data_manager.py

# data_manager.py (Updated to V10.2 - Whale Learning Data Link)
import os
import asyncio
import httpx
import traceback
import time
from datetime import datetime
import ccxt
import numpy as np
import logging
from typing import List, Dict, Any
import pandas as pd

try:
    import pandas_ta as ta
except ImportError:
    print("⚠️ مكتبة pandas_ta غير موجودة. النظام سيفشل.")
    ta = None

# (V10.0) استيراد العقل الحسابي المطور
from ml_engine.indicators import AdvancedTechnicalAnalyzer
# (V10.0) استيراد مونت كارلو المطور
from ml_engine.monte_carlo import MonteCarloAnalyzer 
from ml_engine.patterns import ChartPatternAnalyzer
# (V9.1) استيراد "العقل الذكي"
from ml_engine.ranker import Layer1Ranker 

logging.getLogger("httpx").setLevel(logging.WARNING)
logging.getLogger("httpcore").setLevel(logging.WARNING)

class DataManager:
    def __init__(self, contracts_db, whale_monitor, r2_service=None):
        self.contracts_db = contracts_db or {}
        self.whale_monitor = whale_monitor
        self.r2_service = r2_service
        
        try:
            self.exchange = ccxt.kucoin({
                'sandbox': False, 'enableRateLimit': True,
                'timeout': 30000, 'verbose': False,
            })
            print("✅ تم تهيئة اتصال KuCoin بنجاح")
        except Exception as e:
            print(f"❌ فشل تهيئة اتصال KuCoin: {e}")
            self.exchange = None
            
        self.http_client = None
        self.market_cache = {}
        self.last_market_load = None

        # (V10.0) تهيئة العقول الحسابية
        self.technical_analyzer = AdvancedTechnicalAnalyzer()
        self.pattern_analyzer = None
        self.mc_analyzer = MonteCarloAnalyzer() 
        # (V9.1) تهيئة "العقل الذكي" (النموذج)
        self.layer1_ranker = None 
        
    async def initialize(self):
        self.http_client = httpx.AsyncClient(timeout=30.0)
        await self._load_markets()
        
        print("   > [DataManager] تهيئة محرك الأنماط V8 (ML-Based)...")
        try:
            self.pattern_analyzer = ChartPatternAnalyzer(r2_service=self.r2_service)
            await self.pattern_analyzer.initialize()
        except Exception as e:
            print(f"❌ [DataManager] فشل تهيئة محرك الأنماط V8: {e}")
            self.pattern_analyzer = ChartPatternAnalyzer(r2_service=None)
            
        print("   > [DataManager] تهيئة الكاشف المصغر (Layer1 Ranker V9.8)...")
        try:
            # (تأكد من أن النموذج V9.8 موجود ومسمى بهذا الاسم)
            model_file_path = "ml_models/layer1_ranker.lgbm"
            self.layer1_ranker = Layer1Ranker(model_path=model_file_path)
            await self.layer1_ranker.initialize()
            if self.layer1_ranker.model is None:
                print("   ⚠️ [DataManager V9.8] الرانكر في وضع 'وهمي' (Placeholder).")
            else:
                 print(f"   ✅ [DataManager V9.8] الرانكر {self.layer1_ranker.model_name} جاهز للعمل.")
        except Exception as e:
            print(f"❌ [DataManager V9.8] فشل تهيئة الرانكر V9.8: {e}")
            self.layer1_ranker = None 

        print("✅ DataManager initialized - V10.2 (Whale Learning Data Link)")

    async def _load_markets(self):
        try:
            if not self.exchange: return
            print("🔄 جلب أحدث بيانات الأسواق من KuCoin...")
            self.exchange.load_markets()
            self.market_cache = self.exchange.markets
            self.last_market_load = datetime.now()
            print(f"✅ تم تحميل {len(self.market_cache)} سوق من KuCoin")
        except Exception as e: print(f"❌ فشل تحميل بيانات الأسواق: {e}")

    async def close(self):
        if self.http_client and not self.http_client.is_closed: await self.http_client.aclose()
        if self.exchange:
            try: await self.exchange.close()
            except Exception: pass

