learning_hub/hub_manager.py

# learning_hub/hub_manager.py
# (محدث بالكامل - V2 - VADER Learning)
import asyncio
from typing import Any, Dict

# (استيراد جميع المكونات الداخلية للمركز)
from .schemas import *
from .policy_engine import PolicyEngine
from .memory_store import MemoryStore
from .statistical_analyzer import StatisticalAnalyzer
from .reflector import Reflector
from .curator import Curator

class LearningHubManager:
    def __init__(self, r2_service: Any, llm_service: Any, data_manager: Any):
        print("🚀 Initializing Learning Hub Manager...")
        
        # 1. الخدمات الأساسية (يتم تمريرها من app.py)
        self.r2_service = r2_service
        self.llm_service = llm_service
        self.data_manager = data_manager

        # 2. تهيئة المكونات (بناء النظام)
        self.policy_engine = PolicyEngine()
        self.memory_store = MemoryStore(
            r2_service=self.r2_service, 
            policy_engine=self.policy_engine,
            llm_service=self.llm_service 
        )
        self.reflector = Reflector(
            llm_service=self.llm_service,
            memory_store=self.memory_store
        )
        self.curator = Curator(
            llm_service=self.llm_service,
            memory_store=self.memory_store
        )
        self.statistical_analyzer = StatisticalAnalyzer(
            r2_service=self.r2_service,
            data_manager=self.data_manager
        )
        
        self.initialized = False
        print("✅ Learning Hub Manager constructed. Ready for initialization.")

    async def initialize(self):
        """
        تهيئة جميع الأنظمة الفرعية، وخاصة تحميل الإحصائيات والأوزان.
        """
        if self.initialized:
            return
            
        print("🔄 [HubManager] Initializing all sub-modules...")
        await self.statistical_analyzer.initialize()
        self.initialized = True
        print("✅ [HubManager] All sub-modules initialized. Learning Hub is LIVE.")

    async def analyze_trade_and_learn(self, trade_object: Dict[str, Any], close_reason: str):
        """
        هذه هي الدالة الرئيسية التي يستدعيها TradeManager.
        إنها تشغل كلاً من نظام التعلم السريع (Reflector) والبطيء (StatsAnalyzer).
        """
        if not self.initialized:
            print("⚠️ [HubManager] Learning Hub not initialized. Skipping learning.")
            return

        print(f"🧠 [HubManager] Learning from trade {trade_object.get('symbol')}...")

        try:
            # 1. التعلم السريع (Reflector):
            await self.reflector.analyze_trade_outcome(trade_object, close_reason)
        except Exception as e:
            print(f"❌ [HubManager] Reflector (Fast-Learner) failed: {e}")

        try:
            # 2. التعلم البطيء (StatisticalAnalyzer):
            await self.statistical_analyzer.update_statistics(trade_object, close_reason)
        except Exception as e:
            print(f"❌ [HubManager] StatisticalAnalyzer (Slow-Learner) failed: {e}")
            
        print(f"✅ [HubManager] Learning complete for {trade_object.get('symbol')}.")
        
    async def get_active_context_for_llm(self, domain: str, query: str) -> str:
        """
        يُستخدم بواسطة LLMService لجلب "الدفتر" (Playbook) / القواعد (Deltas).
        """
        if not self.initialized:
            return "Learning Hub not initialized."
            
        return await self.memory_store.get_active_context(domain, query)
        
    async def get_statistical_feedback_for_llm(self, entry_strategy: str) -> str:
        """
        يُستخدم بواسطة LLMService لجلب أفضل ملف خروج (إحصائياً).
        """
        if not self.initialized:
            return "Learning Hub not initialized."

        best_profile = await self.statistical_analyzer.get_best_exit_profile(entry_strategy)
        
        if best_profile != "unknown":
            # (Prompt in English as requested)
            feedback = f"Statistical Feedback: For the '{entry_strategy}' strategy, the '{best_profile}' exit profile has historically performed best."
            return feedback
        else:
            return "No statistical feedback available for this strategy yet."
            
    # 🔴 --- START OF CHANGE (V2 - VADER Learning) --- 🔴
    async def get_statistical_news_score(self, raw_vader_score: float) -> float:
        """
        يحول درجة VADER الخام إلى متوسط الربح/الخسارة التاريخي المتوقع.
        (يُستخدم بواسطة app.py / MLProcessor للترتيب الداخلي)
        """
        if not self.initialized:
            return 0.0 # محايد
            
        # (جلب متوسط الربح/الخسارة الفعلي من المحلل الإحصائي)
        historical_pnl = await self.statistical_analyzer.get_statistical_vader_pnl(raw_vader_score)
        
        # (إرجاع النسبة المئوية للربح/الخسارة مباشرة، مثلاً: 1.1 أو -0.5)
        return historical_pnl
    # 🔴 --- END OF CHANGE --- 🔴

    # 🔴 --- START OF CHANGE --- 🔴
    async def get_optimized_weights(self, market_condition: str) -> Dict[str, float]:
        """
        يُستخدم بواسطة MLProcessor/StrategyEngine/Sentry لجلب الأوزان المعدلة إحصائياً.
        """
        if not self.initialized:
            # (الحصول على كل الأوزان الافتراضية)
            return await self.statistical_analyzer.get_default_strategy_weights()
            
        # (الحصول على كل الأوزان المحسنة)
        return await self.statistical_analyzer.get_optimized_weights(market_condition)
    # 🔴 --- END OF CHANGE --- 🔴
        
    async def run_distillation_check(self):
        """
        (يتم استدعاؤها دورياً من app.py)
        للتحقق من جميع المجالات وتشغيل التقطير إذا لزم الأمر.
        """
        if not self.initialized:
            return
            
        print("ℹ️ [HubManager] Running periodic distillation check...")
        for domain in self.memory_store.domain_files.keys():
            await self.curator.check_and_distill_domain(domain)
        print("✅ [HubManager] Distillation check complete.")
        
    # (No change to shutdown function)
    async def shutdown(self):
        """
        Saves all persistent data from the statistical analyzer.
        """
        if not self.initialized:
            return
            
        print("🔄 [HubManager] Shutting down... Saving all learning data.")
        try:
            await self.statistical_analyzer.save_weights_to_r2()
            await self.statistical_analyzer.save_performance_history()
            await self.statistical_analyzer.save_exit_profile_effectiveness()
            # 🔴 --- START OF CHANGE (V2 - VADER Learning) --- 🔴
            await self.statistical_analyzer.save_vader_effectiveness()
            # 🔴 --- END OF CHANGE --- 🔴
            print("✅ [HubManager] All statistical (slow-learner) data saved.")
        except Exception as e:
            print(f"❌ [HubManager] Failed to save learning data on shutdown: {e}")
    # 🔴 --- START OF CHANGE --- 🔴
    # (تم حذف القوس } الزائد من هنا)
    # 🔴 --- END OF CHANGE --- 🔴