Create sniper_engine.py

ml_engine/sniper_engine.py

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+# ============================================================
+# 🎯 ml_engine/sniper_engine.py (V1.0 - L2 Entry Sniper)
+# هذا هو "الحارس V3" الذي بنيناه (زناد الدخول)
+# ============================================================
+
+import os
+import sys
+import numpy as np
+import pandas as pd
+import pandas_ta as ta
+import lightgbm as lgb
+import joblib
+import asyncio
+import traceback
+from typing import List, Dict, Any
+
+# --- [ 💡 💡 💡 ] ---
+# [ 🚀 🚀 🚀 ]  العتبة الافتراضية (بناءً على طلبك بعد الاختبار المنفصل)
+DEFAULT_SNIPER_THRESHOLD = 0.60 
+# [ 🚀 🚀 🚀 ]
+# --- [ 💡 💡 💡 ] ---
+
+N_SPLITS = 5
+LOOKBACK_WINDOW = 500 # (الحد الأدنى للشموع 1m لحساب Z-Score (w=500))
+
+# ============================================================
+# 🔧 1. دوال هندسة الميزات (مطابقة 100% للمرحلة 2.ب)
+# ============================================================
+
+def _z_score_rolling(x, w=500): 
+    """حساب Z-Score المتدحرج (آمن من القسمة على صفر)"""
+    r = x.rolling(w).mean()
+    s = x.rolling(w).std().replace(0, np.nan)
+    z = (x - r) / s
+    return z.fillna(0)
+
+def _add_liquidity_proxies(df):
+    """
+    إضافة بدائل السيولة وتدفق الطلب المتقدمة.
+    """
+    df_proxy = df.copy()
+    if 'datetime' not in df_proxy.index:
+        if 'timestamp' in df_proxy.columns:
+            df_proxy['datetime'] = pd.to_datetime(df_proxy['timestamp'], unit='ms')
+            df_proxy = df_proxy.set_index('datetime')
+        else:
+            print("❌ [SniperEngine] خطأ في بدائل السيولة: المؤشر الزمني مفقود.")
+            return df_proxy
+            
+    df_proxy['ret'] = df_proxy['close'].pct_change().fillna(0)
+    df_proxy['dollar_vol'] = df_proxy['close'] * df_proxy['volume']
+    
+    df_proxy['amihud'] = (df_proxy['ret'].abs() / df_proxy['dollar_vol'].replace(0, np.nan)).fillna(np.inf)
+    
+    dp = df_proxy['close'].diff()
+    roll_cov = dp.rolling(64).cov(dp.shift(1))
+    df_proxy['roll_spread'] = (2 * np.sqrt(np.maximum(0, -roll_cov))).fillna(method='bfill')
+
+    sign = np.sign(df_proxy['close'].diff()).fillna(0)
+    df_proxy['signed_vol'] = sign * df_proxy['volume']
+    df_proxy['ofi'] = df_proxy['signed_vol'].rolling(30).sum().fillna(0)
+
+    buy_vol = (sign > 0) * df_proxy['volume']
+    sell_vol = (sign < 0) * df_proxy['volume']
+    imb = (buy_vol.rolling(60).sum() - sell_vol.rolling(60).sum()).abs()
+    tot = df_proxy['volume'].rolling(60).sum()
+    df_proxy['vpin'] = (imb / tot.replace(0, np.nan)).fillna(0)
+
+    df_proxy['rv_gk'] = (np.log(df_proxy['high'] / df_proxy['low'])**2) / 2 - \
+                        (2 * np.log(2) - 1) * (np.log(df_proxy['close'] / df_proxy['open'])**2)
+    
+    vwap_window = 20
+    df_proxy['vwap'] = (df_proxy['close'] * df_proxy['volume']).rolling(vwap_window).sum() / \
+                       df_proxy['volume'].rolling(vwap_window).sum()
+    df_proxy['vwap_dev'] = (df_proxy['close'] - df_proxy['vwap']).fillna(0)
+
+    df_proxy['L_score'] = (
+        _z_score_rolling(df_proxy['volume']) +
