Update LLM.py

LLM.py

@@ -1,5 +1,6 @@
 # LLM.py
 import os, traceback, asyncio, json, time
+import re # ✅ استيراد مكتبة re
 from datetime import datetime
 from functools import wraps
 from backoff import on_exception, expo
@@ -18,9 +19,9 @@ class PatternAnalysisEngine:
 
     def _format_chart_data_for_llm(self, ohlcv_data):
         """تنسيق شامل لبيانات الشموع الخام لتحليل الأنماط"""
-        if not ohlcv_data: 
+        if not ohlcv_data:
             return "Insufficient chart data for pattern analysis"
-        
+
         try:
             # استخدام جميع الأطر الزمنية المتاحة مع البيانات الخام
             all_timeframes = []
@@ -29,58 +30,58 @@ class PatternAnalysisEngine:
                     # تمرير البيانات الخام مباشرة للنموذج
                     raw_candle_summary = self._format_raw_candle_data(candles, timeframe)
                     all_timeframes.append(f"=== {timeframe.upper()} TIMEFRAME ({len(candles)} CANDLES) ===\n{raw_candle_summary}")
-            
+
             return "\n\n".join(all_timeframes) if all_timeframes else "No sufficient timeframe data available"
-        except Exception as e: 
+        except Exception as e:
             return f"Error formatting chart data: {str(e)}"
-    
+
     def _format_raw_candle_data(self, candles, timeframe):
         """تنسيق بيانات الشموع الخام بشكل مفصل للنموذج"""
         try:
             if len(candles) < 10:
                 return f"Only {len(candles)} candles available - insufficient for deep pattern analysis"
-            
+
             # أخذ آخر 50 شمعة كحد أقصى لتجنب السياق الطويل جداً
             analysis_candles = candles[-50:] if len(candles) > 50 else candles
-            
+
             summary = []
             summary.append(f"Total candles: {len(candles)} (showing last {len(analysis_candles)})")
             summary.append("Recent candles (newest to oldest):")
-            
+
             # عرض آخر 15 شمعة بالتفصيل
             for i in range(min(15, len(analysis_candles))):
                 idx = len(analysis_candles) - 1 - i
                 candle = analysis_candles[idx]
-                
+
                 # تحويل الطابع الزمني
                 try:
                     timestamp = datetime.fromtimestamp(candle[0] / 1000).strftime('%Y-%m-%d %H:%M:%S')
                 except:
                     timestamp = "unknown"
-                
+
                 open_price, high, low, close, volume = candle[1], candle[2], candle[3], candle[4], candle[5]
-                
+
                 candle_type = "🟢 BULLISH" if close > open_price else "🔴 BEARISH" if close < open_price else "⚪ NEUTRAL"
                 body_size = abs(close - open_price)
                 body_percent = (body_size / open_price * 100) if open_price > 0 else 0
-                
+
                 wick_upper = high - max(open_price, close)
                 wick_lower = min(open_price, close) - low
                 total_range = high - low
-                
+
                 if total_range > 0:
                     body_ratio = (body_size / total_range) * 100
                     upper_wick_ratio = (wick_upper / total_range) * 100
                     lower_wick_ratio = (wick_lower / total_range) * 100
                 else:
                     body_ratio = upper_wick_ratio = lower_wick_ratio = 0
-                
+
                 summary.append(f"{i+1:2d}. {timestamp} | {candle_type}")
                 summary.append(f"     O:{open_price:.8f} H:{high:.8f} L:{low:.8f} C:{close:.8f}")
                 summary.append(f"     Body: {body_percent:.2f}% | Body/Range: {body_ratio:.1f}%")
                 summary.append(f"     Wicks: Upper {upper_wick_ratio:.1f}% / Lower {lower_wick_ratio:.1f}%")
                 summary.append(f"     Volume: {volume:,.0f}")
-            
+
             # إضافة تحليل إحصائي
             if len(analysis_candles) >= 20:
                 stats = self._calculate_candle_statistics(analysis_candles)
@@ -91,12 +92,12 @@ class PatternAnalysisEngine:
                 summary.append(f"• Trend: {stats['trend']}")
                 summary.append(f"• Support: {stats['support']:.6f}")
                 summary.append(f"• Resistance: {stats['resistance']:.6f}")
-            
+
             return "\n".join(summary)
-            
+
         except Exception as e:
             return f"Error formatting raw candle data: {str(e)}"
-    
+
     def _calculate_candle_statistics(self, candles):
         """حساب الإحصائيات الأساسية للشموع"""
         try:
@@ -104,16 +105,16 @@ class PatternAnalysisEngine:
             opens = [c[1] for c in candles]
             highs = [c[2] for c in candles]
             lows = [c[3] for c in candles]
-            
+
             # حساب التغير في السعر
             first_close = closes[0]
             last_close = closes[-1]
             price_change = ((last_close - first_close) / first_close) * 100
-            
+
             # حساب متوسط حجم الجسم
             body_sizes = [abs(close - open) for open, close in zip(opens, closes)]
             avg_body = (sum(body_sizes) / len(body_sizes)) / first_close * 100
-            
+
             # حساب ATR مبسط
             true_ranges = []
             for i in range(1, len(candles)):
@@ -122,9 +123,9 @@ class PatternAnalysisEngine:
                 tr2 = abs(high - prev_close)
                 tr3 = abs(low - prev_close)
                 true_ranges.append(max(tr1, tr2, tr3))
-            
+
             atr = sum(true_ranges) / len(true_ranges) if true_ranges else 0
-            
+
             # تحديد الاتجاه
             if price_change > 3:
                 trend = "STRONG UPTREND"
@@ -136,11 +137,11 @@ class PatternAnalysisEngine:
                 trend = "DOWNTREND"
             else:
                 trend = "SIDEWAYS"
-            
+
             # مستويات الدعم والمقاومة المبسطة
             support = min(lows)
             resistance = max(highs)
-            
+
             return {
                 'price_change': price_change,
                 'avg_body': avg_body,
@@ -149,7 +150,7 @@ class PatternAnalysisEngine:
                 'support': support,
                 'resistance': resistance
             }
-            
+
         except Exception as e:
             return {
                 'price_change': 0,
@@ -159,14 +160,14 @@ class PatternAnalysisEngine:
                 'support': 0,
                 'resistance': 0
             }
-    
+
     async def analyze_chart_patterns(self, symbol, ohlcv_data):
         try:
             if not ohlcv_data:
                 return {"pattern_detected": "insufficient_data", "pattern_confidence": 0.1, "pattern_analysis": "No candle data available"}
-            
+
             chart_text = self._format_chart_data_for_llm(ohlcv_data)
-            
+
             prompt = f"""
 ANALYZE CHART PATTERNS FOR {symbol}
 
