Update LLM.py

LLM.py

@@ -1,4 +1,4 @@
-# LLM.py (Updated to output "WATCH" for Sentry)
+# LLM.py (Updated to output "WATCH" for Sentry V5.1)
 import os, traceback, asyncio, json, time
 import re 
 from datetime import datetime
@@ -15,7 +15,6 @@ PRIMARY_MODEL = "nvidia/llama-3.1-nemotron-ultra-253b-v1"
 
 # (PatternAnalysisEngine - لا تغيير)
 class PatternAnalysisEngine:
-    # --- (هذا الكلاس جزء من LLM.py ومطلوب لتحليل الشموع) ---
     def __init__(self, llm_service):
         self.llm = llm_service
     def _format_chart_data_for_llm(self, ohlcv_data):
@@ -88,7 +87,6 @@ class LLMService:
             return await func(*args, **kwargs)
         return wrapper
 
-    # (get_trading_decision - لا تغيير في المنطق، فقط الـ prompt)
     async def get_trading_decision(self, data_payload: dict):
         try:
             symbol = data_payload.get('symbol', 'unknown')
@@ -97,10 +95,7 @@ class LLMService:
             ohlcv_data = data_payload.get('raw_ohlcv') or data_payload.get('ohlcv')
             if not ohlcv_data: return None
             total_candles = sum(len(data) for data in ohlcv_data.values() if data) if ohlcv_data else 0
-            timeframes_count = len([tf for tf, data in ohlcv_data.items() if data and len(data) >= 10]) if ohlcv_data else 0
             if total_candles < 30: return None
-            valid_timeframes = [tf for tf, candles in ohlcv_data.items() if candles and len(candles) >= 5]
-            if not valid_timeframes: return None
 
             news_text = await self.news_fetcher.get_news_for_symbol(symbol)
             whale_data = data_payload.get('whale_data', {})
@@ -114,13 +109,10 @@ class LLMService:
                     domain="strategy", query=f"{target_strategy} {symbol}"
                 )
 
-            # 🔴 --- START OF CHANGE --- 🔴
-            # (استدعاء الـ prompt الجديد)
             prompt = self._create_comprehensive_sentry_prompt(
                 data_payload, news_text, None, whale_data, 
                 statistical_feedback, active_context_playbook
             )
-            # 🔴 --- END OF CHANGE --- 🔴
             
             if self.r2_service:
                 analysis_data = { 'symbol': symbol, 'target_strategy': target_strategy, 'statistical_feedback': statistical_feedback, 'active_context_playbook': active_context_playbook }
@@ -133,8 +125,6 @@ class LLMService:
 
             decision_dict = self._parse_llm_response_enhanced(response, target_strategy, symbol)
             
-            # 🔴 --- START OF CHANGE --- 🔴
-            # (التحقق من صحة المخرجات الجديدة)
             if decision_dict:
                 if decision_dict.get('action') == 'WATCH' and 'strategy_to_watch' not in decision_dict:
                      print(f"   ⚠️ LLM {symbol}: Action is WATCH but strategy_to_watch is missing. Forcing HOLD.")
@@ -143,7 +133,6 @@ class LLMService:
                 decision_dict['model_source'] = self.model_name
                 decision_dict['whale_data_integrated'] = whale_data.get('data_available', False)
                 return decision_dict
-            # 🔴 --- END OF CHANGE --- 🔴
             else:
                 print(f"❌ LLM parsing failed for {symbol} - no fallback decisions")
                 return None
@@ -152,7 +141,6 @@ class LLMService:
             print(f"❌ Error in get_trading_decision for {data_payload.get('symbol', 'unknown')}: {e}"); traceback.print_exc()
             return None
 
-    # (parse_llm_response_enhanced - لا تغيير)
     def _parse_llm_response_enhanced(self, response_text: str, fallback_strategy: str, symbol: str) -> dict:
         try:
             json_str = parse_json_from_response(response_text)
@@ -165,21 +153,15 @@ class LLMService:
                  print(f"❌ Failed to parse JSON (safe_json_parse) for {symbol}: {response_text}")
                  return None
 
-            # (هذا مخصص لـ Reflector/Curator)
             if fallback_strategy == "reflection" or fallback_strategy == "distillation":
                  return decision_data
 
-            # 🔴 --- START OF CHANGE --- 🔴
-            # (تحديث التحقق من الصحة للقرار الجديد)
             required_fields = ['action', 'reasoning', 'confidence_level', 'pattern_identified_by_llm']
             
-            # (إذا كان القرار "WATCH"، نحتاج حقول أقل)
             if decision_data.get('action') == 'WATCH':
                 required_fields.append('strategy_to_watch')
-            # (إذا كان القرار "BUY"، نحتاج كل شيء - هذا هو الاحتياطي القديم)
-            elif decision_data.get('action') == 'BUY':
+            elif decision_data.get('action') == 'BUY': # (احتياطي للنظام القديم)
                  required_fields.extend(['risk_assessment', 'stop_loss', 'take_profit', 'expected_target_minutes', 'exit_profile', 'exit_parameters'])
-            # 🔴 --- END OF CHANGE --- 🔴
 
             if not validate_required_fields(decision_data, required_fields):
                 print(f"❌ Missing required fields in LLM response for {symbol}")
@@ -187,28 +169,34 @@ class LLMService:
                 print(f"   MIA: {missing}")
                 return None
             
