Update whale_news_data.py

whale_news_data.py

@@ -4,14 +4,13 @@ import httpx
 import json
 import traceback
 import time
-import math
 from datetime import datetime, timedelta
 from collections import defaultdict, deque
 import ccxt.pro as ccxt
 import numpy as np
 
 class EnhancedWhaleMonitor:
-    def __init__(self, contracts_db=None):
+    def __init__(self, contracts_db=None, r2_service=None):
         self.http_client = httpx.AsyncClient(timeout=15.0, limits=httpx.Limits(max_connections=50, max_keepalive_connections=10))
         
         self.moralis_key = os.getenv("MORALIS_KEY")
@@ -21,6 +20,7 @@ class EnhancedWhaleMonitor:
         self.whale_threshold_usd = 100000
         self.contracts_db = contracts_db or {}
         self.data_manager = None
+        self.r2_service = r2_service
         
         self.address_labels = {}
         self._initialize_dynamic_labels()
@@ -87,6 +87,7 @@ class EnhancedWhaleMonitor:
         
         self.pattern_performance = {}
         self.pattern_success_rates = {}
+        self._load_pattern_performance()
 
     def _initialize_dynamic_labels(self):
         self.address_categories = {
@@ -108,6 +109,45 @@ class EnhancedWhaleMonitor:
         
         self._load_initial_exchange_addresses()
 
+    async def _load_pattern_performance(self):
+        """تحميل بيانات أداء الأنماط من التخزين"""
+        if not self.r2_service:
+            return
+            
+        try:
+            key = "pattern_performance.json"
+            response = self.r2_service.s3_client.get_object(Bucket="trading", Key=key)
+            data = json.loads(response['Body'].read())
+            self.pattern_performance = data.get('pattern_performance', {})
+            self.pattern_success_rates = data.get('pattern_success_rates', {})
+            print(f"✅ تم تحميل بيانات أداء الأنماط: {len(self.pattern_performance)} نمط")
+        except Exception as e:
+            print(f"⚠️ لم يتم العثور على بيانات أداء الأنماط: {e}")
+            self.pattern_performance = {}
+            self.pattern_success_rates = {}
+
+    async def _save_pattern_performance(self):
+        """حفظ بيانات أداء الأنماط إلى التخزين"""
+        if not self.r2_service:
+            return
+            
+        try:
+            key = "pattern_performance.json"
+            data = {
+                'pattern_performance': self.pattern_performance,
+                'pattern_success_rates': self.pattern_success_rates,
+                'last_updated': datetime.now().isoformat(),
+                'total_patterns': len(self.pattern_performance)
+            }
+            
+            data_json = json.dumps(data, indent=2).encode('utf-8')
+            self.r2_service.s3_client.put_object(
+                Bucket="trading", Key=key, Body=data_json, ContentType="application/json"
+            )
+            print(f"✅ تم حفظ بيانات أداء الأنماط: {len(self.pattern_performance)} نمط")
+        except Exception as e:
+            print(f"❌ فشل حفظ بيانات أداء الأنماط: {e}")
+
     def _load_initial_exchange_addresses(self):
         for exchange, addresses in self.exchange_patterns.items():
             for address in addresses:
@@ -200,7 +240,7 @@ class EnhancedWhaleMonitor:
                 self.netflow_data[network][token_symbol]['netflow'].append(current_netflow)
                 
         except Exception as e:
-            print(f"خطأ في تحديث مقاييس صافي التدفق: {e}")
+            print(f"❌ خطأ في تحديث مقاييس صافي التدفق: {e}")
 
     def _initialize_token_metrics(self, network, token_symbol):
         self.netflow_data[network][token_symbol] = {
@@ -240,7 +280,7 @@ class EnhancedWhaleMonitor:
             return zscore
             
         except Exception as e:
-            print(f"خطأ في حساب Z-score: {e}")
+            print(f"❌ خطأ في حساب Z-score: {e}")
             return 0
 
     def _generate_netflow_signal(self, network, token_symbol):
@@ -315,7 +355,7 @@ class EnhancedWhaleMonitor:
             return signal
             
         except Exception as e:
-            print(f"خطأ في توليد إشارة التداول: {e}")
+            print(f"❌ خطأ في توليد إشارة التداول: {e}")
             return None
 
     async def _scan_single_evm_network(self, network):
@@ -397,7 +437,7 @@ class EnhancedWhaleMonitor:
             return whale_alerts, trading_signals
                     
         except Exception as e:
-            print(f"خطأ في مسح شبكة {network}: {e}")
+            print(f"❌ خطأ في مسح شبكة {network}: {e}")
             return [], []
 
     async def get_general_whale_activity(self):
@@ -973,6 +1013,7 @@ class EnhancedWhaleMonitor:
             return None
 
     async def track_pattern_outcome(self, symbol, pattern_analysis, success, profit_percent):
+        """تتبع نتائج الأنماط وحفظها"""
         if not pattern_analysis:
             return
         
@@ -985,20 +1026,47 @@ class EnhancedWhaleMonitor:
                     'total_trades': 0,
                     'successful_trades': 0,
                     'total_profit': 0,
-                    'total_confidence': 0
+                    'total_confidence': 0,
+                    'last_updated': datetime.now().isoformat()
                 }
             
             stats = self.pattern_performance[pattern_name]
             stats['total_trades'] += 1
             stats['total_confidence'] += confidence
+            stats['last_updated'] = datetime.now().isoformat()
             
             if success:
                 stats['successful_trades'] += 1
                 stats['total_profit'] += profit_percent
 
-    def get_pattern_reliability(self, pattern_name):
-        if pattern_name in self.pattern_performance:
-            stats = self.pattern_performance[pattern_name]
+            # تحديث معدلات النجاح
             if stats['total_trades'] > 0:
-                return stats['successful_trades'] / stats['total_trades']
-        return 0.5
+                self.pattern_success_rates[pattern_name] = {
+                    'success_rate': stats['successful_trades'] / stats['total_trades'],
+                    'avg_profit': stats['total_profit'] / stats['total_trades'],
+                    'total_trades': stats['total_trades'],
+                    'last_updated': datetime.now().isoformat()
+                }
+            
+            # الحفظ التلقائي كل 10 تحديثات
+            if stats['total_trades'] % 10 == 0:
+                await self._save_pattern_performance()
+
+    def get_pattern_reliability(self, pattern_name):
+        """الحصول على موثوقية النمط"""
+        if pattern_name in self.pattern_success_rates:
+            return self.pattern_success_rates[pattern_name]['success_rate']
+        return 0.5
+
+    async def save_all_pattern_data(self):
+        """حفظ جميع بيانات الأنماط"""
+        await self._save_pattern_performance()
+
+    def get_pattern_performance_summary(self):
+        """الحصول على ملخص أداء الأنماط"""
+        return {
+            'pattern_performance': self.pattern_performance,
+            'pattern_success_rates': self.pattern_success_rates,
+            'total_patterns_tracked': len(self.pattern_performance),
+            'last_updated': datetime.now().isoformat()
+        }