Update LLM.py

LLM.py

@@ -1,44 +1,56 @@
-# LLM.py (V13.5 - Fixed Variable Name Error)
-import os, traceback, json, re, time
+# LLM.py (V13.6 - Final Production Version - FULL PROMPTS)
+import os
+import traceback
+import json
+import re
+import time
 from datetime import datetime
 from typing import Dict, Any, Optional
 from openai import AsyncOpenAI, RateLimitError, APIError
 
 # ==============================================================================
-# 🔌 إعدادات الاتصال
+# 🔌 إعدادات الاتصال بالنموذج (Model Connection Settings)
 # ==============================================================================
 LLM_API_URL = os.getenv("LLM_API_URL", "https://integrate.api.nvidia.com/v1")
 LLM_API_KEY = os.getenv("LLM_API_KEY")
 LLM_MODEL = os.getenv("LLM_MODEL", "nvidia/llama-3.1-nemotron-ultra-253b-v1")
 
+# بارامترات التوليد (مضبوطة لصرامة التحليل)
 LLM_TEMPERATURE = 0.2
 LLM_TOP_P = 0.7
-LLM_MAX_TOKENS = 16384
+LLM_MAX_TOKENS = 16384  # السماح بأقصى طول استجابة ممكن
 LLM_FREQUENCY_PENALTY = 0.8
 LLM_PRESENCE_PENALTY = 0.5
-CLIENT_TIMEOUT = 300.0 
+CLIENT_TIMEOUT = 300.0  # 5 دقائق مهلة للردود المعقدة
 
 class LLMService:
     def __init__(self):
         if not LLM_API_KEY:
-            raise ValueError("❌ [LLM] LLM_API_KEY is missing!")
+            raise ValueError("❌ [LLM FATAL] LLM_API_KEY environment variable is missing!")
         
         self.client = AsyncOpenAI(
             base_url=LLM_API_URL,
             api_key=LLM_API_KEY,
             timeout=CLIENT_TIMEOUT
         )
+        # سيتم حقن هذه التبعيات لاحقاً من app.py
         self.r2_service = None
         self.learning_hub = None 
-        print(f"🧠 [LLM V13.5] Brain Online: {LLM_MODEL}")
+        
+        print(f"🧠 [LLM V13.6] Omniscient Brain Online. Model: {LLM_MODEL}")
 
     async def _call_llm(self, prompt: str) -> Optional[str]:
-        system_prompt = "detailed thinking on"
+        """
+        تنفيذ استدعاء API للنموذج مع تفعيل وضع التفكير العميق (Nemotron Specific).
+        """
+        # ⚠️ عبارة تفعيل وضع التفكير الخاصة بنموذج Nemotron
+        system_prompt_trigger = "detailed thinking on"
+
         try:
             response = await self.client.chat.completions.create(
                 model=LLM_MODEL,
                 messages=[
-                    {"role": "system", "content": system_prompt},
+                    {"role": "system", "content": system_prompt_trigger},
                     {"role": "user", "content": prompt}
                 ],
                 temperature=LLM_TEMPERATURE,
@@ -47,184 +59,271 @@ class LLMService:
                 frequency_penalty=LLM_FREQUENCY_PENALTY,
                 presence_penalty=LLM_PRESENCE_PENALTY,
                 stream=False,
-                response_format={"type": "json_object"}
+                response_format={"type": "json_object"} # إجبار النموذج على الرد بصيغة JSON
             )
-            return response.choices[0].message.content
+            
+            if response.choices and response.choices[0].message.content:
+                return response.choices[0].message.content
+            else:
+                print("⚠️ [LLM Warning] Received empty response from model.")
+                return None
+                
         except Exception as e:
-            print(f"❌ [LLM Call Error] {e}")
+            print(f"❌ [LLM Call Error] API request failed: {e}")
             return None
 
