Update LLM.py

LLM.py

@@ -1,4 +1,4 @@
-# LLM.py
+# LLM.py (محدث بالكامل مع ملفات الخروج الديناميكية والتغذية الراجعة)
 import os, traceback, asyncio, json, time
 import re # ✅ استيراد مكتبة re
 from datetime import datetime
@@ -7,9 +7,8 @@ from backoff import on_exception, expo
 from openai import OpenAI, RateLimitError, APITimeoutError
 import numpy as np
 from sentiment_news import NewsFetcher
-# ✅ تعديل الاستيراد: parse_json_from_response لم يعد مستخدماً هنا بشكل مباشر لتحليل استجابة النموذج الرئيسية
 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 # ✅ الإصلاح: استيراد المحلل الآمن من الموجه الجديد
+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"
@@ -164,16 +163,11 @@ class PatternAnalysisEngine:
                 'resistance': 0
             }
 
-    # --- (analyze_chart_patterns و _parse_pattern_response لم تعد تُستخدم مباشرة للـ LLM prompt الأساسي، لكن قد تُستخدم في مكان آخر) ---
     async def analyze_chart_patterns(self, symbol, ohlcv_data):
-        # ... (الكود الأصلي هنا - لم يتم تغييره)
-        # هذا التحليل يتم الآن في الطبقة الثانية (MLProcessor)
-        # ولا يتم إرسال نتيجته إلى الـ LLM في الطبقة الثالثة
-        pass # Placeholder to indicate no change here
+        pass 
 
     def _parse_pattern_response(self, response_text):
-        # ... (الكود الأصلي هنا - لم يتم تغييره)
-        pass # Placeholder to indicate no change here
+        pass 
 
 
 class LLMService:
@@ -183,9 +177,10 @@ class LLMService:
         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) # Still needed for formatting candle data
+        self.pattern_engine = PatternAnalysisEngine(self) 
         self.semaphore = asyncio.Semaphore(5)
         self.r2_service = None  # سيتم تعيينه من app.py
+        self.learning_engine = None # 🔴 جديد: سيتم تعيينه من app.py
 
     def _rate_limit_nvidia_api(func):
         @wraps(func)
@@ -220,12 +215,17 @@ class LLMService:
             print(f"   ✅ أطر زمنية صالحة لـ {symbol}: {', '.join(valid_timeframes)}")
 
             news_text = await self.news_fetcher.get_news_for_symbol(symbol)
-            # ❗ لا نستخدم _get_pattern_analysis هنا لأن التحليل المسبق لا يُرسل
-            # pattern_analysis = await self._get_pattern_analysis(data_payload) # <-- تم إزالة هذا السطر
             whale_data = data_payload.get('whale_data', {})
 
-            # ❗ تعديل: تمرير None لـ pattern_analysis
-            prompt = self._create_comprehensive_trading_prompt(data_payload, news_text, None, whale_data)
+            # 🔴 جديد: الحصول على تغذية راجعة من محرك التعلم
+            best_learned_exit = "None"
+            learning_feedback = "No learning data yet."
+            if self.learning_engine and self.learning_engine.initialized:
+                best_learned_exit = await self.learning_engine.get_best_exit_profile(target_strategy)
+                if best_learned_exit != "unknown":
+                    learning_feedback = f"Learning System Feedback: For the '{target_strategy}' strategy, the '{best_learned_exit}' exit profile has historically performed best. Please consider this."
+            
+            prompt = self._create_comprehensive_trading_prompt(data_payload, news_text, None, whale_data, learning_feedback)
 
