LLM.py

# LLM.py (Updated to V5.4 - Fixed Re-Analysis TP/SL Wipe Bug)
import os, traceback, asyncio, json, time
import 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
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 

NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY")
PRIMARY_MODEL = "nvidia/llama-3.1-nemotron-ultra-253b-v1"

# (PatternAnalysisEngine - لا تغيير)
class PatternAnalysisEngine:
    def __init__(self, llm_service):
        self.llm = llm_service
    def _format_chart_data_for_llm(self, ohlcv_data):
        if not ohlcv_data: return "Insufficient chart data for pattern analysis"
        try:
            all_timeframes = []
            for timeframe, candles in ohlcv_data.items():
                if candles and len(candles) >= 10:
                    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):
        try:
            if len(candles) < 10: return f"Only {len(candles)} candles available - insufficient for deep pattern analysis"
            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):")
            for i in range(min(15, len(analysis_candles))):
                idx = len(analysis_candles) - 1 - i; candle = analysis_candles[idx]
                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}")
            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):
        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]
            first_close = closes[0]; last_close = closes[-1]; price_change = ((last_close - first_close) / first_close) * 100
            body_sizes = [abs(close - open) for open, close in zip(opens, closes)]; avg_body = (sum(body_sizes) / len(body_sizes)) / first_close * 100 if first_close > 0 else 0
            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
            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"
            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): pass 
    def _parse_pattern_response(self, response_text): pass 


class LLMService:
    def __init__(self, api_key=NVIDIA_API_KEY, model_name=PRIMARY_MODEL, temperature=0.7):
        self.api_key = api_key
        self.model_name = model_name
        self.temperature = temperature
        self.client = OpenAI(base_url="https://integrate.api.nvidia.com/v1", api_key=self.api_key)
        self.news_fetcher = NewsFetcher()
        self.pattern_engine = PatternAnalysisEngine(self) 
        self.semaphore = asyncio.Semaphore(5)
        self.r2_service = None
        self.learning_hub = None

    def _rate_limit_nvidia_api(func):
        @wraps(func)
        @on_exception(expo, RateLimitError, max_tries=5)
        async def wrapper(*args, **kwargs):
            return await func(*args, **kwargs)
        return wrapper

    async def get_trading_decision(self, data_payload: dict):
        try:
            symbol = data_payload.get('symbol', 'unknown')
            target_strategy = data_payload.get('target_strategy', 'GENERIC')

            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
            if total_candles < 30: return None

            news_text = await self.news_fetcher.get_news_for_symbol(symbol)
            whale_data = data_payload.get('whale_data', {})

            statistical_feedback = "No statistical learning data yet."
            active_context_playbook = "No active learning rules available."
            
            if self.learning_hub and self.learning_hub.initialized:
                statistical_feedback = await self.learning_hub.get_statistical_feedback_for_llm(target_strategy)
                active_context_playbook = await self.learning_hub.get_active_context_for_llm(
                    domain="strategy", query=f"{target_strategy} {symbol}"
                )

            prompt = self._create_comprehensive_sentry_prompt(
                data_payload, news_text, None, whale_data, 
                statistical_feedback, active_context_playbook
            )
            
            if self.r2_service:
                analysis_data = { 'symbol': symbol, 'target_strategy': target_strategy, 'statistical_feedback': statistical_feedback, 'active_context_playbook': active_context_playbook }
                await self.r2_service.save_llm_prompts_async(
                    symbol, 'sentry_watchlist_decision_v5', prompt, analysis_data
                )

            async with self.semaphore:
                response = await self._call_llm(prompt)

            decision_dict = self._parse_llm_response_enhanced(response, target_strategy, symbol)
            