    async def get_market_context_async(self):
        try:
            sentiment_data = await self.get_sentiment_safe_async()
            price_data = await self._get_prices_with_fallback()
            bitcoin_price = price_data.get('bitcoin'); ethereum_price = price_data.get('ethereum')
            return {
                'timestamp': datetime.now().isoformat(),
                'bitcoin_price_usd': bitcoin_price, 'ethereum_price_usd': ethereum_price,
                'fear_and_greed_index': sentiment_data.get('feargreed_value') if sentiment_data else None,
                'sentiment_class': sentiment_data.get('feargreed_class') if sentiment_data else 'NEUTRAL',
                'market_trend': self._determine_market_trend(bitcoin_price, sentiment_data),
                'btc_sentiment': self._get_btc_sentiment(bitcoin_price),
                'data_quality': 'HIGH' if bitcoin_price and ethereum_price else 'LOW'
            }
        except Exception as e: return self._get_minimal_market_context()

    async def get_sentiment_safe_async(self):
        try:
            async with httpx.AsyncClient(timeout=10) as client:
                response = await client.get("https://api.alternative.me/fng/")
                response.raise_for_status(); data = response.json()
                if 'data' not in data or not data['data']: raise ValueError("بيانات المشاعر غير متوفرة")
                latest_data = data['data'][0]
                return { "feargreed_value": int(latest_data['value']), "feargreed_class": latest_data['value_classification'], "source": "alternative.me", "timestamp": datetime.now().isoformat() }
        except Exception as e: return None
    def _determine_market_trend(self, bitcoin_price, sentiment_data):
        if bitcoin_price is None: return "UNKNOWN"
        if bitcoin_price > 60000: score = 1
        elif bitcoin_price < 55000: score = -1
        else: score = 0
        if sentiment_data and sentiment_data.get('feargreed_value') is not None:
            fear_greed = sentiment_data.get('feargreed_value')
            if fear_greed > 60: score += 1
            elif fear_greed < 40: score -= 1
        if score >= 1: return "bull_market"
        elif score <= -1: return "bear_market"
        else: return "sideways_market"
    def _get_btc_sentiment(self, bitcoin_price):
        if bitcoin_price is None: return 'UNKNOWN'
        elif bitcoin_price > 60000: return 'BULLISH'
        elif bitcoin_price < 55000: return 'BEARISH'
        else: return 'NEUTRAL'
    async def _get_prices_with_fallback(self):
        try:
            prices = await self._get_prices_from_kucoin_safe()
            if prices.get('bitcoin') and prices.get('ethereum'): return prices
            return await self._get_prices_from_coingecko()
        except Exception as e: return {'bitcoin': None, 'ethereum': None}
    async def _get_prices_from_kucoin_safe(self):
        if not self.exchange: return {'bitcoin': None, 'ethereum': None}
        try:
            prices = {'bitcoin': None, 'ethereum': None}
            btc_ticker = self.exchange.fetch_ticker('BTC/USDT'); btc_price = float(btc_ticker.get('last', 0)) if btc_ticker.get('last') else None
            if btc_price and btc_price > 0: prices['bitcoin'] = btc_price
            eth_ticker = self.exchange.fetch_ticker('ETH/USDT'); eth_price = float(eth_ticker.get('last', 0)) if eth_ticker.get('last') else None
            if eth_price and eth_price > 0: prices['ethereum'] = eth_price
            return prices
        except Exception as e: return {'bitcoin': None, 'ethereum': None}
    async def _get_prices_from_coingecko(self):
        try:
            await asyncio.sleep(0.5)
            url = "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin,ethereum&vs_currencies=usd"
            headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36', 'Accept': 'application/json'}
            async with httpx.AsyncClient(headers=headers) as client:
                response = await client.get(url, timeout=10)
                if response.status_code == 429: await asyncio.sleep(2); response = await client.get(url, timeout=10)
                response.raise_for_status(); data = response.json()
                btc_price = data.get('bitcoin', {}).get('usd'); eth_price = data.get('ethereum', {}).get('usd')
                if btc_price and eth_price: return {'bitcoin': btc_price, 'ethereum': eth_price}
                else: return {'bitcoin': None, 'ethereum': None}
        except Exception as e: return {'bitcoin': None, 'ethereum': None}
    def _get_minimal_market_context(self):
        return { 'timestamp': datetime.now().isoformat(), 'data_available': False, 'market_trend': 'UNKNOWN', 'btc_sentiment': 'UNKNOWN', 'data_quality': 'LOW' }