             if self.r2_service:
                 analysis_data = {
@@ -234,7 +234,7 @@ class LLMService:
                     'final_score': data_payload.get('final_score'),
                     'enhanced_final_score': data_payload.get('enhanced_final_score'),
                     'target_strategy': target_strategy,
-                    'pattern_analysis': None, # ❗ تم التغيير: لا يوجد تحليل مسبق
+                    'learning_feedback_provided': learning_feedback, # 🔴 جديد
                     'whale_data_available': whale_data.get('data_available', False),
                     'total_candles': total_candles,
                     'timeframes_count': timeframes_count,
@@ -242,7 +242,7 @@ class LLMService:
                     'timestamp': datetime.now().isoformat()
                 }
                 await self.r2_service.save_llm_prompts_async(
-                    symbol, 'comprehensive_trading_decision_llm_pattern', prompt, analysis_data # غيرت النوع قليلاً للتمييز
+                    symbol, 'comprehensive_trading_decision_v2', prompt, analysis_data
                 )
 
             async with self.semaphore:
@@ -251,7 +251,6 @@ class LLMService:
             decision_dict = self._parse_llm_response_enhanced(response, target_strategy, symbol)
             if decision_dict:
                 decision_dict['model_source'] = self.model_name
-                # ❗ لا نضيف pattern_analysis هنا لأنه غير متوفر
                 decision_dict['whale_data_integrated'] = whale_data.get('data_available', False)
                 decision_dict['total_candles_analyzed'] = total_candles
                 decision_dict['timeframes_analyzed'] = timeframes_count
@@ -277,10 +276,22 @@ class LLMService:
                  print(f"❌ فشل تحليل JSON (safe_json_parse) لـ {symbol}: {response_text}")
                  return None
 
-            # ❗ تعديل: تحديث الحقول المطلوبة لتعكس التغييرات في الـ prompt
-            required_fields = ['action', 'reasoning', 'risk_assessment', 'stop_loss', 'take_profit', 'expected_target_minutes', 'confidence_level', 'pattern_identified_by_llm']
+            # 🔴 تحديث: إضافة الحقول الجديدة للخروج
+            required_fields = [
+                'action', 'reasoning', 'risk_assessment', 'stop_loss', 'take_profit', 
+                'expected_target_minutes', 'confidence_level', 'pattern_identified_by_llm',
+                'exit_profile', 'exit_parameters'
+            ]
             if not validate_required_fields(decision_data, required_fields):
                 print(f"❌ حقول مطلوبة مفقودة في استجابة النموذج لـ {symbol}")
+                # طباعة الحقول المفقودة
+                missing = [f for f in required_fields if f not in decision_data]
+                print(f"   MIA: {missing}")
+                return None
+            
+            # 🔴 التحقق من exit_parameters
+            if not isinstance(decision_data['exit_parameters'], dict):
+                print(f"❌ الحقل 'exit_parameters' ليس قاموساً (dict) صالحاً لـ {symbol}")
                 return None
 
             action = decision_data.get('action')
@@ -302,22 +313,18 @@ class LLMService:
             print(f"❌ خطأ في تحليل استجابة النموذج لـ {symbol}: {e}")
             return None
 
-    # ❗ دالة _get_pattern_analysis لم تعد مستخدمة هنا، لكن قد تُستخدم في إعادة التحليل
     async def _get_pattern_analysis(self, data_payload):
-        # ... (الكود الأصلي هنا)
         try:
             symbol = data_payload['symbol']
             ohlcv_data = data_payload.get('raw_ohlcv') or data_payload.get('ohlcv')
             if ohlcv_data:
-                 # Note: This analysis result is used ONLY for re-analysis prompts now.
                  return await self.pattern_engine.analyze_chart_patterns(symbol, ohlcv_data)
             return None
         except Exception as e:
             print(f"❌ فشل تحليل الأنماط (قد يكون لإعادة التحليل) لـ {data_payload.get('symbol')}: {e}")
             return None
 
