Update LLM.py

LLM.py

@@ -1,4 +1,4 @@
-# LLM.py (V13.8 - Production Fix - Robust Null Safety)
+# LLM.py (V14.3 - Independent Analyst & Raw Data Verifier)
 import os
 import traceback
 import json
@@ -6,7 +6,7 @@ import re
 import time
 from datetime import datetime
 from typing import Dict, Any, Optional
-from openai import AsyncOpenAI, RateLimitError, APIError
+from openai import AsyncOpenAI
 
 # ==============================================================================
 # 🔌 إعدادات الاتصال بالنموذج (Model Connection Settings)
@@ -15,12 +15,12 @@ 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
+# بارامترات التوليد (مضبوطة للتفكير العميق والتحليل الدقيق)
+LLM_TEMPERATURE = 0.2       # حرارة منخفضة للدقة والمنطق
 LLM_TOP_P = 0.7
-LLM_MAX_TOKENS = 16384
-LLM_FREQUENCY_PENALTY = 0.8
-LLM_PRESENCE_PENALTY = 0.5
+LLM_MAX_TOKENS = 16384      # الحد الأقصى للسماح بتحليل كميات ضخمة من البيانات
+LLM_FREQUENCY_PENALTY = 0.0
+LLM_PRESENCE_PENALTY = 0.0
 CLIENT_TIMEOUT = 300.0
 
 class LLMService:
@@ -36,10 +36,12 @@ class LLMService:
         self.r2_service = None
         self.learning_hub = None 
         
-        print(f"🧠 [LLM V13.8] Omniscient Brain Online. Model: {LLM_MODEL}")
+        print(f"🧠 [LLM V14.3] Independent Analyst (Reasoning Mode) Online. Model: {LLM_MODEL}")
 
     async def _call_llm(self, prompt: str) -> Optional[str]:
-        """تنفيذ استدعاء API للنموذج مع حماية ضد الاستجابات الفارغة."""
+        """تنفيذ استدعاء API للنموذج مع تفعيل وضع التفكير."""
+        
+        # [ 🔥 مفتاح التفعيل ]: هذه العبارة تجبر النموذج على الدخول في وضع التفكير المتسلسل
         system_prompt_trigger = "detailed thinking on"
 
         try:
@@ -58,13 +60,13 @@ class LLMService:
                 response_format={"type": "json_object"}
             )
             
-            # [ 🚀 ] حماية ضد NoneType في الاستجابة
+            # حماية ضد الاستجابات الفارغة
             if response and response.choices and len(response.choices) > 0:
                 message = response.choices[0].message
                 if message and message.content:
                     return message.content
             
-            print("⚠️ [LLM Warning] Received empty or malformed response from model.")
+            print("⚠️ [LLM Warning] Received empty response from model.")
             return None
                 
         except Exception as e:
@@ -72,9 +74,10 @@ class LLMService:
             return None
 
     def _parse_json_secure(self, text: str) -> Optional[Dict]:
-        """محلل JSON قوي."""
+        """محلل JSON قوي يستخرج الكائن من بين نصوص التفكير."""
         try:
             if not text: return None
+            # البحث عن أول كائن JSON صالح يبدأ بـ { وينتهي بـ }
             json_match = re.search(r'\{.*\}', text, re.DOTALL)
             if json_match:
                 return json.loads(json_match.group(0))
@@ -89,24 +92,29 @@ class LLMService:
             return None
 
     # ==================================================================
-    # 🧠 الوظيفة الرئيسية 1: قرار الدخول الاستراتيجي (Layer 3)
+    # 🧠 الوظيفة الرئيسية 1: قرار الدخول الاستراتيجي (Layer 3 Analysis)
     # ==================================================================
     async def get_trading_decision(self, candidate_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
         symbol = candidate_data.get('symbol', 'UNKNOWN_ASSET')
         try:
+            # جلب سياق التعلم السابق
             learning_context = "Playbook: No specific prior learning records found."
             if self.learning_hub:
                 learning_context = await self.learning_hub.get_active_context_for_llm("general", f"{symbol} entry analysis")
 
