ml_engine/processor.py

# ============================================================
# 🧠 ml_engine/processor.py 
# (V68.1 - GEM-Architect: Realistic Oracle Fallback)
# ============================================================

import asyncio
import traceback
import logging
import os
import sys
import numpy as np
from typing import Dict, Any, List, Optional

# --- استيراد المحركات (كما هي) ---
try: from .titan_engine import TitanEngine
except ImportError: TitanEngine = None
try: from .patterns import ChartPatternAnalyzer
except ImportError: ChartPatternAnalyzer = None
try: from .monte_carlo import MonteCarloEngine
except ImportError: MonteCarloEngine = None
try: from .oracle_engine import OracleEngine
except ImportError: OracleEngine = None
try: from .sniper_engine import SniperEngine
except ImportError: SniperEngine = None
try: from .hybrid_guardian import HybridDeepSteward
except ImportError: HybridDeepSteward = None
try: from .guardian_hydra import GuardianHydra
except ImportError: GuardianHydra = None

# ============================================================
# 📂 مسارات النماذج
# ============================================================
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
MODELS_L2_DIR = os.path.join(BASE_DIR, "ml_models", "layer2")
MODELS_PATTERN_DIR = os.path.join(BASE_DIR, "ml_models", "xgboost_pattern2")
MODELS_UNIFIED_DIR = os.path.join(BASE_DIR, "ml_models", "Unified_Models_V1")
MODELS_SNIPER_DIR = os.path.join(BASE_DIR, "ml_models", "guard_v2") 
MODELS_HYDRA_DIR = os.path.join(BASE_DIR, "ml_models", "guard_v1")
MODEL_V2_PATH = os.path.join(BASE_DIR, "ml_models", "DeepSteward_V2_Production.json")
MODEL_V3_PATH = os.path.join(BASE_DIR, "ml_models", "DeepSteward_V3_Production.json")
MODEL_V3_FEAT = os.path.join(BASE_DIR, "ml_models", "DeepSteward_V3_Features.json")

# ============================================================
# 🎛️ SYSTEM LIMITS (Realistic Defaults)
# ============================================================
class SystemLimits:
    """
    GEM-Architect: Adjusted Defaults based on real-world calibration.
    Oracle Ceiling detected at ~0.75, so Threshold set to 0.60.
    """
    
    # --- Layer 1 ---
    L1_MIN_AFFINITY_SCORE = 15.0  
    
    # --- Layer 2 Hard Gates (Loosened) ---
    L2_GATE_TITAN    = 0.60 
    L2_GATE_PATTERN  = 0.50 
    L2_GATE_MC       = 0.50 
    
    # --- Layer 2 Weights & Min Score ---
    L2_MIN_SCORE       = 0.65 
    L2_WEIGHT_TITAN    = 0.40
    L2_WEIGHT_PATTERNS = 0.40
    L2_WEIGHT_MC       = 0.20

    # Pattern Config
    PATTERN_TF_WEIGHTS = {'1h': 0.35, '15m': 0.25, '1d': 0.20, '5m': 0.10, '4h': 0.10}
    PATTERN_THRESH_BULLISH = 0.50 
    PATTERN_THRESH_BEARISH = 0.40

    # --- Layer 3 ---
    L3_CONFIDENCE_THRESHOLD = 0.60 # ✅ Adjusted: Realistic Entry (>55)
    L3_WHALE_IMPACT_MAX = 0.10
    L3_NEWS_IMPACT_MAX  = 0.05
    L3_MC_ADVANCED_MAX  = 0.10

    # --- Layer 4 ---
    L4_ENTRY_THRESHOLD  = 0.40 
    L4_WEIGHT_ML        = 0.60
    L4_WEIGHT_OB        = 0.40
    L4_OB_WALL_RATIO    = 0.35 

    # --- Layer 0: Hydra & Guardian Defaults ---
    HYDRA_CRASH_THRESH      = 0.60 
    HYDRA_GIVEBACK_THRESH   = 0.80 
    HYDRA_STAGNATION_THRESH = 0.60 