-    def _create_comprehensive_trading_prompt(self, payload: dict, news_text: str, pattern_analysis: dict, whale_data: dict) -> str:
-        # ❗ pattern_analysis لم يعد مستخدماً هنا وسيتم تجاهله (أو يكون None)
+    def _create_comprehensive_trading_prompt(self, payload: dict, news_text: str, pattern_analysis: dict, whale_data: dict, learning_feedback: str) -> str:
         symbol = payload.get('symbol', 'N/A')
         current_price = payload.get('current_price', 'N/A')
         reasons = payload.get('reasons_for_candidacy', [])
@@ -331,15 +338,16 @@ class LLMService:
         ohlcv_data = payload.get('raw_ohlcv') or payload.get('ohlcv', {})
 
         final_score_display = f"{final_score:.3f}" if isinstance(final_score, (int, float)) else str(final_score)
-        enhanced_score_display = f"{enhanced_final_score:.3f}" if isinstance(enhanced_final_score, (int, float)) else str(enhanced_final_score)
+        enhanced_score_display = f"{enhanced_score_display:.3f}" if isinstance(enhanced_score, (int, float)) else str(enhanced_score)
 
         indicators_summary = format_technical_indicators(advanced_indicators)
         strategies_summary = format_strategy_scores(strategy_scores, recommended_strategy)
-        # ❗ تم حذف pattern_summary = self._format_pattern_analysis(pattern_analysis)
         whale_analysis_section = format_whale_analysis_for_llm(whale_data)
-        candle_data_section = self._format_candle_data_comprehensive(ohlcv_data) # يعتمد على PatternAnalysisEngine للتنسيق
+        candle_data_section = self._format_candle_data_comprehensive(ohlcv_data) 
         market_context_section = self._format_market_context(sentiment_data)
-        # ⚠️ ملاحظة: تأكد من أن market_context_section ليست فارغة
+        
+        # 🔴 جديد: إضافة التغذية الراجعة للتعلم
+        learning_feedback_section = f"🧠 LEARNING ENGINE FEEDBACK:\n{learning_feedback}"
 
         prompt = f"""
 COMPREHENSIVE TRADING ANALYSIS FOR {symbol}
@@ -353,6 +361,8 @@ COMPREHENSIVE TRADING ANALYSIS FOR {symbol}
 - System Score: {final_score_display}
 - Enhanced Score: {enhanced_score_display}
 
+{learning_feedback_section}
+
 📊 TECHNICAL INDICATORS (ALL TIMEFRAMES):
 {indicators_summary}
 
@@ -375,40 +385,46 @@ COMPREHENSIVE TRADING ANALYSIS FOR {symbol}
 📋 REASONS FOR SYSTEM CANDIDACY (Layer 1 & 2 Screening):
 {chr(10).join([f"• {reason}" for reason in reasons]) if reasons else "No specific reasons provided"}
 