     def _parse_json_secure(self, text: str) -> Optional[Dict]:
+        """
+        محلل JSON قوي يحاول استخراج أول كائن JSON صالح من النص،
+        حتى لو كان الرد يحتوي على نصوص إضافية قبل أو بعد الـ JSON.
+        """
         try:
-            match = re.search(r'\{.*\}', text, re.DOTALL)
-            if match: return json.loads(match.group(0))
-        except: pass
-        return None
+            # البحث عن نمط {...} عبر الأسطر المتعددة
+            json_match = re.search(r'\{.*\}', text, re.DOTALL)
+            if json_match:
+                return json.loads(json_match.group(0))
+            else:
+                print("⚠️ [LLM Parser] No JSON object found in response text.")
+                return None
+        except json.JSONDecodeError as e:
+            print(f"⚠️ [LLM Parser] JSON decode failed: {e}")
+            return None
+        except Exception as e:
+            print(f"❌ [LLM Parser] Unexpected error: {e}")
+            return None
 
     # ==================================================================
-    # 🧠 الوظائف الرئيسية
+    # 🧠 الوظيفة الرئيسية 1: قرار الدخول الاستراتيجي (Layer 3)
     # ==================================================================
     async def get_trading_decision(self, candidate_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
-        symbol = candidate_data.get('symbol', 'UNKNOWN')
+        """
+        تحليل بيانات المرشح الكاملة واتخاذ قرار نهائي بالمراقبة أو الرفض.
+        """
+        symbol = candidate_data.get('symbol', 'UNKNOWN_ASSET')
         try:
-            learning_context = "Playbook: No specific prior learning."
+            # 1. جلب سياق التعلم المؤسسي (Institutional Memory)
+            learning_context = "Playbook: No specific prior learning records found for this context."
             if self.learning_hub:
                 learning_context = await self.learning_hub.get_active_context_for_llm("general", f"{symbol} entry analysis")
 
+            # 2. بناء البرومبت الشامل (Heavyweight Prompt)
             prompt = self._create_heavyweight_entry_prompt(candidate_data, learning_context)
+            
+            # 3. استشارة النموذج
             response_text = await self._call_llm(prompt)
+            if not response_text: return None
+            
+            # 4. تحليل الرد
             decision = self._parse_json_secure(response_text)
 
-            if self.r2_service and response_text:
+            # 5. أرشفة عملية اتخاذ القرار (للشفافية والتدقيق المستقبلي)
+            if self.r2_service and decision:
                 await self.r2_service.save_llm_prompt_async(symbol, "entry_decision_full", prompt, response_text)
 
             return decision
+
         except Exception as e:
-            print(f"❌ [LLM Entry Error] {symbol}: {e}")
+            print(f"❌ [LLM Entry Error] Critical failure for {symbol}: {e}")
             traceback.print_exc()
             return None
 
+    # ==================================================================
+    # 🔄 الوظيفة الرئيسية 2: إعادة التحليل الدوري (Strategic Re-eval)
+    # ==================================================================
     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')
+        """
+        مراجعة صفقة مفتوحة بناءً على تغيرات السوق وإصدار أوامر تحديث أو خروج.
+        """
+        symbol = trade_data.get('symbol', 'UNKNOWN_ASSET')
         try:
-            strategy = trade_data.get('entry_reason', 'GENERIC')
-            learning_context = "Playbook: Maintain original strategy unless validated invalidation occurs."
+            # 1. جلب سياق التعلم الخاص بالاستراتيجية
+            strategy_name = trade_data.get('entry_reason', 'GENERIC_STRATEGY')
+            learning_context = "Playbook: Stick to original trading plan unless validated invalidation criteria are met."
             if self.learning_hub:
-                learning_context = await self.learning_hub.get_active_context_for_llm("strategy", f"{symbol} re-eval {strategy}")
+                learning_context = await self.learning_hub.get_active_context_for_llm("strategy", f"{symbol} re-eval {strategy_name}")
 
+            # 2. بناء برومبت إعادة التحليل
             prompt = self._create_heavyweight_reanalysis_prompt(trade_data, current_data, learning_context)
+            
+            # 3. استشارة النموذج
             response_text = await self._call_llm(prompt)
+            if not response_text: return None
+            
+            # 4. تحليل الرد
             decision = self._parse_json_secure(response_text)
 
