Create hub_manager.py

learning_hub/hub_manager.py

@@ -0,0 +1,147 @@
+# learning_hub/hub_manager.py
+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."
+
+    async def get_optimized_weights(self, market_condition: str) -> Dict[str, float]:
+        """
+        يُستخدم بواسطة MLProcessor/StrategyEngine لجلب الأوزان المعدلة إحصائياً.
+        """
+        if not self.initialized:
+            defaults = await self.statistical_analyzer.get_default_strategy_weights()
+            return defaults.get("strategy_weights", {})
+            
+        return await self.statistical_analyzer.get_optimized_strategy_weights(market_condition)
+        
+    async def run_distillation_check(self):
+        """
+        (��تم 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.")
+        
+    # 🔴 --- START OF CHANGE --- 🔴
+    # (New function to save all data on shutdown)
+    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()
+            print("✅ [HubManager] All statistical (slow-learner) data saved.")
+        except Exception as e:
+            print(f"❌ [HubManager] Failed to save learning data on shutdown: {e}")
+    # 🔴 --- END OF CHANGE --- 🔴