-🎯 TRADING DECISION INSTRUCTIONS (SPOT ONLY - LLM MUST ANALYZE PATTERNS):
-
-1.  **PERFORM CHART PATTERN ANALYSIS:** Based *ONLY* on the provided 'RAW CANDLE DATA SUMMARY & STATISTICS' section above, identify relevant chart patterns (Triangles, Flags, Head & Shoulders, Double Tops/Bottoms, etc.) and candlestick patterns (Engulfing, Doji, Hammer, etc.) across the available timeframes. Determine the likely direction and strength implied by these patterns.
-2.  **INTEGRATE ALL DATA:** Combine YOUR pattern analysis with the technical indicators, strategy analysis, whale activity, market context, and news.
-3.  **ADHERE STRICTLY TO SPOT TRADING RULES:** Only decide 'BUY' (for a LONG position) if your comprehensive analysis indicates a high probability of upward movement. If the analysis suggests downward movement or high uncertainty, the ONLY valid action is 'HOLD'. DO NOT suggest 'SELL'.
-4.  **RISK ASSESSMENT:** Evaluate the risk-reward ratio specifically for a potential 'BUY' (LONG) trade based on support/resistance levels derived from the candle data summary.
-5.  **JUSTIFY YOUR DECISION:** Clearly explain your reasoning, detailing how YOUR pattern analysis and other data points led to the final 'BUY' or 'HOLD' decision.
-
-CRITICAL: You MUST explicitly state the pattern(s) you identified from the candle data summary and how they influenced your decision. Provide specific price levels (Stop Loss, Take Profit) suitable for a BUY/LONG trade if recommending 'BUY'.
-
-OUTPUT FORMAT (JSON - SPOT ONLY):
+🎯 TRADING DECISION INSTRUCTIONS (SPOT ONLY - LLM MUST ANALYZE PATTERNS AND DEFINE EXIT STRATEGY):
+
+1.  **PERFORM CHART PATTERN ANALYSIS:** Based *ONLY* on the provided 'RAW CANDLE DATA SUMMARY & STATISTICS' section above, identify relevant chart patterns (Triangles, Flags, Head & Shoulders, etc.) and candlestick patterns (Engulfing, Doji, etc.).
+2.  **INTEGRATE ALL DATA:** Combine YOUR pattern analysis with technicals, strategy analysis, whale activity, market context, news, and the 'LEARNING ENGINE FEEDBACK'.
+3.  **ADHERE STRICTLY TO SPOT TRADING RULES:** Only decide 'BUY' (LONG) or 'HOLD'. DO NOT suggest 'SELL'.
+4.  **DEFINE EXIT STRATEGY (CRITICAL):** If (and only if) action is 'BUY', you MUST define the dynamic exit strategy (Exit Profile) and its parameters. This profile will be executed by a separate tactical bot.
+    -   `"exit_profile"`: Choose one: "ATR_TRAILING" (Recommended for trends/breakouts), "FIXED_TARGET" (Recommended for mean reversion/scalping), "TIME_BASED" (Exit after X minutes regardless), "SIGNAL_BASED" (Emergency exit on opposite signal - *Use with caution*).
+    -   `"exit_parameters"`: Define parameters for the chosen profile.
+        -   For "ATR_TRAILING": {{"atr_multiplier": 2.0, "atr_period": 14, "break_even_trigger_percent": 1.5}} (break_even_trigger_percent moves stop to entry when profit hits 1.5%)
+        -   For "FIXED_TARGET": {{"time_stop_minutes": 120}} (Hard stop if target not hit in 120 mins)
+        -   For "TIME_BASED": {{"exit_after_minutes": 60}}
+        -   For "SIGNAL_BASED": {{"emergency_volume_spike_multiplier": 5.0}} (Exit if reverse volume spike > 5x average)
+5.  **DEFINE HARD STOPS:** You must still provide the initial "hard" stop_loss (catastrophic failure stop) and the final "take_profit" target. The dynamic exit profile operates *within* these boundaries.
+
+OUTPUT FORMAT (JSON - SPOT ONLY - INCLUDE EXIT PROFILE):
 {{
     "action": "BUY/HOLD",
-    "reasoning": "Detailed explanation integrating ALL data sources, starting with the patterns identified from the candle summary, and justifying the BUY or HOLD decision based on SPOT trading logic.",
-    "pattern_identified_by_llm": "Name of the primary pattern(s) identified by analyzing the provided candle data summary (e.g., 'Bull Flag on 1H', 'Double Bottom on 4H', 'No Clear Pattern')",
-    "pattern_influence": "Explain how the identified pattern(s) influenced the decision (e.g., 'Bull flag confirmed uptrend, supporting BUY', 'Descending triangle indicated weakness, leading to HOLD')",
+    "reasoning": "Detailed explanation integrating ALL data sources, starting with the patterns identified from the candle summary, and justifying the BUY or HOLD decision. Explain *why* the chosen exit_profile is appropriate.",
+    "pattern_identified_by_llm": "Name of the primary pattern(s) identified (e.g., 'Bull Flag on 1H', 'No Clear Pattern')",
+    "pattern_influence": "Explain how the identified pattern(s) influenced the decision.",
     "risk_assessment": "low/medium/high",
-    "stop_loss": 0.000000, # Required if action is BUY, 0 if HOLD
-    "take_profit": 0.000000, # Required if action is BUY, 0 if HOLD
-    "expected_target_minutes": 15, # Required if action is BUY, 0 if HOLD
+    
+    "stop_loss": 0.000000, # Required if action is BUY (Hard stop loss), 0 if HOLD
+    "take_profit": 0.000000, # Required if action is BUY (Final target), 0 if HOLD
+    
+    "exit_profile": "FIXED_TARGET", # (Required if BUY, "None" if HOLD). Choose from: "ATR_TRAILING", "FIXED_TARGET", "TIME_BASED", "SIGNAL_BASED"
+    "exit_parameters": {{ "time_stop_minutes": 120 }}, # (Required if BUY, {{}} if HOLD). Must match the chosen exit_profile.
+    
+    "expected_target_minutes": 15, # Required if action is BUY (Time to reach final TP), 0 if HOLD
     "confidence_level": 0.85, # Confidence in the BUY or HOLD decision
     "strategy": "{target_strategy}", # The strategy context provided
-    "whale_influence": "How whale data influenced the BUY/HOLD decision (e.g., 'Lack of whale activity supported HOLD', 'Whale buying pressure reinforced BUY')",
+    "whale_influence": "How whale data influenced the BUY/HOLD decision",
     "key_support_level": 0.000000, # Derived from candle data analysis
     "key_resistance_level": 0.000000, # Derived from candle data analysis
-    "risk_reward_ratio": 2.5 # Calculated ONLY for a potential BUY trade, 0 if HOLD
+    "risk_reward_ratio": 2.5 # Calculated for the HARD SL/TP, 0 if HOLD
 }}
 """
         return prompt
 
