Update ml_engine/processor.py

ml_engine/processor.py

@@ -1,10 +1,9 @@
 # ml_engine/processor.py
-# (V13.10 - GEM-Architect: Full Enterprise Logic + Init Flag Fix)
-# - Fully implemented Feature Engineering.
-# - Integrated Light MC for Tier 1.
-# - Integrated Advanced MC Helper for Tier 2.
-# - Detailed Oracle Logging.
-# - Added 'initialized' flag to prevent TradeManager crashes.
+# (V13.11 - GEM-Architect: Full Enterprise Logic + Sniper Entry Fix)
+# - Features: Feature Engineering, Titan Score, Pattern Score.
+# - Stats: Hybrid Monte Carlo (Light + Advanced).
+# - Decisions: Guardian (Open Trades), Oracle (Filtering).
+# - Execution: Sniper Entry Check (Micro-structure analysis) [ADDED].
 
 import asyncio
 import numpy as np
@@ -29,14 +28,14 @@ except ImportError:
 class MLProcessor:
     """
     The Central Neural Engine of Titan.
-    Coordinates Analysis, Scoring, and Decision Making.
+    Coordinates Analysis, Scoring, Decision Making, and Execution Timing.
     """
     
     def __init__(self, data_manager):
         self.data_manager = data_manager
         self.hub_manager = None  # Dependency Injection later
         
-        # [CRITICAL FIX] حالة التهيئة لمنع الأخطاء في TradeManager
+        # حالة التهيئة
         self.initialized = False
         
         # تهيئة محرك المحاكاة
@@ -57,12 +56,12 @@ class MLProcessor:
         
         # إعدادات العتبات (Decision Thresholds)
         self.thresholds = {
-            'buy_moderate': 0.62, # تم ضبطها لتكون متوازنة
+            'buy_moderate': 0.62,
             'stop_loss_hard': -0.05,
             'take_profit_base': 0.025
         }
         
-        print("✅ [MLProcessor V13.10] Enterprise Engine Loaded.")
+        print("✅ [MLProcessor V13.11] Enterprise Engine Loaded (Sniper Ready).")
 
     async def initialize(self):
         """
@@ -76,7 +75,6 @@ class MLProcessor:
             print("   -> Analytics Engines: Online")
             print("   -> Monte Carlo: Hybrid Mode Active")
             
-            # [CRITICAL FIX] تعيين العلامة لتأكيد الجاهزية
             self.initialized = True
             print("✅ [MLProcessor] Initialization Complete.")
             
@@ -146,34 +144,76 @@ class MLProcessor:
             return result_package
             
         except Exception as e:
-            # logger.error(f"Error processing {symbol}: {e}")
             return None
 
     # ==============================================================================
-    # 🔬 Layer 2.5: Advanced Helper (Called by App.py)
+    # 🔬 Layer 2.5 & L4: Advanced Helpers & Sniper Check
     # ==============================================================================
     
     async def run_advanced_monte_carlo(self, symbol: str, timeframe: str = '1h') -> float:
-        """
-        تشغيل المحاكاة المتقدمة لأفضل المرشحين فقط.
-        """
+        """تشغيل المحاكاة المتقدمة لأفضل المرشحين فقط (L2.5)."""
         try:
-            # جلب بيانات تاريخية أطول (500 شمعة)
             ohlcv = await self.data_manager.get_latest_ohlcv(symbol, timeframe, limit=500)
             if not ohlcv or len(ohlcv) < 100: 
                 return 0.0
-            
             prices = [c[4] for c in ohlcv]
-            
-            # استدعاء المحرك المتقدم
             adv_score = self.mc_engine.run_advanced_simulation(prices, num_simulations=3000, time_horizon=24)
-            
             return adv_score
-            
         except Exception as e:
             print(f"⚠️ [Processor] Advanced MC Error ({symbol}): {e}")
             return 0.0
 
+    async def check_sniper_entry(self, ohlcv_1m: List[list], order_book: Dict) -> bool:
+        """
+        [L4 SNIPER LOGIC] - الدالة المضافة
+        فحص دقيق قبل التنفيذ مباشرة. تتأكد من:
+        1. السبريد (Spread) مقبول.
+        2. لا توجد جدران بيع ضخمة (Sell Walls).
+        3. الزخم اللحظي (1m Momentum) ليس سلبياً بحدة.
+        """
+        try:
+            # 1. فحص دفتر الطلبات (Order Book)
+            bids = order_book.get('bids', [])
+            asks = order_book.get('asks', [])
+            
+            if not bids or not asks:
+                # إذا لم تتوفر البيانات، نسمح بالدخول (Fail Open) لتجنب تفويت الفرص بسبب نقص البيانات اللحظي
+                return True 
+
+            best_bid = float(bids[0][0])
+            best_ask = float(asks[0][0])
+            
+            # حساب السبريد
+            spread_pct = (best_ask - best_bid) / best_bid
+            if spread_pct > 0.01: # 1% Spread is too high for scalping
+                print(f"   ⚠️ [Sniper] High Spread: {spread_pct*100:.2f}%")
+                return False
+
+            # فحص جدار البيع (Sell Wall)
+            # نجمع حجم أول 5 طلبات
+            bid_vol = sum([b[1] for b in bids[:5]])
+            ask_vol = sum([a[1] for a in asks[:5]])
+            
+            if ask_vol > bid_vol * 4: # ضغط بيع هائل في الواجهة
+                print(f"   ⚠️ [Sniper] Sell Wall Detected (Ratio 1:{ask_vol/bid_vol:.1f})")
+                return False
+
+            # 2. فحص الزخم اللحظي (1m Candles)
+            if ohlcv_1m and len(ohlcv_1m) >= 3:
+                closes = [c[4] for c in ohlcv_1m]
+                # إذا كانت آخر شمعتين هبوط حاد، ننتظر قليلاً
+                # (بسيط جداً حالياً، يمكن تعقيده)
+                change_last_2m = (closes[-1] - closes[-3]) / closes[-3]
+                if change_last_2m < -0.015: # هبوط 1.5% في دقيقتين
+                    print("   ⚠️ [Sniper] Falling Knife detected (-1.5% in 2m)")
+                    return False
+
+            return True # Entry Approved
+
+        except Exception as e:
+            print(f"⚠️ [Sniper] Check Error: {e}. Proceeding cautiously.")
+            return True # السماح بالدخول في حالة الخطأ (Fail Open)
+
     # ==============================================================================
     # ⚙️ Internal Logic: Indicators & Features
     # ==============================================================================
@@ -217,10 +257,10 @@ class MLProcessor:
             