+        _z_score_rolling(1 / df_proxy['amihud'].replace(np.inf, np.nan)) + 
+        _z_score_rolling(-df_proxy['roll_spread']) + 
+        _z_score_rolling(-df_proxy['rv_gk'].abs()) + 
+        _z_score_rolling(-df_proxy['vwap_dev'].abs()) + 
+        _z_score_rolling(df_proxy['ofi']) 
+    )
+    
+    return df_proxy
+
+def _add_standard_features(df):
+    """إضافة الميزات القياسية (عوائد، زخم، حجم)"""
+    df_feat = df.copy()
+    
+    df_feat['return_1m'] = df_feat['close'].pct_change(1)
+    df_feat['return_3m'] = df_feat['close'].pct_change(3)
+    df_feat['return_5m'] = df_feat['close'].pct_change(5)
+    df_feat['return_15m'] = df_feat['close'].pct_change(15)
+
+    df_feat['rsi_14'] = ta.rsi(df_feat['close'], length=14)
+    ema_9 = ta.ema(df_feat['close'], length=9)
+    ema_21 = ta.ema(df_feat['close'], length=21)
+    df_feat['ema_9_slope'] = (ema_9 - ema_9.shift(1)) / ema_9.shift(1)
+    df_feat['ema_21_dist'] = (df_feat['close'] - ema_21) / ema_21 
+    
+    df_feat['atr'] = ta.atr(df_feat['high'], df_feat['low'], df_feat['close'], length=100)
+    df_feat['vol_zscore_50'] = _z_score_rolling(df_feat['volume'], w=50) 
+    
+    df_feat['candle_range'] = df_feat['high'] - df_feat['low']
+    df_feat['close_pos_in_range'] = (df_feat['close'] - df_feat['low']) / (df_feat['candle_range'].replace(0, np.nan))
+    
+    return df_feat
+
+# ============================================================
+# 🎯 2. كلاس المحرك الرئيسي (SniperEngine V1)
+# ============================================================
+
+class SniperEngine:
+    def __init__(self, base_project_dir: str):
+        """
+        تهيئة محرك قناص الدخول V1 (L2 Sniper).
+        Args:
+            base_project_dir: المسار الرئيسي للمشروع (Guard_Project)
+        """
+        # (نحمل النماذج من 'Models_V3' الذي أنشأناه)
+        self.models_dir = os.path.join(base_project_dir, "Models_V3") 
+        self.models: List[lgb.Booster] = []
+        self.feature_names: List[str] = []
+        
+        self.threshold = DEFAULT_SNIPER_THRESHOLD
+        self.initialized = False
+        
+        # (جعل LOOKBACK_WINDOW متاحاً للكود الخارجي)
+        self.LOOKBACK_WINDOW = LOOKBACK_WINDOW
+        
+        print("🎯 [SniperEngine V1] تم الإنشاء. جاهز للتهيئة.")
+
+    async def initialize(self):
+        """
+        تحميل النماذج الخمسة (Ensemble) وقائمة الميزات.
+        """
+        print(f"🎯 [SniperEngine V1] جاري التهيئة من {self.models_dir}...")
+        try:
+            model_files = [f for f in os.listdir(self.models_dir) if f.startswith('lgbm_guard_v3_fold_')]
+            if len(model_files) < N_SPLITS:
+                print(f"❌ [SniperEngine V1] خطأ فادح: تم العثور على {len(model_files)} نماذج فقط، مطلوب {N_SPLITS}.")
+                return
+
+            for f in sorted(model_files):
+                model_path = os.path.join(self.models_dir, f)
+                self.models.append(lgb.Booster(model_file=model_path))
+            
+            self.feature_names = self.models[0].feature_name()
+            self.initialized = True
+            print(f"✅ [SniperEngine V1] تم تحميل {len(self.models)} نماذج قنص بنجاح.")
