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import os, traceback, json, time, re |
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import httpx |
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from datetime import datetime |
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from typing import List, Dict, Any, Optional |
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from openai import AsyncOpenAI, RateLimitError, APIError |
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try: |
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from r2 import R2Service |
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from learning_hub.hub_manager import LearningHubManager |
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except ImportError: |
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print("❌ [LLMService] فشل استيراد R2Service أو LearningHubManager") |
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R2Service = None |
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LearningHubManager = None |
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try: |
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from sentiment_news import NewsFetcher |
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except ImportError: |
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NewsFetcher = None |
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try: |
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from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer |
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except ImportError: |
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SentimentIntensityAnalyzer = None |
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LLM_API_URL = os.getenv("LLM_API_URL", "https://integrate.api.nvidia.com/v1") |
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LLM_API_KEY = os.getenv("LLM_API_KEY") |
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LLM_MODEL = os.getenv("LLM_MODEL", "nvidia/llama-3.1-nemotron-ultra-253b-v1") |
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LLM_TEMPERATURE = 0.2 |
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LLM_TOP_P = 0.7 |
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LLM_MAX_TOKENS = 16384 |
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LLM_FREQUENCY_PENALTY = 0.8 |
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LLM_PRESENCE_PENALTY = 0.5 |
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CLIENT_TIMEOUT = 300.0 |
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class LLMService: |
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def __init__(self): |
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if not LLM_API_KEY: |
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raise ValueError("❌ [LLMService] متغير بيئة LLM_API_KEY غير موجود!") |
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try: |
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self.client = AsyncOpenAI( |
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base_url=LLM_API_URL, |
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api_key=LLM_API_KEY, |
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timeout=CLIENT_TIMEOUT |
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) |
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print(f"✅ [LLMService V19.3] مهيأ. النموذج: {LLM_MODEL}") |
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print(f" -> Endpoint: {LLM_API_URL}") |
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except Exception as e: |
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print(f"❌ [LLMService V19.3] فشل تهيئة AsyncOpenAI: {e}") |
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traceback.print_exc() |
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raise |
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self.r2_service: Optional[R2Service] = None |
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self.learning_hub: Optional[LearningHubManager] = None |
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self.news_fetcher: Optional[NewsFetcher] = None |
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self.vader_analyzer: Optional[SentimentIntensityAnalyzer] = None |
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async def _call_llm(self, prompt: str) -> Optional[str]: |
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""" |
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(محدث V19.2) |
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إجراء استدعاء API للنموذج الضخم (يستخدم الآن "detailed thinking on" كـ system prompt). |
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""" |
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system_prompt = "detailed thinking on" |
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payload = { |
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"model": LLM_MODEL, |
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"messages": [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": prompt} |
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], |
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"temperature": LLM_TEMPERATURE, |
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"top_p": LLM_TOP_P, |
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"max_tokens": LLM_MAX_TOKENS, |
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"frequency_penalty": LLM_FREQUENCY_PENALTY, |
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"presence_penalty": LLM_PRESENCE_PENALTY, |
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"stream": False, |
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"response_format": {"type": "json_object"} |
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} |
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try: |
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response = await self.client.chat.completions.create(**payload) |
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if response.choices and len(response.choices) > 0: |
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content = response.choices[0].message.content |
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if content: |
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return content.strip() |
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print(f"❌ [LLMService] استجابة API غير متوقعة: {response.model_dump_json()}") |
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return None |
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except RateLimitError as e: |
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print(f"❌ [LLMService] خطأ Rate Limit من NVIDIA API: {e}") |
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except APIError as e: |
