# ============================================================
# 🎯 ml_engine/sniper_engine.py 
# (V2.3 - GEM-Architect: Dynamic Window Fix)
# ============================================================
# - Fixed: Rolling window error (min_periods > window).
# - Logic: Auto-adjusts min_periods to fit requested window size.
# - Integrity: Full ML & OB functionality restored.
# ============================================================

import os
import time
import numpy as np
import pandas as pd
import pandas_ta as ta
import lightgbm as lgb
import traceback
from typing import List, Dict, Any, Optional

N_SPLITS = 5
# السماح ببيانات أقل للمرونة (بدلاً من 500)
LOOKBACK_WINDOW = 150 

# ============================================================
# 🔧 1. Feature Engineering (Standard + Liquidity Proxies)
# ============================================================

def _z_score_rolling(x, w=500): 
    # ✅ FIX: Ensure min_periods never exceeds window size (w)
    # If w=50 (like in vol_zscore), min_periods becomes 50.
    # If w=500, min_periods becomes 100 (allowing partial calculation).
    effective_min = min(w, 100)
    
    r = x.rolling(w, min_periods=effective_min).mean()
    s = x.rolling(w, min_periods=effective_min).std().replace(0, np.nan)
    z = (x - r) / s
    return z.fillna(0)

def _add_liquidity_proxies(df):
    """حساب مؤشرات السيولة المتقدمة"""
    df_proxy = df.copy()
    if 'datetime' not in df_proxy.index.names and 'timestamp' in df_proxy.columns:
        df_proxy['datetime'] = pd.to_datetime(df_proxy['timestamp'], unit='ms')
        df_proxy = df_proxy.set_index('datetime')
            
    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()
    # Reduced min_periods for reliability
    roll_cov = dp.rolling(64, min_periods=20).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.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):
    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)
    
    if ema_9 is not None:
        df_feat['ema_9_slope'] = (ema_9 - ema_9.shift(1)) / ema_9.shift(1)
    else:
        df_feat['ema_9_slope'] = 0
        
    if ema_21 is not None:
        df_feat['ema_21_dist'] = (df_feat['close'] - ema_21) / ema_21 
    else:
        df_feat['ema_21_dist'] = 0
    
    df_feat['atr'] = ta.atr(df_feat['high'], df_feat['low'], df_feat['close'], length=100)
    
    # This was causing the error (w=50 vs min=100). Now fixed in helper.
    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. SniperEngine Class (Robust)
# ============================================================

class SniperEngine:
    
    def __init__(self, models_dir: str):
        self.models_dir = models_dir 
        self.models: List[lgb.Booster] = []
        self.feature_names: List[str] = []
        
        # --- Configurable Thresholds ---
        self.entry_threshold = 0.40
        self.wall_ratio_limit = 0.40   
        self.weight_ml = 0.60
        self.weight_ob = 0.40
        
        self.ob_depth_decay = 0.15      
        self.max_wall_dist = 0.005      
        self.max_spread_pct = 0.002     
        self.spoof_patience = 0         
        
        self.initialized = False
        self.LOOKBACK_WINDOW = LOOKBACK_WINDOW
        self.ORDER_BOOK_DEPTH = 20
        
        self._wall_cache = {}

        print("🎯 [SniperEngine V2.3] Dynamic Window Logic Loaded.")

    def configure_settings(self, 
                           threshold: float, 
                           wall_ratio: float, 
                           w_ml: float = 0.60, 
                           w_ob: float = 0.40,
                           max_wall_dist: float = 0.005,
                           max_spread: float = 0.002):
        self.entry_threshold = threshold
        self.wall_ratio_limit = wall_ratio
        self.weight_ml = w_ml
        self.weight_ob = w_ob
        self.max_wall_dist = max_wall_dist
        self.max_spread_pct = max_spread

    async def initialize(self):
        print(f"🎯 [SniperEngine] Loading models from {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] Error: Found {len(model_files)} models, need {N_SPLITS}.")
            
            for f in sorted(model_files):
                model_path = os.path.join(self.models_dir, f)
                self.models.append(lgb.Booster(model_file=model_path))
            
            if self.models:
                self.feature_names = self.models[0].feature_name()
                
            self.initialized = True
            print(f"✅ [SniperEngine] Active. WallLimit: {self.wall_ratio_limit}, MaxDist: {self.max_wall_dist*100}%")

        except Exception as e:
            print(f"❌ [SniperEngine] Init failed: {e}")
            traceback.print_exc()
            self.initialized = False

    def _calculate_features_live(self, df_1m: pd.DataFrame) -> pd.DataFrame:
        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] Feature calc error: {e}")
            return pd.DataFrame() 

    # ==============================================================================
    # 📊 3. Smart Order Book Logic (OKX Safe)
    # ==============================================================================
    def _score_order_book(self, order_book: Dict[str, Any], symbol: str = None) -> Dict[str, Any]:
        try:
            bids = order_book.get('bids', [])
            asks = order_book.get('asks', [])
            
            if not bids or not asks:
                return {'score': 0.0, 'imbalance': 0.0, 'veto': True, 'reason': 'Empty OB'}