            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.")
                     decision_dict['action'] = 'HOLD'
                
                decision_dict['model_source'] = self.model_name
                decision_dict['whale_data_integrated'] = whale_data.get('data_available', False)
                return decision_dict
            else:
                print(f"❌ LLM parsing failed for {symbol} - no fallback decisions")
                return None

        except Exception as e:
            print(f"❌ Error in get_trading_decision for {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:
                print(f"❌ Failed to extract JSON from LLM response for {symbol}")
                return None

            decision_data = safe_json_parse(json_str)
            if not decision_data:
                 print(f"❌ Failed to parse JSON (safe_json_parse) for {symbol}: {response_text}")
                 return None

            if fallback_strategy == "reflection" or fallback_strategy == "distillation":
                 return decision_data

            required_fields = ['action', 'reasoning', 'confidence_level', 'pattern_identified_by_llm']
            
            if decision_data.get('action') == 'WATCH':
                required_fields.append('strategy_to_watch')
            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'])

            if not validate_required_fields(decision_data, required_fields):
                print(f"❌ Missing required fields in LLM response for {symbol}")
                missing = [f for f in required_fields if f not in decision_data]
                print(f"   MIA: {missing}")
                return None
            
            action = decision_data.get('action')
            if action not in ['WATCH', 'HOLD']:
                # (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

            # (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

    async def _get_pattern_analysis(self, data_payload):
        try:
            symbol = data_payload['symbol']
            ohlcv_data = data_payload.get('raw_ohlcv') or data_payload.get('ohlcv')
            if ohlcv_data: return None 
            return None
        except Exception as e:
            print(f"❌ Pattern analysis failed for {data_payload.get('symbol')}: {e}")
            return None

    def _create_comprehensive_sentry_prompt(
        self, 
        payload: dict, 
        news_text: str, 
        pattern_analysis: dict, 
        whale_data: dict, 
        statistical_feedback: str, 
        active_context_playbook: str
    ) -> str:
        
        symbol = payload.get('symbol', 'N/A')
        current_price = payload.get('current_price', 'N/A')
        
        price_change_24h_raw = payload.get('price_change_24h', 0)
        price_change_24h_display = f"{price_change_24h_raw:+.2f}%" if isinstance(price_change_24h_raw, (int, float)) else "N/A"
        
        reasons = payload.get('reasons_for_candidacy', [])
        sentiment_data = payload.get('sentiment_data', {})
        advanced_indicators = payload.get('advanced_indicators', {})
        strategy_scores = payload.get('strategy_scores', {})
        recommended_strategy = payload.get('recommended_strategy', 'N/A')
        target_strategy = payload.get('target_strategy', 'GENERIC')
        enhanced_final_score = payload.get('enhanced_final_score', 0)
        enhanced_score_display = f"{enhanced_final_score:.3f}" if isinstance(enhanced_final_score, (int, float)) else str(enhanced_final_score)

        indicators_summary = format_technical_indicators(advanced_indicators)
        strategies_summary = format_strategy_scores(strategy_scores, recommended_strategy)
        whale_analysis_section = format_whale_analysis_for_llm(whale_data)
        ohlcv_data = payload.get('raw_ohlcv') or payload.get('ohlcv', {})
        candle_data_section = self._format_candle_data_comprehensive(ohlcv_data) 
        market_context_section = self._format_market_context(sentiment_data)
        
        statistical_feedback_section = f"🧠 STATISTICAL FEEDBACK (Slow-Learner):\n{statistical_feedback}"
        playbook_section = f"📚 LEARNING PLAYBOOK (Fast-Learner Active Rules):\n{active_context_playbook}"

        exhaustion_warning = ""
        try:
            rsi_1d = advanced_indicators.get('1d', {}).get('rsi', 50)
            rsi_4h = advanced_indicators.get('4h', {}).get('rsi', 50)
            if price_change_24h_raw > 40 and (rsi_1d > 75 or rsi_4h > 75):
                exhaustion_warning = (
                    "🚩 **تنبيه استراتيجي: تم رصد زخم مرتفع (احتمال إرهاق)** 🚩\n"
                    f"الأصل مرتفع {price_change_24h_display} خلال 24 ساعة ومؤشر RSI على 1D/4H في منطقة تشبع شرائي.\n"
                    "هذا ليس أمر 'إيقاف'، بل هو 'تحدي تحليلي'. مهمتك هي التحقيق وتحديد ما إذا كان هذا:\n"
                    "1.  **فخ إرهاق (Exhaustion Trap):** (يجب 'HOLD')\n"
                    "2.  **اختراق استمراري حقيقي (Sustainable Continuation):** (يمكن 'WATCH')\n"
                    "------------------------------------------------------------------"
                )
        except Exception:
            pass 


        prompt = f"""
COMPREHENSIVE STRATEGIC ANALYSIS FOR {symbol} (FOR SENTRY WATCHLIST)

🚨 IMPORTANT: You are a STRATEGIC EXPLORER. Your job is NOT to execute a trade. Your job is to decide if this asset is interesting enough to be passed to the "SENTRY" (a high-speed tactical agent) for real-time monitoring and execution.

