Update LLM.py

LLM.py

@@ -1,19 +1,21 @@
 # LLM.py (Updated to integrate LearningHub and English-only prompts)
 import os, traceback, asyncio, json, time
-import re
+import re # ✅ استيراد مكتبة re
 from datetime import datetime
 from functools import wraps
 from backoff import on_exception, expo
 from openai import OpenAI, RateLimitError, APITimeoutError
 import numpy as np
 from sentiment_news import NewsFetcher
+# ✅ تعديل الاستيراد: parse_json_from_response لم يعد مستخدماً هنا بشكل مباشر لتحليل استجابة النموذج الرئيسية
 from helpers import validate_required_fields, format_technical_indicators, format_strategy_scores, format_candle_data_for_pattern_analysis, format_whale_analysis_for_llm, parse_json_from_response
-from ml_engine.processor import safe_json_parse
+from ml_engine.processor import safe_json_parse # ✅ الإصلاح: استيراد المحلل الآمن من الموجه الجديد
+
+NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY")
+PRIMARY_MODEL = "nvidia/llama-3.1-nemotron-ultra-253b-v1"
 
-# (Note: PatternAnalysisEngine code remains unchanged, so it is omitted here for brevity)
-# ... (PatternAnalysisEngine class code as before) ...
 class PatternAnalysisEngine:
-    # --- (كود PatternAnalysisEngine كما هو بدون تغيير) ---
+    # --- (هذا الكلاس جزء من LLM.py ومطلوب لتحليل الشموع) ---
     def __init__(self, llm_service):
         self.llm = llm_service
 
@@ -163,9 +165,11 @@ class PatternAnalysisEngine:
             }
 
     async def analyze_chart_patterns(self, symbol, ohlcv_data):
+        """(تم تركها فارغة عمداً لأن النموذج الضخم يقوم بها الآن)"""
         pass 
 
     def _parse_pattern_response(self, response_text):
+        """(تم تركها فارغة عمداً)"""
         pass 
 
 
@@ -331,8 +335,16 @@ class LLMService:
             return None
 
     async def _get_pattern_analysis(self, data_payload):
-        # (Omitted for brevity - same as original file)
-        pass
+        try:
+            symbol = data_payload['symbol']
+            ohlcv_data = data_payload.get('raw_ohlcv') or data_payload.get('ohlcv')
+            if ohlcv_data:
+                 # (This is a placeholder, as PatternAnalysisEngine.analyze_chart_patterns is not implemented)
+                 return None 
+            return None
+        except Exception as e:
+            print(f"❌ Pattern analysis failed for {data_payload.get('symbol')}: {e}")
+            return None
 
     # 🔴 --- START OF PROMPT CHANGE --- 🔴
     def _create_comprehensive_trading_prompt(
@@ -458,16 +470,104 @@ OUTPUT FORMAT (JSON - SPOT ONLY - INCLUDE EXIT PROFILE AND SELF-CRITIQUE):
 
 
     def _format_candle_data_comprehensive(self, ohlcv_data):
-        # (Omitted for brevity - same as original file)
-        pass
+        """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:
+                    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"
+
+            raw_candle_analysis_text = self.pattern_engine._format_chart_data_for_llm(ohlcv_data)
+
+            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):
-        # (Omitted for brevity - same as original file)
-        pass
+        """تحليل الشموع لإطار زمني محدد - (تستخدم داخلياً بواسطة _format_raw_candle_data)"""
+        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)}"
 
     def _format_market_context(self, sentiment_data):
-        # (Omitted for brevity - same as original file)
-        pass
+        """تنسيق سياق السوق"""
+        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') 
+
+        lines = [
+            f"• Bitcoin Sentiment: {btc_sentiment}",
+            f"• Fear & Greed Index: {fear_greed} ({sentiment_data.get('sentiment_class', 'Neutral')})",
+            f"• Overall Market Trend: {market_trend.replace('_', ' ').title() if isinstance(market_trend, str) else 'N/A'}"
+        ]
+
+        general_whale = sentiment_data.get('general_whale_activity', {})
+        if general_whale and general_whale.get('sentiment') != 'NEUTRAL': 
+             whale_sentiment = general_whale.get('sentiment', 'N/A')
+             critical_alert = general_whale.get('critical_alert', False)
+             lines.append(f"• General Whale Sentiment: {whale_sentiment.replace('_', ' ').title() if isinstance(whale_sentiment, str) else 'N/A'}")
+             if critical_alert:
+                 lines.append("  ⚠️ CRITICAL WHALE ALERT ACTIVE")
+
+        return "\n".join(lines)
 
 
     async def re_analyze_trade_async(self, trade_data: dict, processed_data: dict):