ml_engine/sniper_engine.py

# ============================================================
# 🎯 ml_engine/sniper_engine.py (V1.1 - L2 Entry Sniper)
# (معدل ليقرأ النماذج من نفس مجلد Guard V2)
# ============================================================

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 0.60)
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))).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:
    
    # [ 🚀 🚀 🚀 ]
    # [ 💡 💡 💡 ]  التعديل: تغيير __init__ ليطابق GuardEngine
    def __init__(self, models_dir: str):
        """
        تهيئة محرك قناص الدخول V1 (L2 Sniper).
        Args:
            models_dir: المسار المباشر للمجلد (e.g., "ml_models/guard_v2")
        """
        self.models_dir = models_dir 
        # [ 🚀 🚀 🚀 ]
        
        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:
            # (سيبحث الآن داخل 'ml_models/guard_v2/' عن هذه الملفات)
            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}.")
                print(f"   -> (تأكد من وجود ملفات 'lgbm_guard_v3...' داخل {self.models_dir})")
                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 <= 0.90: # (توسيع النطاق)
            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}'}