-            prompt = self._create_heavyweight_entry_prompt(candidate_data, learning_context)
+            # إنشاء البرومبت التحليلي (البيانات الخام)
+            prompt = self._create_raw_data_analyst_prompt(candidate_data, learning_context)
             
+            # استدعاء النموذج
             response_text = await self._call_llm(prompt)
             if not response_text: return None
             
+            # تحليل النتيجة
             decision = self._parse_json_secure(response_text)
 
+            # أرشفة القرار
             if self.r2_service and decision:
-                await self.r2_service.save_llm_prompts_async(symbol, "entry_decision_full", prompt, response_text)
+                await self.r2_service.save_llm_prompts_async(symbol, "raw_analyst_decision", prompt, response_text)
 
             return decision
 
@@ -121,20 +129,24 @@ class LLMService:
     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_ASSET')
         try:
-            strategy_name = trade_data.get('entry_reason', 'GENERIC_STRATEGY')
-            learning_context = "Playbook: Stick to original trading plan."
+            # جلب سياق الاستراتيجية
+            learning_context = "Playbook: Stick to original trading plan unless invalidated."
             if self.learning_hub:
-                learning_context = await self.learning_hub.get_active_context_for_llm("strategy", f"{symbol} re-eval {strategy_name}")
+                learning_context = await self.learning_hub.get_active_context_for_llm("strategy", f"{symbol} re-eval")
 
-            prompt = self._create_heavyweight_reanalysis_prompt(trade_data, current_data, learning_context)
+            # إنشاء برومبت إعادة التحليل (البيانات الخام)
+            prompt = self._create_raw_reanalysis_prompt(trade_data, current_data, learning_context)
             
+            # استدعاء النموذج
             response_text = await self._call_llm(prompt)
             if not response_text: return None
             
+            # تحليل النتيجة
             decision = self._parse_json_secure(response_text)
 
+            # أرشفة القرار
             if self.r2_service and decision:
-                await self.r2_service.save_llm_prompts_async(symbol, "re_analysis_full", prompt, response_text)
+                await self.r2_service.save_llm_prompts_async(symbol, "raw_reanalysis_decision", prompt, response_text)
 
             return decision
 
@@ -143,101 +155,131 @@ class LLMService:
             return None
 
     # ==================================================================
-    # 📝 قسم هندسة البرومبتات الكاملة (Full Prompt Engineering)
+    # 📝 أدوات المساعدة في التنسيق (Helpers)
     # ==================================================================
-    def _create_heavyweight_entry_prompt(self, data: Dict[str, Any], learning_context: str) -> str:
+    def _format_candles_to_csv(self, ohlcv_dict: Dict[str, list], limit: int = 200) -> str:
+        """
+        تحويل بيانات الشموع من صيغة JSON/List إلى صيغة CSV مضغوطة ليفهمها النموذج بسهولة.
+        """
+        csv_output = ""
+        # تحديد الأولويات للفريمات الزمنية (الأهم فالأهم)
+        priority_tfs = ['1h', '4h', '1d'] 
+        
+        for tf in priority_tfs:
+            if tf not in ohlcv_dict: continue
+            candles = ohlcv_dict[tf]
+            if not candles: continue
+            
+            # اقتطاع آخر 'limit' شمعة فقط
+            sliced_candles = candles[-limit:]
+            
+            csv_output += f"\n=== TIMEFRAME {tf.upper()} (Last {len(sliced_candles)} Candles) ===\n"
+            csv_output += "Index,Open,High,Low,Close,Volume\n"
+            
+            for idx, c in enumerate(sliced_candles):
+                # التنسيق: Index, Open, High, Low, Close, Volume
+                # (نتأكد من وجود العناصر لتجنب الأخطاء)
+                if len(c) >= 6:
+                    csv_output += f"{idx},{c[1]},{c[2]},{c[3]},{c[4]},{c[5]}\n"
+        
+        if not csv_output:
+            return "NO CANDLE DATA AVAILABLE."
+            
+        return csv_output
+
+    # ==================================================================
+    # 🧠 هندسة البرومبت: الدخول (Raw Data Analyst Prompt)
+    # ==================================================================
+    def _create_raw_data_analyst_prompt(self, data: Dict[str, Any], learning_context: str) -> str:
         symbol = data.get('symbol', 'UNKNOWN')
-        current_price = data.get('current_price', 0.0)
         