 
-    # ❗ تم حذف دالة _format_pattern_analysis لأنها لم تعد مستخدمة هنا
-
-
     def _format_candle_data_comprehensive(self, ohlcv_data):
         """تنسيق شامل لبيانات الشموع الخام"""
         if not ohlcv_data:
@@ -429,8 +445,6 @@ OUTPUT FORMAT (JSON - SPOT ONLY):
             summary = f"📊 Available Timeframes: {', '.join(timeframes_available)}\n"
             summary += f"📈 Total Candles Available: {total_candles}\n\n"
 
-            # استخدام محرك الأنماط فقط لتنسيق البيانات الخام نصياً
-            # The LLM will perform the actual pattern *analysis* based on this text.
             raw_candle_analysis_text = self.pattern_engine._format_chart_data_for_llm(ohlcv_data)
 
             summary += raw_candle_analysis_text
@@ -488,7 +502,6 @@ OUTPUT FORMAT (JSON - SPOT ONLY):
 
     def _format_market_context(self, sentiment_data):
         """تنسيق سياق السوق"""
-        # ⚠️ تأكد من أن هذه الدالة تتلقى بيانات صالحة من app.py/data_manager.py
         if not sentiment_data or sentiment_data.get('data_quality', 'LOW') == 'LOW':
             return "Market context data not available or incomplete."
 
@@ -497,13 +510,11 @@ OUTPUT FORMAT (JSON - SPOT ONLY):
         market_trend = sentiment_data.get('market_trend', 'N/A') # e.g., 'bull_market', 'bear_market', 'sideways_market'
 
         lines = [
-            # "🌍 GLOBAL MARKET CONTEXT:", # Changed title for clarity
             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'}"
         ]
 
-        # Including General Whale Activity Sentiment if available
         general_whale = sentiment_data.get('general_whale_activity', {})
         if general_whale and general_whale.get('sentiment') != 'NEUTRAL': # Only show if not neutral
              whale_sentiment = general_whale.get('sentiment', 'N/A')
@@ -526,12 +537,18 @@ OUTPUT FORMAT (JSON - SPOT ONLY):
                 return None
 
             news_text = await self.news_fetcher.get_news_for_symbol(symbol)
-            # ❗ نحتاج إلى تحليل الأنماط *هنا* لإعادة التحليل
             pattern_analysis = await self._get_pattern_analysis(processed_data)
             whale_data = processed_data.get('whale_data', {})
 