{exhaustion_warning}

🎯 STRATEGY CONTEXT:
* Target Strategy: {target_strategy}
* Recommended Strategy (from ML): {recommended_strategy}
* Current Price: ${current_price}
* 24H Price Change: {price_change_24h_display}
* Enhanced System Score: {enhanced_score_display}

--- LEARNING HUB INPUT (CRITICAL) ---
{playbook_section}
{statistical_feedback_section}
--- END OF LEARNING INPUT ---

📊 TECHNICAL INDICATORS (ALL TIMEFRAMES):
{indicators_summary}

📈 RAW CANDLE DATA SUMMARY & STATISTICS (FOR YOUR PATTERN ANALYSIS):
{candle_data_section}
{chr(10)}--- END OF CANDLE DATA ---{chr(10)}

🎯 STRATEGY ANALYSIS (System's recommendation based on various factors):
{strategies_summary}

🐋 WHALE ACTIVITY ANALYSIS:
{whale_analysis_section}

🌍 MARKET CONTEXT:
{market_context_section if market_context_section and "No market context" not in market_context_section else "Market context data not available for this analysis."}

---
🎯 SENTRY DECISION INSTRUCTIONS (WATCH or HOLD):

1.  **PERFORM CHART PATTERN ANALYSIS:** Based *ONLY* on the provided 'RAW CANDLE DATA SUMMARY & STATISTICS', identify relevant patterns.
2.  **[CRITICAL] INVESTIGATE THE STRATEGIC ALERT (if present):**
    * ** للتحقق من الاستمرارية (Continuation):** هل الارتفاع مدعوم بـ 'volume_ratio' عالي (موجود في المؤشرات)؟ هل هو اختراق واضح لنمط تجميعي (مثل 'Bull Flag' أو 'Consolidation Breakout')؟ هل الشموع قوية (أجسام كبيرة)؟
    * ** للتحقق من الإرهاق (Exhaustion):** هل ترى 'Bearish Divergence' (السعر يصنع قمة جديدة بينما RSI/MACD لا يفعل)؟ هل يضعف 'volume_ratio' مع الصعود؟ هل تظهر شموع انعكاسية (Doji, Shooting Star) على 4H/1D؟
3.  **INTEGRATE ALL DATA:** ادمج 'تحقيقك' مع باقي البيانات (Learning Hub, Whale Activity).
4.  **DECIDE ACTION (WATCH or HOLD):**
    * **WATCH:** فقط إذا أكد تحقيقك أنها 'Sustainable Continuation' ولديك ثقة عالية (>= 0.75).
    * **HOLD:** إذا أظهر تحقيقك أنها 'Exhaustion Trap'، أو إذا كان الوضع غير واضح ومحفوف بالمخاطر.
5.  **DEFINE STRATEGY:** If (and only if) action is 'WATCH', you MUST specify which strategy the Sentry should use (e.g., 'breakout_momentum', 'mean_reversion').
6.  **SELF-CRITIQUE:** Justify your decision. Why is this strong enough for the Sentry?
7.  **[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):
{{
  "action": "WATCH/HOLD",
  "reasoning": "Detailed explanation integrating ALL data sources. If the Strategic Alert was present, *explicitly state your investigation findings* (e.g., 'I confirmed this is continuation because volume is increasing and a bull flag is forming on 1H...')",
  "pattern_identified_by_llm": "Name of the primary pattern(s) identified (e.g., 'Bull Flag on 1H', 'No Clear Pattern')",
  