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print(f"❌ [LLMService] خطأ API من NVIDIA API: {e}") |
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except json.JSONDecodeError: |
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print(f"❌ [LLMService] فشل في تحليل استجابة JSON.") |
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except Exception as e: |
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print(f"❌ [LLMService] خطأ غير متوقع في _call_llm: {e}") |
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traceback.print_exc() |
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return None |
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def _parse_llm_response_enhanced(self, |
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response_text: str, |
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fallback_strategy: str = "decision", |
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symbol: str = "N/A") -> Optional[Dict[str, Any]]: |
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""" |
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(محدث V8) محلل JSON ذكي ومتسامح مع الأخطاء. |
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""" |
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if not response_text: |
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print(f" ⚠️ [LLMParser] الاستجابة فارغة لـ {symbol}.") |
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return self._get_fallback_response(fallback_strategy, "Empty response") |
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try: |
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return json.loads(response_text) |
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except json.JSONDecodeError: |
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print(f" ⚠️ [LLMParser] فشل تحليل JSON المباشر لـ {symbol}. محاولة استخراج JSON...") |
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pass |
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try: |
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json_match = re.search(r'\{.*\}', response_text, re.DOTALL) |
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if json_match: |
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json_string = json_match.group(0) |
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return json.loads(json_string) |
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else: |
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print(f" ⚠️ [LLMParser] لم يتم العثور على JSON مطابق لـ {symbol}.") |
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raise json.JSONDecodeError("No JSON object found in text", response_text, 0) |
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except json.JSONDecodeError as e: |
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print(f" ❌ [LLMParser] فشل الاستخراج النهائي لـ {symbol}. نص الاستجابة: {response_text[:200]}...") |
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return self._get_fallback_response(fallback_strategy, f"Final JSON parse fail: {e}") |
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except Exception as e: |
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print(f" ❌ [LLMParser] خطأ غير متوقع في المحلل لـ {symbol}: {e}") |
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return self._get_fallback_response(fallback_strategy, f"Unexpected parser error: {e}") |
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def _get_fallback_response(self, strategy: str, reason: str) -> Optional[Dict[str, Any]]: |
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""" |
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(محدث V8) إرجاع استجابة آمنة عند فشل النموذج الضخم. |
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""" |
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print(f" 🚨 [LLMService] تفعيل الاستجابة الاحتياطية (Fallback) لاستراتيجية '{strategy}' (السبب: {reason})") |
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if strategy == "decision": |
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return { |
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"action": "NO_DECISION", |
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"strategy_to_watch": "GENERIC", |
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"confidence_level": 0, |
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"reasoning": f"LLM analysis failed: {reason}", |
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"exit_profile": "Standard" |
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} |
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elif strategy == "reanalysis": |
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return { |
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"action": "HOLD", |
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"strategy": "MAINTAIN_CURRENT", |
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"reasoning": f"LLM re-analysis failed: {reason}. Maintaining current trade strategy." |
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} |
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elif strategy == "reflection": |
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return None |
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elif strategy == "distillation": |
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return None |
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return None |
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async def get_trading_decision(self, candidate_data: Dict[str, Any]) -> Optional[Dict[str, Any]]: |
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""" |
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(محدث V8.1) |
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يستدعي النموذج الضخم لاتخاذ قرار "WATCH" استراتيجي (Explorer Brain). |
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""" |
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symbol = candidate_data.get('symbol', 'UNKNOWN') |
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try: |
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learning_context_prompt = "Playbook: No learning context available." |
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if self.learning_hub: |
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learning_context_prompt = await self.learning_hub.get_active_context_for_llm( |
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domain="general", |
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query=f"{symbol} strategy decision" |
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) |
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prompt = self._create_trading_prompt(candidate_data, learning_context_prompt) |
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if self.r2_service: |