-            # ❗ تعديل: تمرير pattern_analysis لدالة إنشاء الـ prompt
-            prompt = self._create_re_analysis_prompt(trade_data, processed_data, news_text, pattern_analysis, whale_data)
+            # 🔴 جديد: الحصول على تغذية راجعة لإعادة التحليل
+            best_learned_exit = "None"
+            learning_feedback = "No learning data for re-analysis."
+            if self.learning_engine and self.learning_engine.initialized:
+                best_learned_exit = await self.learning_engine.get_best_exit_profile(original_strategy)
+                if best_learned_exit != "unknown":
+                    learning_feedback = f"Learning System Feedback: For the '{original_strategy}' strategy, the '{best_learned_exit}' exit profile is typically best. Does this still apply?"
+
+            prompt = self._create_re_analysis_prompt(trade_data, processed_data, news_text, pattern_analysis, whale_data, learning_feedback)
 
             if self.r2_service:
                 analysis_data = {
@@ -539,11 +556,12 @@ OUTPUT FORMAT (JSON - SPOT ONLY):
                     'entry_price': trade_data.get('entry_price'),
                     'current_price': processed_data.get('current_price'),
                     'original_strategy': original_strategy,
-                    'pattern_analysis': pattern_analysis, # نتيجة التحليل المسبق
+                    'learning_feedback_provided': learning_feedback, # 🔴 جديد
+                    'pattern_analysis': pattern_analysis, 
                     'whale_data_available': whale_data.get('data_available', False)
                 }
                 await self.r2_service.save_llm_prompts_async(
-                    symbol, 'trade_reanalysis_with_pattern', prompt, analysis_data # تمييز النوع
+                    symbol, 'trade_reanalysis_v2', prompt, analysis_data
                 )
 
             async with self.semaphore:
@@ -579,6 +597,19 @@ OUTPUT FORMAT (JSON - SPOT ONLY):
                  print(f"⚠️ النموذج اقترح إجراء إعادة تحليل غير مدعوم ({action}) لـ {symbol}. سيتم اعتباره HOLD.")
                  decision_data['action'] = 'HOLD'
 
+            # 🔴 تحديث: إذا كان UPDATE_TRADE، يجب أن يتضمن ملف خروج جديد
+            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"❌ حقول مطلوبة مفقودة لـ UPDATE_TRADE لـ {symbol}")
+                    missing = [f for f in required_update_fields if f not in decision_data]
+                    print(f"   MIA: {missing}")
+                    decision_data['action'] = 'HOLD' # العودة إلى HOLD إذا كان التحديث غير مكتمل
+                elif not isinstance(decision_data['new_exit_parameters'], dict):
+                    print(f"❌ الحقل 'new_exit_parameters' ليس قاموساً صالحاً لـ {symbol}")
+                    decision_data['action'] = 'HOLD'
+
+
             strategy_value = decision_data.get('strategy')
             if not strategy_value or strategy_value == 'unknown':
                 decision_data['strategy'] = fallback_strategy
@@ -588,13 +619,19 @@ OUTPUT FORMAT (JSON - SPOT ONLY):
             print(f"Error parsing re-analysis response for {symbol}: {e}")
             return None
 
-    def _create_re_analysis_prompt(self, trade_data: dict, processed_data: dict, news_text: str, pattern_analysis: dict, whale_data: dict) -> str:
-        # ❗ pattern_analysis مطلوب هنا
+    def _create_re_analysis_prompt(self, trade_data: dict, processed_data: dict, news_text: str, pattern_analysis: dict, whale_data: dict, learning_feedback: str) -> str:
         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')
         original_trade_type = "LONG" # SPOT only
+        
+        # 🔴 جديد: جلب ملف الخروج الحالي
+        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', {}))
+        
+        # 🔴 جديد: إضافة التغذية الراجعة للتعلم
+        learning_feedback_section = f"🧠 LEARNING ENGINE FEEDBACK:\n{learning_feedback}"
 
         try:
             price_change = ((current_price - entry_price) / entry_price) * 100 if entry_price else 0
@@ -603,11 +640,9 @@ OUTPUT FORMAT (JSON - SPOT ONLY):
             price_change_display = "N/A"
 
         indicators_summary = format_technical_indicators(processed_data.get('advanced_indicators', {}))
-        # ❗ استخدام دالة التنسيق _format_pattern_analysis هنا
         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', {}))
-        # ❗ إضافة ملخص الشموع الخام لإعادة التحليل أيضاً
         ohlcv_data = processed_data.get('raw_ohlcv') or processed_data.get('ohlcv', {})
         candle_data_section = self._format_candle_data_comprehensive(ohlcv_data)
 