@@ -197,7 +198,7 @@ OUTPUT FORMAT (JSON):
     "pattern_analysis": "Detailed explanation covering multiple timeframes and specific candle patterns",
     "timeframe_confirmations": {{
         "1h": "pattern_details",
-        "4h": "pattern_details", 
+        "4h": "pattern_details",
         "1d": "pattern_details"
     }},
     "candlestick_patterns": ["Hammer", "Bullish Engulfing", ...],
@@ -216,18 +217,18 @@ OUTPUT FORMAT (JSON):
     def _parse_pattern_response(self, response_text):
         try:
             json_str = parse_json_from_response(response_text)
-            if not json_str: 
+            if not json_str:
                 return {"pattern_detected": "parse_error", "pattern_confidence": 0.1, "pattern_analysis": "Could not parse pattern analysis response"}
-            
+
             # ✅ الإصلاح: استخدام safe_json_parse بدلاً من json.loads
             pattern_data = safe_json_parse(json_str)
             if not pattern_data:
-                return {"pattern_detected": "parse_error", "pattern_confidence": 0.1, "pattern_analysis": f"Failed to parse JSON string: {json_str[:200]}"}
-            
+                 return {"pattern_detected": "parse_error", "pattern_confidence": 0.1, "pattern_analysis": f"Failed to parse JSON string: {json_str[:200]}"}
+
             required = ['pattern_detected', 'pattern_confidence', 'predicted_direction']
-            if not validate_required_fields(pattern_data, required): 
+            if not validate_required_fields(pattern_data, required):
                 return {"pattern_detected": "incomplete_data", "pattern_confidence": 0.1, "pattern_analysis": "Incomplete pattern analysis data"}
-            
+
             return pattern_data
         except Exception as e:
             print(f"Error parsing pattern response: {e}")
@@ -247,7 +248,7 @@ class LLMService:
     def _rate_limit_nvidia_api(func):
         @wraps(func)
         @on_exception(expo, RateLimitError, max_tries=5)
-        async def wrapper(*args, **kwargs): 
+        async def wrapper(*args, **kwargs):
             return await func(*args, **kwargs)
         return wrapper
 