    def _create_dataframe(self, candles: List) -> pd.DataFrame:
        """(V9.1) إنشاء DataFrame (تحتاج 200 شمعة للميزات)"""
        try:
            if not candles: return pd.DataFrame()
            df = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
            df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
            df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
            df.set_index('timestamp', inplace=True)
            df.sort_index(inplace=True)
            return df
        except Exception as e:
            print(f"❌ خطأ في إنشاء DataFrame: {e}")
            return pd.DataFrame()

    async def layer1_rapid_screening(self) -> List[Dict[str, Any]]:
        """
        الطبقة 1: فحص سريع - (محدث V10.1)
        - إصلاح مونت كارلو المفقود.
        - استخدام عتبة التوازن 53% (0.53) التي اخترتها.
        """
        print("📊 الطبقة 1 (V10.1 - Balanced 53% + MC Fix): بدء الغربلة...")
        
        if not self.layer1_ranker:
            print("❌ [V10.1] الرانكر غير مهيأ. إيقاف الغربلة.")
            return []

        # الخطوة 1: جلب أفضل 100 عملة حسب الحجم
        volume_data = await self._get_volume_data_optimal()
        if not volume_data: volume_data = await self._get_volume_data_direct_api()
        if not volume_data:
            print("❌ [V10.1] فشل جلب بيانات الأحجام.")
            return []
        
        volume_data.sort(key=lambda x: x['dollar_volume'], reverse=True)
        top_100_by_volume = volume_data[:100]
        print(f"✅ [V10.1] تم تحديد أفضل {len(top_100_by_volume)} عملة. بدء حساب الميزات الذكية...")
        
        final_candidates_with_scores = []
        batch_symbols_data = top_100_by_volume
        batch_symbols = [s['symbol'] for s in batch_symbols_data]
            
        # الخطوة 2: جلب بيانات 1H (200 شمعة)
        tasks = [self._fetch_1h_ohlcv_for_screening(symbol, limit=200) for symbol in batch_symbols]
        results_candles = await asyncio.gather(*tasks, return_exceptions=True)
        
        valid_symbol_data_for_ranking = []
        for j, (candles) in enumerate(results_candles):
            symbol_data = batch_symbols_data[j]
            if isinstance(candles, Exception) or not candles or len(candles) < 200: continue
            symbol_data['ohlcv_1h_raw'] = candles 
            valid_symbol_data_for_ranking.append(symbol_data)

        if not valid_symbol_data_for_ranking:
            print("❌ [V10.1] لا توجد عملات صالحة (تحتاج 200 شمعة 1H).")
            return []

        print(f"   🔄 [V10.1] حساب الميزات (الذكية + مونت كارلو) لـ {len(valid_symbol_data_for_ranking)} عملة...")
        
        # الخطوة 3: حساب "الميزات الذكية V9.8" (الكاملة)
        all_features_list = []
        symbols_in_order = []
        for symbol_data in valid_symbol_data_for_ranking:
            try:
                df = self._create_dataframe(symbol_data['ohlcv_1h_raw'])
                if df.empty: continue
                
                # (أ. حساب الميزات الذكية V9.8 - من indicators.py)
                smart_features = self.technical_analyzer.calculate_v9_smart_features(df)
                if not smart_features:
                    # (فشل حساب الميزات الرئيسية، تجاهل العملة)
                    continue
                
                # 🔴 --- (V10.0 - الإصلاح) --- 🔴
                # (ب. حساب ميزات مونت كارلو V9.8 - من monte_carlo.py)
                # (نستخدم آخر 100 شمعة فقط للمحاكاة البسيطة كما في التدريب)
                closes_np = df['close'].tail(100).to_numpy()
                mc_features = self.mc_analyzer.generate_1h_price_distribution_simple(closes_np)
                
                # (إضافة ميزات مونت كارلو إلى قاموس الميزات)
                smart_features['mc_prob_gain'] = mc_features['mc_prob_gain']
                smart_features['mc_var_95_pct'] = mc_features['mc_var_95_pct']
                # 🔴 --- (نهاية الإصلاح) --- 🔴

                all_features_list.append(smart_features)
                symbols_in_order.append(symbol_data)
                
            except Exception:
                pass # (تجاهل العملة إذا فشل أي جزء)
        
        if not all_features_list:
            print("❌ [V10.1] فشل حساب الميزات الذكية.")
            return []