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await self.r2_service.save_llm_prompts_async(symbol, "trading_decision", prompt, candidate_data) |
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response_text = await self._call_llm(prompt) |
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decision_json = self._parse_llm_response_enhanced( |
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response_text, |
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fallback_strategy="decision", |
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symbol=symbol |
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) |
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return decision_json |
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except Exception as e: |
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print(f"❌ [LLMService] فشل فادح في get_trading_decision لـ {symbol}: {e}") |
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traceback.print_exc() |
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return self._get_fallback_response("decision", str(e)) |
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async def re_analyze_trade_async(self, trade_data: Dict[str, Any], current_data: Dict[str, Any]) -> Optional[Dict[str, Any]]: |
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""" |
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(محدث V19.3) |
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يستدعي النموذج الضخم لإعادة تحليل صفقة مفتوحة (Reflector Brain). |
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""" |
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symbol = trade_data.get('symbol', 'UNKNOWN') |
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try: |
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learning_context_prompt = "Playbook: No learning context available." |
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if self.learning_hub: |
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learning_context_prompt = await self.learning_hub.get_active_context_for_llm( |
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domain="strategy", |
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query=f"{symbol} re-analysis {trade_data.get('strategy', 'GENERIC')}" |
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) |
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latest_news_text = "News data unavailable for re-analysis." |
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latest_news_score = 0.0 |
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if self.news_fetcher: |
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latest_news_text = await self.news_fetcher.get_news_for_symbol(symbol) |
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if self.vader_analyzer and latest_news_text: |
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vader_scores = self.vader_analyzer.polarity_scores(latest_news_text) |
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latest_news_score = vader_scores.get('compound', 0.0) |
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current_data['latest_news_text'] = latest_news_text |
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current_data['latest_news_score'] = latest_news_score |
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prompt = await self._create_reanalysis_prompt(trade_data, current_data, learning_context_prompt) |
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if self.r2_service: |
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await self.r2_service.save_llm_prompts_async(symbol, "trade_reanalysis", prompt, current_data) |
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response_text = await self._call_llm(prompt) |
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decision_json = self._parse_llm_response_enhanced( |
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response_text, |
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fallback_strategy="reanalysis", |
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symbol=symbol |
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) |
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return decision_json |
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except Exception as e: |
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print(f"❌ [LLMService] فشل فادح في re_analyze_trade_async لـ {symbol}: {e}") |
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traceback.print_exc() |
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return self._get_fallback_response("reanalysis", str(e)) |
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def _create_trading_prompt(self, |
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candidate_data: Dict[str, Any], |
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learning_context: str) -> str: |
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""" |
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(معدل V19.2) |
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إنشاء الـ Prompt (باللغة الإنجليزية) لاتخاذ قرار التداول الأولي (Explorer). |
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(تم نقل جميع التعليمات إلى هنا لتناسب system prompt الجديد) |
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""" |
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symbol = candidate_data.get('symbol', 'N/A') |
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l1_score = candidate_data.get('enhanced_final_score', 0) |
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l1_reasons = candidate_data.get('reasons_for_candidacy', []) |
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pattern_data = candidate_data.get('pattern_analysis', {}) |
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mc_data = candidate_data.get('monte_carlo_distribution', {}) |
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news_text = candidate_data.get('news_text', 'No news text provided.') |
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news_score_raw = candidate_data.get('news_score_raw', 0.0) |
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statistical_news_pnl = candidate_data.get('statistical_news_pnl', 0.0) |
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whale_data = candidate_data.get('whale_data', {}) |
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whale_summary = whale_data.get('llm_friendly_summary', {}) |
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exchange_flows = whale_data.get('exchange_flows', {}) |
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whale_signal = whale_summary.get('recommended_action', 'HOLD') |
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whale_confidence = whale_summary.get('confidence', 0.3) |