@@ -255,43 +256,43 @@ class LLMService:
         try:
             symbol = data_payload.get('symbol', 'unknown')
             target_strategy = data_payload.get('target_strategy', 'GENERIC')
-            
+
             # ✅ التحقق من بيانات الشموع بشكل صحيح - الإصلاح الرئيسي هنا
             ohlcv_data = data_payload.get('raw_ohlcv') or data_payload.get('ohlcv')
             if not ohlcv_data:
                 print(f"⚠️ لا توجد بيانات شموع لـ {symbol} - تخطي التحليل")
                 return None
-            
+
             # ✅ حساب إجمالي الشموع المتاحة
             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
-            
+
             print(f"   📊 بيانات {symbol}: {total_candles} شمعة في {timeframes_count} إطار زمني")
-            
+
             if total_candles < 30:  # تخفيف الشرط من 50 إلى 30 شمعة
                 print(f"   ⚠️ بيانات شموع غير كافية لـ {symbol}: {total_candles} شمعة فقط")
                 return None
-            
+
             # ✅ تأكيد وجود بيانات شموع صالحة
             valid_timeframes = []
             for timeframe, candles in ohlcv_data.items():
                 if candles and len(candles) >= 5:  # تخفيف الشرط
                     valid_timeframes.append(timeframe)
-            
+
             if not valid_timeframes:
                 print(f"   ⚠️ لا توجد أطر زمنية صالحة لـ {symbol}")
                 return None
-            
+
             print(f"   ✅ أطر زمنية صالحة لـ {symbol}: {', '.join(valid_timeframes)}")
-            
+
             # جلب جميع البيانات المطلوبة
             news_text = await self.news_fetcher.get_news_for_symbol(symbol)
             pattern_analysis = await self._get_pattern_analysis(data_payload)
             whale_data = data_payload.get('whale_data', {})
-            
+
             # إنشاء الـ prompt الشامل
             prompt = self._create_comprehensive_trading_prompt(data_payload, news_text, pattern_analysis, whale_data)
-            
+
             # ✅ حفظ الـ Prompt في R2 قبل إرساله للنموذج
             if self.r2_service:
                 analysis_data = {
@@ -310,10 +311,10 @@ class LLMService:
                 await self.r2_service.save_llm_prompts_async(
                     symbol, 'comprehensive_trading_decision', prompt, analysis_data
                 )
-            
-            async with self.semaphore: 
+
+            async with self.semaphore:
                 response = await self._call_llm(prompt)
-            
+
             decision_dict = self._parse_llm_response_enhanced(response, target_strategy, symbol)
             if decision_dict:
                 decision_dict['model_source'] = self.model_name
@@ -321,36 +322,53 @@ class LLMService:
                 decision_dict['whale_data_integrated'] = whale_data.get('data_available', False)
                 decision_dict['total_candles_analyzed'] = total_candles
                 decision_dict['timeframes_analyzed'] = timeframes_count
+                # ✅ الإصلاح: التأكد من أن نوع الصفقة هو LONG إذا كان القرار BUY
+                if decision_dict.get('action') == 'BUY':
+                    decision_dict['trade_type'] = 'LONG'
                 return decision_dict
             else:
                 print(f"❌ فشل تحليل النموذج الضخم لـ {symbol} - لا توجد قرارات بديلة")
                 return None
-                
+
         except Exception as e:
             print(f"❌ خطأ في قرار التداول لـ {data_payload.get('symbol', 'unknown')}: {e}")
             traceback.print_exc()
             return None
-    
+
     def _parse_llm_response_enhanced(self, response_text: str, fallback_strategy: str, symbol: str) -> dict:
         try:
             json_str = parse_json_from_response(response_text)
-            if not json_str: 
+            if not json_str:
                 print(f"❌ فشل استخراج JSON من استجابة النموذج لـ {symbol}")
                 return None
 