    # Fixed Legacy Guards
    LEGACY_V2_PANIC_THRESH = 0.98 
    LEGACY_V3_HARD_THRESH  = 0.95
    LEGACY_V3_SOFT_THRESH  = 0.88
    LEGACY_V3_ULTRA_THRESH = 0.99

    @classmethod
    def to_dict(cls) -> Dict[str, Any]:
        return {k: v for k, v in cls.__dict__.items() if not k.startswith('__') and not callable(v)}

# ============================================================
# 🧠 MLProcessor Class
# ============================================================
class MLProcessor:
    def __init__(self, data_manager=None):
        self.data_manager = data_manager
        self.initialized = False
        
        self.titan = TitanEngine(model_dir=MODELS_L2_DIR) if TitanEngine else None
        self.pattern_engine = ChartPatternAnalyzer(models_dir=MODELS_PATTERN_DIR) if ChartPatternAnalyzer else None
        self.mc_analyzer = MonteCarloEngine() if MonteCarloEngine else None
        self.oracle = OracleEngine(model_dir=MODELS_UNIFIED_DIR) if OracleEngine else None
        self.sniper = SniperEngine(models_dir=MODELS_SNIPER_DIR) if SniperEngine else None
        
        self.guardian_hydra = None
        if GuardianHydra:
            self.guardian_hydra = GuardianHydra(model_dir=MODELS_HYDRA_DIR)

        self.guardian_legacy = None
        if HybridDeepSteward:
            self.guardian_legacy = HybridDeepSteward(
                v2_model_path=MODEL_V2_PATH,
                v3_model_path=MODEL_V3_PATH,
                v3_features_map_path=MODEL_V3_FEAT
            )

        print(f"🧠 [MLProcessor V68.1] Realistic Mode Loaded (Oracle 0.60).")

    async def initialize(self):
        if self.initialized: return
        print("⚙️ [Processor] Initializing Neural Grid...")
        try:
            tasks = []
            if self.titan: tasks.append(self.titan.initialize())
            
            if self.pattern_engine: 
                self.pattern_engine.configure_thresholds(
                    weights=SystemLimits.PATTERN_TF_WEIGHTS, 
                    bull_thresh=SystemLimits.PATTERN_THRESH_BULLISH, 
                    bear_thresh=SystemLimits.PATTERN_THRESH_BEARISH
                )
                tasks.append(self.pattern_engine.initialize())
            
            if self.oracle: 
                if hasattr(self.oracle, 'set_threshold'): 
                    self.oracle.set_threshold(SystemLimits.L3_CONFIDENCE_THRESHOLD)
                tasks.append(self.oracle.initialize())
            
            if self.sniper: 
                if hasattr(self.sniper, 'configure_settings'): 
                    self.sniper.configure_settings(
                        threshold=SystemLimits.L4_ENTRY_THRESHOLD, 
                        wall_ratio=SystemLimits.L4_OB_WALL_RATIO,
                        w_ml=SystemLimits.L4_WEIGHT_ML,
                        w_ob=SystemLimits.L4_WEIGHT_OB
                    )
                tasks.append(self.sniper.initialize())
            
            if tasks: await asyncio.gather(*tasks)

            if self.guardian_hydra: 
                self.guardian_hydra.initialize()
                print("   🛡️ [Guard 1] Hydra X-Ray: Active")
            
            if self.guardian_legacy:
                if asyncio.iscoroutinefunction(self.guardian_legacy.initialize):
                    await self.guardian_legacy.initialize()
                else:
                    self.guardian_legacy.initialize()
                
                # Default init
                self.guardian_legacy.configure_thresholds(
                    v2_panic=SystemLimits.LEGACY_V2_PANIC_THRESH,
                    v3_hard=SystemLimits.LEGACY_V3_HARD_THRESH,
                    v3_soft=SystemLimits.LEGACY_V3_SOFT_THRESH,
                    v3_ultra=SystemLimits.LEGACY_V3_ULTRA_THRESH
                )
                print(f"   🛡️ [Guard 2] Legacy Steward: Active")

            self.initialized = True
            print("✅ [Processor] All Systems Operational.")
            