-            if self.r2_service and response_text:
+            # 5. الأرشفة
+            if self.r2_service and decision:
                 await self.r2_service.save_llm_prompt_async(symbol, "re_analysis_full", prompt, response_text)
 
             return decision
+
         except Exception as e:
-            print(f"❌ [LLM Re-Eval Error] {symbol}: {e}")
+            print(f"❌ [LLM Re-Eval Error] Critical failure for {symbol}: {e}")
             return None
 
     # ==================================================================
-    # 📝 هندسة البرومبتات (مصححة)
+    # 📝 قسم هندسة البرومبتات الكاملة (Full Prompt Engineering)
     # ==================================================================
     def _create_heavyweight_entry_prompt(self, data: Dict[str, Any], learning_context: str) -> str:
-        symbol = data.get('symbol')
-        current_price = data.get('current_price')
+        """
+        إنشاء برومبت ضخم يحتوي على كل التفاصيل المتاحة بدون أي اختصار.
+        """
+        symbol = data.get('symbol', 'UNKNOWN')
+        current_price = data.get('current_price', 0.0)
+        
+        # --- بيانات الطبقات السابقة (Preliminary Scores) ---
+        l1_total_score = data.get('enhanced_final_score', 0.0)
+        l2_total_score = data.get('layer2_score', 0.0)
         
-        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)
+        titan_raw_score = data.get('titan_details', {}).get('score', 0.0)
+        titan_trend_label = "STRONG_UPTREND" if titan_raw_score > 0.7 else "UPTREND" if titan_raw_score > 0.5 else "NEUTRAL/WEAK"
         
-        # ✅ تصحيح الاسم هنا واستخدامه بالأسفل
-        l1_total = data.get('enhanced_final_score', 0)
-        l2_total = data.get('layer2_score', 0)
+        pat_data = data.get('pattern_details', {})
+        pattern_name = pat_data.get('pattern_detected', 'None detected')
+        pattern_confidence = pat_data.get('pattern_confidence', 0.0)
+        
+        mc_prob = data.get('components', {}).get('mc_score', 0.0)
 
+        # --- الأدلة الخام (Raw Evidence Data) ---
+        # 1. بيانات الحيتان
         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)}
-   - 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_evidence_block = f"""
+   - [1H Window] Net Flow to Exchanges: ${whale_1h.get('net_flow_usd', 0):,.2f}
+   - [1H Window] Deposit Tx Count: {whale_1h.get('deposit_count', 0)} | Withdrawal Tx Count: {whale_1h.get('withdrawal_count', 0)}
+   - [24H Window] Net Accumulation Flow: ${whale_24h.get('net_flow_usd', 0):,.2f}
+   - [24H Window] Total Large Transactions: {whale_24h.get('whale_transfers_count', 0)}
+   - [24H Window] Relative Flow Impact: {whale_24h.get('relative_net_flow_percent', 0):.4f}% of daily volume
 """
-        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:
-                price_section += f"   - {tf.upper()}: Open={candle[1]}, High={candle[2]}, Low={candle[3]}, Close={candle[4]}, Vol={candle[5]}\n"
 
+        # 2. نص الأخبار الكامل
+        raw_news_text = data.get('news_text', 'No specific news articles found for this asset in the last 12 hours.')
+
+        # 3. لقطة حركة السعر (OHLCV Snapshot)
+        ohlcv_data = data.get('ohlcv_sample', {})
+        price_snapshot_block = ""
+        for tf, candle in ohlcv_data.items():
+            if candle and len(candle) >= 6:
+                # Format: [Timestamp, Open, High, Low, Close, Volume]
+                price_snapshot_block += f"   - {tf.upper()} Frame: Open={candle[1]}, High={candle[2]}, Low={candle[3]}, Close={candle[4]}, Vol={candle[5]}\n"
+
+        # --- تجميع البرومبت النهائي ---
         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_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}
-* Layer 2 Enhanced Score: {l2_total:.4f} / 1.0 (After initial whale/news weighting)
+YOU ARE THE OMNISCIENT BRAIN. A skeptical, master-level crypto trading AI with absolute veto power.
+Your goal is to validate the preliminary findings of your sub-systems and make the FINAL GO/NO-GO decision for asset: {symbol}.
+Current Market Price: {current_price}
+
+========== 🧠 PART 1: SUB-SYSTEM REPORTS (PRELIMINARY OPINIONS) ==========
+Your subordinate analytical layers have flagged this asset with the following scores:
+* Layer 1 Technical Score: {l1_total_score:.4f} / 1.0
+  - Titan ML Trend Model: {titan_raw_score:.4f} ({titan_trend_label})
+  - Chart Pattern Recognition: {pattern_name} (Confidence: {pattern_confidence:.2f})
+  - Monte Carlo Win Probability (1H): {mc_prob:.4f}
+* Layer 2 Enhanced Score: {l2_total_score:.4f} / 1.0 (Adjusted for initial whale/news sentiment)
 