  "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.')",
          "What is the second reason? (e.g., 'The Sentry might enter too late.')"
      ],
      "confidence_adjustment_reason": "Brief reason if confidence was adjusted post-critique."
  }}
}}
"""
        return prompt

    def _format_candle_data_comprehensive(self, ohlcv_data):
        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):
        # (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') 
        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):
        try:
            symbol = trade_data['symbol']; original_strategy = trade_data.get('strategy', 'GENERIC')
            ohlcv_data = processed_data.get('raw_ohlcv') or processed_data.get('ohlcv')
            if not ohlcv_data: return None
            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', {})

            statistical_feedback = "No statistical learning data yet."
            active_context_playbook = "No active learning rules available."
            if self.learning_hub and self.learning_hub.initialized:
                statistical_feedback = await self.learning_hub.get_statistical_feedback_for_llm(original_strategy)
                active_context_playbook = await self.learning_hub.get_active_context_for_llm(
                    domain="strategy", query=f"{original_strategy} {symbol} re-analysis"
                )

            prompt = self._create_re_analysis_prompt(
                trade_data, processed_data, news_text, pattern_analysis, 
                whale_data, statistical_feedback, active_context_playbook
            )

            if self.r2_service:
                analysis_data = { 'symbol': symbol, 'original_strategy': original_strategy }
                await self.r2_service.save_llm_prompts_async(
                    symbol, 'trade_reanalysis_v5_hub', prompt, analysis_data
                )

            async with self.semaphore:
                response = await self._call_llm(prompt)

            # 🔴 --- START OF CHANGE (V5.4) --- 🔴
            # (تمرير كائن الصفقة الأصلي بالكامل لنسخ القيم القديمة إذا لزم الأمر)
            re_analysis_dict = self._parse_re_analysis_response(response, original_strategy, symbol, trade_data)
            # 🔴 --- END OF CHANGE --- 🔴
            
            if re_analysis_dict:
                re_analysis_dict['model_source'] = self.model_name
                return re_analysis_dict
            else:
                print(f"❌ LLM re-analysis parsing failed for {symbol}")
                return None
        except Exception as e:
            print(f"❌ Error in LLM re-analysis: {e}"); traceback.print_exc()
            return None

    # 🔴 --- START OF CHANGE (V5.4) --- 🔴
    def _parse_re_analysis_response(self, response_text: str, fallback_strategy: str, symbol: str, original_trade: dict) -> dict:
        """(محدث V5.4) يضمن عدم مسح قيم SL/TP أبداً."""
        try:
            json_str = parse_json_from_response(response_text)
            if not json_str: return None
            decision_data = safe_json_parse(json_str)
            if not decision_data: print(f"❌ Failed to parse JSON (safe_json_parse) for re-analysis of {symbol}: {response_text}"); return None

            action = decision_data.get('action')
            if action not in ['HOLD', 'CLOSE_TRADE', 'UPDATE_TRADE']:
                 print(f"⚠️ LLM suggested unsupported re-analysis action ({action}) for {symbol}. Forcing HOLD.")
                 decision_data['action'] = 'HOLD'
            
            # (منطق التحقق الجديد V5.4)
            if action == 'UPDATE_TRADE':
                required_update_fields = ['new_stop_loss', 'new_take_profit', 'new_exit_profile', 'new_exit_parameters']
                if not validate_required_fields(decision_data, required_update_fields):
                    print(f"❌ Missing required fields for UPDATE_TRADE for {symbol}"); decision_data['action'] = 'HOLD' 
                elif not isinstance(decision_data['new_exit_parameters'], dict):
                    print(f"❌ 'new_exit_parameters' is not a valid dict for {symbol}"); decision_data['action'] = 'HOLD'
            
            # (آلية الحماية من المسح)
            if action in ['HOLD', 'UPDATE_TRADE']:
                # التحقق من new_stop_loss
                new_sl = decision_data.get('new_stop_loss')
                if not isinstance(new_sl, (int, float)) or new_sl <= 0:
                    print(f"⚠️ LLM Re-Analysis {symbol}: new_stop_loss is invalid ({new_sl}). Reverting to original SL.")
                    decision_data['new_stop_loss'] = original_trade.get('stop_loss')
                
                # التحقق من new_take_profit
                new_tp = decision_data.get('new_take_profit')
                if not isinstance(new_tp, (int, float)) or new_tp <= 0:
                    print(f"⚠️ LLM Re-Analysis {symbol}: new_take_profit is invalid ({new_tp}). Reverting to original TP.")
                    decision_data['new_take_profit'] = original_trade.get('take_profit')
            
            strategy_value = decision_data.get('strategy')
            if not strategy_value or strategy_value == 'unknown':
                decision_data['strategy'] = fallback_strategy
            
            return decision_data
        except Exception as e:
            print(f"Error parsing re-analysis response for {symbol}: {e}")
            return None
    # 🔴 --- END OF CHANGE --- 🔴