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whale_reason = whale_summary.get('whale_activity_summary', 'No whale data.') |
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net_flow_usd = exchange_flows.get('net_flow_usd', 0.0) |
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accumulation_data_24h = whale_data.get('accumulation_analysis_24h', {}) |
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net_flow_24h_usd = accumulation_data_24h.get('net_flow_usd', 0.0) |
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total_inflow_24h_usd = accumulation_data_24h.get('to_exchanges_usd', 0.0) |
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total_outflow_24h_usd = accumulation_data_24h.get('from_exchanges_usd', 0.0) |
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relative_net_flow_24h_percent = accumulation_data_24h.get('relative_net_flow_percent', 0.0) |
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market_context = candidate_data.get('sentiment_data', {}) |
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market_trend = market_context.get('market_trend', 'UNKNOWN') |
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btc_sentiment = market_context.get('btc_sentiment', 'UNKNOWN') |
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playbook_prompt = f""" |
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--- START OF LEARNING PLAYBOOK --- |
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{learning_context} |
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--- END OF PLAYBOOK --- |
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""" |
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ml_summary_prompt = f""" |
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1. **ML Analysis (Score: {l1_score:.3f}):** |
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* Reasons: {', '.join(l1_reasons)} |
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* Chart Pattern: {pattern_data.get('pattern_detected', 'None')} (Conf: {pattern_data.get('pattern_confidence', 0):.2f}) |
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* Monte Carlo (1h): {mc_data.get('probability_of_gain', 0):.1f}% chance of profit (Expected: {mc_data.get('expected_return_pct', 0):.2f}%) |
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""" |
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news_prompt = f""" |
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2. **News & Sentiment Analysis:** |
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* Market Trend: {market_trend} (BTC: {btc_sentiment}) |
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* VADER (Raw): {news_score_raw:.3f} |
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* Statistical PnL (Learned): {statistical_news_pnl:+.2f}% |
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* News Text: {news_text[:300]}... |
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""" |
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whale_activity_prompt = f""" |
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3. **Whale Activity (Real-time Flow - Optimized Window):** |
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* Signal: {whale_signal} (Confidence: {whale_confidence:.2f}) |
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* Reason: {whale_reason} |
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* Net Flow (to/from Exchanges): ${net_flow_usd:,.2f} |
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4. **Whale Activity (24h Accumulation):** |
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* 24h Net Flow (Accumulation): ${net_flow_24h_usd:,.2f} |
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* 24h Total Deposits: ${total_inflow_24h_usd:,.2f} |
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* 24h Total Withdrawals: ${total_outflow_24h_usd:,.2f} |
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* Relative 24h Net Flow (vs Daily Volume): {relative_net_flow_24h_percent:+.2f}% |
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""" |
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task_prompt = f""" |
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CONTEXT: |
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You are an expert AI trading analyst (Explorer Brain). |
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Analyze the provided data for {symbol} and decide if it's a high-potential candidate to 'WATCH'. |
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{playbook_prompt} |
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--- START OF CANDIDATE DATA --- |
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{ml_summary_prompt} |
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{news_prompt} |
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{whale_activity_prompt} |
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--- END OF CANDIDATE DATA --- |
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TASK: |
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1. **Internal Thinking (Private):** Perform a step-by-step analysis (as triggered by the system prompt). |
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|
* Synthesize all data points (ML, News, Whale Flow, 24h Accumulation). |
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* Are the signals aligned? (e.g., ML breakout + Whale Accumulation = Strong). |
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* Are there conflicts? (e.g., Good ML Score but high 24h Deposits = Risky). |
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* Consult the "Playbook" for learned rules. |
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2. **Final Decision:** Based on your internal thinking, decide the final action. |
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3. **Output Constraint:** Provide your final answer ONLY in the requested JSON object format, with no introductory text, markdown formatting, or explanations. |
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OUTPUT (JSON Object ONLY): |
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{{ |
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"action": "WATCH" or "NO_DECISION", |
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"strategy_to_watch": "STRATEGY_NAME", |
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|
"confidence_level": 0.0_to_1.0, |
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|
"reasoning": "Brief justification (max 40 words) synthesizing all data points.", |
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"exit_profile": "Aggressive" or "Standard" or "Patient" |
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}} |
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""" |