             # ✅ الإصلاح: استخدام safe_json_parse بدلاً من json.loads
             decision_data = safe_json_parse(json_str)
             if not decision_data:
-                print(f"❌ فشل تحليل JSON (safe_json_parse) لـ {symbol}: {response_text}")
-                return None
+                 print(f"❌ فشل تحليل JSON (safe_json_parse) لـ {symbol}: {response_text}")
+                 return None
 
-            required_fields = ['action', 'reasoning', 'risk_assessment', 'trade_type', 'stop_loss', 'take_profit', 'expected_target_minutes', 'confidence_level']
-            if not validate_required_fields(decision_data, required_fields): 
+            # ✅ الإصلاح: تعديل الحقول المطلوبة لـ SPOT
+            required_fields = ['action', 'reasoning', 'risk_assessment', 'stop_loss', 'take_profit', 'expected_target_minutes', 'confidence_level']
+            # 'trade_type' لم يعد مطلوباً هنا لأنه دائماً LONG أو غير محدد (HOLD)
+            if not validate_required_fields(decision_data, required_fields):
                 print(f"❌ حقول مطلوبة مفقودة في استجابة النموذج لـ {symbol}")
                 return None
 
+            # ✅ الإصلاح: التحقق من أن الإجراء هو BUY أو HOLD فقط
+            action = decision_data.get('action')
+            if action not in ['BUY', 'HOLD']:
+                print(f"⚠️ النموذج اقترح إ��راء غير مدعوم ({action}) لـ {symbol}. سيتم اعتباره HOLD.")
+                decision_data['action'] = 'HOLD' # فرض HOLD إذا كان الإجراء غير صالح
+
+            # ✅ الإصلاح: تحديد trade_type بناءً على الإجراء
+            if decision_data['action'] == 'BUY':
+                decision_data['trade_type'] = 'LONG'
+            else: # إذا كان HOLD
+                decision_data['trade_type'] = None # لا يوجد نوع صفقة لـ HOLD
+
             strategy_value = decision_data.get('strategy')
-            if not strategy_value or strategy_value == 'unknown': 
+            if not strategy_value or strategy_value == 'unknown':
                 decision_data['strategy'] = fallback_strategy
 
             return decision_data
@@ -363,16 +381,16 @@ class LLMService:
             symbol = data_payload['symbol']
             # ✅ استخدام raw_ohlcv أولاً ثم ohlcv - الإصلاح الرئيسي
             ohlcv_data = data_payload.get('raw_ohlcv') or data_payload.get('ohlcv')
-            
+
             if ohlcv_data:
                 # ✅ تمرير البيانات الخام مباشرة لمحرك تحليل الأنماط
                 return await self.pattern_engine.analyze_chart_patterns(symbol, ohlcv_data)
-            
+
             return None
         except Exception as e:
             print(f"❌ فشل تحليل الأنماط 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:
         symbol = payload.get('symbol', 'N/A')
         current_price = payload.get('current_price', 'N/A')
@@ -386,7 +404,7 @@ class LLMService:
         enhanced_final_score = payload.get('enhanced_final_score', 'N/A')
         # ✅ استخدام raw_ohlcv أولاً - الإصلاح الرئيسي
         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)
 
@@ -401,6 +419,8 @@ class LLMService:
         prompt = f"""
 COMPREHENSIVE TRADING ANALYSIS FOR {symbol}
 
+🚨 IMPORTANT SYSTEM CONSTRAINT: This is a SPOT TRADING system ONLY. Decisions MUST be limited to BUY (LONG) or HOLD. SHORT selling is NOT possible.
+
 🎯 STRATEGY CONTEXT:
 - Target Strategy: {target_strategy}
 - Recommended Strategy: {recommended_strategy}
@@ -432,47 +452,47 @@ COMPREHENSIVE TRADING ANALYSIS FOR {symbol}
 📋 REASONS FOR CANDIDACY:
 {chr(10).join([f"• {reason}" for reason in reasons]) if reasons else "No specific reasons provided"}
 