@@ -623,6 +658,10 @@ TRADE RE-ANALYSIS FOR {symbol} (SPOT ONLY - Currently Open LONG Position)
 - Current Price: {current_price}
 - Current Performance: {price_change_display}
 - Trade Age: {trade_data.get('hold_duration_minutes', 'N/A')} minutes
+- Current Exit Profile: {current_exit_profile}
+- Current Exit Parameters: {current_exit_params}
+
+{learning_feedback_section}
 
 🔄 UPDATED TECHNICAL ANALYSIS:
 {indicators_summary}
@@ -645,30 +684,50 @@ TRADE RE-ANALYSIS FOR {symbol} (SPOT ONLY - Currently Open LONG Position)
 
 🎯 RE-ANALYSIS INSTRUCTIONS (SPOT - LONG POSITION):
 
-1.  **ANALYZE UPDATED DATA:** Evaluate if the original LONG thesis still holds based on the updated raw candle data summary, technicals, patterns (provided above), whale activity, and market context.
-2.  **FOCUS ON RECENT PRICE ACTION:** Pay close attention to the latest candle formations and volume in the 'UPDATED RAW CANDLE DATA' section.
-3.  **VALIDATE PATTERNS:** Consider the 'UPDATED PATTERN ANALYSIS RESULTS' provided. Does the recent price action confirm or invalidate these patterns?
-4.  **ASSESS RISK:** Evaluate the current risk-reward ratio for HOLDING the LONG position using latest price action and updated support/resistance levels derived from the candle data.
-5.  **DECIDE ACTION (HOLD/CLOSE/UPDATE):** Based on the comprehensive analysis, decide whether to HOLD, CLOSE_TRADE (exit the LONG position), or UPDATE_TRADE (adjust SL/TP for the LONG position). DO NOT suggest SHORTING.
-6.  **PROVIDE DETAILS:** If updating, provide specific new SL/TP levels. Justify your decision clearly, integrating all data points.
+1.  **ANALYZE UPDATED DATA:** Evaluate if the original LONG thesis still holds based on the updated raw candle data summary, technicals, patterns (provided above), whale activity, market context, and learning feedback.
+2.  **VALIDATE PATTERNS:** Consider the 'UPDATED PATTERN ANALYSIS RESULTS' provided. Does the recent price action confirm or invalidate these patterns?
+3.  **DECIDE ACTION (HOLD/CLOSE/UPDATE):** Based on the comprehensive analysis, decide whether to HOLD, CLOSE_TRADE (exit the LONG position), or UPDATE_TRADE (adjust SL/TP and/or the Exit Profile for the LONG position).
+4.  **IF UPDATING (CRITICAL):** If action is UPDATE_TRADE, you MUST provide:
+    -   `new_stop_loss` (New hard stop)
+    -   `new_take_profit` (New final target)
+    -   `new_exit_profile`: (e.g., "ATR_TRAILING") - Can be the same or different.
+    -   `new_exit_parameters`: (e.g., {{"atr_multiplier": 1.5}}) - Must match the new profile.
+5.  **PROVIDE DETAILS:** Justify your decision clearly, integrating all data points.
 
-CRITICAL: The decision must be one of HOLD, CLOSE_TRADE, or UPDATE_TRADE for the existing LONG position. Explain how recent candle data and provided patterns influenced your decision.
+CRITICAL: The decision must be one of HOLD, CLOSE_TRADE, or UPDATE_TRADE for the existing LONG position.
 