        # الخطوة 4: التنبؤ (التصنيف)
        print(f"   🧠 [V10.1] إرسال {len(all_features_list)} عملة إلى نموذج الرانكر...")
        features_dataframe = pd.DataFrame(all_features_list)
        probabilities = self.layer1_ranker.predict_proba(features_dataframe)
        
        # (الطباعة التشخيصية)
        print(f"   🔍 [V10.1 DEBUG] تم استلام {len(probabilities)} نتيجة من الرانكر.")
        debug_scores = []
        for i, (symbol_data) in enumerate(symbols_in_order):
            debug_scores.append((symbol_data['symbol'], probabilities[i]))
        debug_scores.sort(key=lambda x: x[1], reverse=True)
        print("   --- 📋 [V10.1 DEBUG] أعلى 10 درجات خام (قبل الفلترة) ---")
        for i, (symbol, score) in enumerate(debug_scores[:10]):
            print(f"      {i+1}. {symbol}: {score:.4f}") 
        print("   -------------------------------------------------")

        # الخطوة 5: تجميع النتائج (مع عتبة التوازن 53%)
        for i, (symbol_data) in enumerate(symbols_in_order):
            score = probabilities[i]
            
            # 🔴 (V10.1: عتبة التوازن 53% - بناءً على اختيارك) 🔴
            if score >= 0.50: 
                symbol = symbol_data['symbol']
                print(f"      ✅ {symbol}: نجح (الاحتمالية: {score:.3f})")
                symbol_data['layer1_score'] = float(score)
                symbol_data['reasons_for_candidacy'] = ["V9_SMART_RANKER_BALANCED_53"]
                if 'ohlcv_1h_raw' in symbol_data: del symbol_data['ohlcv_1h_raw'] 
                final_candidates_with_scores.append(symbol_data)

        print(f"🎯 اكتملت الغربلة (V10.1). تم تأهيل {len(final_candidates_with_scores)} عملة (ثقة >= 53%).")
        
        if final_candidates_with_scores:
            final_candidates_with_scores.sort(key=lambda x: x['layer1_score'], reverse=True)
            print("🏆 المرشحون الناجحون (Top Candidates):")
            for k, candidate in enumerate(final_candidates_with_scores[:5]):
                print(f"   {k+1}. {candidate['symbol']}: (Score: {candidate.get('layer1_score'):.3f})")
        else:
            print("⚠️ [V10.1] لم تنجح أي عملة في تجاوز عتبة الثقة 53%.")

        return final_candidates_with_scores[:20]

    async def _fetch_1h_ohlcv_for_screening(self, symbol: str, limit: int = 200) -> List:
        try:
            ohlcv_data = self.exchange.fetch_ohlcv(symbol, '1h', limit=limit)
            if not ohlcv_data or len(ohlcv_data) < limit: return None
            return ohlcv_data
        except Exception: return None

    async def _get_volume_data_optimal(self) -> List[Dict[str, Any]]:
        try:
            if not self.exchange: return []
            tickers = self.exchange.fetch_tickers()
            volume_data = []
            for symbol, ticker in tickers.items():
                if not symbol.endswith('/USDT') or not ticker.get('active', True): continue
                current_price = ticker.get('last', 0)
                quote_volume = ticker.get('quoteVolume', 0)
                if current_price is None or current_price <= 0: continue
                
                if quote_volume is not None and quote_volume > 0:
                    dollar_volume = quote_volume
                else:
                    base_volume = ticker.get('baseVolume', 0)
                    if base_volume is None: continue
                    dollar_volume = base_volume * current_price
                    
                if dollar_volume is None or dollar_volume < 50000: continue
                
                price_change_24h = ticker.get('percentage', 0) or 0

                volume_data.append({ 
                    'symbol': symbol, 
                    'dollar_volume': dollar_volume, 
                    'current_price': current_price, 
                    'volume_24h': ticker.get('baseVolume', 0) or 0, 
                    'price_change_24h': price_change_24h 
                })
            print(f"✅ تم معالجة {len(volume_data)} عملة في الطريقة المثلى")
            return volume_data
        except Exception as e: 
            print(f"❌ خطأ في جلب بيانات الحجم المثلى: {e}")
            return []
            