 ========== 🔍 PART 2: RAW EVIDENCE FOR VERIFICATION (THE TRUTH) ==========
-Do not trust the scores above blindly. Verify them against this raw data:
+Do NOT trust the scores above blindly. Verify them against this raw data yourself:
 
-[A] RAW PRICE ACTION SNAPSHOT (OHLCV Last Closed Candles):
-{price_section}
--> TASK: Does this price action confirm the 'Titan Trend' reported above?
+[A] RAW PRICE ACTION SNAPSHOT (Latest Closed Candles OHLCV):
+{price_snapshot_block}
+-> VERIFICATION TASK: Does this raw price action actually confirm the 'Titan Trend' reported above? Look for contradictions.
 
-[B] RAW WHALE ON-CHAIN ACTIVITY:
-{whale_section}
--> TASK: Is there hidden distribution (selling) despite the technical uptrend?
+[B] RAW WHALE ON-CHAIN ACTIVITY (Flows & Accumulation):
+{whale_evidence_block}
+-> VERIFICATION TASK: Is there hidden distribution (selling) occurring despite the technical uptrend? High inflows to exchanges are a red flag.
 
 [C] RAW NEWSWIRE FEED (Latest Headlines & Summaries):
-\"\"\"{news_text}\"\"\"
--> TASK: Are there any immediate red flags, FUD, or regulatory risks in this text?
+\"\"\"
+{raw_news_text}
+\"\"\"
+-> VERIFICATION TASK: Read the text above. Are there any immediate red flags, FUD (Fear, Uncertainty, Doubt), or regulatory risks that the sentiment score might have missed?
 
 ========== 📖 PART 3: INSTITUTIONAL MEMORY (LEARNING PLAYBOOK) ==========
+The following rules have been learned from previous trading outcomes:
 {learning_context}
 
 ========== 🛑 FINAL DECISION TASK ==========
-Perform a deep, step-by-step internal analysis (triggered by your system mode).
+Perform a deep, step-by-step internal analysis (triggered by your 'detailed thinking' mode).
 Compare PART 1 (Opinions) vs PART 2 (Facts).
-If FACTS contradict OPINIONS, you MUST reject the trade.
+If FACTS strongly contradict OPINIONS, you MUST reject the trade regardless of the high scores.
 
-REQUIRED OUTPUT (Strict JSON format ONLY):
+REQUIRED OUTPUT (Strict JSON format ONLY, no other text):
 {{
   "action": "WATCH" or "IGNORE",
-  "confidence_level": 0.00 to 1.00,
-  "reasoning": "A rigorous, professional justification citing specific raw evidence.",
+  "confidence_level": 0.00 to 1.00 (Two decimal places),
+  "reasoning": "A rigorous, professional justification citing specific raw evidence points that swayed your decision.",
   "strategy_directive": "MOMENTUM_BREAKOUT" or "DIP_ACCUMULATION" or "SCALP_REVERSAL",
-  "key_risk_factor": "Identify the single biggest risk based on raw evidence."
+  "key_risk_factor": "Identify the single biggest risk factor based on the raw evidence provided."
 }}
 """
 