+            print(f"   -> تم تحديد {len(self.feature_names)} ميزة مطلوبة.")
+            print(f"   -> تم ضبط عتبة الدخول الافتراضية على: {self.threshold * 100:.1f}%")
+
+        except Exception as e:
+            print(f"❌ [SniperEngine V1] فشل التهيئة: {e}")
+            traceback.print_exc()
+            self.initialized = False
+
+    def set_entry_threshold(self, new_threshold: float):
+        """
+        السماح بتغيير العتبة أثناء التشغيل.
+        """
+        if 0.30 <= new_threshold <= 0.70:
+            print(f"🎯 [SniperEngine V1] تم تحديث العتبة من {self.threshold} إلى {new_threshold}")
+            self.threshold = new_threshold
+        else:
+            print(f"⚠️ [SniperEngine V1] تم تجاهل العتبة (خارج النطاق): {new_threshold}")
+
+    def _calculate_features_live(self, df_1m: pd.DataFrame) -> pd.DataFrame:
+        """
+        الدالة الخاصة لتطبيق خط أنابيب الميزات الكامل.
+        """
+        try:
+            df_with_std_feats = _add_standard_features(df_1m)
+            df_with_all_feats = _add_liquidity_proxies(df_with_std_feats)
+            df_final = df_with_all_feats.replace([np.inf, -np.inf], np.nan)
+            return df_final
+            
+        except Exception as e:
+            print(f"❌ [SniperEngine V1] فشل حساب الميزات: {e}")
+            return pd.DataFrame() 
+
+    async def check_entry_signal_async(self, ohlcv_1m_data: List[List]) -> Dict[str, Any]:
+        """
+        الدالة الرئيسية: التحقق من إشارة الدخول لأحدث شمعة.
+        Args:
+            ohlcv_1m_data: قائمة بالشموع (آخر 500+ شمعة 1m)
+        """
+        if not self.initialized:
+            return {'signal': 'WAIT', 'reason': 'Sniper Engine not initialized'}
+        
+        if len(ohlcv_1m_data) < self.LOOKBACK_WINDOW:
+            return {'signal': 'WAIT', 'reason': f'Insufficient 1m data ({len(ohlcv_1m_data)} < {self.LOOKBACK_WINDOW})'}
+
+        try:
+            df = pd.DataFrame(ohlcv_1m_data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+            df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
+            
+            df_features = self._calculate_features_live(df)
+            
+            if df_features.empty:
+                return {'signal': 'WAIT', 'reason': 'Feature calculation failed'}
+
+            latest_features_row = df_features.iloc[-1:]
+            
+            X_live = latest_features_row[self.feature_names].fillna(0)
+            
+            all_probs = []
+            for model in self.models:
+                all_probs.append(model.predict(X_live))
+            
+            stacked_probs = np.stack(all_probs)    
+            mean_probs = np.mean(stacked_probs, axis=0) 
+            
+            avg_prob_1 = mean_probs[0][1] 
+
+            if avg_prob_1 >= self.threshold:
+                # (طباعة مخففة، لأنها قد تتكرر كثيراً في وضع L2)
+                # print(f"🔥 [Sniper V1] إشارة شراء! (الثقة: {avg_prob_1*100:.2f}% > {self.threshold*100:.2f}%)")
+                return {
+                    'signal': 'BUY',
+                    'confidence_prob': float(avg_prob_1),
+                    'threshold': self.threshold
+                }
+            else:
+                return {
+                    'signal': 'WAIT',
+                    'reason': 'Sniper confidence below threshold',
+                    'confidence_prob': float(avg_prob_1),
+                    'threshold': self.threshold
+                }
+
+        except Exception as e:
+            print(f"❌ [SniperEngine V1] خطأ فادح في التحقق من الإشارة: {e}")
+            traceback.print_exc()
+            return {'signal': 'WAIT', 'reason': f'Exception: {e}'}