    def _create_re_analysis_prompt(
        self, 
        trade_data: dict, processed_data: dict, news_text: str, 
        pattern_analysis: dict, whale_data: dict, 
        statistical_feedback: str, active_context_playbook: str
    ) -> str:
        
        symbol = trade_data.get('symbol', 'N/A'); entry_price = trade_data.get('entry_price', 'N/A'); current_price = processed_data.get('current_price', 'N/A'); strategy = trade_data.get('strategy', 'GENERIC'); current_exit_profile = trade_data.get('decision_data', {}).get('exit_profile', 'N/A'); current_exit_params = json.dumps(trade_data.get('decision_data', {}).get('exit_parameters', {}))
        
        # 🔴 --- START OF CHANGE (V5.4) --- 🔴
        # (تمرير الأهداف الحالية إلى النموذج)
        current_sl = trade_data.get('stop_loss', 'N/A')
        current_tp = trade_data.get('take_profit', 'N/A')
        # 🔴 --- END OF CHANGE --- 🔴

        statistical_feedback_section = f"🧠 STATISTICAL FEEDBACK (Slow-Learner):\n{statistical_feedback}"; playbook_section = f"📚 LEARNING PLAYBOOK (Fast-Learner Active Rules):\n{active_context_playbook}"
        try: price_change = ((current_price - entry_price) / entry_price) * 100 if entry_price else 0; price_change_display = f"{price_change:+.2f}%"
        except (TypeError, ZeroDivisionError): price_change_display = "N/A"
        
        price_change_24h_raw = processed_data.get('price_change_24h', 0)
        price_change_24h_display = f"{price_change_24h_raw:+.2f}%" if isinstance(price_change_24h_raw, (int, float)) else "N/A"

        indicators_summary = format_technical_indicators(processed_data.get('advanced_indicators', {})); pattern_summary = self._format_pattern_analysis(pattern_analysis) if pattern_analysis else "Pattern analysis data not available for re-analysis."; whale_analysis_section = format_whale_analysis_for_llm(whale_data); market_context_section = self._format_market_context(processed_data.get('sentiment_data', {})); ohlcv_data = processed_data.get('raw_ohlcv') or processed_data.get('ohlcv', {}); candle_data_section = self._format_candle_data_comprehensive(ohlcv_data)

        exhaustion_warning = ""
        try:
            rsi_1d = processed_data.get('advanced_indicators', {}).get('1d', {}).get('rsi', 50)
            if price_change_24h_raw > 40 and rsi_1d > 75:
                exhaustion_warning = (
                    "🚩 **تنبيه استراتيجي: تم رصد زخم مرتفع (احتمال إرهاق)** 🚩\n"
                    f"الأصل مرتفع {price_change_24h_display} خلال 24 ساعة ومؤشر RSI على 1D/4H في منطقة تشبع شرائي.\n"
                    "هذا يزيد من خطورة الاستمرار في الصفقة. قم بالتحقيق في قوة الاتجاه الحالية.\n"
                    "هل بدأ الحجم (Volume) يضعف؟ هل تظهر إشارات انعكاس على 1H/4H؟\n"
                    "------------------------------------------------------------------"
                )
        except Exception:
            pass
            
        # 🔴 --- START OF CHANGE (V5.4) --- 🔴
        prompt = f"""
TRADE RE-ANALYSIS FOR {symbol} (SPOT ONLY - Currently Open LONG Position)

{exhaustion_warning}

📊 CURRENT TRADE CONTEXT:
* Strategy: {strategy}
* Entry Price: {entry_price} (LONG position)
* Current Price: {current_price}
* 24H Price Change: {price_change_24h_display}
* Current Performance: {price_change_display}
* Current Exit Profile: {current_exit_profile}
* CURRENT Stop Loss: {current_sl}
* CURRENT Take Profit: {current_tp}

--- LEARNING HUB INPUT (CRITICAL) ---
{playbook_section}
{statistical_feedback_section}
--- END OF LEARNING INPUT ---

🔄 UPDATED TECHNICAL ANALYSIS:
{indicators_summary}

📈 UPDATED RAW CANDLE DATA SUMMARY & STATISTICS:
{candle_data_section}
{chr(10)}--- END OF CANDLE DATA ---{chr(10)}