        except Exception as e:
            print(f"❌ [Processor FATAL] Init failed: {e}")
            traceback.print_exc()

    async def process_compound_signal(self, raw_data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        L2 Processing with Hybrid Gated Scoring (Full Visibility).
        """
        if not self.initialized: await self.initialize()
        
        symbol = raw_data.get('symbol')
        ohlcv_data = raw_data.get('ohlcv')
        current_price = raw_data.get('current_price', 0.0)
        
        # ✅ الحقن المباشر للقيم
        limits = raw_data.get('dynamic_limits', {})
        
        if not symbol or not ohlcv_data: return None
        
        try:
            # 1. Titan Prediction
            score_titan = 0.5
            titan_res = {}
            if self.titan:
                titan_res = await asyncio.to_thread(self.titan.predict, ohlcv_data)
                score_titan = titan_res.get('score', 0.5)

            # 2. Pattern Analysis
            score_patterns = 0.5
            pattern_res = {}
            pattern_name = "Neutral"
            if self.pattern_engine:
                pattern_res = await self.pattern_engine.detect_chart_patterns(ohlcv_data)
                score_patterns = pattern_res.get('pattern_confidence', 0.5)
                pattern_name = pattern_res.get('pattern_detected', 'Neutral')

            # 3. Monte Carlo Light
            mc_score = 0.5
            if self.mc_analyzer and '1h' in ohlcv_data:
                closes = [c[4] for c in ohlcv_data['1h']]
                raw_mc = self.mc_analyzer.run_light_check(closes)
                mc_score = 0.5 + (raw_mc * 5.0)
                mc_score = max(0.0, min(1.0, mc_score))

            # --- 4. Hybrid Gated Logic (Aggressive) ---
            
            # A) Extract Gates (Injectable, fallback to Aggressive SystemLimits)
            gate_titan = limits.get('l2_gate_titan', SystemLimits.L2_GATE_TITAN)
            gate_patt  = limits.get('l2_gate_pattern', SystemLimits.L2_GATE_PATTERN)
            gate_mc    = limits.get('l2_gate_mc', SystemLimits.L2_GATE_MC)
            
            rejection_reason = None
            is_valid = True

            # B) HARD GATES Check
            if score_titan < gate_titan: 
                is_valid = False
                rejection_reason = f"Titan {score_titan:.2f} < {gate_titan}"
            elif score_patterns < gate_patt:
                is_valid = False
                rejection_reason = f"Pattern {score_patterns:.2f} < {gate_patt}"
            elif mc_score < gate_mc:
                is_valid = False
                rejection_reason = f"MC {mc_score:.2f} < {gate_mc}"

            # C) Weighted Score Calculation
            w_titan = limits.get('w_titan', SystemLimits.L2_WEIGHT_TITAN)
            w_patt = limits.get('w_patt', SystemLimits.L2_WEIGHT_PATTERNS)
            w_mc = limits.get('w_mc', SystemLimits.L2_WEIGHT_MC)
            
            total_w = w_titan + w_patt + w_mc
            if total_w <= 0: total_w = 1.0
            
            hybrid_score = ((score_titan * w_titan) + (score_patterns * w_patt) + (mc_score * w_mc)) / total_w
            
            # D) Final Score Gate
            min_l2_score = limits.get('l2_min_score', SystemLimits.L2_MIN_SCORE)
            if is_valid and hybrid_score < min_l2_score:
                is_valid = False
                rejection_reason = f"Hybrid {hybrid_score:.2f} < {min_l2_score}"
            