            # ✅ Safe Indexing
            best_bid = float(bids[0][0])
            best_ask = float(asks[0][0])
            spread_pct = (best_ask - best_bid) / best_bid
            
            if spread_pct > self.max_spread_pct:
                return {
                    'score': 0.0, 
                    'veto': True, 
                    'reason': f"Wide Spread ({spread_pct:.2%})"
                }

            w_bid_vol = 0.0
            w_ask_vol = 0.0
            total_raw_ask_vol = 0.0 
            
            depth = min(len(bids), len(asks), self.ORDER_BOOK_DEPTH)
            
            for i in range(depth):
                weight = 1.0 / (1.0 + (self.ob_depth_decay * i))
                
                bid_vol = float(bids[i][1])
                ask_vol = float(asks[i][1])
                
                w_bid_vol += bid_vol * weight
                w_ask_vol += ask_vol * weight
                total_raw_ask_vol += ask_vol

            total_w_vol = w_bid_vol + w_ask_vol
            weighted_imbalance = w_bid_vol / total_w_vol if total_w_vol > 0 else 0.5
            
            max_valid_wall = 0.0
            limit_price = best_ask * (1 + self.max_wall_dist)
            
            for item in asks[:depth]:
                p = float(item[0])
                v = float(item[1])
                
                if p <= limit_price:
                    if v > max_valid_wall: max_valid_wall = v
            
            wall_ratio = max_valid_wall / total_raw_ask_vol if total_raw_ask_vol > 0 else 0
            
            veto_wall = False
            veto_reason = "OK"
            
            if wall_ratio >= self.wall_ratio_limit:
                veto_wall = True
                veto_reason = f"Sell Wall ({wall_ratio:.2f})"
                
                if symbol:
                    curr_time = time.time()
                    cache = self._wall_cache.get(symbol, {'last_check': 0, 'count': 0})
                    if curr_time - cache['last_check'] > 5.0:
                        cache['count'] = 1
                    else:
                        cache['count'] += 1
                    cache['last_check'] = curr_time
                    self._wall_cache[symbol] = cache
            else:
                if symbol and symbol in self._wall_cache:
                    self._wall_cache[symbol]['count'] = 0

            return {
                'score': float(weighted_imbalance),
                'imbalance': float(weighted_imbalance), 
                'wall_ratio': float(wall_ratio),
                'veto': veto_wall,
                'spread_ok': True,
                'reason': veto_reason
            }

        except Exception as e:
            return {'score': 0.0, 'veto': True, 'reason': f"OB Error: {e}"}

    # ==============================================================================
    # 🎯 4. Main Signal Check (Fixed Logging)
    # ==============================================================================
    async def check_entry_signal_async(self, 
                                     ohlcv_1m_data: List[List], 
                                     order_book_data: Dict[str, Any] = None,
                                     symbol: str = None) -> Dict[str, Any]:
        
        if not self.initialized:
            return {'signal': 'WAIT', 'reason': 'Not initialized'}
        
        ml_score = 0.5
        ml_reason = "No Data"
        
        # ✅ Relaxed Check: Allow partial data (min 100) instead of strict 500
        if len(ohlcv_1m_data) >= 100 and self.models:
            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 not df_features.empty:
                    X_live = df_features.iloc[-1:][self.feature_names].fillna(0)
                    preds = [m.predict(X_live)[0] for m in self.models] 
                    ml_score = float(np.mean(preds))
                    ml_reason = f"ML:{ml_score:.2f}"
            except Exception as e:
                print(f"❌ [Sniper] ML Error: {e}")
                ml_reason = "ML Err"

        ob_res = {'score': 0.5, 'imbalance': 0.5, 'veto': False, 'reason': 'No OB'}
        if order_book_data:
            ob_res = self._score_order_book(order_book_data, symbol=symbol)

        ob_str = f"OB:{ob_res['score']:.2f}"
        
        if ob_res.get('veto', False):
            final_score = 0.0
            signal = 'WAIT'
            reason_str = f"⛔ {ob_res['reason']} | {ml_reason} | {ob_str}"
        else:
            final_score = (ml_score * self.weight_ml) + (ob_res['score'] * self.weight_ob)
            
            if final_score >= self.entry_threshold:
                signal = 'BUY'
                reason_str = f"✅ GO: {final_score:.2f} | {ml_reason} | {ob_str}"
            else:
                signal = 'WAIT'
                reason_str = f"📉 Low Score: {final_score:.2f} | {ml_reason} | {ob_str}"

        return {
            'signal': signal,
            'confidence_prob': final_score,
            'ml_score': ml_score,
            'ob_score': ob_res['score'],
            'entry_price': float(order_book_data['asks'][0][0]) if order_book_data and order_book_data.get('asks') else 0.0,
            'reason': reason_str
        }