Update ml_engine/data_manager.py

ml_engine/data_manager.py

@@ -1,6 +1,6 @@
 # ============================================================
 # 📂 ml_engine/data_manager.py 
-# (V63.0 - GEM-Architect: High Volume Filter & Depth Scan)
+# (V63.1 - GEM-Architect: Fixed Missing Attribute & Full Integrity)
 # ============================================================
 
 import asyncio
@@ -44,7 +44,7 @@ class DataManager:
             'UP', 'DOWN', 'BEAR', 'BULL', '3S', '3L'
         ]
         
-        print(f"📦 [DataManager V63.0] High Liquidity (1M+) & Depth Scan Active.")
+        print(f"📦 [DataManager V63.1] Integrity Restored (Regime Fix).")
 
     async def initialize(self):
         print("   > [DataManager] Starting initialization...")
@@ -79,7 +79,7 @@ class DataManager:
         self.adaptive_hub_ref = adaptive_hub_ref
         print(f"🔍 [Layer 1] Screening for High Vol Assets (Bottom/Acc/Mom)...")
         
-        # 1. فلتر السيولة الأساسي (الآن صارم: 1 مليون دولار)
+        # 1. فلتر السيولة الأساسي (1 مليون دولار)
         initial_candidates = await self._stage0_universe_filter()
         if not initial_candidates:
             print("⚠️ [Layer 1] Stage 0 returned 0 candidates.")
@@ -96,7 +96,9 @@ class DataManager:
             classification = self._classify_opportunity_type(item)
             
             if classification['type'] != 'NONE':
+                # ✅ استدعاء الدالة المفقودة سابقاً
                 regime_info = self._diagnose_asset_regime(item)
+                
                 item['asset_regime'] = regime_info['regime']
                 item['asset_regime_conf'] = regime_info['conf']
                 
@@ -115,7 +117,7 @@ class DataManager:
         final_list = []
         # نأخذ أفضل 50 مرشحاً لفحص دفتر الطلبات
         semi_final_list.sort(key=lambda x: x['l1_sort_score'], reverse=True)
-        candidates_for_depth = semi_final_list[:50]
+        candidates_for_depth = semi_final_list[:300]
         
         if candidates_for_depth:
             print(f"   🛡️ [Layer 1.5] Checking Depth Support for {len(candidates_for_depth)} candidates...")
@@ -144,7 +146,7 @@ class DataManager:
             final_list.append(item)
 
         final_list.sort(key=lambda x: x['l1_sort_score'], reverse=True)
-        selection = final_list[:60]
+        selection = final_list[:300]
         
         print(f"✅ [Layer 1] Passed {len(selection)} active candidates.")
         return selection
@@ -231,8 +233,8 @@ class DataManager:
     # ==================================================================
     async def _stage0_universe_filter(self) -> List[Dict[str, Any]]:
         try:
-            # 🔥 إعداد الحد الأدنى لحجم التداول (يمكنك تغييره إلى 5 مليون من هنا)
-            MIN_VOLUME_THRESHOLD = 1000000.0  # 1 Million USDT
+            # 🔥 إعداد الحد الأدنى لحجم التداول
+            MIN_VOLUME_THRESHOLD = 5000000.0  # 1 Million USDT
             
             print(f"   🛡️ [Stage 0] Fetching Tickers (Min Vol: ${MIN_VOLUME_THRESHOLD:,.0f})...")
             tickers = await self.exchange.fetch_tickers()
@@ -284,6 +286,47 @@ class DataManager:
             traceback.print_exc()
             return []
 
+    # ------------------------------------------------------------------
+    # 🧭 The Diagnoser (تمت استعادتها)
+    # ------------------------------------------------------------------
+    def _diagnose_asset_regime(self, item: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        تقوم بتشخيص حالة السوق للأصل (Regime) لتحديد ما إذا كان مناسباً للدخول
+        """
+        try:
+            if 'df_1h' not in item: 
+                # محاولة استخراج الداتا فريم إذا لم تكن موجودة
+                if 'ohlcv_1h_raw' in item:
+                    item['df_1h'] = self._calc_indicators(item['ohlcv_1h_raw'])
+                else:
+                    return {'regime': 'RANGE', 'conf': 0.0}
+
+            df = item['df_1h']
+            if df.empty: return {'regime': 'RANGE', 'conf': 0.0}
+
+            curr = df.iloc[-1]
+            price = curr['close']
+            ema20 = curr['ema20']
+            ema50 = curr['ema50']
+            rsi = curr['rsi']
+            atr = curr['atr']
+            atr_pct = (atr / price) * 100 if price > 0 else 0
+            
+            regime = "RANGE"
+            conf = 0.5
+            
+            if atr_pct < 0.4: return {'regime': 'DEAD', 'conf': 0.9}
+            
+            if price > ema20 and ema20 > ema50 and rsi > 50:
+                regime = "BULL"
+                conf = 0.8 if rsi > 55 else 0.6
+            elif price < ema20 and ema20 < ema50 and rsi < 50:
+                regime = "BEAR"
+                conf = 0.8 if rsi < 45 else 0.6
+                
+            return {'regime': regime, 'conf': conf}
+        except Exception: return {'regime': 'RANGE', 'conf': 0.0}
+
     # ------------------------------------------------------------------
     # Helpers & Indicators
     # ------------------------------------------------------------------
@@ -305,6 +348,8 @@ class DataManager:
             c['ohlcv'] = {'1h': h1, '15m': m15} 
             c['ohlcv_1h_raw'] = h1 
             c['ohlcv_15m_raw'] = m15
+            # حساب المؤشرات هنا لتوفير الوقت لاحقاً
+            c['df_1h'] = self._calc_indicators(h1)
             return c
         except: return None