-        l1_total_score = data.get('enhanced_final_score', 0.0)
-        l2_total_score = data.get('layer2_score', 0.0)
+        # 1. استخراج بيانات الطبقة الأولى (HEARSAY)
+        l1_components = data.get('components', {})
+        titan_score = l1_components.get('titan_score', 0.0)
         
-        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"
+        pat_details = data.get('pattern_details', {})
+        pat_conf = pat_details.get('pattern_confidence', 0.0)
         
-        pat_data = data.get('pattern_details', {})
-        pattern_name = pat_data.get('pattern_detected', 'None detected')
-        pattern_confidence = pat_data.get('pattern_confidence', 0.0)
+        simple_mc_score = l1_components.get('mc_score', 0.0)
         
-        mc_prob = data.get('components', {}).get('mc_score', 0.0)
-
-        # [ 🚀 ] حماية استخراج بيانات الحيتان
-        whale = data.get('whale_data') or {} # (استخدم قاموس فارغ إذا كان None)
-        whale_1h = whale.get('exchange_flows') or {}
-        whale_24h = whale.get('accumulation_analysis_24h') or {}
+        # 2. استخراج بيانات الطبقة الثانية (CONTEXT)
+        # الحيتان
+        whale_raw = data.get('l2_raw_values', {}).get('whale', 'No Data')
+        if whale_raw == "No Data" or whale_raw == "$0" or whale_raw == "No Data ($0)":
+             whale_section = "WHALE DATA: ⚠️ NO DATA FOUND (Assume Neutral/Unknown)"
+        else:
+             whale_section = f"WHALE DATA (Net Flow): {whale_raw} (Positive=Outflow/Sell, Negative=Inflow/Buy? Check Context)"
+
+        # الأخبار
+        news_raw = data.get('news_text', 'No specific news found.')
         
-        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
-"""
-
-        raw_news_text = data.get('news_text', 'No specific news articles found.')
-
-        # [ 🚀 ] حماية حلقة الشموع (OHLCV)
-        ohlcv_data = data.get('ohlcv_sample') or {}
-        price_snapshot_block = ""
-        for tf, candle in ohlcv_data.items():
-            # التأكد من أن candle ليس None وأنه قائمة تحتوي على بيانات
-            if candle and isinstance(candle, (list, tuple)) and len(candle) >= 6:
-                price_snapshot_block += f"   - {tf.upper()} Frame: Open={candle[1]}, High={candle[2]}, Low={candle[3]}, Close={candle[4]}, Vol={candle[5]}\n"
+        # مونت كارلو المتقدمة
+        adv_mc_raw = data.get('l2_raw_values', {}).get('adv_mc', 'No Data')
+        
+        # 3. استخراج البيانات الخام (THE TRUTH)
+        ohlcv_raw = data.get('ohlcv', {}) 
+        candles_csv_block = self._format_candles_to_csv(ohlcv_raw, limit=200)
 