-🎯 TRADING DECISION INSTRUCTIONS:
+🎯 TRADING DECISION INSTRUCTIONS (SPOT ONLY):
 
-1. ANALYZE ALL PROVIDED DATA: technical indicators, whale activity, raw candle patterns, market context
-2. FOCUS ON RAW CANDLE DATA for pattern recognition and price action analysis
-3. CONSIDER STRATEGY ALIGNMENT: {target_strategy}
-4. EVALUATE RISK-REWARD RATIO based on support/resistance levels from candle data
-5. INTEGRATE WHALE ACTIVITY signals into your decision
-6. ASSESS PATTERN STRENGTH and timeframe confirmations from raw candles
-7. CONSIDER MARKET SENTIMENT impact
+1. ANALYZE ALL PROVIDED DATA: technical indicators, whale activity, raw candle patterns, market context.
+2. FOCUS ON RAW CANDLE DATA for pattern recognition and price action analysis.
+3. ADHERE STRICTLY TO SPOT TRADING RULES: Only consider BUY (LONG) opportunities or HOLD. DO NOT suggest SELL (SHORT).
+4. EVALUATE RISK-REWARD RATIO based on support/resistance for a LONG position.
+5. INTEGRATE WHALE ACTIVITY signals into your decision (relevant for potential buy pressure or lack thereof).
+6. ASSESS PATTERN STRENGTH and timeframe confirmations from raw candles, looking for bullish signals.
+7. CONSIDER MARKET SENTIMENT impact.
 
-CRITICAL: You MUST provide specific price levels and time expectations based on the raw candle analysis.
+CRITICAL: Provide specific price levels (Stop Loss, Take Profit) suitable for a BUY/LONG trade OR justify a HOLD decision. If analysis suggests a price decrease, the ONLY valid action is HOLD.
 
-OUTPUT FORMAT (JSON):
+OUTPUT FORMAT (JSON - SPOT ONLY):
 {{
-    "action": "BUY/SELL/HOLD",
-    "reasoning": "Detailed explanation integrating ALL data sources with emphasis on raw candle patterns and price action",
+    "action": "BUY/HOLD", # Only BUY or HOLD allowed
+    "reasoning": "Detailed explanation integrating ALL data sources, justifying BUY or HOLD based on SPOT trading logic.",
     "risk_assessment": "low/medium/high",
-    "trade_type": "LONG/SHORT",
-    "stop_loss": 0.000000,
-    "take_profit": 0.000000,
-    "expected_target_minutes": 15,
-    "confidence_level": 0.85,
+    # "trade_type": "LONG", # Implicitly LONG if action is BUY
+    "stop_loss": 0.000000, # Required if action is BUY
+    "take_profit": 0.000000, # Required if action is BUY
+    "expected_target_minutes": 15, # Required if action is BUY
+    "confidence_level": 0.85, # Confidence in the BUY or HOLD decision
     "strategy": "{target_strategy}",
-    "whale_influence": "How whale data influenced the decision",
-    "pattern_influence": "How raw candle patterns influenced the decision",
+    "whale_influence": "How whale data influenced the BUY/HOLD decision",
+    "pattern_influence": "How raw candle patterns influenced the BUY/HOLD decision",
     "key_support_level": 0.000000,
     "key_resistance_level": 0.000000,
-    "risk_reward_ratio": 2.5
+    "risk_reward_ratio": 2.5 # Calculated for the potential BUY trade
 }}
 """
         return prompt
 
