Update LLM.py

LLM.py

@@ -1,5 +1,5 @@
 # LLM.py
-import os, traceback, asyncio, json
+import os, traceback, asyncio, json, time
 from datetime import datetime
 from functools import wraps
 from backoff import on_exception, expo
@@ -16,22 +16,149 @@ class PatternAnalysisEngine:
         self.llm = llm_service
 
     def _format_chart_data_for_llm(self, ohlcv_data):
-        """تنسيق شامل لبيانات الشموع لتحليل الأنماط"""
+        """تنسيق شامل لبيانات الشموع الخام لتحليل الأنماط"""
         if not ohlcv_data: 
             return "Insufficient chart data for pattern analysis"
         
         try:
-            # استخدام جميع الأطر الزمنية المتاحة
+            # استخدام جميع الأطر الزمنية المتاحة مع البيانات الخام
             all_timeframes = []
             for timeframe, candles in ohlcv_data.items():
-                if candles and len(candles) >= 20:
-                    candle_summary = format_candle_data_for_pattern_analysis({timeframe: candles}, timeframe)
-                    all_timeframes.append(f"=== {timeframe.upper()} TIMEFRAME ===\n{candle_summary}")
+                if candles and len(candles) >= 10:  # تخفيف الشرط من 20 إلى 10 شموع
+                    # 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: 
             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)
+                summary.append(f"\n📊 STATISTICAL ANALYSIS:")
+                summary.append(f"• Price Change: {stats['price_change']:+.2f}%")
+                summary.append(f"• Average Body Size: {stats['avg_body']:.4f}%")
+                summary.append(f"• Volatility (ATR): {stats['atr']:.6f}")
+                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:
+            closes = [c[4] for c in candles]
+            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)):
+                high, low, prev_close = highs[i], lows[i], closes[i-1]
+                tr1 = high - low
+                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"
+            elif price_change > 1:
+                trend = "UPTREND"
+            elif price_change < -3:
+                trend = "STRONG DOWNTREND"
+            elif price_change < -1:
+                trend = "DOWNTREND"
+            else:
+                trend = "SIDEWAYS"
+            
+            # مستويات الدعم والمقاومة المبسطة
+            support = min(lows)
+            resistance = max(highs)
+            
+            return {
+                'price_change': price_change,
+                'avg_body': avg_body,
+                'atr': atr,
+                'trend': trend,
+                'support': support,
+                'resistance': resistance
+            }
+            
+        except Exception as e:
+            return {
+                'price_change': 0,
+                'avg_body': 0,
+                'atr': 0,
+                'trend': 'UNKNOWN',
+                'support': 0,
+                'resistance': 0
+            }
+    
     async def analyze_chart_patterns(self, symbol, ohlcv_data):
         try:
             if not ohlcv_data:
@@ -42,19 +169,19 @@ class PatternAnalysisEngine:
             prompt = f"""
 ANALYZE CHART PATTERNS FOR {symbol}
 
-CANDLE DATA FOR TECHNICAL ANALYSIS:
+RAW CANDLE DATA FOR TECHNICAL ANALYSIS:
 {chart_text}
 
 PATTERN ANALYSIS INSTRUCTIONS:
-1. Analyze ALL available timeframes (1w, 1d, 4h, 1h, 15m, 5m)
-2. Identify clear chart patterns (Double Top/Bottom, Head & Shoulders, Triangles, Flags, etc.)
-3. Assess trend direction and strength
-4. Identify key support and resistance levels
-5. Evaluate volume patterns
-6. Look for convergence/divergence across timeframes
-7. Consider candlestick patterns and formations
+1. Analyze ALL available timeframes with the raw candle data provided
+2. Identify clear chart patterns (Double Top/Bottom, Head & Shoulders, Triangles, Flags, Wedges, etc.)
+3. Assess trend direction and strength across multiple timeframes
+4. Identify key support and resistance levels from price action
+5. Evaluate volume patterns and candle formations
+6. Look for convergence/divergence across different timeframes
+7. Analyze candlestick patterns (Doji, Hammer, Engulfing, Morning/Evening Star, etc.)
 
