Update LLM.py

LLM.py

@@ -1,13 +1,11 @@
-# LLM.py (V13.4 - The "Heavyweight" Omniscient Brain)
-# استعادة كامل التفاصيل في البرومبتات لضمان عدم وجود أي اختصارات.
-
+# LLM.py (V13.5 - Fixed Variable Name Error)
 import os, traceback, json, re, time
 from datetime import datetime
 from typing import Dict, Any, Optional
 from openai import AsyncOpenAI, RateLimitError, APIError
 
 # ==============================================================================
-# 🔌 إعدادات الاتصال (مطابقة للأصل تماماً)
+# 🔌 إعدادات الاتصال
 # ==============================================================================
 LLM_API_URL = os.getenv("LLM_API_URL", "https://integrate.api.nvidia.com/v1")
 LLM_API_KEY = os.getenv("LLM_API_KEY")
@@ -32,14 +30,10 @@ class LLMService:
         )
         self.r2_service = None
         self.learning_hub = None 
-        
-        print(f"🧠 [LLM V13.4] Heavyweight Brain Initialized: {LLM_MODEL}")
+        print(f"🧠 [LLM V13.5] Brain Online: {LLM_MODEL}")
 
     async def _call_llm(self, prompt: str) -> Optional[str]:
-        """إرسال الطلب مع تفعيل وضع التفكير العميق بدقة"""
-        # ⚠️ هام: هذا الإعداد دقيق جداً لتفعيل قدرات Nemotron الخاصة
         system_prompt = "detailed thinking on"
-
         try:
             response = await self.client.chat.completions.create(
                 model=LLM_MODEL,
@@ -61,7 +55,6 @@ class LLMService:
             return None
 
     def _parse_json_secure(self, text: str) -> Optional[Dict]:
-        """محلل JSON قوي يستخرج البيانات من أي نص"""
         try:
             match = re.search(r'\{.*\}', text, re.DOTALL)
             if match: return json.loads(match.group(0))
@@ -69,24 +62,19 @@ class LLMService:
         return None
 
     # ==================================================================
-    # 🧠 الوظيفة 1: قرار الدخول الاستراتيجي (تحليل شامل ومفصل)
+    # 🧠 الوظائف الرئيسية
     # ==================================================================
     async def get_trading_decision(self, candidate_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
         symbol = candidate_data.get('symbol', 'UNKNOWN')
         try:
-            # جلب سياق التعلم السابق إن وجد
-            learning_context = "Playbook: No specific prior learning for this scenario."
+            learning_context = "Playbook: No specific prior learning."
             if self.learning_hub:
                 learning_context = await self.learning_hub.get_active_context_for_llm("general", f"{symbol} entry analysis")
 
-            # 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)
 
-            # 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)
 
@@ -96,9 +84,6 @@ class LLMService:
             traceback.print_exc()
             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]]:
         symbol = trade_data.get('symbol', 'UNKNOWN')
         try:
@@ -108,7 +93,6 @@ class LLMService:
                 learning_context = await self.learning_hub.get_active_context_for_llm("strategy", f"{symbol} re-eval {strategy}")
 
             prompt = self._create_heavyweight_reanalysis_prompt(trade_data, current_data, learning_context)
-            
             response_text = await self._call_llm(prompt)
             decision = self._parse_json_secure(response_text)
 
@@ -121,32 +105,26 @@ class LLMService:
             return None
 
     # ==================================================================
-    # 📝 قسم هندسة البرومبتات (تفاصيل كاملة بدون اختصارات)
+    # 📝 هندسة البرومبتات (مصححة)
     # ==================================================================
     def _create_heavyweight_entry_prompt(self, data: Dict[str, Any], learning_context: str) -> str:
-        """
-        إنشاء برومبت ضخم يحتوي على كل شاردة وواردة من البيانات المتاحة.
-        """
         symbol = data.get('symbol')
         current_price = data.get('current_price')
         
-        # 1. تفاصيل الطبقات الس��بقة (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"
-        
         pat_details = data.get('pattern_details', {})
         pat_name = pat_details.get('pattern_detected', 'None')
         pat_conf = pat_details.get('pattern_confidence', 0)
-        
         mc_score = data.get('components', {}).get('mc_score', 0)
+        
+        # ✅ تصحيح الاسم هنا واستخدامه بالأسفل
         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', {})
-        
         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)}
@@ -154,16 +132,11 @@ class LLMService:
    - 24H Whale Transaction Count: {whale_24h.get('whale_transfers_count', 0)}
    - Relative Flow Impact (24H): {whale_24h.get('relative_net_flow_percent', 0):.4f}%
 """
-
-        # 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)
+        news_text = data.get('news_text', 'No specific news available.')
         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"""
@@ -173,7 +146,7 @@ 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
+* Layer 1 Technical Score: {l1_total:.4f} / 1.0   <-- ✅ تم التصحيح هنا
   - 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}
@@ -206,28 +179,21 @@ REQUIRED OUTPUT (Strict JSON format ONLY):
 {{
   "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').",
+  "reasoning": "A rigorous, professional justification citing specific raw evidence.",
   "strategy_directive": "MOMENTUM_BREAKOUT" or "DIP_ACCUMULATION" or "SCALP_REVERSAL",
   "key_risk_factor": "Identify the single biggest risk based on raw evidence."
 }}
 """
 
     def _create_heavyweight_reanalysis_prompt(self, trade: Dict, current: Dict, learning_context: str) -> str:
-        """
-        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
+        duration_min = (datetime.now(timezone.utc) - datetime.fromisoformat(trade.get('entry_time').replace('Z', '+00:00'))).total_seconds() / 60
         titan_now = current.get('titan_score', 0)
-        
         whale_now = current.get('whale_data', {})
         whale_1h_net = whale_now.get('exchange_flows', {}).get('net_flow_usd', 0)
-        
         news_now = current.get('news_text', 'No new significant news.')
 
         return f"""
@@ -245,7 +211,6 @@ TIME IN TRADE: {duration_min:.1f} minutes
 ========== 🆕 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]}\"\"\"
 
@@ -262,5 +227,4 @@ Output Strict JSON ONLY:
   "reasoning": "Professional assessment of current risk vs original thesis."
 }}
 """
-
 print("✅ LLM Service V13.4 (Heavyweight Omniscient Brain) Loaded - NO SHORTCUTS")