 OUTPUT FORMAT (JSON - SPOT RE-ANALYSIS):
 {{
     "action": "HOLD/CLOSE_TRADE/UPDATE_TRADE",
-    "reasoning": "Comprehensive justification for HOLD, CLOSE, or UPDATE of the LONG position, based on updated analysis with emphasis on recent candle patterns and the provided pattern analysis results.",
-    "new_stop_loss": 0.000000, # If action is UPDATE_TRADE, else 0
-    "new_take_profit": 0.000000, # If action is UPDATE_TRADE, else 0
+    "reasoning": "Comprehensive justification for HOLD, CLOSE, or UPDATE of the LONG position, based on updated analysis. If UPDATE, explain why the new exit profile/parameters are better.",
+    
+    "new_stop_loss": 0.000000, # (Required if UPDATE_TRADE, else 0)
+    "new_take_profit": 0.000000, # (Required if UPDATE_TRADE, else 0)
+    "new_exit_profile": "None", # (Required if UPDATE_TRADE, else "None")
+    "new_exit_parameters": {{}}, # (Required if UPDATE_TRADE, else {{}})
+    
     "new_expected_minutes": 15, # If action is UPDATE_TRADE or HOLD (new expectation), else 0
     "confidence_level": 0.85, # Confidence in the re-analysis decision
     "strategy": "{strategy}", # Original strategy context
-    "whale_influence_reanalysis": "How updated whale data influenced the decision for the LONG trade",
-    "pattern_influence_reanalysis": "How updated raw candle patterns AND the provided pattern analysis results influenced the decision",
+    "whale_influence_reanalysis": "How updated whale data influenced the decision",
+    "pattern_influence_reanalysis": "How updated candle patterns AND provided patterns influenced the decision",
     "risk_adjustment": "low/medium/high" # Current risk level if HOLDING
 }}
 """
         return prompt
+        
+    # ❗ دالة _format_pattern_analysis مُضافة هنا لأنها أُزيلت من الأعلى
+    def _format_pattern_analysis(self, pattern_analysis):
+        """تنسيق تحليل الأنماط للنموذج الضخم"""
+        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')
+
+        return f"System Pattern Analysis: Detected '{pattern}' on {timeframe} timeframe with {confidence:.2f} confidence. Predicted direction: {direction}."
 
     @_rate_limit_nvidia_api
     async def _call_llm(self, prompt: str) -> str:
@@ -681,29 +740,28 @@ OUTPUT FORMAT (JSON - SPOT RE-ANALYSIS):
                         messages=[{"role": "user", "content": prompt}],
                         temperature=self.temperature,
                         seed=int(time.time()), # Use time for seed
-                        max_tokens=4000 # Increased max_tokens slightly
+                        max_tokens=4000 
                     )
-                    # Basic validation of response content
                     content = response.choices[0].message.content
                     if content and '{' in content and '}' in content:
                         return content
                     else:
                         print(f"⚠️ LLM returned invalid content (attempt {attempt+1}): {content[:100]}...")
-                        if attempt == 0: await asyncio.sleep(1) # Wait before retry
+                        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 # Let backoff handle retries for these specific errors
+                    raise 
                 except Exception as e:
                     print(f"❌ Unexpected LLM API error (attempt {attempt+1}): {e}")
-                    if attempt == 0: await asyncio.sleep(2) # Wait longer for unexpected errors
-                    elif attempt == 1: raise # Raise error after second failed attempt
+                    if attempt == 0: await asyncio.sleep(2) 
+                    elif attempt == 1: raise 
 
             print("❌ LLM failed to return valid content after retries.")
-            return "" # Return empty string if all attempts fail
+            return "" 
 
         except Exception as e:
             print(f"❌ Final failure in _call_llm after backoff retries: {e}")
-            raise # Re-raise the exception after backoff fails
+            raise 
 
-print("✅ LLM Service loaded - Updated Prompts for SPOT ONLY & LLM Pattern Analysis")
+print("✅ LLM Service loaded - V2 (Dynamic Exit Profiles & Learning Feedback)")