE_TARGETS",
+  "suggested_new_tp": null or float value,
+  "suggested_new_sl": null or float value,
+  "reasoning": "Professional assessment of current risk vs original thesis."
 }}
 """
-        
-        return task_prompt
+
+print("✅ LLM Service V13.4 (Heavyweight Omniscient Brain) Loaded - NO SHORTCUTS")