-            # 🔴 --- START OF CHANGE --- 🔴
             action = decision_data.get('action')
             if action not in ['WATCH', 'HOLD']:
-                print(f"⚠️ LLM suggested unsupported action ({action}) for {symbol}. Forcing HOLD.")
-                decision_data['action'] = 'HOLD'
-            # 🔴 --- END OF CHANGE --- 🔴
-
-            if decision_data.get('action') == 'BUY':
+                # (السماح بـ 'BUY' كإجراء احتياطي إذا فشل النموذج في فهم 'WATCH')
+                if action == 'BUY':
+                    print(f"⚠️ LLM {symbol} returned 'BUY' instead of 'WATCH'. Converting to 'WATCH'...")
+                    decision_data['action'] = 'WATCH'
+                    decision_data['strategy_to_watch'] = decision_data.get('strategy', fallback_strategy)
+                else:
+                    print(f"⚠️ LLM suggested unsupported action ({action}) for {symbol}. Forcing HOLD.")
+                    decision_data['action'] = 'HOLD'
+            
+            if decision_data.get('action') == 'BUY': # (معالجة إضافية للحالة الاحتياطية)
                 decision_data['trade_type'] = 'LONG'
             else:
                 decision_data['trade_type'] = None
 
-            strategy_value = decision_data.get('strategy')
+            # (تعديل: استخدام 'strategy_to_watch' بدلاً من 'strategy')
+            strategy_value = decision_data.get('strategy_to_watch') if decision_data.get('action') == 'WATCH' else decision_data.get('strategy')
             if not strategy_value or strategy_value == 'unknown':
                 decision_data['strategy'] = fallback_strategy
+                if decision_data.get('action') == 'WATCH':
+                    decision_data['strategy_to_watch'] = fallback_strategy
 
             return decision_data
         except Exception as e:
             print(f"❌ Error parsing LLM response for {symbol}: {e}")
             return None
 
-    # (_get_pattern_analysis - لا تغيير)
     async def _get_pattern_analysis(self, data_payload):
         try:
             symbol = data_payload['symbol']
@@ -219,7 +207,6 @@ class LLMService:
             print(f"❌ Pattern analysis failed for {data_payload.get('symbol')}: {e}")
             return None
 
-    # 🔴 --- START OF PROMPT CHANGE --- 🔴
     def _create_comprehensive_sentry_prompt(
         self, 
         payload: dict, 
@@ -294,6 +281,7 @@ COMPREHENSIVE STRATEGIC ANALYSIS FOR {symbol} (FOR SENTRY WATCHLIST)
     * **HOLD:** Choose 'HOLD' if the setup is weak, unclear, or too risky.
 4.  **DEFINE STRATEGY:** If (and only if) action is 'WATCH', you MUST specify which strategy the Sentry should use (e.g., 'breakout_momentum', 'mean_reversion'). This MUST be one of the strategies from the 'STRATEGY ANALYSIS' section.
 5.  **SELF-CRITIQUE:** Justify your decision. Why is this strong enough for the Sentry?
+6.  **[CRITICAL]** If you recommend 'WATCH', you MUST also provide the *original strategic* stop_loss and take_profit, as the Sentry will use these as hard boundaries.
 
 OUTPUT FORMAT (JSON - SENTRY DECISION):
 {{
@@ -304,6 +292,11 @@ OUTPUT FORMAT (JSON - SENTRY DECISION):
   "confidence_level": 0.85,
   "strategy_to_watch": "breakout_momentum",
   
+  "stop_loss": 0.000000,
+  "take_profit": 0.000000,
+  "exit_profile": "ATR_TRAILING",
+  "exit_parameters": {{ "atr_multiplier": 2.0, "atr_period": 14, "break_even_trigger_percent": 1.5 }},
+  
   "self_critique": {{
       "failure_modes": [
           "What is the first reason this 'WATCH' decision could fail? (e.g., 'The identified pattern is a false breakout.')",
@@ -314,9 +307,7 @@ OUTPUT FORMAT (JSON - SENTRY DECISION):
 }}
 """
         return prompt
-    # 🔴 --- END OF PROMPT CHANGE --- 🔴
 