         return f"""
-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 yourself:
-
-[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 (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):
-\"\"\"
-{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 'detailed thinking' mode).
-Compare PART 1 (Opinions) vs PART 2 (Facts).
-If FACTS strongly contradict OPINIONS, you MUST reject the trade regardless of the high scores.
-
-REQUIRED OUTPUT (Strict JSON format ONLY, no other text):
+ROLE: You are the 'Omniscient Brain', the Lead Quantitative Analyst.
+OBJECTIVE: Analyze the RAW DATA for {symbol} and decide to TRADE or IGNORE.
+
+⚠️ CRITICAL INSTRUCTION:
+The "Local Model Reports" below are generated by smaller, potentially flawed algorithms. 
+They are strictly HEARSAY. You must NOT trust them blindly.
+Your job is to verify them against the RAW CANDLE DATA provided below.
+If your visual/statistical analysis of the candles contradicts the Local Models, YOUR ANALYSIS PREVAILS.
+
+========== 1. LOCAL MODEL REPORTS (HEARSAY - TREAT WITH SKEPTICISM) ==========
+[A] Technical Indicator Analysis (formerly Titan): Score {titan_score:.2f}/1.00
+    - Note: Based on standard RSI/MACD/Bollinger calculations.
+[B] Pattern Recognition Model: Confidence {pat_conf:.2f}/1.00
+    - Note: This model DOES NOT identify pattern names. It only outputs a confidence score.
+[C] Simple Monte Carlo: Score {simple_mc_score:.2f}
+    - Method: Basic Random Walk based on recent volatility.
+[D] Advanced Monte Carlo: Probability {adv_mc_raw}
+    - Method: GARCH(1,1) model with Fat-Tailed distribution simulation.
+
+========== 2. EXTERNAL FACTORS (CONTEXT) ==========
+[A] {whale_section}
+[B] RAW NEWS FEED:
+    "{news_raw}"
+
+========== 3. THE TRUTH (RAW CANDLE DATA) ==========
+Below are the last 200 candles for key timeframes (1H, 4H, 1D).
+Index 199 is the MOST RECENT candle.
+Format: Index,Open,High,Low,Close,Volume
+
+{candles_csv_block}
+
+========== 4. YOUR ANALYTICAL TASKS ==========
+1. **Pattern Hunting:** Scan the CSV data above (especially 1H and 4H). Do you see any classic patterns (Bull Flag, Head & Shoulders, Double Bottom, etc.)?
+2. **Trend Verification:** Compare the 'Technical Indicator Analysis' score with the slope of the raw Close prices. Is the score hallucinating a trend where there is actually a range or downtrend?
+3. **Volume Analysis:** Look at the Volume column. Is there a volume spike supporting the price move?
+4. **Conflict Resolution:** If Local Models say "UP" but Raw Candles show "Lower Highs" (Downtrend), you must REJECT.
+
+========== 5. FINAL DECISION OUTPUT ==========
+Based ONLY on your analysis of the raw data:
+
+REQUIRED JSON FORMAT:
 {{
   "action": "WATCH" or "IGNORE",
-  "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 factor based on the raw evidence provided."
+  "confidence": 0.00 to 1.00,
+  "identified_patterns": ["List specific patterns YOU found in the raw data (e.g., 'Bull Flag on 1H')"],
+  "analysis_summary": "Your reasoning. Explicitly mention if you agreed or disagreed with the Local Models.",
+  "key_raw_evidence": "Cite specific candle indexes or volume figures that support your decision."
 }}
 """
 
-    def _create_heavyweight_reanalysis_prompt(self, trade: Dict, current: Dict, learning_context: str) -> str:
+    # ==================================================================
+    # 🧠 هندسة البرومبت: إعادة التحليل (Raw Re-Analysis Prompt)
+    # ==================================================================
+    def _create_raw_reanalysis_prompt(self, trade: Dict, current: Dict, learning_context: str) -> str:
         symbol = trade.get('symbol', 'UNKNOWN')
         entry_price = trade.get('entry_price', 0.0)
         current_price = current.get('current_price', 0.0)
         
+        # حساب مدة الصفقة والربح
         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
@@ -245,53 +287,66 @@ REQUIRED OUTPUT (Strict JSON format ONLY, no other text):
             duration_minutes = 0.0
             
         pnl_percentage = ((current_price - entry_price) / entry_price) * 100
+        
+        # 1. البيانات الجديدة (Local Models)
         titan_score_now = current.get('titan_score', 0.0)
         