-CRITICAL: You MUST analyze at least 3 different timeframes to confirm patterns.
+CRITICAL: You MUST analyze the actual raw candle data provided across multiple timeframes.
 
 OUTPUT FORMAT (JSON):
 {{
@@ -66,12 +193,13 @@ OUTPUT FORMAT (JSON):
     "timeframe_expectation": "15-25 minutes",
     "key_support_levels": [0.1200, 0.1180, 0.1150],
     "key_resistance_levels": [0.1300, 0.1320, 0.1350],
-    "pattern_analysis": "Detailed explanation covering multiple timeframes",
+    "pattern_analysis": "Detailed explanation covering multiple timeframes and specific candle patterns",
     "timeframe_confirmations": {{
         "1h": "pattern_details",
         "4h": "pattern_details", 
         "1d": "pattern_details"
     }},
+    "candlestick_patterns": ["Hammer", "Bullish Engulfing", ...],
     "risk_assessment": "low/medium/high",
     "recommended_entry": 0.1234,
     "recommended_targets": [0.1357, 0.1400],
@@ -123,6 +251,22 @@ class LLMService:
             symbol = data_payload.get('symbol', 'unknown')
             target_strategy = data_payload.get('target_strategy', 'GENERIC')
             
+            # ✅ التحقق من بيانات الشموع بشكل صحيح
+            ohlcv_data = data_payload.get('ohlcv') or data_payload.get('raw_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)
+            timeframes_count = len([tf for tf, data in ohlcv_data.items() if data and len(data) >= 10])
+            
+            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
+            
             # جلب جميع البيانات المطلوبة
             news_text = await self.news_fetcher.get_news_for_symbol(symbol)
             pattern_analysis = await self._get_pattern_analysis(data_payload)
@@ -141,6 +285,8 @@ class LLMService:
                     'target_strategy': target_strategy,
                     'pattern_analysis': pattern_analysis,
                     'whale_data_available': whale_data.get('data_available', False),
+                    'total_candles': total_candles,
+                    'timeframes_count': timeframes_count,
                     'timestamp': datetime.now().isoformat()
                 }
                 await self.r2_service.save_llm_prompts_async(
@@ -155,6 +301,7 @@ class LLMService:
                 decision_dict['model_source'] = self.model_name
                 decision_dict['pattern_analysis'] = pattern_analysis
                 decision_dict['whale_data_integrated'] = whale_data.get('data_available', False)
+                decision_dict['total_candles_analyzed'] = total_candles
                 return decision_dict
             else:
                 print(f"❌ فشل تحليل النموذج الضخم لـ {symbol} - لا توجد قرارات بديلة")
@@ -193,6 +340,7 @@ class LLMService:
             ohlcv_data = data_payload.get('ohlcv') or data_payload.get('raw_ohlcv')
             
             if ohlcv_data:
+                # ✅ تمرير البيانات الخام مباشرة لمحرك تحليل الأنماط
                 return await self.pattern_engine.analyze_chart_patterns(symbol, ohlcv_data)
             
             return None
@@ -237,7 +385,7 @@ COMPREHENSIVE TRADING ANALYSIS FOR {symbol}
 📊 TECHNICAL INDICATORS (ALL TIMEFRAMES):
 {indicators_summary}
 
-📈 CANDLE DATA & PATTERN ANALYSIS:
+📈 RAW CANDLE DATA & PATTERN ANALYSIS:
 {candle_data_section}
 
 🔍 PATTERN ANALYSIS RESULTS:
@@ -260,19 +408,20 @@ COMPREHENSIVE TRADING ANALYSIS FOR {symbol}
 
 🎯 TRADING DECISION INSTRUCTIONS:
 
-1. ANALYZE ALL PROVIDED DATA: technical indicators, whale activity, patterns, market context
-2. CONSIDER STRATEGY ALIGNMENT: {target_strategy}
-3. EVALUATE RISK-REWARD RATIO based on support/resistance levels
-4. INTEGRATE WHALE ACTIVITY signals into your decision
-5. CONSIDER PATTERN STRENGTH and timeframe confirmations
-6. ASSESS 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. 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
 
-CRITICAL: You MUST provide specific price levels and time expectations.
+CRITICAL: You MUST provide specific price levels and time expectations based on the raw candle analysis.
 