     def _format_pattern_analysis(self, pattern_analysis):
-        if not pattern_analysis: 
+        if not pattern_analysis:
             return "No clear patterns detected across analyzed timeframes"
-        
+
         confidence = pattern_analysis.get('pattern_confidence', 0)
         pattern_name = pattern_analysis.get('pattern_detected', 'unknown')
         predicted_direction = pattern_analysis.get('predicted_direction', 'N/A')
         movement_percent = pattern_analysis.get('predicted_movement_percent', 'N/A')
-        
+
         analysis_lines = [
             f"🎯 Pattern: {pattern_name}",
             f"📊 Confidence: {confidence:.1%}",
@@ -480,49 +500,49 @@ OUTPUT FORMAT (JSON):
             f"💰 Expected Movement: {movement_percent}%",
             f"📝 Analysis: {pattern_analysis.get('pattern_analysis', 'No detailed analysis')}"
         ]
-        
+
         # إضافة مستويات الدعم والمقاومة إذا كانت متوفرة
         support_levels = pattern_analysis.get('key_support_levels', [])
         resistance_levels = pattern_analysis.get('key_resistance_levels', [])
-        
+
         if support_levels:
             analysis_lines.append(f"🛟 Support Levels: {', '.join([f'{level:.6f}' for level in support_levels[:3]])}")
         if resistance_levels:
             analysis_lines.append(f"🚧 Resistance Levels: {', '.join([f'{level:.6f}' for level in resistance_levels[:3]])}")
-        
+
         # إضافة أنماط الشموع إذا كانت متوفرة
         candlestick_patterns = pattern_analysis.get('candlestick_patterns', [])
         if candlestick_patterns:
             analysis_lines.append(f"🕯️ Candlestick Patterns: {', '.join(candlestick_patterns)}")
-        
+
         return "\n".join(analysis_lines)
 
     def _format_candle_data_comprehensive(self, ohlcv_data):
         """تنسيق شامل لبيانات الشموع الخام"""
         if not ohlcv_data:
             return "No raw candle data available for analysis"
-        
+
         try:
             timeframes_available = []
             total_candles = 0
-            
+
             for timeframe, candles in ohlcv_data.items():
                 if candles and len(candles) >= 5:  # تخفيف الشرط
                     timeframes_available.append(f"{timeframe.upper()} ({len(candles)} candles)")
                     total_candles += len(candles)
-            
+
             if not timeframes_available:
                 return "Insufficient candle data across all timeframes"
-            
+
             summary = f"📊 Available Timeframes: {', '.join(timeframes_available)}\n"
             summary += f"📈 Total Candles Available: {total_candles}\n\n"
-            
+
             # استخدام محرك الأنماط لتنسيق البيانات الخام
             pattern_engine = PatternAnalysisEngine(self)
             raw_candle_analysis = pattern_engine._format_chart_data_for_llm(ohlcv_data)
-            
+
             summary += raw_candle_analysis
-            
+
             return summary
         except Exception as e:
             return f"Error formatting raw candle data: {str(e)}"
@@ -532,20 +552,20 @@ OUTPUT FORMAT (JSON):
         try:
             if len(candles) < 10:  # تخفيف الشرط
                 return f"Insufficient data ({len(candles)} candles)"
-            
+
             recent_candles = candles[-15:]  # آخر 15 شمعة فقط
-            
+
             # حساب المتغيرات الأساسية
             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
-            
+
             # تحليل الاتجاه
             if price_change > 2:
                 trend = "🟢 UPTREND"
@@ -553,22 +573,22 @@ OUTPUT FORMAT (JSON):
                 trend = "🔴 DOWNTREND"
             else:
                 trend = "⚪ SIDEWAYS"
-            
+
             # تحليل التقلب
             high_max = max(highs)
             low_min = min(lows)
             volatility = ((high_max - low_min) / low_min) * 100
-            
+
             # تحليل الحجم
             avg_volume = sum(volumes) / len(volumes)
             current_volume = volumes[-1]
             volume_ratio = current_volume / avg_volume if avg_volume > 0 else 1
-            
+
             # تحليل الشموع
             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)
-            
+
             analysis = [
                 f"📈 Trend: {trend} ({price_change:+.2f}%)",
                 f"🌊 Volatility: {volatility:.2f}%",
@@ -577,7 +597,7 @@ OUTPUT FORMAT (JSON):
                 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)}"
@@ -586,18 +606,18 @@ OUTPUT FORMAT (JSON):
         """تنسيق سياق السوق"""
         if not sentiment_data:
             return "No market context data available"
-        
+
         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')
-        
+
         lines = [
             "🌍 MARKET CONTEXT:",
             f"• Bitcoin Sentiment: {btc_sentiment}",
             f"• Fear & Greed Index: {fear_greed}",
             f"• Market Trend: {market_trend}"
         ]
-        
+
         general_whale = sentiment_data.get('general_whale_activity', {})
         if general_whale:
             whale_sentiment = general_whale.get('sentiment', 'N/A')
@@ -605,27 +625,27 @@ OUTPUT FORMAT (JSON):
             lines.append(f"• General Whale Sentiment: {whale_sentiment}")
             if critical_alert:
                 lines.append("• ⚠️ CRITICAL WHALE ALERT")
-        
+
         return "\n".join(lines)
 