     def _create_heavyweight_reanalysis_prompt(self, trade: Dict, current: Dict, learning_context: str) -> str:
-        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(timezone.utc) - datetime.fromisoformat(trade.get('entry_time').replace('Z', '+00:00'))).total_seconds() / 60
-        titan_now = current.get('titan_score', 0)
+        """
+        إنشاء برومبت مفصل لإعادة تقييم صفقة مفتوحة، مقارنة ظروف الدخول بالوضع الحالي.
+        """
+        symbol = trade.get('symbol', 'UNKNOWN')
+        entry_price = trade.get('entry_price', 0.0)
+        current_price = current.get('current_price', 0.0)
+        
+        # حساب مدة الصفقة بالدقائق
+        try:
+            entry_time = datetime.fromisoformat(trade.get('entry_time').replace('Z', '+00:00'))
+            duration_minutes = (datetime.now(entry_time.tzinfo) - entry_time).total_seconds() / 60
+        except:
+            duration_minutes = 0.0
+            
+        pnl_percentage = ((current_price - entry_price) / entry_price) * 100
+        
+        # البيانات الحالية المقارنة
+        titan_score_now = current.get('titan_score', 0.0)
+        
         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.')
+        whale_1h_net = whale_now.get('exchange_flows', {}).get('net_flow_usd', 0.0)
+        whale_24h_net = whale_now.get('accumulation_analysis_24h', {}).get('net_flow_usd', 0.0)
+        
+        news_text_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.
+EVENT: Mandatory periodic strategic re-evaluation of an OPEN POSITION.
 ASSET: {symbol}
-TIME IN TRADE: {duration_min:.1f} minutes
+TIME IN TRADE: {duration_minutes:.1f} minutes
 
-========== 📉 POSITION STATUS ==========
+========== 📉 CURRENT POSITION STATUS ==========
 * Entry Price: {entry_price}
-* Current Price: {current_price}
-* Unrealized PnL: {pnl_pct:+.2f}%
-* Original Entry Reason: "{trade.get('entry_reason')}"
+* Current Market Price: {current_price}
+* Unrealized PnL: {pnl_percentage:+.2f}%
+* Original Entry Reason: "{trade.get('entry_reason', 'N/A')}"
+* Current Targets -> TP: {trade.get('tp_price', 'N/A')} | SL: {trade.get('sl_price', 'N/A')}
+
+========== 🆕 CHANGED MARKET CONDITIONS (RAW DATA UPDATE) ==========
+1. ML Trend Model Update (Titan): Currently at {titan_score_now:.4f}
+   (Is the trend weakening compared to entry?)
+
+2. Fresh Whale Activity (Last 1H Window): Net Flow ${whale_1h_net:,.0f}
+   (Positive value = potential selling pressure flowing to exchanges. Negative = accumulation.)
 
-========== 🆕 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}
-3. Latest News Update:
-   \"\"\"{news_now[:1000]}\"\"\"
+3. 24H Cumulative Whale Flow: Net ${whale_24h_net:,.0f}
 
-========== 📖 PLAYBOOK GUIDELINES ==========
+4. Latest News Update Raw Text:
+   \"\"\"{news_text_now[:1500]}\"\"\"
+
+========== 📖 PLAYBOOK & STRATEGY GUIDELINES ==========
 {learning_context}
 
-========== 🛡️ GUARDIAN DECISION ==========
-Analyze if the original investment thesis is still valid based on the NEW raw data.
-Output Strict JSON ONLY:
+========== 🛡️ GUARDIAN DECISION TASK ==========
+Analyze if the original investment thesis is still valid based on the NEW raw data above.
+Do NOT recommend closing just because of small fluctuations. Look for FUNDAMENTAL thesisINVALIDATION.
+
+REQUIRED OUTPUT (Strict JSON format ONLY):
 {{
   "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."
+  "new_tp": null or a specific new float value (if action is UPDATE_TARGETS),
+  "new_sl": null or a specific new float value (if action is UPDATE_TARGETS),
+  "reasoning": "Concise professional assessment of current risk vs original thesis based on new data."
 }}
 """
 print("✅ LLM Service V13.4 (Heavyweight Omniscient Brain) Loaded - NO SHORTCUTS")