    async def _get_volume_data_direct_api(self) -> List[Dict[str, Any]]:
        try:
            url = "https://api.kucoin.com/api/v1/market/allTickers"
            async with httpx.AsyncClient(timeout=15) as client:
                response = await client.get(url)
                response.raise_for_status()
                data = response.json()
                if data.get('code') != '200000': raise ValueError(f"استجابة API غير متوقعة: {data.get('code')}")
                
                tickers = data['data']['ticker']
                volume_data = []
                for ticker in tickers:
                    symbol = ticker['symbol']
                    if not symbol.endswith('USDT'): continue
                    formatted_symbol = symbol.replace('-', '/')
                    try:
                        vol_value = ticker.get('volValue')
                        last_price = ticker.get('last')
                        change_rate = ticker.get('changeRate')
                        vol = ticker.get('vol')
                        
                        if vol_value is None or last_price is None or change_rate is None or vol is None: continue
                        
                        dollar_volume = float(vol_value) if vol_value else 0
                        current_price = float(last_price) if last_price else 0
                        price_change = (float(change_rate) * 100) if change_rate else 0
                        volume_24h = float(vol) if vol else 0
                        
                        if dollar_volume >= 50000 and current_price > 0:
                            volume_data.append({ 
                                'symbol': formatted_symbol, 
                                'dollar_volume': dollar_volume, 
                                'current_price': current_price, 
                                'volume_24h': volume_24h, 
                                'price_change_24h': price_change 
                            })
                    except (ValueError, TypeError, KeyError):
                        continue
                        
                print(f"✅ تم معالجة {len(volume_data)} عملة في الطريقة المباشرة")
                return volume_data
        except Exception as e:
            print(f"❌ خطأ في جلب بيانات الحجم المباشر: {e}")
            return []
            
    async def stream_ohlcv_data(self, symbols: List[Dict[str, Any]], queue: asyncio.Queue):
        print(f"📊 بدء تدفق بيانات OHLCV (الكاملة) لـ {len(symbols)} عملة (مصنفة)...")
        batch_size = 15
        batches = [symbols[i:i + batch_size] for i in range(0, len(symbols), batch_size)]
        total_successful = 0
        
        for batch_num, batch in enumerate(batches):
            print(f"   🔄 [المنتج] جلب الدفعة {batch_num + 1}/{len(batches)} ({len(batch)} عملة)...")
            batch_tasks = []
            for symbol_data in batch:
                symbol_str = symbol_data['symbol'] 
                task = asyncio.create_task(self._fetch_complete_ohlcv_parallel(symbol_str))
                batch_tasks.append(task)
                
            batch_results = await asyncio.gather(*batch_tasks, return_exceptions=True)
            
            successful_data_for_batch = []
            for i, result in enumerate(batch_results):
                original_symbol_data = batch[i]
                symbol_str = original_symbol_data['symbol']
                
                if isinstance(result, Exception):
                    print(f"      ❌ [المنتج] فشل جلب {symbol_str}: {result}")
                elif result is not None:
                    result.update(original_symbol_data) 
                    successful_data_for_batch.append(result)
                    print(f"      ✅ [المنتج] {symbol_str}: {result.get('successful_timeframes', 0)}/6 أطر زمنية")
                else:
                    print(f"      ⚠️ [المنتج] {symbol_str}: بيانات غير كافية، تم التجاهل")

            if successful_data_for_batch:
                try:
                    await queue.put(successful_data_for_batch)
                    total_successful += len(successful_data_for_batch)
                except Exception as q_err:
                    print(f"      ❌ [المنتج] فشل إرسال الدفعة للطابور: {q_err}")

            if batch_num < len(batches) - 1:
                await asyncio.sleep(1) # (إضافة تأخير بسيط بين الدفعات)
                
        print(f"✅ [المنتج] اكتمل التدفق. تم إرسال {total_successful} عملة للمعالجة.")
        await queue.put(None)
        print("      📬 [المنتج] تم إرسال إشارة الإنهاء (None) إلى الطابور.")