 OUTPUT FORMAT (JSON):
 {{
     "action": "BUY/SELL/HOLD",
-    "reasoning": "Detailed explanation integrating ALL data sources (technical, whale, patterns, news)",
+    "reasoning": "Detailed explanation integrating ALL data sources with emphasis on raw candle patterns and price action",
     "risk_assessment": "low/medium/high",
     "trade_type": "LONG/SHORT",
     "stop_loss": 0.000000,
@@ -281,7 +430,7 @@ OUTPUT FORMAT (JSON):
     "confidence_level": 0.85,
     "strategy": "{target_strategy}",
     "whale_influence": "How whale data influenced the decision",
-    "pattern_influence": "How chart patterns influenced the decision",
+    "pattern_influence": "How raw candle patterns influenced the decision",
     "key_support_level": 0.000000,
     "key_resistance_level": 0.000000,
     "risk_reward_ratio": 2.5
@@ -315,43 +464,50 @@ OUTPUT FORMAT (JSON):
         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"""
+        """تنسيق شامل لبيانات الشموع الخام"""
         if not ohlcv_data:
-            return "No candle data available for analysis"
+            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) >= 20:
+                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\n"
+            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)
             
-            # تحليل لكل إطار زمني رئيسي
-            for timeframe in ['1d', '4h', '1h', '15m']:
-                if timeframe in ohlcv_data and ohlcv_data[timeframe]:
-                    candles = ohlcv_data[timeframe]
-                    if len(candles) >= 20:
-                        timeframe_analysis = self._analyze_timeframe_candles(candles, timeframe)
-                        summary += f"⏰ {timeframe.upper()} ANALYSIS:\n{timeframe_analysis}\n\n"
+            summary += raw_candle_analysis
             
             return summary
         except Exception as e:
-            return f"Error formatting candle data: {str(e)}"
+            return f"Error formatting raw candle data: {str(e)}"
 
     def _analyze_timeframe_candles(self, candles, timeframe):
         """تحليل الشموع لإطار زمني محدد"""
         try:
-            if len(candles) < 20:
-                return "Insufficient data"
+            if len(candles) < 10:  # تخفيف الشرط
+                return f"Insufficient data ({len(candles)} candles)"
             
-            recent_candles = candles[-20:]  # آخر 20 شمعة
+            recent_candles = candles[-15:]  # آخر 15 شمعة فقط
             
             # حساب المتغيرات الأساسية
             closes = [c[4] for c in recent_candles]
@@ -431,6 +587,12 @@ OUTPUT FORMAT (JSON):
             symbol = trade_data['symbol']
             original_strategy = trade_data.get('strategy', 'GENERIC')
             
+            # ✅ التحقق من بيانات الشموع المحدثة
+            ohlcv_data = processed_data.get('ohlcv') or processed_data.get('raw_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)
@@ -529,23 +691,23 @@ TRADE RE-ANALYSIS FOR {symbol}
 
 🎯 RE-ANALYSIS INSTRUCTIONS:
 
-1. Evaluate if the original thesis still holds
-2. Consider new whale activity and patterns
-3. Assess current risk-reward ratio
-4. Decide whether to hold, close, or adjust the trade
+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
 
 OUTPUT FORMAT (JSON):
 {{
     "action": "HOLD/CLOSE_TRADE/UPDATE_TRADE",
-    "reasoning": "Comprehensive justification based on updated analysis",
+    "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,
     "strategy": "{strategy}",
     "whale_influence_reanalysis": "How updated whale data influenced decision",
-    "pattern_influence_reanalysis": "How updated patterns influenced decision",
+    "pattern_influence_reanalysis": "How updated raw candle patterns influenced decision",
     "risk_adjustment": "low/medium/high"
 }}
 """
@@ -569,4 +731,4 @@ OUTPUT FORMAT (JSON):
             print(f"❌ Unexpected LLM API error: {e}")
             raise
 
-print("✅ LLM Service loaded - Comprehensive Analysis with Whale Data & Pattern Integration")
+print("✅ LLM Service loaded - Comprehensive Analysis with Raw Candle Data & Enhanced Pattern Integration")