            return {
                'symbol': symbol, 
                'current_price': current_price, 
                'enhanced_final_score': hybrid_score,
                'is_valid': is_valid, # ✅ Validity Flag
                'rejection_reason': rejection_reason, # ✅ Reason
                'dynamic_limits': limits, 
                'asset_regime': raw_data.get('asset_regime', 'UNKNOWN'),
                'strategy_type': raw_data.get('strategy_type', 'NORMAL'),
                'titan_score': score_titan, 
                'patterns_score': score_patterns, 
                'mc_score': mc_score, 
                'components': {
                    'titan_score': score_titan, 
                    'patterns_score': score_patterns, 
                    'mc_score': mc_score
                }, 
                'pattern_name': pattern_name, 
                'ohlcv': ohlcv_data, 
                'titan_details': titan_res, 
                'pattern_details': pattern_res.get('details', {})
            }
        except Exception as e: 
            print(f"❌ [Processor] Error processing {symbol}: {e}")
            return None

    async def consult_oracle(self, symbol_data: Dict[str, Any]) -> Dict[str, Any]:
        if not self.initialized: await self.initialize()
        
        # ✅ الحقن المباشر للعتبة
        limits = symbol_data.get('dynamic_limits', {})
        threshold = limits.get('l3_oracle_thresh', SystemLimits.L3_CONFIDENCE_THRESHOLD)
        
        if self.oracle:
            if hasattr(self.oracle, 'set_threshold'):
                self.oracle.set_threshold(threshold)
                
            decision = await self.oracle.predict(symbol_data)
            conf = decision.get('confidence', 0.0)
            
            if decision.get('action') in ['WATCH', 'BUY'] and conf < threshold:
                decision['action'] = 'WAIT'
                decision['reason'] = f"Context Veto: Conf {conf:.2f} < Limit {threshold:.2f}"
            
            return decision
        return {'action': 'WAIT', 'reason': 'Oracle Engine Missing'}

    async def check_sniper_entry(self, ohlcv_1m_data: List, order_book_data: Dict[str, Any], context_data: Dict = None) -> Dict[str, Any]:
        if not self.initialized: await self.initialize()
        
        limits = context_data.get('dynamic_limits', {}) if context_data else {}
        thresh = limits.get('l4_sniper_thresh', SystemLimits.L4_ENTRY_THRESHOLD)
        wall_r = limits.get('l4_ob_wall_ratio', SystemLimits.L4_OB_WALL_RATIO)

        if self.sniper: 
            if hasattr(self.sniper, 'configure_settings'):
                self.sniper.configure_settings(
                    threshold=thresh,
                    wall_ratio=wall_r,
                    w_ml=SystemLimits.L4_WEIGHT_ML,
                    w_ob=SystemLimits.L4_WEIGHT_OB
                )
            return await self.sniper.check_entry_signal_async(ohlcv_1m_data, order_book_data)
            
        return {'signal': 'WAIT', 'reason': 'Sniper Engine Missing'}

    def consult_dual_guardians(self, symbol, ohlcv_1m, ohlcv_5m, ohlcv_15m, trade_context, order_book_snapshot=None):
        """
        💎 GEM-Architect: Conditional Hydra & Fixed Legacy Logic
        """
        response = {'action': 'HOLD', 'detailed_log': '', 'probs': {}}
        
        # 1. استخراج الحدود الديناميكية من سياق الصفقة
        limits = trade_context.get('dynamic_limits', {})
        
        # ✅ سحب القيم مع Fallback آمن
        h_crash_thresh = limits.get('hydra_crash', SystemLimits.HYDRA_CRASH_THRESH)
        h_giveback_thresh = limits.get('hydra_giveback', SystemLimits.HYDRA_GIVEBACK_THRESH)
        h_stag_thresh = limits.get('hydra_stagnation', SystemLimits.HYDRA_STAGNATION_THRESH)
        
        # ✅ Context Data
        entry_price = float(trade_context.get('entry_price', 0.0))
        highest_price = trade_context.get('highest_price', entry_price)
        max_pnl_pct = ((highest_price - entry_price) / entry_price) * 100 if entry_price > 0 else 0.0
        time_in_trade_mins = trade_context.get('time_in_trade_mins', 0.0)