    async def _fetch_complete_ohlcv_parallel(self, symbol: str) -> Dict[str, Any]:
        try:
            ohlcv_data = {}
            timeframes = [('5m', 200), ('15m', 200), ('1h', 200), ('4h', 200), ('1d', 200), ('1w', 200)]
            timeframe_tasks = []
            
            for timeframe, limit in timeframes:
                task = asyncio.create_task(self._fetch_single_timeframe_improved(symbol, timeframe, limit))
                timeframe_tasks.append(task)
                
            timeframe_results = await asyncio.gather(*timeframe_tasks, return_exceptions=True)
            successful_timeframes = 0
            
            for i, (timeframe, limit) in enumerate(timeframes):
                result = timeframe_results[i]
                if isinstance(result, Exception): continue
                if result and len(result) >= 200: # (التأكد من وجود بيانات كافية)
                    ohlcv_data[timeframe] = result
                    successful_timeframes += 1
                    
            if successful_timeframes >= 3 and ohlcv_data: # (نحتاج 3 أطر زمنية على الأقل)
                try:
                    current_price = await self.get_latest_price_async(symbol)
                    if current_price is None:
                        # (محاولة احتياطية للحصول على السعر من آخر شمعة)
                        for tf_data in ohlcv_data.values():
                            if tf_data and len(tf_data) > 0: current_price = tf_data[-1][4]; break
                    if current_price is None: return None
                    
                    return { 
                        'symbol': symbol, 
                        'ohlcv': ohlcv_data, 
                        'raw_ohlcv': ohlcv_data, 
                        'current_price': current_price, 
                        'timestamp': datetime.now().isoformat(), 
                        'candles_count': {tf: len(data) for tf, data in ohlcv_data.items()},
                        'successful_timeframes': successful_timeframes 
                    }
                except Exception: 
                    return None
            else: 
                return None
        except Exception: 
            return None
            
    async def _fetch_single_timeframe_improved(self, symbol: str, timeframe: str, limit: int):
        max_retries = 3
        retry_delay = 2
        for attempt in range(max_retries):
            try:
                ohlcv_data = self.exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
                if ohlcv_data and len(ohlcv_data) > 0: 
                    return ohlcv_data
                else: 
                    return []
            except Exception:
                if attempt < max_retries - 1:
                    await asyncio.sleep(retry_delay * (attempt + 1))
                else: 
                    return []
                    
    async def get_latest_price_async(self, symbol):
        try:
            if not self.exchange: return None
            ticker = self.exchange.fetch_ticker(symbol)
            return float(ticker['last']) if ticker and ticker.get('last') else None
        except Exception: 
            return None
            
    # 🔴 --- START OF CHANGE (V10.2) --- 🔴
    async def get_symbol_daily_volume(self, symbol: str) -> float:
        """
        (جديد) دالة مساعدة لجلب حجم التداول اليومي (بالدولار) لعملة واحدة.
        (تستخدم في إعادة التحليل).
        """
        try:
            if not self.exchange: return 0.0
            ticker = self.exchange.fetch_ticker(symbol)
            if not ticker: return 0.0
            
            current_price = ticker.get('last', 0)
            quote_volume = ticker.get('quoteVolume', 0)
            
            if quote_volume is not None and quote_volume > 0:
                return float(quote_volume)
            elif current_price is not None and current_price > 0:
                base_volume = ticker.get('baseVolume', 0)
                if base_volume is not None:
                    return float(base_volume) * float(current_price)
            
            return 0.0
        except Exception:
            return 0.0

    async def get_whale_data_for_symbol(self, symbol: str, daily_volume_usd: float = 0.0):
        """
        (محدث V10.2)
        تمرير حجم التداول اليومي إلى مراقب الحيتان لتفعيل المقاييس النسبية.
        """
        try:
            if self.whale_monitor:
                return await self.whale_monitor.get_symbol_whale_activity(symbol, daily_volume_usd=daily_volume_usd)
            else:
                return None
        except Exception: 
            return None
    # 🔴 --- END OF CHANGE --- 🔴
            
    async def get_whale_trading_signal(self, symbol, whale_data, market_context):
        try:
            return await self.whale_monitor.generate_whale_trading_signal(symbol, whale_data, market_context) if self.whale_monitor else {'action': 'HOLD', 'confidence': 0.3, 'reason': 'Whale monitor not available', 'source': 'whale_analysis'}
        except Exception as e:
            return {'action': 'HOLD', 'confidence': 0.3, 'reason': f'Error: {str(e)}', 'source': 'whale_analysis'}

print("✅ DataManager loaded - V10.2 (Whale Learning Data Link)")