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return task_prompt |
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|
|
async def _create_reanalysis_prompt(self, |
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trade_data: Dict[str, Any], |
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|
current_data: Dict[str, Any], |
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learning_context: str) -> str: |
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""" |
|
|
(معدل V19.2) |
|
|
إنشاء الـ Prompt (باللغة الإنجليزية) لإعادة تحليل صفقة مفتوحة (Reflector Brain). |
|
|
""" |
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|
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|
symbol = trade_data.get('symbol', 'N/A') |
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original_strategy = trade_data.get('strategy', 'N/A') |
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|
original_reasoning = trade_data.get('decision_data', {}).get('reasoning', 'N/A') |
|
|
entry_price = trade_data.get('entry_price', 0) |
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|
current_pnl = trade_data.get('pnl_percent', 0) |
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|
current_sl = trade_data.get('stop_loss', 0) |
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|
current_tp = trade_data.get('take_profit', 0) |
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current_price = current_data.get('current_price', 0) |
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|
mc_data = current_data.get('monte_carlo_distribution', {}) |
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|
mc_prob = mc_data.get('probability_of_gain', 0) |
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|
mc_expected_return = mc_data.get('expected_return_pct', 0) |
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latest_news_text = current_data.get('latest_news_text', 'No news.') |
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latest_news_score = current_data.get('latest_news_score', 0.0) |
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statistical_feedback = "" |
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if self.learning_hub: |
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statistical_feedback = await self.learning_hub.get_statistical_feedback_for_llm(original_strategy) |
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playbook_prompt = f""" |
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--- START OF LEARNING PLAYBOOK --- |
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{learning_context} |
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{statistical_feedback} |
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--- END OF PLAYBOOK --- |
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""" |
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trade_status_prompt = f""" |
|
|
1. **Open Trade Status ({symbol}):** |
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* Current PnL: {current_pnl:+.2f}% |
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* Original Strategy: {original_strategy} |
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* Original Reasoning: {original_reasoning} |
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|
* Entry Price: {entry_price} |
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* Current Price: {current_price} |
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* Current StopLoss: {current_sl} |
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* Current TakeProfit: {current_tp} |
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""" |
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current_analysis_prompt = f""" |
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|
2. **Current Real-time Analysis:** |
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|
* Monte Carlo (1h): {mc_prob:.1f}% chance of profit (Expected: {mc_expected_return:.2f}%) |
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* Latest News (VADER: {latest_news_score:.3f}): {latest_news_text[:300]}... |
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""" |
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task_prompt = f""" |
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|
CONTEXT: |
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You are an expert AI trading analyst (Reflector Brain). |
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An open trade for {symbol} has triggered a mandatory re-analysis. Analyze the new data and decide the next action. |
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{playbook_prompt} |
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--- START OF TRADE DATA --- |
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|
{trade_status_prompt} |
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{current_analysis_prompt} |
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--- END OF TRADE DATA --- |
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|
|
TASK: |
|
|
1. **Internal Thinking (Private):** Perform a step-by-step analysis (as triggered by the system prompt). |
|
|
* Compare the "Open Trade Status" with the "Current Real-time Analysis". |
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* Has the situation improved or deteriorated? (e.g., PnL is good, but new Monte Carlo is negative). |
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* Are there new critical news? |
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* Consult the "Playbook" for learned rules and statistical feedback. |
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2. **Final Decision:** Based on your internal thinking, decide the best course of action (HOLD, UPDATE_TRADE, CLOSE_TRADE). |
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3. **Output Constraint:** Provide your final answer ONLY in the requested JSON object format, with no introductory text, markdown formatting, or explanations. |
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OUTPUT (JSON Object ONLY): |
|
|
{{ |
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|
"action": "HOLD" or "UPDATE_TRADE" or "CLOSE_TRADE", |
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|
"strategy": "MAINTAIN_CURRENT" or "ADAPTIVE_EXIT" or "IMMEDIATE_EXIT", |
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|
"reasoning": "Brief justification (max 40 words) for the decision.", |
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|
"new_stop_loss": (float or null, required if action is 'UPDATE_TRADE'), |
|
|
"new_take_profit": (float or null, required if action is 'UPDATE_TRADE') |
|
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}} |
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|
""" |
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return task_prompt |