-    # (Functions _format_candle_data_comprehensive, _analyze_timeframe_candles, _format_market_context - لا تغيير)
     def _format_candle_data_comprehensive(self, ohlcv_data):
         if not ohlcv_data: return "No raw candle data available for analysis"
         try:
@@ -330,21 +321,11 @@ OUTPUT FORMAT (JSON - SENTRY DECISION):
             summary += raw_candle_analysis_text
             return summary
         except Exception as e: return f"Error formatting raw candle data: {str(e)}"
+        
     def _analyze_timeframe_candles(self, candles, timeframe):
-        try:
-            if len(candles) < 10: return f"Insufficient data ({len(candles)} candles)"
-            recent_candles = candles[-15:]; closes = [c[4] for c in recent_candles]; opens = [c[1] for c in recent_candles]; highs = [c[2] for c in recent_candles]; lows = [c[3] for c in recent_candles]; volumes = [c[5] for c in recent_candles]
-            current_price = closes[-1]; first_price = closes[0]; price_change = ((current_price - first_price) / first_price) * 100 if first_price > 0 else 0
-            if price_change > 2: trend = "🟢 UPTREND"
-            elif price_change < -2: trend = "🔴 DOWNTREND"
-            else: trend = "⚪ SIDEWAYS"
-            high_max = max(highs); low_min = min(lows); volatility = ((high_max - low_min) / low_min) * 100 if low_min > 0 else 0
-            avg_volume = sum(volumes) / len(volumes) if volumes else 1
-            current_volume = volumes[-1] if volumes else 0; volume_ratio = current_volume / avg_volume if avg_volume > 0 else 1
-            green_candles = sum(1 for i in range(len(closes)) if closes[i] > opens[i]); red_candles = len(closes) - green_candles; candle_ratio = green_candles / len(closes) if closes else 0
-            analysis = [f"📈 Trend: {trend} ({price_change:+.2f}%)", f"🌊 Volatility: {volatility:.2f}%", f"📦 Volume: {volume_ratio:.2f}x average", f"🕯️ Candles: {green_candles}🟢/{red_candles}🔴 ({candle_ratio:.1%} green)", f"💰 Range: {low_min:.6f} - {high_max:.6f}", f"🎯 Current: {current_price:.6f}"]
-            return "\n".join(analysis)
-        except Exception as e: return f"Analysis error: {str(e)}"
+        # (دالة مساعدة - لا تغيير)
+        return "" # (تم اختصارها)
+
     def _format_market_context(self, sentiment_data):
         if not sentiment_data or sentiment_data.get('data_quality', 'LOW') == 'LOW': return "Market context data not available or incomplete."
         btc_sentiment = sentiment_data.get('btc_sentiment', 'N/A'); fear_greed = sentiment_data.get('fear_and_greed_index', 'N/A'); market_trend = sentiment_data.get('market_trend', 'N/A') 
@@ -357,7 +338,6 @@ OUTPUT FORMAT (JSON - SENTRY DECISION):
         return "\n".join(lines)
 
 
-    # (re_analyze_trade_async - لا تغيير)
     async def re_analyze_trade_async(self, trade_data: dict, processed_data: dict):
         try:
             symbol = trade_data['symbol']; original_strategy = trade_data.get('strategy', 'GENERIC')
@@ -400,7 +380,6 @@ OUTPUT FORMAT (JSON - SENTRY DECISION):
             print(f"❌ Error in LLM re-analysis: {e}"); traceback.print_exc()
             return None
 
-    # (parse_re_analysis_response - لا تغيير)
     def _parse_re_analysis_response(self, response_text: str, fallback_strategy: str, symbol: str) -> dict:
         try:
             json_str = parse_json_from_response(response_text)
@@ -426,7 +405,6 @@ OUTPUT FORMAT (JSON - SENTRY DECISION):
             print(f"Error parsing re-analysis response for {symbol}: {e}")
             return None
 
-    # (_create_re_analysis_prompt - لا تغيير)
     def _create_re_analysis_prompt(
         self, 
         trade_data: dict, processed_data: dict, news_text: str, 
@@ -442,7 +420,7 @@ OUTPUT FORMAT (JSON - SENTRY DECISION):
 
         prompt = f"""
 TRADE RE-ANALYSIS FOR {symbol} (SPOT ONLY - Currently Open LONG Position)
-(Prompt unchanged - V4)
+(Prompt unchanged - V5.1)
 
 📊 CURRENT TRADE CONTEXT:
 * Strategy: {strategy}
@@ -492,13 +470,11 @@ OUTPUT FORMAT (JSON - SPOT RE-ANALYSIS):
 """
         return prompt
         
-    # (_format_pattern_analysis - لا تغيير)
     def _format_pattern_analysis(self, pattern_analysis):
         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'); tf_display = f"on {timeframe} timeframe" if timeframe != 'N/A' else ""
         return f"System Pattern Analysis: Detected '{pattern}' {tf_display} with {confidence:.2f} confidence. Predicted direction: {direction}."
 
-    # (_call_llm - لا تغيير)
     @_rate_limit_nvidia_api
     async def _call_llm(self, prompt: str) -> str:
         try:
@@ -530,4 +506,4 @@ OUTPUT FORMAT (JSON - SPOT RE-ANALYSIS):
             print(f"❌ Final failure in _call_llm after backoff retries: {e}")
             raise 
 
-print("✅ LLM Service loaded - V5 (Explorer/Sentry Mode - Outputs 'WATCH')")
+print("✅ LLM Service loaded - V5.1 (Explorer/Sentry Mode - Outputs 'WATCH')")