     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')
-            
+
             # ✅ التحقق من بيانات الشموع المحدثة - الإصلاح الرئيسي
             ohlcv_data = processed_data.get('raw_ohlcv') or processed_data.get('ohlcv')
             if not ohlcv_data:
                 print(f"⚠️ لا توجد بيانات شموع محدثة لـ {symbol} - تخطي إعادة التحليل")
                 return None
-            
+
             # جلب جميع البيانات المحدثة
             news_text = await self.news_fetcher.get_news_for_symbol(symbol)
             pattern_analysis = await self._get_pattern_analysis(processed_data)
             whale_data = processed_data.get('whale_data', {})
-            
+
             prompt = self._create_re_analysis_prompt(trade_data, processed_data, news_text, pattern_analysis, whale_data)
-            
+
             # ✅ حفظ الـ Prompt في R2
             if self.r2_service:
                 analysis_data = {
@@ -639,10 +659,10 @@ OUTPUT FORMAT (JSON):
                 await self.r2_service.save_llm_prompts_async(
                     symbol, 'trade_reanalysis', prompt, analysis_data
                 )
-            
-            async with self.semaphore: 
+
+            async with self.semaphore:
                 response = await self._call_llm(prompt)
-            
+
             re_analysis_dict = self._parse_re_analysis_response(response, original_strategy, symbol)
             if re_analysis_dict:
                 re_analysis_dict['model_source'] = self.model_name
@@ -651,7 +671,7 @@ OUTPUT FORMAT (JSON):
             else:
                 print(f"❌ فشل إعادة تحليل النموذج الضخم لـ {symbol}")
                 return None
-                
+
         except Exception as e:
             print(f"❌ خطأ في إعادة تحليل LLM: {e}")
             traceback.print_exc()
@@ -660,17 +680,23 @@ OUTPUT FORMAT (JSON):
     def _parse_re_analysis_response(self, response_text: str, fallback_strategy: str, symbol: str) -> dict:
         try:
             json_str = parse_json_from_response(response_text)
-            if not json_str: 
+            if not json_str:
                 return None
 
             # ✅ الإصلاح: استخدام safe_json_parse بدلاً من json.loads
             decision_data = safe_json_parse(json_str)
             if not decision_data:
-                print(f"❌ فشل تحليل JSON (safe_json_parse) لإعادة التحليل لـ {symbol}: {response_text}")
-                return None
+                 print(f"❌ فشل تحليل JSON (safe_json_parse) لإعادة التحليل لـ {symbol}: {response_text}")
+                 return None
+
+            # ✅ الإصلاح: ��لتحقق من أن الإجراء هو HOLD, CLOSE_TRADE, أو UPDATE_TRADE
+            action = decision_data.get('action')
+            if action not in ['HOLD', 'CLOSE_TRADE', 'UPDATE_TRADE']:
+                 print(f"⚠️ النموذج اقترح إجراء إعادة تحليل غير مدعوم ({action}) لـ {symbol}. سيتم اعتباره HOLD.")
+                 decision_data['action'] = 'HOLD' # فرض HOLD
 
             strategy_value = decision_data.get('strategy')
-            if not strategy_value or strategy_value == 'unknown': 
+            if not strategy_value or strategy_value == 'unknown':
                 decision_data['strategy'] = fallback_strategy
 