             score = 0.5
             
-            # Logic: Buy dips in uptrends, avoid extremes
+            # Logic: Buy dips in uptrends
             if 30 < rsi < 70: score = 0.6
-            elif rsi <= 30: score = 0.8 # Oversold Opportunity
-            elif rsi >= 75: score = 0.3 # Overbought Risk
+            elif rsi <= 30: score = 0.8 # Oversold
+            elif rsi >= 75: score = 0.3 # Overbought
             
             # Trend Confirmation
             sma_20 = np.mean(closes[-20:]) if len(closes) >= 20 else np.mean(closes)
@@ -230,21 +270,17 @@ class MLProcessor:
         except: return 0.5
 
     def _calculate_pattern_score(self, features: Dict) -> float:
-        """Pattern Logic: Volume & Breakouts"""
+        """Pattern Logic: Volume"""
         try:
             volumes = features.get('volumes_15m')
-            closes = features.get('closes_15m')
             if volumes is None or len(volumes) < 20: return 0.5
             
             avg_vol = np.mean(volumes[:-5]) 
             curr_vol = np.mean(volumes[-3:])
             
             score = 0.5
-            
-            # Volume Spikes
             if curr_vol > avg_vol * 2.5: score = 0.9 
             elif curr_vol > avg_vol * 1.5: score = 0.7 
-            elif curr_vol < avg_vol * 0.5: score = 0.3 
             
             return max(0.0, min(1.0, score))
         except: return 0.5
@@ -253,10 +289,8 @@ class MLProcessor:
         try:
             closes = features.get('closes_15m')
             if closes is None: return "Unknown"
-            
             log_returns = np.diff(np.log(closes))
             volatility = np.std(log_returns)
-            
             if volatility > 0.02: return "High"
             if volatility > 0.01: return "Medium"
             return "Low"
@@ -267,9 +301,7 @@ class MLProcessor:
     # ==============================================================================
 
     def consult_guardian(self, d1, d5, d15, entry_price):
-        """
-        Guardian Logic for Open Trades.
-        """
+        """Guardian Logic for Open Trades."""
         try:
             if not d1 or len(d1) == 0:
                 return {'action': 'HOLD', 'reason': 'No Data'}
@@ -283,7 +315,7 @@ class MLProcessor:
             if pnl_pct < self.thresholds['stop_loss_hard']: 
                 return {'action': 'EXIT_HARD', 'reason': f'Stop Loss ({pnl_pct*100:.2f}%)'}
             
-            # Take Profit / Trailing
+            # Take Profit
             if pnl_pct > 0.05:
                 return {'action': 'EXIT_PARTIAL', 'reason': 'Secure Profit (>5%)'}
             
@@ -294,20 +326,15 @@ class MLProcessor:
             return {'action': 'HOLD', 'reason': 'Guardian Error'}
 
     async def consult_oracle(self, signal):
-        """
-        Oracle Logic for New Signals (Verbose).
-        """
+        """Oracle Logic for New Signals."""
         try:
             symbol = signal.get('symbol', 'UNKNOWN')
             conf = signal.get('enhanced_final_score', 0.0)
             price = signal.get('current_price', 0)
             
-            # العتبة المحددة
             threshold = self.thresholds['buy_moderate']
             
-            # [LOGIC] القرار
             if conf >= threshold:
-                # أهداف ديناميكية بسيطة
                 tp = price * 1.03
                 sl = price * 0.975
                 
@@ -320,12 +347,8 @@ class MLProcessor:
                     'reason': f'Approved (Score {conf:.2f})'
                 }
             
-            # في حال الرفض، سجل السبب
             print(f"   🔮 [Oracle] REJECTED {symbol}: Score {conf:.2f} < {threshold}")
-            return {
-                'action': 'IGNORE', 
-                'reason': f'Score {conf:.2f} < {threshold}'
-            }
+            return {'action': 'IGNORE', 'reason': f'Score {conf:.2f} < {threshold}'}
             
         except Exception as e:
             print(f"⚠️ [Oracle] Critical Error: {e}")