-        # [ 🚀 ] حماية بيانات الحيتان في إعادة التحليل
-        whale_now = current.get('whale_data') or {}
-        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)
+        # 2. البيانات الخارجية (الحيتان والأخبار)
+        whale_now = current.get('whale_data', {})
+        # محاولة استخراج القيمة الخام بشكل آمن
+        whale_net_flow = whale_now.get('accumulation_analysis_24h', {}).get('net_flow_usd', 'No Data')
         
+        if whale_net_flow == 'No Data':
+             whale_section = "WHALE DATA: ⚠️ NO DATA FOUND"
+        else:
+             whale_section = f"WHALE DATA (24H Net Flow): ${whale_net_flow}"
+
         news_text_now = current.get('news_text', 'No new significant news.')
 
+        # 3. البيانات الخام (الشموع الجديدة)
+        ohlcv_raw = current.get('ohlcv', {})
+        # نكتفي بـ 100 شمعة للمتابعة لتقليل الحمل
+        candles_csv_block = self._format_candles_to_csv(ohlcv_raw, limit=100)
+
         return f"""
-ROLE: Omniscient Brain (Trade Guardian Mode).
-EVENT: Mandatory periodic strategic re-evaluation of an OPEN POSITION.
-ASSET: {symbol}
-TIME IN TRADE: {duration_minutes:.1f} minutes
+ROLE: You are the 'Omniscient Brain', the Lead Quantitative Analyst (Guardian Mode).
+EVENT: RE-EVALUATION of an OPEN POSITION.
+ASSET: {symbol} | DURATION: {duration_minutes:.1f} min | PnL: {pnl_percentage:+.2f}%
 
-========== 📉 CURRENT POSITION STATUS ==========
+⚠️ CRITICAL INSTRUCTION:
+You must analyze the RAW DATA to see if the original trade thesis is INVALIDATED.
+Do NOT panic over small fluctuations. Look for structural breaks or trend reversals in the raw candles.
+
+========== 1. POSITION STATUS ==========
 * Entry Price: {entry_price}
-* Current Market Price: {current_price}
-* Unrealized PnL: {pnl_percentage:+.2f}%
-* Original Entry Reason: "{trade.get('entry_reason', 'N/A')}"
+* Current Price: {current_price}
 * 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.)
+========== 2. NEW CONTEXT (HEARSAY) ==========
+[A] Tech Indicator Score Now: {titan_score_now:.2f}/1.00
+[B] {whale_section}
+[C] LATEST NEWS: "{news_text_now[:500]}..."
 
-3. 24H Cumulative Whale Flow: Net ${whale_24h_net:,.0f}
+========== 3. THE TRUTH (LATEST RAW CANDLES) ==========
+Index 99 is the MOST RECENT candle.
+Format: Index,Open,High,Low,Close,Volume
 
-4. Latest News Update Raw Text:
-   \"\"\"{news_text_now[:1500]}\"\"\"
+{candles_csv_block}
 
-========== 📖 PLAYBOOK & STRATEGY GUIDELINES ==========
-{learning_context}
+========== 4. YOUR ANALYTICAL TASKS ==========
+1. **Momentum Check:** Look at the last 10-20 candles. Is the momentum stalling or reversing against you?
+2. **Volume check:** Is there heavy volume selling (red candles with high volume)?
+3. **Thesis Validation:** Does the raw price action still support the original direction (UP)?
 
-========== 🛡️ 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 thesis INVALIDATION.
+========== 5. FINAL DECISION OUTPUT ==========
+Based ONLY on raw data analysis:
 
-REQUIRED OUTPUT (Strict JSON format ONLY):
+REQUIRED JSON FORMAT:
 {{
   "action": "HOLD" or "EMERGENCY_EXIT" or "UPDATE_TARGETS",
-  "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."
+  "new_tp": null or float (only if action is UPDATE_TARGETS),
+  "new_sl": null or float (only if action is UPDATE_TARGETS),
+  "reasoning": "Concise analysis of why the trade is still valid or broken based on raw candles."
 }}
 """
 print("✅ LLM Service V13.8 (Robust Null Safety) Loaded")