             return decision_data
@@ -683,24 +709,29 @@ OUTPUT FORMAT (JSON):
         entry_price = trade_data.get('entry_price', 'N/A')
         current_price = processed_data.get('current_price', 'N/A')
         strategy = trade_data.get('strategy', 'GENERIC')
-        
-        try: 
+        # ✅ الإصلاح: التأكيد على أن الصفقة الحالية هي LONG
+        original_trade_type = "LONG" # Since the system is SPOT only
+
+        try:
+            # حساب PnL بناءً على LONG
             price_change = ((current_price - entry_price) / entry_price) * 100
             price_change_display = f"{price_change:+.2f}%"
-        except (TypeError, ZeroDivisionError): 
+        except (TypeError, ZeroDivisionError):
             price_change_display = "N/A"
-            
+
         indicators_summary = format_technical_indicators(processed_data.get('advanced_indicators', {}))
         pattern_summary = self._format_pattern_analysis(pattern_analysis)
         whale_analysis_section = format_whale_analysis_for_llm(whale_data)
         market_context_section = self._format_market_context(processed_data.get('sentiment_data', {}))
 
         prompt = f"""
-TRADE RE-ANALYSIS FOR {symbol}
+TRADE RE-ANALYSIS FOR {symbol} (SPOT ONLY)
+
+🚨 IMPORTANT SYSTEM CONSTRAINT: This is a SPOT TRADING system ONLY. The open trade is implicitly LONG. Re-analysis should decide to HOLD, CLOSE, or UPDATE this LONG position. SHORT selling is NOT possible.
 
 📊 TRADE CONTEXT:
 - Strategy: {strategy}
-- Entry Price: {entry_price}
+- Entry Price: {entry_price} (LONG position)
 - Current Price: {current_price}
 - Performance: {price_change_display}
 - Trade Age: {trade_data.get('hold_duration_minutes', 'N/A')} minutes
@@ -720,26 +751,28 @@ TRADE RE-ANALYSIS FOR {symbol}
 📰 LATEST NEWS:
 {news_text if news_text else "No significant news found"}
 
-🎯 RE-ANALYSIS INSTRUCTIONS:
+🎯 RE-ANALYSIS INSTRUCTIONS (SPOT - LONG POSITION):
 
-1. Evaluate if the original thesis still holds based on updated raw candle data
-2. Consider new whale activity and pattern developments
-3. Assess current risk-reward ratio using latest price action
-4. Decide whether to hold, close, or adjust the trade based on comprehensive analysis
-5. Provide specific updated levels if adjusting
+1. Evaluate if the original LONG thesis still holds based on updated raw candle data.
+2. Consider new whale activity and pattern developments (bullish or bearish implications for the LONG trade).
+3. Assess current risk-reward ratio for HOLDING the LONG position using latest price action.
+4. Decide whether to HOLD, CLOSE_TRADE (exit the LONG position), or UPDATE_TRADE (adjust SL/TP for the LONG position).
+5. Provide specific updated levels if adjusting the LONG trade. DO NOT suggest SHORTING.
 
-OUTPUT FORMAT (JSON):
+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 based on updated analysis with emphasis on recent candle patterns",
-    "new_stop_loss": 0.000000,
-    "new_take_profit": 0.000000,
-    "new_expected_minutes": 15,
-    "confidence_level": 0.85,
+    "action": "HOLD/CLOSE_TRADE/UPDATE_TRADE", # Only these three actions are allowed
+    "reasoning": "Comprehensive justification for HOLD, CLOSE, or UPDATE of the LONG position, based on updated analysis with emphasis on recent candle patterns and SPOT logic.",
+    "new_stop_loss": 0.000000, # If updating the LONG trade
+    "new_take_profit": 0.000000, # If updating the LONG trade
+    "new_expected_minutes": 15, # If updating the LONG trade
+    "confidence_level": 0.85, # Confidence in the re-analysis decision
     "strategy": "{strategy}",
-    "whale_influence_reanalysis": "How updated whale data influenced decision",
-    "pattern_influence_reanalysis": "How updated raw candle patterns influenced decision",
-    "risk_adjustment": "low/medium/high"
+    "whale_influence_reanalysis": "How updated whale data influenced the decision for the LONG trade",
+    "pattern_influence_reanalysis": "How updated raw candle patterns influenced the decision for the LONG trade",
+    "risk_adjustment": "low/medium/high" # Risk associated with continuing to HOLD
 }}
 """
         return prompt