🐋 UPDATED WHALE ACTIVITY:
{whale_analysis_section}

🌍 UPDATED MARKET CONTEXT:
{market_context_section if market_context_section and "No market context" not in market_context_section else "Market context data not available for this re-analysis."}

---
🎯 RE-ANALYSIS INSTRUCTIONS (SPOT - LONG POSITION):

1.  **Analyze the Data:** Review all new data (Indicators, Candles, Whale, Market).
2.  **Evaluate Current State:** Is the original reason for entry still valid? Has the risk changed?
3.  **Investigate Alert (if present):** If the 'Exhaustion Alert' is active, determine if the trend is weakening (exit) or just consolidating (hold).
4.  **Decide Action (HOLD, CLOSE_TRADE, or UPDATE_TRADE):**
    * **HOLD:** The trade is still valid. The current strategy/targets are fine.
    * **CLOSE_TRADE:** The trade is invalidated (e.g., trend reversal, risk too high).
    * **UPDATE_TRADE:** The trade is valid, but the exit parameters need adjustment (e.g., raise stop loss to lock profit, or change exit profile).

**[CRITICAL OUTPUT RULES - YOU MUST FOLLOW THESE]:**
1.  You **MUST** return one of three actions: `HOLD`, `CLOSE_TRADE`, or `UPDATE_TRADE`.
2.  If `action` is `HOLD` or `UPDATE_TRADE`, you **MUST** provide valid (non-zero) numeric values for `new_stop_loss` and `new_take_profit`.
3.  **If `action` is `HOLD` and you do not want to change the targets,** you **MUST** return the *CURRENT* values (Current SL: {current_sl}, Current TP: {current_tp}) in the `new_stop_loss` and `new_take_profit` fields.
4.  If `action` is `CLOSE_TRADE`, the values for targets/exit profile are irrelevant.

OUTPUT FORMAT (JSON - SPOT RE-ANALYSIS):
{{
    "action": "HOLD/CLOSE_TRADE/UPDATE_TRADE",
    "reasoning": "Comprehensive justification. If 'HOLD' or 'UPDATE', justify the new (or existing) SL/TP values.",
    "new_stop_loss": 0.000000,
    "new_take_profit": 0.000000,
    "new_exit_profile": "The new exit profile (or the existing one if HOLD)",
    "new_exit_parameters": {{ "example_key": "example_value" }},
    "new_expected_minutes": 15,
    "confidence_level": 0.85,
    "strategy": "{strategy}",
    "self_critique": {{
        "failure_modes": ["Primary risk of this new decision?", "Second risk?"],
        "confidence_adjustment_reason": "Brief reason if confidence was adjusted."
    }}
}}
"""
        # 🔴 --- END OF CHANGE --- 🔴
        return prompt
        
    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}."

    @_rate_limit_nvidia_api
    async def _call_llm(self, prompt: str) -> str:
        try:
            for attempt in range(2): 
                try:
                    response = self.client.chat.completions.create(
                        model=self.model_name,
                        messages=[{"role": "user", "content": prompt}],
                        temperature=self.temperature,
                        seed=int(time.time()), 
                        max_tokens=4000 
                    )
                    
                    content = None
                    if response.choices and response.choices[0].message:
                        content = response.choices[0].message.content
                    
                    if content and '{' in content and '}' in content:
                        return content
                    else:
                        if content is None:
                            print(f"⚠️ LLM returned NO content (None) (attempt {attempt+1}). Check content filters or API status.")
                        else:
                            print(f"⚠️ LLM returned invalid content (not JSON) (attempt {attempt+1}): {content[:100]}...")
                    
                    if attempt == 0: await asyncio.sleep(1) 

                except (RateLimitError, APITimeoutError) as e:
                    print(f"❌ LLM API Error (Rate Limit/Timeout): {e}. Retrying via backoff...")
                    raise 
                except Exception as e:
                    print(f"❌ Unexpected LLM API error (attempt {attempt+1}): {e}")
                    if attempt == 0: await asyncio.sleep(2) 
                    elif attempt == 1: raise 
            
            print("❌ LLM failed to return valid content after retries.")
            return "" 
            
        except Exception as e:
            print(f"❌ Final failure in _call_llm after backoff retries: {e}")
            raise 

print("✅ LLM Service loaded - V5.4 (Fixed Re-Analysis TP/SL Wipe Bug)")