        # -----------------------------------------------
        # 1. Hydra Execution (Conditional)
        # -----------------------------------------------
        hydra_result = {'action': 'HOLD', 'reason': 'Disabled', 'probs': {}}
        if self.guardian_hydra and self.guardian_hydra.initialized:
            hydra_result = self.guardian_hydra.analyze_position(symbol, ohlcv_1m, ohlcv_5m, ohlcv_15m, trade_context)
            h_probs = hydra_result.get('probs', {})
            
            p_crash = h_probs.get('crash', 0.0)
            p_giveback = h_probs.get('giveback', 0.0)
            p_stagnation = h_probs.get('stagnation', 0.0)
            
            # 🛑 CRASH: Always Active (Safety Net)
            if p_crash >= h_crash_thresh:
                hydra_result['action'] = 'EXIT_HARD'
                hydra_result['reason'] = f"Hydra Crash Risk {p_crash:.2f} >= {h_crash_thresh}"
            
            # 🛑 GIVEBACK: Conditional (Profit > 0.6%)
            elif p_giveback >= h_giveback_thresh:
                if max_pnl_pct >= 0.6: 
                    hydra_result['action'] = 'EXIT_SOFT'
                    hydra_result['reason'] = f"Hydra Giveback {p_giveback:.2f} (Max PnL {max_pnl_pct:.2f}%)"
                else:
                    hydra_result['action'] = 'HOLD' # Ignore noise

            # 🛑 STAGNATION: Conditional (Time > 90 mins)
            elif p_stagnation >= h_stag_thresh:
                if time_in_trade_mins > 90:
                    hydra_result['action'] = 'EXIT_SOFT'
                    hydra_result['reason'] = f"Hydra Stagnation {p_stagnation:.2f} (>90m)"
                else:
                    hydra_result['action'] = 'HOLD' # Too early
        
        # -----------------------------------------------
        # 2. Legacy Execution (Fixed Thresholds)
        # -----------------------------------------------
        legacy_result = {'action': 'HOLD', 'reason': 'Disabled', 'scores': {}}
        if self.guardian_legacy and self.guardian_legacy.initialized:
            self.guardian_legacy.configure_thresholds(
                v2_panic=0.98,
                v3_hard=0.95,
                v3_soft=0.88,
                v3_ultra=0.99
            )
            vol_30m = trade_context.get('volume_30m_usd', 0.0)
            legacy_result = self.guardian_legacy.analyze_position(
                ohlcv_1m, ohlcv_5m, ohlcv_15m, entry_price, 
                order_book=order_book_snapshot,
                volume_30m_usd=vol_30m
            )

        # -----------------------------------------------
        # 3. Final Arbitration
        # -----------------------------------------------
        h_probs = hydra_result.get('probs', {})
        l_scores = legacy_result.get('scores', {})
        
        h_c = h_probs.get('crash', 0.0)
        h_g = h_probs.get('giveback', 0.0)
        l_v2 = l_scores.get('v2', 0.0)
        
        stamp_str = f"🐲[C:{h_c:.2f}|G:{h_g:.2f}] 🕸️[V2:{l_v2:.2f}]"

        final_action = 'HOLD'
        final_reason = f"Safe. {stamp_str}"
        
        if hydra_result['action'] in ['EXIT_HARD', 'EXIT_SOFT', 'TIGHTEN_SL', 'TRAIL_SL']:
             final_action = hydra_result['action']
             final_reason = f"🐲 HYDRA: {hydra_result['reason']}"
        elif legacy_result['action'] in ['EXIT_HARD', 'EXIT_SOFT']:
             final_action = legacy_result['action']
             final_reason = f"🕸️ LEGACY: {legacy_result['reason']}"

        return {
            'action': final_action, 
            'reason': final_reason, 
            'detailed_log': f"{final_action} | {stamp_str}", 
            'probs': h_probs, 
            'scores': l_scores
        }

    async def run_advanced_monte_carlo(self, symbol, timeframe='1h'):
        if self.mc_analyzer and self.data_manager:
            try:
                ohlcv = await self.data_manager.get_latest_ohlcv(symbol, timeframe, limit=300)
                if ohlcv: return self.mc_analyzer.run_advanced_simulation([c[4] for c in ohlcv])
            except Exception: pass
        return 0.0